From d97d00d3814a69797c8032e65a973844a07fbbd9 Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Thu, 28 Jul 2022 03:08:49 -0700
Subject: [PATCH 01/14] [PROD-325] Added archive handling, updated project
 toml, tests pass!

---
 pyproject.toml               |  21 +++-
 spark_log_parser/__main__.py |  29 +++--
 spark_log_parser/eventlog.py | 238 +++++++++++++++++++++++++++++++++++
 tests/logs/databricks.zip    | Bin 0 -> 403955 bytes
 tests/logs/emr.zip           | Bin 0 -> 3908769 bytes
 tests/test_eventlog.py       |  35 ++++++
 tests/test_parse.py          |  39 ++++++
 7 files changed, 342 insertions(+), 20 deletions(-)
 create mode 100644 spark_log_parser/eventlog.py
 create mode 100644 tests/logs/databricks.zip
 create mode 100644 tests/logs/emr.zip
 create mode 100644 tests/test_eventlog.py
 create mode 100644 tests/test_parse.py

diff --git a/pyproject.toml b/pyproject.toml
index 80f4331..593c762 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "flit.buildapi"
 name = "spark_log_parser"
 authors = [{"name" = "Sync Computing"}]
 readme = "README.md"
-requires-python = ">=3.6.1"
+requires-python = ">=3.10.3"
 classifiers = [
     "Intended Audience :: Information Technology",
     "Intended Audience :: System Administrators",
@@ -16,10 +16,6 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.6",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
 ]
 dependencies = [
@@ -27,9 +23,16 @@ dependencies = [
     "numpy==1.21.4",
     "pandas==1.3.5",
     "ujson==5.3.0",
+    "pydantic==1.9.0",
+    "requests==2.26.0",
 ]
 dynamic = ["version", "description"]
 
+[project.optional-dependencies]
+test = [
+    "pytest==7.1.2",
+]
+
 [project.urls]
 Home = "https://github.com/synccomputingcode/spark_log_parser"
 
@@ -38,3 +41,11 @@ line-length = 100
 
 [tool.isort]
 profile = "black"
+
+[tool.pytest.ini_options]
+pythonpath = [
+    "."
+]
+filterwarnings = [
+    "ignore::UserWarning"
+]
diff --git a/spark_log_parser/__main__.py b/spark_log_parser/__main__.py
index 159e4ad..728ef62 100644
--- a/spark_log_parser/__main__.py
+++ b/spark_log_parser/__main__.py
@@ -2,36 +2,35 @@
 
 logging.captureWarnings(True)
 
+import tempfile
+from spark_log_parser.eventlog import EventLog
+
 from spark_log_parser.parsing_models.application_model_v2 import sparkApplication
 
 import os
 import argparse
+from pathlib import Path
 import sys
 
 logger = logging.getLogger("spark_log_parser")
-            
+
 parser = argparse.ArgumentParser("spark_log_parser")
-parser.add_argument("-l", "--log-file", required=True, help="path to event log")
+parser.add_argument("-l", "--log-file", required=True, type=Path, help="path to event log")
 parser.add_argument("-r", "--result-dir", required=True, help="path to directory in which to save parsed logs")
 args = parser.parse_args()  
 
-print("\n" + "*" * 12 + " Running the Log Parser for Spark Predictor " + "*" * 12 + "\n")
-
-log_path = os.path.abspath(args.log_file)
-
 if not os.path.isdir(args.result_dir):
     logger.error("%s is not a directory", args.result_dir)
     sys.exit(1)
 
-print("\n--Processing log file: " + log_path)
-
-log_name = os.path.basename(log_path)
-result_path = os.path.join(args.result_dir, "parsed-" + log_name)
+print("\n" + "*" * 12 + " Running the Log Parser for Spark Predictor " + "*" * 12 + "\n")
+print("--Processing log file: " + str(args.log_file))
 
-if os.path.exists(result_path):
-    os.remove(result_path)
+with tempfile.TemporaryDirectory() as work_dir:
+    event_log = EventLog(url=args.log_file.resolve().as_uri(), work_dir=work_dir)
+    app = sparkApplication(eventlog=str(event_log.event_log))
 
-appobj = sparkApplication(eventlog=log_path)
-appobj.save(result_path)
+result_path = os.path.join(args.result_dir, "parsed-" + args.log_file.name[:-len("".join(args.log_file.suffixes))])
+app.save(result_path)
 
-print(f"--Log directory saved to: {result_path}")
+print(f"--Result saved to: {result_path}.json")
diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
new file mode 100644
index 0000000..834c767
--- /dev/null
+++ b/spark_log_parser/eventlog.py
@@ -0,0 +1,238 @@
+import gzip
+import json
+import pandas as pd
+import shutil
+import struct
+import tarfile
+import zipfile
+import boto3 as boto
+import requests
+from pathlib import Path
+from pydantic import BaseModel, root_validator, stricturl
+
+
+AllowedURLs = stricturl(host_required=False, allowed_schemes={"https", "s3", "file"})
+
+
+class EventLog(BaseModel):
+    url: AllowedURLs
+    work_dir: Path
+    event_log: Path | None
+    file_total = 0
+    size_total = 0
+    file_limit = 15
+    size_limit = 5000000000
+    compression_ratio_limit = 100
+
+    @root_validator()
+    def validate_event_log(cls, values):
+        return vars(EventLogBuilder(values['url'], values['work_dir'], values['file_limit'], values['size_limit'], values['compression_ratio_limit']).build())
+
+
+class EventLogBuilder:
+    def __init__(self, url, work_dir, file_limit, size_limit, compression_ratio_limit):
+        self.url = url
+        self.work_dir = work_dir
+        self.file_total = 0
+        self.size_total = 0
+        self.file_limit = file_limit
+        self.size_limit = size_limit
+        self.compression_ratio_limit = compression_ratio_limit
+
+
+    def build(self) -> "EventLogBuilder":
+        if self.url.scheme in {"https", "s3"}:
+            local_path = self.work_dir.joinpath(self._basename())
+            self._download(local_path)
+        else:
+            local_path = Path(self.url.path)
+
+        event_logs = self._extract_archive(local_path, self.work_dir)
+
+        # Remove the file we downloaded to free up space
+        if self.url.scheme in {"https", "s3"}:
+            local_path.unlink()
+
+        self.event_log = self._concat(event_logs)
+
+        return self
+
+
+    def _extract_archive(self, event_log: Path, extract_dir: Path | None = None):
+        extension = "".join(event_log.suffixes)
+        if self._is_supported_archive_extension(extension):
+            if not extract_dir:
+                extract_dir = event_log.parent
+
+            if extension.endswith(".tar.gz"):
+                return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tar.gz")]))
+            if extension.endswith(".tgz"):
+                return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tgz")]))
+            if extension.endswith(".zip"):
+                return self._extract_zip(event_log, extract_dir.joinpath(event_log.name[:-len(".zip")]))
+            if extension == ".gz":
+                return self._extract_gz(event_log, extract_dir.joinpath(event_log.name[:-len(".gz")]))
+            if extension in {".json", ".log", ""}:
+                return [event_log]
+
+            raise ValueError("Unsupported extension")
+
+        else:
+            return [event_log]
+
+
+    def _concat(self, event_logs: list[Path]):
+        if len(event_logs) == 1:
+            return event_logs[0]
+
+        dat = []
+        for log in event_logs:
+            with open(log) as log_file:
+                line = json.loads(log_file.readline())
+                if line['Event'] == "DBCEventLoggingListenerMetadata":
+                    dat.append((line['Rollover Number'], line['SparkContext Id'], log))
+
+        df = pd.DataFrame(dat, columns=["rollover_index", "context_id", "path"]).sort_values("rollover_index")
+        
+        if not all(df.rollover_index.diff()[1:] == 1):
+            raise ValueError("Rollover file appears to be missing")
+
+        if not len(df.context_id.unique() == 1):
+            raise ValueError("Not all rollover files have the same Spark context ID")
+
+        event_log = self.work_dir.joinpath(self._basename() + "-concatenated.json")
+        with open(event_log, "w") as fobj:
+            for path in df.path:
+                with open(path) as part_fobj:
+                    for line in part_fobj:
+                        fobj.write(line)
+
+        return event_log
+
+
+    def _extract_tgz(self, archive_path: Path, extract_dir: Path):
+        paths = []
+
+        with tarfile.open(archive_path) as tar_file:
+            for tf in tar_file:
+                if self._should_skip_file(tf.name):
+                    continue
+                if tf.isdir():
+                    continue
+
+                self.file_total += 1
+                self.size_total += tf.size
+                self._safety_check(tf.size)
+
+                extract_path = extract_dir.joinpath(tf.name)
+                extract_path.parent.mkdir(parents=True, exist_ok=True)
+                with tar_file.extract(tf.name) as source, open(extract_path, "wb") as target:
+                    shutil.copyfileobj(source, target)
+
+                # Remove the archive if it was extracted to free up space
+                sub_paths = self._extract_archive(extract_path)
+                if extract_path not in sub_paths:
+                    extract_path.unlink()
+
+                paths += sub_paths
+
+        return paths
+
+
+    def _extract_zip(self, archive_path: Path, extract_dir: Path):
+        paths = []
+
+        with zipfile.ZipFile(archive_path) as zfile:
+            for zinfo in zfile.infolist():
+                if self._should_skip_file(zinfo.filename):
+                    continue
+                if zinfo.is_dir():
+                    continue
+
+                self.file_total += 1
+                self.size_total += zinfo.file_size
+                self._safety_check(zinfo.file_size)
+
+                extract_path = extract_dir.joinpath(zinfo.filename)
+                extract_path.parent.mkdir(parents=True, exist_ok=True)
+                with zfile.open(zinfo.filename) as source, open(extract_path, "wb") as target:
+                    shutil.copyfileobj(source, target)
+
+                # Remove the archive if it was extracted to free up space
+                sub_paths = self._extract_archive(extract_path)
+                if extract_path not in sub_paths:
+                    extract_path.unlink()
+
+                paths += sub_paths
+
+        return paths
+
+
+    def _extract_gz(self, archive_path: Path, extract_path: Path):
+        with open(archive_path, "rb") as fobj:
+            fobj.seek(-4, 2)
+            size = struct.unpack("I", fobj.read(4))[0]
+
+        self.size_total += size
+        self._safety_check(size)
+
+        with gzip.open(archive_path, "rb") as source, open(extract_path, "wb") as target:
+            shutil.copyfileobj(source, target)
+
+        return [extract_path]
+
+
+    def _is_supported_archive_extension(self, extension: str):
+        if extension in {".tar.gz", ".tgz", ".gz", ".zip"}:
+            return True
+        return False
+
+
+    FILE_SKIP_PATTERNS = [
+        ".DS_Store".lower(),
+        "__MACOSX".lower(),
+        "/."
+    ]
+
+    def _should_skip_file(self, filename: str):
+        if filename.startswith("."):
+            return True
+
+        filename = filename.lower()
+        for name in EventLogBuilder.FILE_SKIP_PATTERNS:
+            if name in filename:
+                return True
+
+        return False
+
+
+    def _safety_check(self, size):
+        ratio = size / self.size_total
+        if ratio > self.compression_ratio_limit:
+            raise ValueError("Suspicious ratio size")
+
+        if self.size_total > self.size_limit:
+            raise ValueError("Arhive too big")
+
+        if self.file_total > self.file_limit:
+            raise ValueError("Too many filesin archive")
+
+
+    def _download(self, path):
+        with open(path, 'wb') as fobj:
+            if self.url.scheme == "https":
+                response = requests.get(self.url, stream=True)
+                for chunk in response.iter_content():
+                        fobj.write(chunk)
+            elif self.url.scheme == "s3":
+                s3 = boto.client("s3")
+                s3.download_fileobj(self.url.host, self.url.path, fobj)
+
+        return path
+
+
+    def _basename(self):
+        if self.url.scheme in {"https", "s3"}:
+            return self.url.path.split("/")[-1]
+        elif self.url.scheme == "file":
+            return Path(self.url.path).name
diff --git a/tests/logs/databricks.zip b/tests/logs/databricks.zip
new file mode 100644
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diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
new file mode 100644
index 0000000..9ebadcf
--- /dev/null
+++ b/tests/test_eventlog.py
@@ -0,0 +1,35 @@
+import json
+from pathlib import Path
+from spark_log_parser import eventlog
+import tempfile
+
+
+def test_simple_databricks_log():
+    event_log = Path("tests", "logs", "databricks.zip").resolve()
+
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+
+        with open(log.event_log) as log_fobj:
+            event = json.loads(log_fobj.readline())
+
+    assert log.file_total == 1, "One file in log archive"
+
+    assert all(key in event for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]), "All keys are present"
+
+    assert event['Event'] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
+
+def test_simple_emr_log():
+    event_log = Path("tests", "logs", "emr.zip").resolve()
+
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+
+        with open(log.event_log) as log_fobj:
+            event = json.loads(log_fobj.readline())
+
+    assert log.file_total == 1, "One file in log archive"
+
+    assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
+
+    assert event['Event'] == "SparkListenerLogStart", "Expected first event is present"
diff --git a/tests/test_parse.py b/tests/test_parse.py
new file mode 100644
index 0000000..830e509
--- /dev/null
+++ b/tests/test_parse.py
@@ -0,0 +1,39 @@
+import json
+from pathlib import Path
+from spark_log_parser import eventlog
+import tempfile
+
+from spark_log_parser.parsing_models.application_model_v2 import sparkApplication
+
+
+def test_simple_databricks_log():
+    event_log = Path("tests", "logs", "databricks.zip").resolve()
+
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+
+        result_path = str(Path(temp_dir, "result"))
+        sparkApplication(eventlog=str(log.event_log)).save(result_path)
+
+        with open(result_path + ".json") as result_fobj:
+            parsed = json.load(result_fobj)
+
+    assert all(key in parsed for key in ["accumData", "executors", "jobData", "metadata", "sqlData", "stageData", "taskData"]), "All keys are present"
+
+    assert parsed['metadata']['application_info']['name'] == "Databricks Shell", "Name is as expected"
+
+def test_simple_emr_log():
+    event_log = Path("tests", "logs", "emr.zip").resolve()
+
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+
+        result_path = str(Path(temp_dir, "result"))
+        sparkApplication(eventlog=str(log.event_log)).save(result_path)
+
+        with open(str(result_path) + ".json") as result_fobj:
+            parsed = json.load(result_fobj)
+
+    assert all(key in parsed for key in ["accumData", "executors", "jobData", "metadata", "sqlData", "stageData", "taskData"]), "All keys are present"
+
+    assert parsed['metadata']['application_info']['name'] == "Text Similarity", "Name is as expected"

From ca9f43af57a7e5c9c897f147353783b447ab3f5a Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Thu, 28 Jul 2022 03:18:00 -0700
Subject: [PATCH 02/14] [PROD-325] Appease Sonar

---
 tests/test_eventlog.py | 4 ++--
 tests/test_parse.py    | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
index 9ebadcf..4aa059a 100644
--- a/tests/test_eventlog.py
+++ b/tests/test_eventlog.py
@@ -7,7 +7,7 @@
 def test_simple_databricks_log():
     event_log = Path("tests", "logs", "databricks.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
         log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
@@ -22,7 +22,7 @@ def test_simple_databricks_log():
 def test_simple_emr_log():
     event_log = Path("tests", "logs", "emr.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
         log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
diff --git a/tests/test_parse.py b/tests/test_parse.py
index 830e509..5933428 100644
--- a/tests/test_parse.py
+++ b/tests/test_parse.py
@@ -9,7 +9,7 @@
 def test_simple_databricks_log():
     event_log = Path("tests", "logs", "databricks.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
         log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
 
         result_path = str(Path(temp_dir, "result"))
@@ -25,7 +25,7 @@ def test_simple_databricks_log():
 def test_simple_emr_log():
     event_log = Path("tests", "logs", "emr.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))], dir="/tmp") as temp_dir:
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
         log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
 
         result_path = str(Path(temp_dir, "result"))

From 5688ef7d9ff013772e84188ddb11fc13eb770f96 Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Thu, 28 Jul 2022 13:12:30 -0700
Subject: [PATCH 03/14] [PROD-325] Bug fixin, test adding

---
 spark_log_parser/eventlog.py       |  14 ++++++++------
 tests/logs/databricks-rollover.zip | Bin 0 -> 4086138 bytes
 tests/test_eventlog.py             |  25 +++++++++++++++++++++++++
 3 files changed, 33 insertions(+), 6 deletions(-)
 create mode 100644 tests/logs/databricks-rollover.zip

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index 834c767..d633253 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -11,7 +11,7 @@
 from pydantic import BaseModel, root_validator, stricturl
 
 
-AllowedURLs = stricturl(host_required=False, allowed_schemes={"https", "s3", "file"})
+AllowedURLs = stricturl(host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"})
 
 
 class EventLog(BaseModel):
@@ -53,7 +53,7 @@ def build(self) -> "EventLogBuilder":
         if self.url.scheme in {"https", "s3"}:
             local_path.unlink()
 
-        self.event_log = self._concat(event_logs)
+        self.event_log = self._concat(event_logs, self.work_dir.joinpath(local_path.name[:-len("".join(local_path.suffixes))] + "-concatenated.json"))
 
         return self
 
@@ -81,14 +81,17 @@ def _extract_archive(self, event_log: Path, extract_dir: Path | None = None):
             return [event_log]
 
 
-    def _concat(self, event_logs: list[Path]):
+    def _concat(self, event_logs: list[Path], event_log: Path):
         if len(event_logs) == 1:
             return event_logs[0]
 
         dat = []
         for log in event_logs:
             with open(log) as log_file:
-                line = json.loads(log_file.readline())
+                try:
+                    line = json.loads(log_file.readline())
+                except ValueError:
+                    continue # Maybe a Databricks pricing file
                 if line['Event'] == "DBCEventLoggingListenerMetadata":
                     dat.append((line['Rollover Number'], line['SparkContext Id'], log))
 
@@ -100,7 +103,6 @@ def _concat(self, event_logs: list[Path]):
         if not len(df.context_id.unique() == 1):
             raise ValueError("Not all rollover files have the same Spark context ID")
 
-        event_log = self.work_dir.joinpath(self._basename() + "-concatenated.json")
         with open(event_log, "w") as fobj:
             for path in df.path:
                 with open(path) as part_fobj:
@@ -126,7 +128,7 @@ def _extract_tgz(self, archive_path: Path, extract_dir: Path):
 
                 extract_path = extract_dir.joinpath(tf.name)
                 extract_path.parent.mkdir(parents=True, exist_ok=True)
-                with tar_file.extract(tf.name) as source, open(extract_path, "wb") as target:
+                with tar_file.extractfile(tf.name) as source, open(extract_path, "wb") as target:
                     shutil.copyfileobj(source, target)
 
                 # Remove the archive if it was extracted to free up space
diff --git a/tests/logs/databricks-rollover.zip b/tests/logs/databricks-rollover.zip
new file mode 100644
index 0000000000000000000000000000000000000000..bc6d5a051add38e5a9598a539a160a1259add002
GIT binary patch
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HcmV?d00001

diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
index 4aa059a..12a1339 100644
--- a/tests/test_eventlog.py
+++ b/tests/test_eventlog.py
@@ -19,6 +19,7 @@ def test_simple_databricks_log():
 
     assert event['Event'] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
 
+
 def test_simple_emr_log():
     event_log = Path("tests", "logs", "emr.zip").resolve()
 
@@ -33,3 +34,27 @@ def test_simple_emr_log():
     assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
 
     assert event['Event'] == "SparkListenerLogStart", "Expected first event is present"
+
+
+def test_databricks_rollover_log():
+    event_log = Path("tests", "logs", "databricks-rollover.zip").resolve()
+
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+
+        with open(log.event_log) as log_fobj:
+            event = json.loads(log_fobj.readline())
+            log_fobj.seek(0)
+
+            rollover_count = 0
+            for i, event_str in enumerate(log_fobj):
+                event = json.loads(event_str)
+                if "Rollover Number" in event:
+                    assert all(key in event for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]), "All keys are present"
+                    assert rollover_count == event['Rollover Number'], "Contiguous monotonically increasing IDs"
+                    rollover_count += 1
+
+            assert rollover_count == 3, "All log parts are present"
+            assert i + 1 == 16945, "All events are present"
+
+    assert log.file_total == 4, "4 files in the log archive - 3 event log parts and 1 pricing file"
\ No newline at end of file

From 618f168b3588448b3faddef73531458b455b1173 Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Thu, 28 Jul 2022 17:53:16 -0700
Subject: [PATCH 04/14] [PROD-325] More bug fixin, test adding

---
 spark_log_parser/__main__.py             |   2 +-
 spark_log_parser/eventlog.py             | 103 ++++++++++++-----------
 tests/logs/bad/duplicate-part.tgz        | Bin 0 -> 426 bytes
 tests/logs/bad/missing-dbc-event.zip     | Bin 0 -> 1370 bytes
 tests/logs/bad/missing-part.zip          | Bin 0 -> 933 bytes
 tests/logs/bad/non-unique-context-id.zip | Bin 0 -> 1555 bytes
 tests/logs/databricks-rollover-messy.zip | Bin 0 -> 4103745 bytes
 tests/test_eventlog.py                   |  30 ++++++-
 tests/test_parse.py                      |   4 +-
 9 files changed, 86 insertions(+), 53 deletions(-)
 create mode 100644 tests/logs/bad/duplicate-part.tgz
 create mode 100644 tests/logs/bad/missing-dbc-event.zip
 create mode 100644 tests/logs/bad/missing-part.zip
 create mode 100644 tests/logs/bad/non-unique-context-id.zip
 create mode 100644 tests/logs/databricks-rollover-messy.zip

diff --git a/spark_log_parser/__main__.py b/spark_log_parser/__main__.py
index 728ef62..b4d3ad7 100644
--- a/spark_log_parser/__main__.py
+++ b/spark_log_parser/__main__.py
@@ -27,7 +27,7 @@
 print("--Processing log file: " + str(args.log_file))
 
 with tempfile.TemporaryDirectory() as work_dir:
-    event_log = EventLog(url=args.log_file.resolve().as_uri(), work_dir=work_dir)
+    event_log = EventLog(source_url=args.log_file.resolve().as_uri(), work_dir=work_dir)
     app = sparkApplication(eventlog=str(event_log.event_log))
 
 result_path = os.path.join(args.result_dir, "parsed-" + args.log_file.name[:-len("".join(args.log_file.suffixes))])
diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index d633253..4c8b97c 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -11,11 +11,11 @@
 from pydantic import BaseModel, root_validator, stricturl
 
 
-AllowedURLs = stricturl(host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"})
+AllowedURL = stricturl(host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"})
 
 
 class EventLog(BaseModel):
-    url: AllowedURLs
+    source_url: AllowedURL
     work_dir: Path
     event_log: Path | None
     file_total = 0
@@ -26,12 +26,12 @@ class EventLog(BaseModel):
 
     @root_validator()
     def validate_event_log(cls, values):
-        return vars(EventLogBuilder(values['url'], values['work_dir'], values['file_limit'], values['size_limit'], values['compression_ratio_limit']).build())
+        return vars(EventLogBuilder(values['source_url'], values['work_dir'], values['file_limit'], values['size_limit'], values['compression_ratio_limit']).build())
 
 
 class EventLogBuilder:
-    def __init__(self, url, work_dir, file_limit, size_limit, compression_ratio_limit):
-        self.url = url
+    def __init__(self, source_url, work_dir, file_limit, size_limit, compression_ratio_limit):
+        self.source_url = source_url
         self.work_dir = work_dir
         self.file_total = 0
         self.size_total = 0
@@ -41,16 +41,16 @@ def __init__(self, url, work_dir, file_limit, size_limit, compression_ratio_limi
 
 
     def build(self) -> "EventLogBuilder":
-        if self.url.scheme in {"https", "s3"}:
+        if self.source_url.scheme in {"https", "s3"}:
             local_path = self.work_dir.joinpath(self._basename())
             self._download(local_path)
         else:
-            local_path = Path(self.url.path)
+            local_path = Path(self.source_url.path)
 
         event_logs = self._extract_archive(local_path, self.work_dir)
 
-        # Remove the file we downloaded to free up space
-        if self.url.scheme in {"https", "s3"}:
+        # Remove the file if we downloaded it to free up space
+        if self.source_url.scheme in {"https", "s3"}:
             local_path.unlink()
 
         self.event_log = self._concat(event_logs, self.work_dir.joinpath(local_path.name[:-len("".join(local_path.suffixes))] + "-concatenated.json"))
@@ -59,27 +59,23 @@ def build(self) -> "EventLogBuilder":
 
 
     def _extract_archive(self, event_log: Path, extract_dir: Path | None = None):
-        extension = "".join(event_log.suffixes)
-        if self._is_supported_archive_extension(extension):
-            if not extract_dir:
-                extract_dir = event_log.parent
-
-            if extension.endswith(".tar.gz"):
-                return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tar.gz")]))
-            if extension.endswith(".tgz"):
-                return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tgz")]))
-            if extension.endswith(".zip"):
-                return self._extract_zip(event_log, extract_dir.joinpath(event_log.name[:-len(".zip")]))
-            if extension == ".gz":
-                return self._extract_gz(event_log, extract_dir.joinpath(event_log.name[:-len(".gz")]))
-            if extension in {".json", ".log", ""}:
-                return [event_log]
-
-            raise ValueError("Unsupported extension")
+        if not extract_dir:
+            extract_dir = event_log.parent
 
-        else:
+        extension = "".join(event_log.suffixes)
+        if extension.endswith(".tar.gz"):
+            return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tar.gz")]))
+        if extension.endswith(".tgz"):
+            return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tgz")]))
+        if extension.endswith(".zip"):
+            return self._extract_zip(event_log, extract_dir.joinpath(event_log.name[:-len(".zip")]))
+        if extension.endswith(".gz"):
+            return self._extract_gz(event_log, extract_dir.joinpath(event_log.name[:-len(".gz")]))
+        if extension in {".json", ".log", ""}:
             return [event_log]
 
+        raise ValueError("Unsupported extension found in the archive")
+
 
     def _concat(self, event_logs: list[Path], event_log: Path):
         if len(event_logs) == 1:
@@ -94,14 +90,21 @@ def _concat(self, event_logs: list[Path], event_log: Path):
                     continue # Maybe a Databricks pricing file
                 if line['Event'] == "DBCEventLoggingListenerMetadata":
                     dat.append((line['Rollover Number'], line['SparkContext Id'], log))
+                else:
+                    raise ValueError("Expected DBC event not found")
 
         df = pd.DataFrame(dat, columns=["rollover_index", "context_id", "path"]).sort_values("rollover_index")
         
-        if not all(df.rollover_index.diff()[1:] == 1):
+        if not len(df.context_id.unique()) == 1:
+            raise ValueError("Not all rollover files have the same Spark context ID")
+
+        diffs = df.rollover_index.diff()[1:]
+
+        if any(diffs > 1):
             raise ValueError("Rollover file appears to be missing")
 
-        if not len(df.context_id.unique() == 1):
-            raise ValueError("Not all rollover files have the same Spark context ID")
+        if any(diffs < 1):
+            raise ValueError("Duplicate rollover file detected")
 
         with open(event_log, "w") as fobj:
             for path in df.path:
@@ -134,6 +137,8 @@ def _extract_tgz(self, archive_path: Path, extract_dir: Path):
                 # Remove the archive if it was extracted to free up space
                 sub_paths = self._extract_archive(extract_path)
                 if extract_path not in sub_paths:
+                    self.file_total -= 1
+                    self.size_total -= tf.size
                     extract_path.unlink()
 
                 paths += sub_paths
@@ -163,6 +168,8 @@ def _extract_zip(self, archive_path: Path, extract_dir: Path):
                 # Remove the archive if it was extracted to free up space
                 sub_paths = self._extract_archive(extract_path)
                 if extract_path not in sub_paths:
+                    self.file_total -= 1
+                    self.size_total -= zinfo.file_size
                     extract_path.unlink()
 
                 paths += sub_paths
@@ -175,19 +182,21 @@ def _extract_gz(self, archive_path: Path, extract_path: Path):
             fobj.seek(-4, 2)
             size = struct.unpack("I", fobj.read(4))[0]
 
+        self.file_total += 1
         self.size_total += size
         self._safety_check(size)
 
         with gzip.open(archive_path, "rb") as source, open(extract_path, "wb") as target:
             shutil.copyfileobj(source, target)
 
-        return [extract_path]
-
+        # Remove the archive if it was extracted to free up space
+        sub_paths = self._extract_archive(extract_path)
+        if extract_path not in sub_paths:
+            self.file_total -= 1
+            self.size_total -= size
+            extract_path.unlink()
 
-    def _is_supported_archive_extension(self, extension: str):
-        if extension in {".tar.gz", ".tgz", ".gz", ".zip"}:
-            return True
-        return False
+        return sub_paths
 
 
     FILE_SKIP_PATTERNS = [
@@ -211,30 +220,30 @@ def _should_skip_file(self, filename: str):
     def _safety_check(self, size):
         ratio = size / self.size_total
         if ratio > self.compression_ratio_limit:
-            raise ValueError("Suspicious ratio size")
+            raise AssertionError("Encountered suspicious compression ratio in the archive")
 
         if self.size_total > self.size_limit:
-            raise ValueError("Arhive too big")
+            raise AssertionError("The archive is too big")
 
         if self.file_total > self.file_limit:
-            raise ValueError("Too many filesin archive")
+            raise AssertionError("Too many files in the archive")
 
 
     def _download(self, path):
         with open(path, 'wb') as fobj:
-            if self.url.scheme == "https":
-                response = requests.get(self.url, stream=True)
+            if self.source_url.scheme == "https":
+                response = requests.get(self.source_url, stream=True)
                 for chunk in response.iter_content():
                         fobj.write(chunk)
-            elif self.url.scheme == "s3":
+            elif self.source_url.scheme == "s3":
                 s3 = boto.client("s3")
-                s3.download_fileobj(self.url.host, self.url.path, fobj)
+                s3.download_fileobj(self.source_url.host, self.source_url.path, fobj)
 
         return path
 
 
     def _basename(self):
-        if self.url.scheme in {"https", "s3"}:
-            return self.url.path.split("/")[-1]
-        elif self.url.scheme == "file":
-            return Path(self.url.path).name
+        if self.source_url.scheme in {"https", "s3"}:
+            return self.source_url.path.split("/")[-1]
+        elif self.source_url.scheme == "file":
+            return Path(self.source_url.path).name
diff --git a/tests/logs/bad/duplicate-part.tgz b/tests/logs/bad/duplicate-part.tgz
new file mode 100644
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HcmV?d00001

diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
index 12a1339..3d69c7c 100644
--- a/tests/test_eventlog.py
+++ b/tests/test_eventlog.py
@@ -8,7 +8,7 @@ def test_simple_databricks_log():
     event_log = Path("tests", "logs", "databricks.zip").resolve()
 
     with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
-        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
@@ -24,7 +24,7 @@ def test_simple_emr_log():
     event_log = Path("tests", "logs", "emr.zip").resolve()
 
     with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
-        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
@@ -40,7 +40,31 @@ def test_databricks_rollover_log():
     event_log = Path("tests", "logs", "databricks-rollover.zip").resolve()
 
     with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
-        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+
+        with open(log.event_log) as log_fobj:
+            event = json.loads(log_fobj.readline())
+            log_fobj.seek(0)
+
+            rollover_count = 0
+            for i, event_str in enumerate(log_fobj):
+                event = json.loads(event_str)
+                if "Rollover Number" in event:
+                    assert all(key in event for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]), "All keys are present"
+                    assert rollover_count == event['Rollover Number'], "Contiguous monotonically increasing IDs"
+                    rollover_count += 1
+
+            assert rollover_count == 3, "All log parts are present"
+            assert i + 1 == 16945, "All events are present"
+
+    assert log.file_total == 4, "4 files in the log archive - 3 event log parts and 1 pricing file"
+
+
+def test_databricks_messy_rollover_log_archive():
+    event_log = Path("tests", "logs", "databricks-rollover-messy.zip").resolve()
+
+    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
diff --git a/tests/test_parse.py b/tests/test_parse.py
index 5933428..cb9b36c 100644
--- a/tests/test_parse.py
+++ b/tests/test_parse.py
@@ -10,7 +10,7 @@ def test_simple_databricks_log():
     event_log = Path("tests", "logs", "databricks.zip").resolve()
 
     with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
-        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         result_path = str(Path(temp_dir, "result"))
         sparkApplication(eventlog=str(log.event_log)).save(result_path)
@@ -26,7 +26,7 @@ def test_simple_emr_log():
     event_log = Path("tests", "logs", "emr.zip").resolve()
 
     with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
-        log = eventlog.EventLog(url=event_log.as_uri(), work_dir=temp_dir)
+        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         result_path = str(Path(temp_dir, "result"))
         sparkApplication(eventlog=str(log.event_log)).save(result_path)

From 65bc4372390b2e555aa681c514b4e92300e149be Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Thu, 28 Jul 2022 18:34:34 -0700
Subject: [PATCH 05/14] [PROD-325] Missed a test file, added makefile and
 linting

---
 makefile                     | 21 ++++++++
 pyproject.toml               |  4 ++
 spark_log_parser/__main__.py | 26 +++++-----
 spark_log_parser/eventlog.py | 92 ++++++++++++++++++++----------------
 tests/test_bad_eventlog.py   | 45 ++++++++++++++++++
 tests/test_eventlog.py       | 60 ++++++++++++++++++-----
 tests/test_parse.py          | 47 ++++++++++++++----
 7 files changed, 222 insertions(+), 73 deletions(-)
 create mode 100644 makefile
 create mode 100644 tests/test_bad_eventlog.py

diff --git a/makefile b/makefile
new file mode 100644
index 0000000..20a802f
--- /dev/null
+++ b/makefile
@@ -0,0 +1,21 @@
+FILES := $(shell git diff --name-only --diff-filter=AM $$(git merge-base origin/main HEAD) -- \*.py)
+
+
+.PHONY: test
+test:
+	pytest
+
+.PHONY: lint
+lint:
+	flake8 --filename ./$(FILES) --max-complexity=10 --ignore=E501,W503
+
+.PHONY: format
+format:
+ifneq ("$(FILES)"," ")
+	black $(FILES)
+	isort $(FILES)
+endif
+
+.PHONY: tidy
+tidy: format lint
+
diff --git a/pyproject.toml b/pyproject.toml
index 593c762..d05b7f8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,6 +31,9 @@ dynamic = ["version", "description"]
 [project.optional-dependencies]
 test = [
     "pytest==7.1.2",
+    "black==22.6.0",
+    "isort==5.10.1",
+    "flake8==4.0.1",
 ]
 
 [project.urls]
@@ -41,6 +44,7 @@ line-length = 100
 
 [tool.isort]
 profile = "black"
+line_length = 100
 
 [tool.pytest.ini_options]
 pythonpath = [
diff --git a/spark_log_parser/__main__.py b/spark_log_parser/__main__.py
index b4d3ad7..c99e3dc 100644
--- a/spark_log_parser/__main__.py
+++ b/spark_log_parser/__main__.py
@@ -1,23 +1,23 @@
+import argparse
 import logging
-
-logging.captureWarnings(True)
-
+import os
+import sys
 import tempfile
-from spark_log_parser.eventlog import EventLog
+from pathlib import Path
 
-from spark_log_parser.parsing_models.application_model_v2 import sparkApplication
+logging.captureWarnings(True)
 
-import os
-import argparse
-from pathlib import Path
-import sys
+from spark_log_parser.eventlog import EventLog  # noqa: E402
+from spark_log_parser.parsing_models.application_model_v2 import sparkApplication  # noqa: E402
 
 logger = logging.getLogger("spark_log_parser")
 
 parser = argparse.ArgumentParser("spark_log_parser")
 parser.add_argument("-l", "--log-file", required=True, type=Path, help="path to event log")
-parser.add_argument("-r", "--result-dir", required=True, help="path to directory in which to save parsed logs")
-args = parser.parse_args()  
+parser.add_argument(
+    "-r", "--result-dir", required=True, help="path to directory in which to save parsed logs"
+)
+args = parser.parse_args()
 
 if not os.path.isdir(args.result_dir):
     logger.error("%s is not a directory", args.result_dir)
@@ -30,7 +30,9 @@
     event_log = EventLog(source_url=args.log_file.resolve().as_uri(), work_dir=work_dir)
     app = sparkApplication(eventlog=str(event_log.event_log))
 
-result_path = os.path.join(args.result_dir, "parsed-" + args.log_file.name[:-len("".join(args.log_file.suffixes))])
+result_path = os.path.join(
+    args.result_dir, "parsed-" + args.log_file.name[: -len("".join(args.log_file.suffixes))]
+)
 app.save(result_path)
 
 print(f"--Result saved to: {result_path}.json")
diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index 4c8b97c..78d25a8 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -1,17 +1,19 @@
 import gzip
 import json
-import pandas as pd
 import shutil
 import struct
 import tarfile
 import zipfile
+from pathlib import Path
+
 import boto3 as boto
+import pandas as pd
 import requests
-from pathlib import Path
 from pydantic import BaseModel, root_validator, stricturl
 
-
-AllowedURL = stricturl(host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"})
+AllowedURL = stricturl(
+    host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"}
+)
 
 
 class EventLog(BaseModel):
@@ -26,7 +28,15 @@ class EventLog(BaseModel):
 
     @root_validator()
     def validate_event_log(cls, values):
-        return vars(EventLogBuilder(values['source_url'], values['work_dir'], values['file_limit'], values['size_limit'], values['compression_ratio_limit']).build())
+        return vars(
+            EventLogBuilder(
+                values["source_url"],
+                values["work_dir"],
+                values["file_limit"],
+                values["size_limit"],
+                values["compression_ratio_limit"],
+            ).build()
+        )
 
 
 class EventLogBuilder:
@@ -39,7 +49,6 @@ def __init__(self, source_url, work_dir, file_limit, size_limit, compression_rat
         self.size_limit = size_limit
         self.compression_ratio_limit = compression_ratio_limit
 
-
     def build(self) -> "EventLogBuilder":
         if self.source_url.scheme in {"https", "s3"}:
             local_path = self.work_dir.joinpath(self._basename())
@@ -53,30 +62,39 @@ def build(self) -> "EventLogBuilder":
         if self.source_url.scheme in {"https", "s3"}:
             local_path.unlink()
 
-        self.event_log = self._concat(event_logs, self.work_dir.joinpath(local_path.name[:-len("".join(local_path.suffixes))] + "-concatenated.json"))
+        self.event_log = self._concat(
+            event_logs,
+            self.work_dir.joinpath(
+                local_path.name[: -len("".join(local_path.suffixes))] + "-concatenated.json"
+            ),
+        )
 
         return self
 
-
     def _extract_archive(self, event_log: Path, extract_dir: Path | None = None):
         if not extract_dir:
             extract_dir = event_log.parent
 
         extension = "".join(event_log.suffixes)
         if extension.endswith(".tar.gz"):
-            return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tar.gz")]))
+            return self._extract_tgz(
+                event_log, extract_dir.joinpath(event_log.name[: -len(".tar.gz")])
+            )
         if extension.endswith(".tgz"):
-            return self._extract_tgz(event_log, extract_dir.joinpath(event_log.name[:-len(".tgz")]))
+            return self._extract_tgz(
+                event_log, extract_dir.joinpath(event_log.name[: -len(".tgz")])
+            )
         if extension.endswith(".zip"):
-            return self._extract_zip(event_log, extract_dir.joinpath(event_log.name[:-len(".zip")]))
+            return self._extract_zip(
+                event_log, extract_dir.joinpath(event_log.name[: -len(".zip")])
+            )
         if extension.endswith(".gz"):
-            return self._extract_gz(event_log, extract_dir.joinpath(event_log.name[:-len(".gz")]))
+            return self._extract_gz(event_log, extract_dir.joinpath(event_log.name[: -len(".gz")]))
         if extension in {".json", ".log", ""}:
             return [event_log]
 
         raise ValueError("Unsupported extension found in the archive")
 
-
     def _concat(self, event_logs: list[Path], event_log: Path):
         if len(event_logs) == 1:
             return event_logs[0]
@@ -87,24 +105,17 @@ def _concat(self, event_logs: list[Path], event_log: Path):
                 try:
                     line = json.loads(log_file.readline())
                 except ValueError:
-                    continue # Maybe a Databricks pricing file
-                if line['Event'] == "DBCEventLoggingListenerMetadata":
-                    dat.append((line['Rollover Number'], line['SparkContext Id'], log))
+                    continue  # Maybe a Databricks pricing file
+                if line["Event"] == "DBCEventLoggingListenerMetadata":
+                    dat.append((line["Rollover Number"], line["SparkContext Id"], log))
                 else:
                     raise ValueError("Expected DBC event not found")
 
-        df = pd.DataFrame(dat, columns=["rollover_index", "context_id", "path"]).sort_values("rollover_index")
-        
-        if not len(df.context_id.unique()) == 1:
-            raise ValueError("Not all rollover files have the same Spark context ID")
-
-        diffs = df.rollover_index.diff()[1:]
+        df = pd.DataFrame(dat, columns=["rollover_index", "context_id", "path"]).sort_values(
+            "rollover_index"
+        )
 
-        if any(diffs > 1):
-            raise ValueError("Rollover file appears to be missing")
-
-        if any(diffs < 1):
-            raise ValueError("Duplicate rollover file detected")
+        self._validate_rollover_logs(df)
 
         with open(event_log, "w") as fobj:
             for path in df.path:
@@ -114,6 +125,17 @@ def _concat(self, event_logs: list[Path], event_log: Path):
 
         return event_log
 
+    def _validate_rollover_logs(self, df: pd.DataFrame):
+        if not len(df.context_id.unique()) == 1:
+            raise ValueError("Not all rollover files have the same Spark context ID")
+
+        diffs = df.rollover_index.diff()[1:]
+
+        if any(diffs > 1):
+            raise ValueError("Rollover file appears to be missing")
+
+        if any(diffs < 1):
+            raise ValueError("Duplicate rollover file detected")
 
     def _extract_tgz(self, archive_path: Path, extract_dir: Path):
         paths = []
@@ -145,7 +167,6 @@ def _extract_tgz(self, archive_path: Path, extract_dir: Path):
 
         return paths
 
-
     def _extract_zip(self, archive_path: Path, extract_dir: Path):
         paths = []
 
@@ -176,7 +197,6 @@ def _extract_zip(self, archive_path: Path, extract_dir: Path):
 
         return paths
 
-
     def _extract_gz(self, archive_path: Path, extract_path: Path):
         with open(archive_path, "rb") as fobj:
             fobj.seek(-4, 2)
@@ -198,12 +218,7 @@ def _extract_gz(self, archive_path: Path, extract_path: Path):
 
         return sub_paths
 
-
-    FILE_SKIP_PATTERNS = [
-        ".DS_Store".lower(),
-        "__MACOSX".lower(),
-        "/."
-    ]
+    FILE_SKIP_PATTERNS = [".DS_Store".lower(), "__MACOSX".lower(), "/."]
 
     def _should_skip_file(self, filename: str):
         if filename.startswith("."):
@@ -216,7 +231,6 @@ def _should_skip_file(self, filename: str):
 
         return False
 
-
     def _safety_check(self, size):
         ratio = size / self.size_total
         if ratio > self.compression_ratio_limit:
@@ -228,20 +242,18 @@ def _safety_check(self, size):
         if self.file_total > self.file_limit:
             raise AssertionError("Too many files in the archive")
 
-
     def _download(self, path):
-        with open(path, 'wb') as fobj:
+        with open(path, "wb") as fobj:
             if self.source_url.scheme == "https":
                 response = requests.get(self.source_url, stream=True)
                 for chunk in response.iter_content():
-                        fobj.write(chunk)
+                    fobj.write(chunk)
             elif self.source_url.scheme == "s3":
                 s3 = boto.client("s3")
                 s3.download_fileobj(self.source_url.host, self.source_url.path, fobj)
 
         return path
 
-
     def _basename(self):
         if self.source_url.scheme in {"https", "s3"}:
             return self.source_url.path.split("/")[-1]
diff --git a/tests/test_bad_eventlog.py b/tests/test_bad_eventlog.py
new file mode 100644
index 0000000..5ea928f
--- /dev/null
+++ b/tests/test_bad_eventlog.py
@@ -0,0 +1,45 @@
+import tempfile
+import unittest
+from pathlib import Path
+
+from spark_log_parser import eventlog
+
+
+class BadEventLog(unittest.TestCase):
+    def test_multiple_context_ids(self):
+        event_log = Path("tests", "logs", "bad", "non-unique-context-id.zip").resolve()
+
+        with tempfile.TemporaryDirectory(
+            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+        ) as temp_dir:
+            with self.assertRaises(
+                ValueError, msg="Not all rollover files have the same Spark context ID"
+            ):
+                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+
+    def test_missing_dbc_event(self):
+        event_log = Path("tests", "logs", "bad", "missing-dbc-event.zip").resolve()
+
+        with tempfile.TemporaryDirectory(
+            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+        ) as temp_dir:
+            with self.assertRaises(ValueError, msg="Expected DBC event not found"):
+                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+
+    def test_duplicate_log_part(self):
+        event_log = Path("tests", "logs", "bad", "duplicate-part.tgz").resolve()
+
+        with tempfile.TemporaryDirectory(
+            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+        ) as temp_dir:
+            with self.assertRaises(ValueError, msg="Duplicate rollover file detected"):
+                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+
+    def test_missing_log_part(self):
+        event_log = Path("tests", "logs", "bad", "missing-part.zip").resolve()
+
+        with tempfile.TemporaryDirectory(
+            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+        ) as temp_dir:
+            with self.assertRaises(ValueError, msg="Rollover file appears to be missing"):
+                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
index 3d69c7c..a791d20 100644
--- a/tests/test_eventlog.py
+++ b/tests/test_eventlog.py
@@ -1,13 +1,16 @@
 import json
+import tempfile
 from pathlib import Path
+
 from spark_log_parser import eventlog
-import tempfile
 
 
 def test_simple_databricks_log():
     event_log = Path("tests", "logs", "databricks.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+    ) as temp_dir:
         log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
@@ -15,15 +18,20 @@ def test_simple_databricks_log():
 
     assert log.file_total == 1, "One file in log archive"
 
-    assert all(key in event for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]), "All keys are present"
+    assert all(
+        key in event
+        for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]
+    ), "All keys are present"
 
-    assert event['Event'] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
+    assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
 
 
 def test_simple_emr_log():
     event_log = Path("tests", "logs", "emr.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+    ) as temp_dir:
         log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
@@ -33,13 +41,15 @@ def test_simple_emr_log():
 
     assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
 
-    assert event['Event'] == "SparkListenerLogStart", "Expected first event is present"
+    assert event["Event"] == "SparkListenerLogStart", "Expected first event is present"
 
 
 def test_databricks_rollover_log():
     event_log = Path("tests", "logs", "databricks-rollover.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+    ) as temp_dir:
         log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
@@ -50,8 +60,19 @@ def test_databricks_rollover_log():
             for i, event_str in enumerate(log_fobj):
                 event = json.loads(event_str)
                 if "Rollover Number" in event:
-                    assert all(key in event for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]), "All keys are present"
-                    assert rollover_count == event['Rollover Number'], "Contiguous monotonically increasing IDs"
+                    assert all(
+                        key in event
+                        for key in [
+                            "Event",
+                            "Spark Version",
+                            "Timestamp",
+                            "Rollover Number",
+                            "SparkContext Id",
+                        ]
+                    ), "All keys are present"
+                    assert (
+                        rollover_count == event["Rollover Number"]
+                    ), "Contiguous monotonically increasing IDs"
                     rollover_count += 1
 
             assert rollover_count == 3, "All log parts are present"
@@ -63,7 +84,9 @@ def test_databricks_rollover_log():
 def test_databricks_messy_rollover_log_archive():
     event_log = Path("tests", "logs", "databricks-rollover-messy.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+    ) as temp_dir:
         log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         with open(log.event_log) as log_fobj:
@@ -74,11 +97,22 @@ def test_databricks_messy_rollover_log_archive():
             for i, event_str in enumerate(log_fobj):
                 event = json.loads(event_str)
                 if "Rollover Number" in event:
-                    assert all(key in event for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]), "All keys are present"
-                    assert rollover_count == event['Rollover Number'], "Contiguous monotonically increasing IDs"
+                    assert all(
+                        key in event
+                        for key in [
+                            "Event",
+                            "Spark Version",
+                            "Timestamp",
+                            "Rollover Number",
+                            "SparkContext Id",
+                        ]
+                    ), "All keys are present"
+                    assert (
+                        rollover_count == event["Rollover Number"]
+                    ), "Contiguous monotonically increasing IDs"
                     rollover_count += 1
 
             assert rollover_count == 3, "All log parts are present"
             assert i + 1 == 16945, "All events are present"
 
-    assert log.file_total == 4, "4 files in the log archive - 3 event log parts and 1 pricing file"
\ No newline at end of file
+    assert log.file_total == 4, "4 files in the log archive - 3 event log parts and 1 pricing file"
diff --git a/tests/test_parse.py b/tests/test_parse.py
index cb9b36c..08aeaea 100644
--- a/tests/test_parse.py
+++ b/tests/test_parse.py
@@ -1,15 +1,17 @@
 import json
-from pathlib import Path
-from spark_log_parser import eventlog
 import tempfile
+from pathlib import Path
 
+from spark_log_parser import eventlog
 from spark_log_parser.parsing_models.application_model_v2 import sparkApplication
 
 
 def test_simple_databricks_log():
     event_log = Path("tests", "logs", "databricks.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+    ) as temp_dir:
         log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         result_path = str(Path(temp_dir, "result"))
@@ -18,14 +20,30 @@ def test_simple_databricks_log():
         with open(result_path + ".json") as result_fobj:
             parsed = json.load(result_fobj)
 
-    assert all(key in parsed for key in ["accumData", "executors", "jobData", "metadata", "sqlData", "stageData", "taskData"]), "All keys are present"
+    assert all(
+        key in parsed
+        for key in [
+            "accumData",
+            "executors",
+            "jobData",
+            "metadata",
+            "sqlData",
+            "stageData",
+            "taskData",
+        ]
+    ), "All keys are present"
+
+    assert (
+        parsed["metadata"]["application_info"]["name"] == "Databricks Shell"
+    ), "Name is as expected"
 
-    assert parsed['metadata']['application_info']['name'] == "Databricks Shell", "Name is as expected"
 
 def test_simple_emr_log():
     event_log = Path("tests", "logs", "emr.zip").resolve()
 
-    with tempfile.TemporaryDirectory(prefix="eventlog-%s-" % event_log.name[:-len("".join(event_log.suffixes))]) as temp_dir:
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+    ) as temp_dir:
         log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
 
         result_path = str(Path(temp_dir, "result"))
@@ -34,6 +52,19 @@ def test_simple_emr_log():
         with open(str(result_path) + ".json") as result_fobj:
             parsed = json.load(result_fobj)
 
-    assert all(key in parsed for key in ["accumData", "executors", "jobData", "metadata", "sqlData", "stageData", "taskData"]), "All keys are present"
+    assert all(
+        key in parsed
+        for key in [
+            "accumData",
+            "executors",
+            "jobData",
+            "metadata",
+            "sqlData",
+            "stageData",
+            "taskData",
+        ]
+    ), "All keys are present"
 
-    assert parsed['metadata']['application_info']['name'] == "Text Similarity", "Name is as expected"
+    assert (
+        parsed["metadata"]["application_info"]["name"] == "Text Similarity"
+    ), "Name is as expected"

From 2710733a2aff5e26134c2c966a026b59cd73eedb Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Thu, 28 Jul 2022 18:45:09 -0700
Subject: [PATCH 06/14] [PROD-325] Maybe appease Sonar?

---
 spark_log_parser/eventlog.py | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index 78d25a8..42e2b23 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -11,6 +11,10 @@
 import requests
 from pydantic import BaseModel, root_validator, stricturl
 
+THRESHOLD_ENTRIES = 100
+THRESHOLD_SIZE = 5000000000
+THRESHOLD_RATIO = 100
+
 AllowedURL = stricturl(
     host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"}
 )
@@ -22,9 +26,6 @@ class EventLog(BaseModel):
     event_log: Path | None
     file_total = 0
     size_total = 0
-    file_limit = 15
-    size_limit = 5000000000
-    compression_ratio_limit = 100
 
     @root_validator()
     def validate_event_log(cls, values):
@@ -32,22 +33,16 @@ def validate_event_log(cls, values):
             EventLogBuilder(
                 values["source_url"],
                 values["work_dir"],
-                values["file_limit"],
-                values["size_limit"],
-                values["compression_ratio_limit"],
             ).build()
         )
 
 
 class EventLogBuilder:
-    def __init__(self, source_url, work_dir, file_limit, size_limit, compression_ratio_limit):
+    def __init__(self, source_url, work_dir):
         self.source_url = source_url
         self.work_dir = work_dir
         self.file_total = 0
         self.size_total = 0
-        self.file_limit = file_limit
-        self.size_limit = size_limit
-        self.compression_ratio_limit = compression_ratio_limit
 
     def build(self) -> "EventLogBuilder":
         if self.source_url.scheme in {"https", "s3"}:
@@ -233,13 +228,13 @@ def _should_skip_file(self, filename: str):
 
     def _safety_check(self, size):
         ratio = size / self.size_total
-        if ratio > self.compression_ratio_limit:
+        if ratio > THRESHOLD_RATIO:
             raise AssertionError("Encountered suspicious compression ratio in the archive")
 
-        if self.size_total > self.size_limit:
+        if self.size_total > THRESHOLD_SIZE:
             raise AssertionError("The archive is too big")
 
-        if self.file_total > self.file_limit:
+        if self.file_total > THRESHOLD_ENTRIES:
             raise AssertionError("Too many files in the archive")
 
     def _download(self, path):

From 0a0f211eab6b8c706ffb307ff45bd42e4191e83d Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Fri, 29 Jul 2022 11:38:13 -0700
Subject: [PATCH 07/14] [PROD-325] Added support for unzipped and raw logs, and
 added tests

---
 spark_log_parser/eventlog.py      |   14 +-
 tests/logs/databricks-parsed.json |    1 +
 tests/logs/databricks.json        | 1162 +++++++++++++++++++++++++++++
 tests/test_eventlog.py            |   65 +-
 4 files changed, 1222 insertions(+), 20 deletions(-)
 create mode 100644 tests/logs/databricks-parsed.json
 create mode 100644 tests/logs/databricks.json

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index 42e2b23..45149a0 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -24,8 +24,7 @@ class EventLog(BaseModel):
     source_url: AllowedURL
     work_dir: Path
     event_log: Path | None
-    file_total = 0
-    size_total = 0
+    is_parsed = False
 
     @root_validator()
     def validate_event_log(cls, values):
@@ -53,8 +52,8 @@ def build(self) -> "EventLogBuilder":
 
         event_logs = self._extract_archive(local_path, self.work_dir)
 
-        # Remove the file if we downloaded it to free up space
-        if self.source_url.scheme in {"https", "s3"}:
+        # Remove the archive if we downloaded it to free up space
+        if local_path not in event_logs and self.source_url.scheme in {"https", "s3"}:
             local_path.unlink()
 
         self.event_log = self._concat(
@@ -64,6 +63,8 @@ def build(self) -> "EventLogBuilder":
             ),
         )
 
+        self.is_parsed = self._is_parsed(self.event_log)
+
         return self
 
     def _extract_archive(self, event_log: Path, extract_dir: Path | None = None):
@@ -249,6 +250,11 @@ def _download(self, path):
 
         return path
 
+    def _is_parsed(self, log_path: Path):
+        with open(log_path) as log:
+            entry = json.loads(log.readline())
+            return "jobData" in entry and "stageData" in entry and "taskData" in entry
+
     def _basename(self):
         if self.source_url.scheme in {"https", "s3"}:
             return self.source_url.path.split("/")[-1]
diff --git a/tests/logs/databricks-parsed.json b/tests/logs/databricks-parsed.json
new file mode 100644
index 0000000..273cedf
--- /dev/null
+++ b/tests/logs/databricks-parsed.json
@@ -0,0 +1 @@
+{"jobData": {"job_id": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], "sql_id": [0, null, 3, 3, 4, 6, 6, 6, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "stage_ids": [[0], [1], [2], [4], [5], [6], [8], [11], [12], [14], [17], [18], [19], [20], [22], [25], [27], [30], [32], [33], [34], [35], [37], [40], [42], [45], [46], [47], [48], [50], [53], [55], [58], [60, 61], [62], [63], [64], [66], [69], [71], [74], [76, 77], [78], [79], [80], [82], [85], [87], [90], [92, 93]], "submission_time": [37.59400010108948, 41.884000062942505, 78.25200009346008, 87.43200016021729, 87.72000002861023, 88.46300005912781, 105.00600004196167, 105.35500001907349, 106.0640001296997, 122.86600017547607, 123.04800009727478, 124.0090000629425, 124.11199998855591, 124.22399997711182, 142.82299995422363, 143.11800003051758, 143.20500016212463, 143.41300010681152, 150.41300010681152, 151.4670000076294, 151.54700016975403, 151.59899997711182, 169.65100002288818, 169.80800008773804, 170.40499997138977, 170.53600001335144, 177.70300006866455, 177.7850000858307, 177.85800004005432, 196.58000016212463, 196.72099995613098, 199.45900011062622, 199.56900000572205, 205.0220000743866, 221.0090000629425, 221.143000125885, 221.239000082016, 239.31100010871887, 239.43799996376038, 240.02600002288818, 240.17799997329712, 245.96099996566772, 261.2640001773834, 261.3250000476837, 261.3800001144409, 279.6410000324249, 279.8220000267029, 281.6160001754761, 281.71200013160706, 286.74000000953674], "completion_time": [41.21300005912781, 77.58700013160706, 87.36500000953674, 87.6010000705719, 87.91600012779236, 104.94000005722046, 105.23099994659424, 105.6800000667572, 122.8270001411438, 123.00600004196167, 123.37800002098083, 150.29100012779236, 143.06900000572205, 142.7020001411438, 142.99100017547607, 143.26200008392334, 143.32500004768372, 143.52999997138977, 150.5329999923706, 177.32800006866455, 170.36300015449524, 169.56299996376038, 169.75, 169.84800004959106, 170.49200010299683, 170.60300016403198, 204.6690001487732, 199.4190001487732, 196.53900003433228, 196.6600000858307, 196.75699996948242, 199.5090000629425, 199.61100006103516, 220.55400013923645, 245.7720000743866, 239.24500012397766, 239.9980001449585, 239.3710000514984, 239.50200009346008, 240.08700013160706, 240.32800006866455, 261.01200008392334, 286.63100004196167, 281.5859999656677, 279.55400013923645, 279.7720000743866, 279.8489999771118, 281.6579999923706, 281.7430000305176, 301.87600016593933], "duration": [3.61899995803833, 35.70300006866455, 9.11299991607666, 0.16899991035461426, 0.1960000991821289, 16.47699999809265, 0.22499990463256836, 0.3250000476837158, 16.763000011444092, 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-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--native_system-java--com.github.fommil.netlib__native_system-java__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--native_system-java-natives--com.github.fommil.netlib__native_system-java-natives__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--netlib-native_ref-linux-x86_64-natives--com.github.fommil.netlib__netlib-native_ref-linux-x86_64-natives__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--netlib-native_system-linux-x86_64-natives--com.github.fommil.netlib__netlib-native_system-linux-x86_64-natives__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.joshelser--dropwizard-metrics-hadoop-metrics2-reporter--com.github.joshelser__dropwizard-metrics-hadoop-metrics2-reporter__0.1.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.luben--zstd-jni--com.github.luben__zstd-jni__1.4.8-1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.wendykierp--JTransforms--com.github.wendykierp__JTransforms__3.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.code.findbugs--jsr305--com.google.code.findbugs__jsr305__3.0.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.code.gson--gson--com.google.code.gson__gson__2.2.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.crypto.tink--tink--com.google.crypto.tink__tink__1.6.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.flatbuffers--flatbuffers-java--com.google.flatbuffers__flatbuffers-java__1.9.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.guava--guava--com.google.guava__guava__15.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.protobuf--protobuf-java--com.google.protobuf__protobuf-java__2.6.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.h2database--h2--com.h2database__h2__1.4.195.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.helger--profiler--com.helger__profiler__1.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.jcraft--jsch--com.jcraft__jsch__0.1.50.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.jolbox--bonecp--com.jolbox__bonecp__0.8.0.RELEASE.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.lihaoyi--sourcecode_2.12--com.lihaoyi__sourcecode_2.12__0.1.9.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.microsoft.azure--azure-data-lake-store-sdk--com.microsoft.azure__azure-data-lake-store-sdk__2.3.9.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.ning--compress-lzf--com.ning__compress-lzf__1.0.3.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.sun.mail--javax.mail--com.sun.mail__javax.mail__1.5.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.tdunning--json--com.tdunning__json__1.8.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.thoughtworks.paranamer--paranamer--com.thoughtworks.paranamer__paranamer__2.8.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.trueaccord.lenses--lenses_2.12--com.trueaccord.lenses__lenses_2.12__0.4.12.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--chill-java--com.twitter__chill-java__0.9.5.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--chill_2.12--com.twitter__chill_2.12__0.9.5.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-app_2.12--com.twitter__util-app_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-core_2.12--com.twitter__util-core_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-function_2.12--com.twitter__util-function_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-jvm_2.12--com.twitter__util-jvm_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-lint_2.12--com.twitter__util-lint_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-registry_2.12--com.twitter__util-registry_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-stats_2.12--com.twitter__util-stats_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.typesafe--config--com.typesafe__config__1.2.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.typesafe.scala-logging--scala-logging_2.12--com.typesafe.scala-logging__scala-logging_2.12__3.7.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.univocity--univocity-parsers--com.univocity__univocity-parsers__2.9.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.zaxxer--HikariCP--com.zaxxer__HikariCP__3.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-beanutils--commons-beanutils--commons-beanutils__commons-beanutils__1.9.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-cli--commons-cli--commons-cli__commons-cli__1.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-codec--commons-codec--commons-codec__commons-codec__1.10.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-collections--commons-collections--commons-collections__commons-collections__3.2.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-configuration--commons-configuration--commons-configuration__commons-configuration__1.6.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-dbcp--commons-dbcp--commons-dbcp__commons-dbcp__1.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-digester--commons-digester--commons-digester__commons-digester__1.8.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-fileupload--commons-fileupload--commons-fileupload__commons-fileupload__1.3.3.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-httpclient--commons-httpclient--commons-httpclient__commons-httpclient__3.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-io--commons-io--commons-io__commons-io__2.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-lang--commons-lang--commons-lang__commons-lang__2.6.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-logging--commons-logging--commons-logging__commons-logging__1.1.3.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-net--commons-net--commons-net__commons-net__3.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-pool--commons-pool--commons-pool__commons-pool__1.5.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--info.ganglia.gmetric4j--gmetric4j--info.ganglia.gmetric4j__gmetric4j__1.0.10.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.airlift--aircompressor--io.airlift__aircompressor__0.10.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.delta--delta-sharing-spark_2.12--io.delta__delta-sharing-spark_2.12__0.2.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-core--io.dropwizard.metrics__metrics-core__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-graphite--io.dropwizard.metrics__metrics-graphite__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-healthchecks--io.dropwizard.metrics__metrics-healthchecks__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-jetty9--io.dropwizard.metrics__metrics-jetty9__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-jmx--io.dropwizard.metrics__metrics-jmx__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-json--io.dropwizard.metrics__metrics-json__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-jvm--io.dropwizard.metrics__metrics-jvm__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-servlets--io.dropwizard.metrics__metrics-servlets__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.netty--netty-all--io.netty__netty-all__4.1.51.Final.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.prometheus--simpleclient--io.prometheus__simpleclient__0.7.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.prometheus--simpleclient_common--io.prometheus__simpleclie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pc-api_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-auth_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-context_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-core_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-grpclb_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-netty-shaded_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-protobuf-lite_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-protobuf_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-stub_shaded.jar:/databricks/jars/third_party--gcp-java--gson_shaded.jar:/databricks/jars/third_party--gcp-java--guava_shaded.jar:/databricks/jars/third_party--gcp-java--httpclient_shaded.jar:/databricks/jars/third_party--gcp-java--httpcore_shaded.jar:/databricks/jars/third_party--gcp-java--j2objc-annotations_shaded.jar:/databricks/jars/third_party--gcp-java--jackson-core_shaded.jar:/databricks/jars/third_party--gcp-java--javax.annotation-api_shaded.jar:/databricks/jars/third_party--gcp-java--jsr305_shaded.jar:/databricks/jars/third_party--gcp-java--listenablefuture_shaded.jar:/databricks/jars/third_party--gcp-java--opencensus-api_shaded.jar:/databricks/jars/third_party--gcp-java--opencensus-contrib-http-util_shaded.jar:/databricks/jars/third_party--gcp-java--perfmark-api_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-cloud-pubsub-v1_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-common-protos_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-iam-v1_shaded.jar:/databricks/jars/third_party--gcp-java--protobuf-java-util_shaded.jar:/databricks/jars/third_party--gcp-java--protobuf-java_shaded.jar:/databricks/jars/third_party--gcp-java--threetenbp_shaded.jar:/databricks/jars/third_party--gcs-private--animal-sniffer-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--annotations_shaded.jar:/databricks/jars/third_party--gcs-private--auto-value-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--checker-compat-qual_shaded.jar:/databricks/jars/third_party--gcs-private--checker-qual_shaded.jar:/databricks/jars/third_party--gcs-private--commons-codec_shaded.jar:/databricks/jars/third_party--gcs-private--commons-logging_shaded.jar:/databricks/jars/third_party--gcs-private--conscrypt-openjdk-uber_shaded.jar:/databricks/jars/third_party--gcs-private--error_prone_annotations_shaded.jar:/databricks/jars/third_party--gcs-private--failureaccess_shaded.jar:/databricks/jars/third_party--gcs-private--flogger-system-backend_shaded.jar:/databricks/jars/third_party--gcs-private--flogger_shaded.jar:/databricks/jars/third_party--gcs-private--gcs-connector-hadoop2_shaded.jar:/databricks/jars/third_party--gcs-private--gcs-shaded-jetty9-hadoop1_2.12_deploy.jar:/databricks/jars/third_party--gcs-private--gcs-shaded-spark_3.1_2.12_deploy.jar:/databricks/jars/third_party--gcs-private--gcsio-hadoop2_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-client-jackson2_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-client_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-services-iamcredentials_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-services-storage_shaded.jar:/databricks/jars/third_party--gcs-private--google-auth-library-credentials_shaded.jar:/databricks/jars/third_party--gcs-private--google-auth-library-oauth2-http_shaded.jar:/databricks/jars/third_party--gcs-private--google-extensions_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client-apache-v2_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client-gson_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client-jackson2_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client_shaded.jar:/databricks/jars/third_party--gcs-private--google-oauth-client_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-alts_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-api_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-auth_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-context_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-core_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-netty-shaded_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-protobuf-lite_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-protobuf_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-stub_shaded.jar:/databricks/jars/third_party--gcs-private--gson_shaded.jar:/databricks/jars/third_party--gcs-private--guava_shaded.jar:/databricks/jars/third_party--gcs-private--httpclient_shaded.jar:/databricks/jars/third_party--gcs-private--httpcore_shaded.jar:/databricks/jars/third_party--gcs-private--j2objc-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--jackson-core_shaded.jar:/databricks/jars/third_party--gcs-private--libmeta-services.jar:/databricks/jars/third_party--gcs-private--listenablefuture_shaded.jar:/databricks/jars/third_party--gcs-private--opencensus-api_shaded.jar:/databricks/jars/third_party--gcs-private--opencensus-contrib-http-util_shaded.jar:/databricks/jars/third_party--gcs-private--perfmark-api_shaded.jar:/databricks/jars/third_party--gcs-private--proto-google-common-protos_shaded.jar:/databricks/jars/third_party--gcs-private--proto-google-iam-v1_shaded.jar:/databricks/jars/third_party--gcs-private--protobuf-java-util_shaded.jar:/databricks/jars/third_party--gcs-private--protobuf-java_shaded.jar:/databricks/jars/third_party--gcs-private--util-hadoop-hadoop2_shaded.jar:/databricks/jars/third_party--gcs-private--util-hadoop2_shaded.jar:/databricks/jars/third_party--hadoop--hadoop-tools--hadoop-aws--lib-spark_3.1_2.12_deploy_shaded.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--aopalliance__aopalliance__1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-annotations__2.7.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-core__2.7.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-core__2.7.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-databind__2.7.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.datatype__jackson-datatype-joda__2.7.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.code.findbugs__jsr305__1.3.9_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.guava__guava__11.0.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.guava__guava__16.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.inject__guice__3.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-annotations__1.2.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-keyvault-core__1.0.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-keyvault-core__1.0.0_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-storage__7.0.0_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-storage__8.6.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.rest__client-runtime__1.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okhttp3__logging-interceptor__3.3.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okhttp3__okhttp-urlconnection__3.3.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okhttp3__okhttp__3.3.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okio__okio__1.8.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.retrofit2__adapter-rxjava__2.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.retrofit2__converter-jackson__2.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.retrofit2__retrofit__2.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.sun.xml.bind__jaxb-impl__2.2.3-1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-codec__commons-codec__1.9_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-codec__commons-codec__1.9_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-logging__commons-logging__1.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-logging__commons-logging__1.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--hadoop-azure-2.7.3-abfs-external-20180625_3682417-spark_3.1_2.12_deploy_shaded.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--hadoop-azure-2.7.3-abfs-external-20180920_b33d810-spark_3.1_2.12_deploy_shaded.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--io.netty__netty-all__4.0.52.Final_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--io.reactivex__rxjava__1.2.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.activation__activation__1.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.inject__javax.inject__1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.xml.bind__jaxb-api__2.2.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.xml.stream__stax-api__1.0-2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--joda-time__joda-time__2.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.htrace__htrace-core__3.1.0-incubating_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpclient__4.5.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpclient__4.5.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpcore__4.4.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpcore__4.4.4_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop_azure_abfs--hadoop-tools--hadoop-azure--lib-spark_3.1_2.12_deploy.jar_shaded.jar:/databricks/jars/third_party--jackson--guava_only_shaded.jar:/databricks/jars/third_party--jackson--jackson-module-scala-shaded_2.12_deploy.jar:/databricks/jars/third_party--jackson--jsr305_only_shaded.jar:/databricks/jars/third_party--jackson--paranamer_only_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-client_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-http_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-io_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-util_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jackson-annotations_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jackson-core_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jackson-databind_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jjwt-api_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jjwt-impl_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jjwt-jackson_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.code.findbugs__jsr305__3.0.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.code.gson__gson__2.8.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.errorprone__error_prone_annotations__2.1.3_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.guava__guava__26.0-android_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.j2objc__j2objc-annotations__1.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.lmax__disruptor__3.4.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.squareup.okhttp3__okhttp__3.9.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.squareup.okio__okio__1.13.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--commons-codec__commons-codec__1.9_shaded.jar:/databricks/jars/third_party--opencensus-shaded--commons-logging__commons-logging__1.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.grpc__grpc-context__1.19.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-client__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-core__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-thrift__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-tracerresolver__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-api__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-exporter-trace-jaeger__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-exporter-trace-util__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-impl-core__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-impl__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing.contrib__opentracing-tracerresolver__0.1.5_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing__opentracing-api__0.31.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing__opentracing-noop__0.31.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing__opentracing-util__0.31.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.apache.httpcomponents__httpclient__4.4.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.apache.httpcomponents__httpcore__4.4.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.apache.thrift__libthrift__0.11.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.checkerframework__checker-compat-qual__2.5.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.codehaus.mojo__animal-sniffer-annotations__1.14_shaded.jar:/databricks/jars/third_party--zeromq--jeromq_shaded.jar:/databricks/jars/third_party--zeromq--jnacl_shaded.jar:/databricks/jars/utils--process_utils-spark_3.1_2.12_deploy.jar:/databricks/jars/workflow--workflow-spark_3.1_2.12_deploy.jar", "spark.hadoop.fs.gs.impl.disable.cache": "true", "spark.hadoop.fs.cpfs-adl.impl": "*********(redacted)", "spark.databricks.passthrough.s3a.tokenProviderClassName": "*********(redacted)", "spark.databricks.clusterUsageTags.isSingleUserCluster": "false", "spark.databricks.cloudfetch.requesterClassName": "*********(redacted)", "spark.databricks.clusterUsageTags.sparkEnvVarContainsEscape": "false", "spark.databricks.clusterUsageTags.attribute_tag_dust_suite": "", "spark.hadoop.hive.server2.keystore.path": "/databricks/keys/jetty-ssl-driver-keystore.jks", "spark.hadoop.fs.wasb.impl.disable.cache": "true", "spark.hadoop.hive.server2.transport.mode": "http", "spark.hadoop.spark.databricks.io.parquet.verifyChecksumOnWrite.enabled": "true", "spark.driver.allowMultipleContexts": "false", "spark.databricks.clusterUsageTags.enableSqlAclsOnly": "false", "spark.databricks.clusterUsageTags.clusterNodeType": "m5.4xlarge", "spark.databricks.clusterUsageTags.instanceProfileArn": "arn:aws:iam::471881062455:instance-profile/databricks-workspace-stack-access-data-buckets", "spark.databricks.clusterUsageTags.containerType": "LXC", "spark.databricks.managedCatalog.adls.gen2.tokenProviderClassName": "*********(redacted)", "spark.files.useFetchCache": "false", "spark.sql.allowMultipleContexts": "false", "spark.databricks.clusterUsageTags.enableDfAcls": "false", "spark.databricks.clusterUsageTags.clusterPythonVersion": "3", "spark.databricks.cloudfetch.hasRegionSupport": "true", "spark.hadoop.fs.s3a.impl": "shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem", "spark.shuffle.memoryFraction": "0.2", "spark.hadoop.fs.mcfs-s3.impl": "com.databricks.sql.acl.fs.ManagedCatalogFileSystem", "spark.sql.sources.default": "delta", "spark.databricks.clusterUsageTags.attribute_tag_dust_maintainer": "", "spark.databricks.clusterUsageTags.clusterAvailability": "SPOT_WITH_FALLBACK", "spark.sql.parquet.compression.codec": "snappy", "spark.hadoop.mapred.output.committer.class": "com.databricks.backend.daemon.data.client.DirectOutputCommitter", "spark.hadoop.fs.s3a.fast.upload.active.blocks": "32", "spark.hadoop.spark.sql.sources.outputCommitterClass": "com.databricks.backend.daemon.data.client.MapReduceDirectOutputCommitter", "spark.hadoop.fs.cpfs-adl.impl.disable.cache": "true", "spark.app.id": "app-20220304222044-0000", "spark.databricks.clusterUsageTags.clusterOwnerUserId": "6878104766663444", "spark.hadoop.fs.s3a.fast.upload": "true", "spark.databricks.clusterUsageTags.clusterLogDeliveryEnabled": "true", "spark.databricks.clusterUsageTags.driverNodeType": "m5.xlarge", "spark.speculation.quantile": "0.9", "spark.databricks.cloudProvider": "AWS", "spark.akka.frameSize": "256", "spark.hadoop.parquet.page.write-checksum.enabled": "true", "spark.eventLog.rolling.enabled": "false", "spark.databricks.clusterUsageTags.clusterAllTags": "[{\"key\":\"Vendor\",\"value\":\"Databricks\"},{\"key\":\"Creator\",\"value\":\"arianna@synccomputing.com\"},{\"key\":\"ClusterName\",\"value\":\"job-703-run-31144\"},{\"key\":\"ClusterId\",\"value\":\"0304-221748-fngq99ev\"},{\"key\":\"JobId\",\"value\":\"703\"},{\"key\":\"RunName\",\"value\":\"AirlineDelayLR_gz\"},{\"key\":\"Name\",\"value\":\"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313-worker\"}]", "spark.sql.hive.metastore.version": "0.13.0"}, "existsSQL": true, "existsExecutors": true}}
\ No newline at end of file
diff --git a/tests/logs/databricks.json b/tests/logs/databricks.json
new file mode 100644
index 0000000..0f43487
--- /dev/null
+++ b/tests/logs/databricks.json
@@ -0,0 +1,1162 @@
+{"Event":"DBCEventLoggingListenerMetadata","Spark Version":"3.1.2","Timestamp":1646432444550,"Rollover Number":0,"SparkContext Id":5706921391408253054}
+{"Event":"SparkListenerResourceProfileAdded","Resource Profile Id":0,"Executor Resource Requests":{"cores":{"Resource Name":"cores","Amount":1,"Discovery Script":"","Vendor":""},"memory":{"Resource Name":"memory","Amount":47016,"Discovery Script":"","Vendor":""},"offHeap":{"Resource Name":"offHeap","Amount":0,"Discovery Script":"","Vendor":""}},"Task Resource Requests":{"cpus":{"Resource Name":"cpus","Amount":1.0}}}
+{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"driver","Host":"10.77.243.103","Port":39191},"Maximum Memory":428762726,"Timestamp":1646432444382,"Maximum Onheap Memory":428762726,"Maximum Offheap Memory":0}
+{"Event":"SparkListenerEnvironmentUpdate","JVM Information":{"Java Home":"/usr/lib/jvm/zulu8-ca-amd64/jre","Java Version":"1.8.0_302 (Azul Systems, Inc.)","Scala Version":"version 2.12.10"},"Spark 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abricks/jars/third_party--gcp-java--commons-logging_shaded.jar:/databricks/jars/third_party--gcp-java--error_prone_annotations_shaded.jar:/databricks/jars/third_party--gcp-java--failureaccess_shaded.jar:/databricks/jars/third_party--gcp-java--gax-grpc_shaded.jar:/databricks/jars/third_party--gcp-java--gax-httpjson_shaded.jar:/databricks/jars/third_party--gcp-java--gax_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-client_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-cloudkms_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-cloudresourcemanager_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-compute_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-container_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-deploymentmanager_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-dns_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-iam_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-networkmanagement_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-servicecontrol_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-serviceusage_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-storage_shaded.jar:/databricks/jars/third_party--gcp-java--google-api-services-sts_shaded.jar:/databricks/jars/third_party--gcp-java--google-auth-library-credentials_shaded.jar:/databricks/jars/third_party--gcp-java--google-auth-library-oauth2-http_shaded.jar:/databricks/jars/third_party--gcp-java--google-cloud-core-http_shaded.jar:/databricks/jars/third_party--gcp-java--google-cloud-core_shaded.jar:/databricks/jars/third_party--gcp-java--google-cloud-notification_shaded.jar:/databricks/jars/third_party--gcp-java--google-cloud-pubsub_shaded.jar:/databricks/jars/third_party--gcp-java--google-cloud-storage_shaded.jar:/databricks/jars/third_party--gcp-java--google-http-client-apache-v2_shaded.jar:/databricks/jars/third_party--gcp-java--google-http-client-appengine_shaded.jar:/databricks/jars/third_party--gcp-java--google-http-client-jackson2_shaded.jar:/databricks/jars/third_party--gcp-java--google-http-client_shaded.jar:/databricks/jars/third_party--gcp-java--google-oauth-client_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-alts_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-api_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-auth_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-context_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-core_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-grpclb_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-netty-shaded_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-protobuf-lite_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-protobuf_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-stub_shaded.jar:/databricks/jars/third_party--gcp-java--gson_shaded.jar:/databricks/jars/third_party--gcp-java--guava_shaded.jar:/databricks/jars/third_party--gcp-java--httpclient_shaded.jar:/databricks/jars/third_party--gcp-java--httpcore_shaded.jar:/databricks/jars/third_party--gcp-java--j2objc-annotations_shaded.jar:/databricks/jars/third_party--gcp-java--jackson-core_shaded.jar:/databricks/jars/third_party--gcp-java--javax.annotation-api_shaded.jar:/databricks/jars/third_party--gcp-java--jsr305_shaded.jar:/databricks/jars/third_party--gcp-java--listenablefuture_shaded.jar:/databricks/jars/third_party--gcp-java--opencensus-api_shaded.jar:/databricks/jars/third_party--gcp-java--opencensus-contrib-http-util_shaded.jar:/databricks/jars/third_party--gcp-java--perfmark-api_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-cloud-pubsub-v1_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-common-protos_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-iam-v1_shaded.jar:/databricks/jars/third_party--gcp-java--protobuf-java-util_shaded.jar:/databricks/jars/third_party--gcp-java--protobuf-java_shaded.jar:/databricks/jars/third_party--gcp-java--threetenbp_shaded.jar:/databricks/jars/third_party--gcs-private--animal-sniffer-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--annotations_shaded.jar:/databricks/jars/third_party--gcs-private--auto-value-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--checker-compat-qual_shaded.jar:/databricks/jars/third_party--gcs-private--checker-qual_shaded.jar:/databricks/jars/third_party--gcs-private--commons-codec_shaded.jar:/databricks/jars/third_party--gcs-private--commons-logging_shaded.jar:/databricks/jars/third_party--gcs-private--conscrypt-openjdk-uber_shaded.jar:/databricks/jars/third_party--gcs-private--error_prone_annotations_shaded.jar:/databricks/jars/third_party--gcs-private--failureaccess_shaded.jar:/databricks/jars/third_party--gcs-private--flogger-system-backend_shaded.jar:/databricks/jar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+{"Event":"SparkListenerExecutorAdded","Timestamp":1646432448005,"Executor ID":"0","Executor Info":{"Host":"10.77.255.181","Total Cores":16,"Log Urls":{"stdout":"http://10.77.255.181:40000/logPage/?appId=app-20220304222044-0000&executorId=0&logType=stdout","stderr":"http://10.77.255.181:40000/logPage/?appId=app-20220304222044-0000&executorId=0&logType=stderr"},"Attributes":{},"Resources":{},"Resource Profile Id":0}}
+{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"0","Host":"10.77.255.181","Port":42251},"Maximum Memory":26104509235,"Timestamp":1646432448325,"Maximum Onheap Memory":26104509235,"Maximum Offheap Memory":0}
+{"Event":"SparkListenerExecutorAdded","Timestamp":1646432448688,"Executor ID":"1","Executor Info":{"Host":"10.77.224.250","Total Cores":16,"Log Urls":{"stdout":"http://10.77.224.250:40000/logPage/?appId=app-20220304222044-0000&executorId=1&logType=stdout","stderr":"http://10.77.224.250:40000/logPage/?appId=app-20220304222044-0000&executorId=1&logType=stderr"},"Attributes":{},"Resources":{},"Resource Profile Id":0}}
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+{"Event":"SparkListenerExecutorAdded","Timestamp":1646432450668,"Executor ID":"3","Executor Info":{"Host":"10.77.234.35","Total Cores":16,"Log Urls":{"stdout":"http://10.77.234.35:40000/logPage/?appId=app-20220304222044-0000&executorId=3&logType=stdout","stderr":"http://10.77.234.35:40000/logPage/?appId=app-20220304222044-0000&executorId=3&logType=stderr"},"Attributes":{},"Resources":{},"Resource Profile Id":0}}
+{"Event":"SparkListenerBlockManagerAdded","Block Manager ID":{"Executor ID":"3","Host":"10.77.234.35","Port":32967},"Maximum Memory":26104509235,"Timestamp":1646432451051,"Maximum Onheap Memory":26104509235,"Maximum Offheap Memory":0}
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+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":1,"Stage Attempt ID":0,"Stage Name":"csv at NativeMethodAccessorImpl.java:0","Number of Tasks":10,"RDD Info":[{"RDD ID":8,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"8\",\"name\":\"mapPartitions\"}","Callsite":"csv at NativeMethodAccessorImpl.java:0","Parent IDs":[7],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":3,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_29\",\"name\":\"Scan text \"}","Callsite":"csv at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":5,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_30\",\"name\":\"DeserializeToObject\"}","Callsite":"csv at NativeMethodAccessorImpl.java:0","Parent IDs":[4],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":7,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"7\",\"name\":\"mapPartitions\"}","Callsite":"csv at NativeMethodAccessorImpl.java:0","Parent IDs":[6],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":6,"Name":"SQLExecutionRDD","Callsite":"csv at NativeMethodAccessorImpl.java:0","Parent IDs":[5],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":4,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_29\",\"name\":\"Scan text \"}","Callsite":"csv at NativeMethodAccessorImpl.java:0","Parent IDs":[3],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.DataFrameReader.csv(DataFrameReader.scala:964)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432483232,"Accumulables":[],"Resource Profile 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+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":3,"physicalPlanDescription":"== Physical Plan ==\nAdaptiveSparkPlan (9)\n+- == Current Plan ==\n   HashAggregate (5)\n   +- ShuffleQueryStage (4)\n      +- Exchange (3)\n         +- * HashAggregate (2)\n            +- Scan csv  (1)\n+- == Initial Plan ==\n   HashAggregate (8)\n   +- Exchange (7)\n      +- HashAggregate (6)\n         +- Scan csv  (1)\n\n\n(1) Scan csv \nOutput: []\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<>\n\n(2) HashAggregate [codegen id : 1]\nInput: []\nKeys: []\nFunctions [1]: [partial_count(1) AS count#90L]\nAggregate Attributes [1]: [count#89L]\nResults [1]: [count#90L]\n\n(3) Exchange\nInput [1]: [count#90L]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#94]\n\n(4) ShuffleQueryStage\nOutput [1]: [count#90L]\nArguments: 0, Statistics(sizeInBytes=3.5 GiB)\n\n(5) HashAggregate\nInput [1]: [count#90L]\nKeys: []\nFunctions [1]: [finalmerge_count(merge count#90L) AS count(1)#86L]\nAggregate Attributes [1]: [count(1)#86L]\nResults [1]: [count(1)#86L AS count#87L]\n\n(6) HashAggregate\nInput: []\nKeys: []\nFunctions [1]: [partial_count(1) AS count#90L]\nAggregate Attributes [1]: [count#89L]\nResults [1]: [count#90L]\n\n(7) Exchange\nInput [1]: [count#90L]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#52]\n\n(8) HashAggregate\nInput [1]: [count#90L]\nKeys: []\nFunctions [1]: [finalmerge_count(merge count#90L) AS count(1)#86L]\nAggregate Attributes [1]: [count(1)#86L]\nResults [1]: [count(1)#86L AS count#87L]\n\n(9) AdaptiveSparkPlan\nOutput [1]: [count#87L]\nArguments: isFinalPlan=false\n\n","sparkPlanInfo":{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[], functions=[finalmerge_count(merge count#90L) AS count(1)#86L])","children":[{"nodeName":"ShuffleQueryStage","simpleString":"ShuffleQueryStage 0, Statistics(sizeInBytes=3.5 GiB)","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#94]","children":[{"nodeName":"WholeStageCodegen (1)","simpleString":"WholeStageCodegen","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[], functions=[partial_count(1) AS count#90L])","children":[{"nodeName":"InputAdapter","simpleString":"InputAdapter","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[{"name":"file sorting by size time","accumulatorId":118,"metricType":"timing"},{"name":"number of files read","accumulatorId":112,"metricType":"sum"},{"name":"filesystem read data size","accumulatorId":117,"metricType":"size"},{"name":"filesystem read data size (sampled)","accumulatorId":116,"metricType":"size"},{"name":"filesystem read time (sampled)","accumulatorId":115,"metricType":"timing"},{"name":"metadata time","accumulatorId":113,"metricType":"timing"},{"name":"size of files read","accumulatorId":114,"metricType":"size"},{"name":"number of output rows","accumulatorId":111,"metricType":"sum"}]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[{"name":"spill size","accumulatorId":133,"metricType":"size"},{"name":"time in aggregation build","accumulatorId":134,"metricType":"timing"},{"name":"peak memory","accumulatorId":132,"metricType":"size"},{"name":"number of output rows","accumulatorId":131,"metricType":"sum"},{"name":"avg hash probe bucket list iters","accumulatorId":135,"metricType":"average"}]}],"metadata":{},"metrics":[{"name":"duration","accumulatorId":130,"metricType":"timing"}]}],"metadata":{},"metrics":[{"name":"shuffle records written","accumulatorId":128,"metricType":"sum"},{"name":"shuffle write time","accumulatorId":129,"metricType":"nsTiming"},{"name":"records read","accumulatorId":126,"metricType":"sum"},{"name":"local bytes read","accumulatorId":124,"metricType":"size"},{"name":"fetch wait time","accumulatorId":125,"metricType":"timing"},{"name":"remote bytes read","accumulatorId":122,"metricType":"size"},{"name":"local blocks read","accumulatorId":121,"metricType":"sum"},{"name":"remote blocks read","accumulatorId":120,"metricType":"sum"},{"name":"data size","accumulatorId":119,"metricType":"size"},{"name":"remote bytes read to disk","accumulatorId":123,"metricType":"size"},{"name":"shuffle bytes written","accumulatorId":127,"metricType":"size"}]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerDriverAccumUpdates","executionId":3,"accumUpdates":[[118,0],[112,10],[113,0],[114,1904190334]]}
+{"Event":"SparkListenerJobStart","Job ID":2,"Submission Time":1646432519595,"Stage Infos":[{"Stage ID":2,"Stage Attempt ID":0,"Stage Name":"count at NativeMethodAccessorImpl.java:0","Number of Tasks":10,"RDD Info":[{"RDD ID":12,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_94\",\"name\":\"Exchange\"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[11],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":10,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_48\",\"name\":\"Scan csv \"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[9],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":9,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_48\",\"name\":\"Scan csv \"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":11,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_93\",\"name\":\"WholeStageCodegen\"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[10],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.count(Dataset.scala:3097)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Accumulables":[],"Resource Profile Id":0}],"Stage 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+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":2,"Stage Attempt ID":0,"Stage Name":"count at NativeMethodAccessorImpl.java:0","Number of Tasks":10,"RDD Info":[{"RDD ID":12,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_94\",\"name\":\"Exchange\"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[11],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":10,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_48\",\"name\":\"Scan csv \"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[9],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":9,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_48\",\"name\":\"Scan csv \"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":11,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_93\",\"name\":\"WholeStageCodegen\"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[10],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.count(Dataset.scala:3097)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432519604,"Accumulables":[],"Resource Profile 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+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":4,"Stage Attempt ID":0,"Stage Name":"count at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":14,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_114\",\"name\":\"WholeStageCodegen\"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[13],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":13,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_0\",\"name\":\"ShuffleQueryStage\"}","Callsite":"count at NativeMethodAccessorImpl.java:0","Parent IDs":[12],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[3],"Details":"org.apache.spark.sql.Dataset.count(Dataset.scala:3097)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432528782,"Completion Time":1646432528942,"Accumulables":[{"ID":120,"Name":"remote blocks read","Value":"8","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":121,"Name":"local blocks read","Value":"2","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":122,"Name":"remote bytes read","Value":"472","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":124,"Name":"local bytes read","Value":"118","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":125,"Name":"fetch wait time","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":126,"Name":"records read","Value":"10","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":166,"Name":"duration","Value":"14","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":167,"Name":"number of output rows","Value":"1","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":170,"Name":"time in aggregation build","Value":"14","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":172,"Name":"internal.metrics.executorDeserializeTime","Value":10,"Internal":true,"Count Failed Values":true},{"ID":173,"Name":"internal.metrics.executorDeserializeCpuTime","Value":10649000,"Internal":true,"Count Failed Values":true},{"ID":174,"Name":"internal.metrics.executorRunTime","Value":120,"Internal":true,"Count Failed Values":true},{"ID":175,"Name":"internal.metrics.executorCpuTime","Value":98407025,"Internal":true,"Count Failed Values":true},{"ID":176,"Name":"internal.metrics.resultSize","Value":1762,"Internal":true,"Count Failed Values":true},{"ID":185,"Name":"internal.metrics.shuffle.read.remoteBlocksFetched","Value":8,"Internal":true,"Count Failed Values":true},{"ID":186,"Name":"internal.metrics.shuffle.read.localBlocksFetched","Value":2,"Internal":true,"Count Failed Values":true},{"ID":187,"Name":"internal.metrics.shuffle.read.remoteBytesRead","Value":472,"Internal":true,"Count Failed Values":true},{"ID":188,"Name":"internal.metrics.shuffle.read.remoteBytesReadToDisk","Value":0,"Internal":true,"Count Failed Values":true},{"ID":189,"Name":"internal.metrics.shuffle.read.localBytesRead","Value":118,"Internal":true,"Count Failed Values":true},{"ID":190,"Name":"internal.metrics.shuffle.read.fetchWaitTime","Value":0,"Internal":true,"Count Failed Values":true},{"ID":191,"Name":"internal.metrics.shuffle.read.recordsRead","Value":10,"Internal":true,"Count Failed Values":true}],"Resource Profile Id":0}}
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ID":15,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_120\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Accumulables":[],"Resource Profile Id":0}],"Stage 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tabricks.clusterUsageTags.clusterOwnerUserId":"6878104766663444","spark.hadoop.fs.s3a.fast.upload":"true","spark.databricks.clusterUsageTags.clusterLogDeliveryEnabled":"true","spark.databricks.clusterUsageTags.driverNodeType":"m5.xlarge","spark.speculation.quantile":"0.9","spark.databricks.cloudProvider":"AWS","spark.akka.frameSize":"256","spark.hadoop.parquet.page.write-checksum.enabled":"true","spark.eventLog.rolling.enabled":"false","spark.databricks.clusterUsageTags.clusterAllTags":"[{\"key\":\"Vendor\",\"value\":\"Databricks\"},{\"key\":\"Creator\",\"value\":\"arianna@synccomputing.com\"},{\"key\":\"ClusterName\",\"value\":\"job-703-run-31144\"},{\"key\":\"ClusterId\",\"value\":\"0304-221748-fngq99ev\"},{\"key\":\"JobId\",\"value\":\"703\"},{\"key\":\"RunName\",\"value\":\"AirlineDelayLR_gz\"},{\"key\":\"Name\",\"value\":\"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313-worker\"}]","spark.sql.hive.metastore.version":"0.13.0"}}
+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":5,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":17,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_137\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[16],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":16,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_120\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[15],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":15,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_120\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432529066,"Accumulables":[],"Resource Profile 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tempfile\nfile = tempfile.NamedTemporary...","spark.databricks.clusterUsageTags.clusterEbsVolumeType":"GENERAL_PURPOSE_SSD","spark.sql.streaming.stopTimeout":"15s","spark.app.startTime":"1646432441343","spark.executor.id":"driver","spark.databricks.job.runId":"31144","spark.hadoop.databricks.s3commit.client.sslTrustAll":"false","spark.hadoop.hive.server2.idle.session.timeout":"900000","spark.hadoop.fs.cpfs-abfss.impl.disable.cache":"true","spark.hadoop.fs.mcfs-s3a.impl":"com.databricks.sql.acl.fs.ManagedCatalogFileSystem","spark.databricks.clusterUsageTags.sparkEnvVarContainsNewline":"false","spark.databricks.clusterUsageTags.clusterPinned":"false","spark.hadoop.fs.fcfs-s3n.impl":"*********(redacted)","spark.hadoop.fs.mcfs-gs.impl":"com.databricks.sql.acl.fs.ManagedCatalogFileSystem","spark.databricks.replId":"ReplId-3fd17-60988-17274-b","spark.hadoop.hive.server2.thrift.http.port":"10000","spark.jobGroup.id":"4598586477831989067_7525196874081667841_job-703-run-31144-action-4960839961566214","spark.databricks.clusterUsageTags.instanceWorkerEnvId":"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313","spark.databricks.io.directoryCommit.enableLogicalDelete":"false","spark.databricks.clusterUsageTags.enableJobsAutostart":"true","spark.databricks.clusterUsageTags.clusterLogDestination":"s3://job-bank-temp/databricks/9.1.x-scala2.12/AirlineDelayLR_gz/arianna","spark.hadoop.parquet.page.verify-checksum.enabled":"true","spark.databricks.clusterUsageTags.clusterResourceClass":"default","spark.shuffle.service.port":"4048","spark.databricks.passthrough.glue.executorServiceFactoryClassName":"*********(redacted)","spark.hadoop.fs.cpfs-s3n.impl":"*********(redacted)","spark.databricks.acl.client":"com.databricks.spark.sql.acl.client.SparkSqlAclClient","spark.sql.legacy.createHiveTableByDefault":"false","spark.databricks.clusterUsageTags.enableJdbcAutoStart":"true","spark.databricks.clusterUsageTags.sparkEnvVarContainsDollarSign":"false","spark.databricks.clusterUsageTags.clusterStateMessage":"Starting Spark","spark.hadoop.spark.sql.parquet.output.committer.class":"org.apache.spark.sql.parquet.DirectParquetOutputCommitter","spark.databricks.preemption.enabled":"true","spark.master":"spark://10.77.243.103:7077","spark.databricks.tahoe.logStore.aws.class":"com.databricks.tahoe.store.MultiClusterLogStore","spark.hadoop.fs.adl.impl.disable.cache":"true","spark.rpc.message.maxSize":"256","spark.hadoop.fs.gs.impl":"shaded.databricks.V2_1_4.com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem","spark.task.reaper.killTimeout":"60s","spark.hadoop.hive.server2.enable.doAs":"false","spark.hadoop.fs.fcfs-s3n.impl.disable.cache":"true","spark.hadoop.fs.s3n.impl":"shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem","spark.databricks.clusterUsageTags.enableLocalDiskEncryption":"false","spark.databricks.clusterUsageTags.numPerClusterInitScriptsV2":"0","spark.databricks.isolationID":"1efbc00e-cb29-4e80-b07b-fe3ae8b92b02","spark.hadoop.fs.azure.skip.metrics":"true","spark.databricks.clusterUsageTags.driverContainerId":"8a041ac64b95432ab1643fe44cf52169","spark.files.overwrite":"true","spark.databricks.clusterUsageTags.sparkEnvVarContainsSingleQuotes":"false","spark.hadoop.fs.s3a.threads.max":"136","spark.databricks.clusterUsageTags.clusterNumSshKeys":"0","spark.sparkr.use.daemon":"false","spark.hadoop.fs.mcfs-abfss.impl.disable.cache":"true","spark.streaming.driver.writeAheadLog.allowBatching":"true","spark.databricks.clusterUsageTags.clusterScalingType":"fixed_size","spark.databricks.workerNodeTypeId":"m5.4xlarge","spark.hadoop.fs.fcfs-s3.impl.disable.cache":"true","spark.sql.sources.commitProtocolClass":"com.databricks.sql.transaction.directory.DirectoryAtomicCommitProtocol","spark.databricks.clusterUsageTags.clusterWorkers":"4","spark.sql.hive.metastore.sharedPrefixes":"org.mariadb.jdbc,com.mysql.jdbc,org.postgresql,com.microsoft.sqlserver,microsoft.sql.DateTimeOffset,microsoft.sql.Types,com.databricks,com.codahale,com.fasterxml.jackson,shaded.databricks","spark.hadoop.fs.AbstractFileSystem.gs.impl":"shaded.databricks.V2_1_4.com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS","spark.executor.memory":"47016m","spark.hadoop.databricks.fs.perfMetrics.enable":"true","spark.databricks.inherited.credentials.keys.spark.databricks.api.token":"*********(redacted)","spark.databricks.clusterUsageTags.dataPlaneRegion":"us-east-1","spark.home":"/databricks/spark","spark.hadoop.fs.cpfs-s3a.impl":"*********(redacted)","spark.databricks.tahoe.logStore.azure.class":"com.databricks.tahoe.store.AzureLogStore","spark.hadoop.parquet.page.size.check.estimate":"false","spark.hadoop.fs.gs.impl.disable.cache":"true","spark.sql.execution.id":"4","spark.sql.catalogImplementation":"hive","spark.hadoop.fs.cpfs-adl.impl":"*********(redacted)","spark.databricks.passthrough.s3a.tokenProviderClassName":"*********(redacted)","spark.databricks.clusterUsageTags.isSingleUserCluster":"false","spark.databricks.cloudfetch.requesterClassName":"*********(redacted)","spark.databricks.clusterUsageTags.sparkEnvVarContainsEscape":"false","spark.databricks.clusterUsageTags.attribute_tag_dust_suite":"","spark.hadoop.hive.server2.keystore.path":"/databricks/keys/jetty-ssl-driver-keystore.jks","spark.hadoop.fs.wasb.impl.disable.cache":"true","spark.hadoop.hive.server2.transport.mode":"http","spark.hadoop.spark.databricks.io.parquet.verifyChecksumOnWrite.enabled":"true","spark.driver.allowMultipleContexts":"false","spark.databricks.clusterUsageTags.enableSqlAclsOnly":"false","spark.databricks.clusterUsageTags.clusterNodeType":"m5.4xlarge","spark.databricks.clusterUsageTags.instanceProfileArn":"arn:aws:iam::471881062455:instance-profile/databricks-workspace-stack-access-data-buckets","spark.databricks.clusterUsageTags.containerType":"LXC","spark.databricks.managedCatalog.adls.gen2.tokenProviderClassName":"*********(redacted)","spark.files.useFetchCache":"false","spark.sql.allowMultipleContexts":"false","spark.databricks.clusterUsageTags.enableDfAcls":"false","spark.databricks.clusterUsageTags.clusterPythonVersion":"3","spark.databricks.cloudfetch.hasRegionSupport":"true","spark.databricks.job.type":"python","spark.hadoop.fs.s3a.impl":"shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem","spark.shuffle.memoryFraction":"0.2","spark.hadoop.fs.mcfs-s3.impl":"com.databricks.sql.acl.fs.ManagedCatalogFileSystem","spark.sql.sources.default":"delta","spark.databricks.clusterUsageTags.attribute_tag_dust_maintainer":"","spark.databricks.clusterUsageTags.clusterAvailability":"SPOT_WITH_FALLBACK","spark.sql.parquet.compression.codec":"snappy","spark.hadoop.mapred.output.committer.class":"com.databricks.backend.daemon.data.client.DirectOutputCommitter","spark.hadoop.fs.s3a.fast.upload.active.blocks":"32","spark.databricks.token":"*********(redacted)","spark.hadoop.spark.sql.sources.outputCommitterClass":"com.databricks.backend.daemon.data.client.MapReduceDirectOutputCommitter","spark.hadoop.fs.cpfs-adl.impl.disable.cache":"true","spark.app.id":"app-20220304222044-0000","spark.databricks.clusterUsageTags.clusterOwnerUserId":"6878104766663444","spark.hadoop.fs.s3a.fast.upload":"true","spark.databricks.clusterUsageTags.clusterLogDeliveryEnabled":"true","spark.databricks.clusterUsageTags.driverNodeType":"m5.xlarge","spark.speculation.quantile":"0.9","spark.databricks.cloudProvider":"AWS","spark.akka.frameSize":"256","spark.hadoop.parquet.page.write-checksum.enabled":"true","spark.eventLog.rolling.enabled":"false","spark.databricks.clusterUsageTags.clusterAllTags":"[{\"key\":\"Vendor\",\"value\":\"Databricks\"},{\"key\":\"Creator\",\"value\":\"arianna@synccomputing.com\"},{\"key\":\"ClusterName\",\"value\":\"job-703-run-31144\"},{\"key\":\"ClusterId\",\"value\":\"0304-221748-fngq99ev\"},{\"key\":\"JobId\",\"value\":\"703\"},{\"key\":\"RunName\",\"value\":\"AirlineDelayLR_gz\"},{\"key\":\"Name\",\"value\":\"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313-worker\"}]","spark.sql.hive.metastore.version":"0.13.0"}}
+{"Event":"SparkListenerTaskStart","Stage ID":5,"Stage Attempt ID":0,"Task Info":{"Task ID":22,"Index":0,"Attempt":0,"Launch Time":1646432529080,"Executor ID":"3","Host":"10.77.234.35","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Killed":false,"Accumulables":[]}}
+{"Event":"SparkListenerTaskEnd","Stage ID":5,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":22,"Index":0,"Attempt":0,"Launch Time":1646432529080,"Executor ID":"3","Host":"10.77.234.35","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1646432529255,"Failed":false,"Killed":false,"Accumulables":[{"ID":213,"Name":"number of output rows","Update":"11","Value":"11","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":217,"Name":"filesystem read time (sampled)","Update":"23","Value":"23","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":218,"Name":"filesystem read data size (sampled)","Update":"1616","Value":"1616","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":219,"Name":"filesystem read data size","Update":"1616","Value":"1616","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":221,"Name":"internal.metrics.executorDeserializeTime","Update":10,"Value":10,"Internal":true,"Count Failed Values":true},{"ID":222,"Name":"internal.metrics.executorDeserializeCpuTime","Update":10098402,"Value":10098402,"Internal":true,"Count Failed Values":true},{"ID":223,"Name":"internal.metrics.executorRunTime","Update":140,"Value":140,"Internal":true,"Count Failed Values":true},{"ID":224,"Name":"internal.metrics.executorCpuTime","Update":86117088,"Value":86117088,"Internal":true,"Count Failed Values":true},{"ID":225,"Name":"internal.metrics.resultSize","Update":1612,"Value":1612,"Internal":true,"Count Failed Values":true},{"ID":244,"Name":"internal.metrics.input.bytesRead","Update":1616,"Value":1616,"Internal":true,"Count Failed Values":true},{"ID":245,"Name":"internal.metrics.input.recordsRead","Update":11,"Value":11,"Internal":true,"Count Failed Values":true},{"ID":246,"Name":"internal.metrics.input.sampledTimeReadNano","Update":23033366,"Value":23033366,"Internal":true,"Count Failed Values":true},{"ID":247,"Name":"internal.metrics.input.sampledBytesRead","Update":1616,"Value":1616,"Internal":true,"Count Failed Values":true}]},"Task Executor Metrics":{"JVMHeapMemory":0,"JVMOffHeapMemory":0,"OnHeapExecutionMemory":0,"OffHeapExecutionMemory":0,"OnHeapStorageMemory":0,"OffHeapStorageMemory":0,"OnHeapUnifiedMemory":0,"OffHeapUnifiedMemory":0,"DirectPoolMemory":0,"MappedPoolMemory":0,"ProcessTreeJVMVMemory":0,"ProcessTreeJVMRSSMemory":0,"ProcessTreePythonVMemory":0,"ProcessTreePythonRSSMemory":0,"ProcessTreeOtherVMemory":0,"ProcessTreeOtherRSSMemory":0,"MinorGCCount":0,"MinorGCTime":0,"MajorGCCount":0,"MajorGCTime":0},"Task Metrics":{"Executor Deserialize Time":10,"Executor Deserialize CPU Time":10098402,"Executor Run Time":140,"Executor CPU Time":86117088,"Peak Execution Memory":0,"Result Size":1612,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":0,"Fetch Wait Time":0,"Remote Bytes Read":0,"Remote Bytes Read To Disk":0,"Local Bytes Read":0,"Total Records Read":0},"Shuffle Write Metrics":{"Shuffle Bytes Written":0,"Shuffle Write Time":0,"Shuffle Records Written":0},"Input Metrics":{"Bytes Read":1616,"Records Read":11},"Output Metrics":{"Bytes Written":0,"Records Written":0},"Updated Blocks":[]}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":5,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":17,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_137\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[16],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":16,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_120\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[15],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":15,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_120\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432529066,"Completion Time":1646432529256,"Accumulables":[{"ID":213,"Name":"number of output rows","Value":"11","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":217,"Name":"filesystem read time (sampled)","Value":"23","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":218,"Name":"filesystem read data size (sampled)","Value":"1616","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":219,"Name":"filesystem read data size","Value":"1616","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":221,"Name":"internal.metrics.executorDeserializeTime","Value":10,"Internal":true,"Count Failed Values":true},{"ID":222,"Name":"internal.metrics.executorDeserializeCpuTime","Value":10098402,"Internal":true,"Count Failed Values":true},{"ID":223,"Name":"internal.metrics.executorRunTime","Value":140,"Internal":true,"Count Failed Values":true},{"ID":224,"Name":"internal.metrics.executorCpuTime","Value":86117088,"Internal":true,"Count Failed Values":true},{"ID":225,"Name":"internal.metrics.resultSize","Value":1612,"Internal":true,"Count Failed Values":true},{"ID":244,"Name":"internal.metrics.input.bytesRead","Value":1616,"Internal":true,"Count Failed Values":true},{"ID":245,"Name":"internal.metrics.input.recordsRead","Value":11,"Internal":true,"Count Failed Values":true},{"ID":246,"Name":"internal.metrics.input.sampledTimeReadNano","Value":23033366,"Internal":true,"Count Failed Values":true},{"ID":247,"Name":"internal.metrics.input.sampledBytesRead","Value":1616,"Internal":true,"Count Failed Values":true}],"Resource Profile Id":0}}
+{"Event":"SparkListenerJobEnd","Job ID":4,"Completion Time":1646432529259,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":4,"time":1646432529303,"errorMessage":""}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":5,"description":"\nimport tempfile\nfile = tempfile.NamedTemporary...","details":"org.apache.spark.sql.Dataset.createOrReplaceTempView(Dataset.scala:3394)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","physicalPlanDescription":"== Physical Plan ==\nExecute CreateViewCommand (1)\n   +- CreateViewCommand (2)\n         +- Window (8)\n            +- Project (7)\n               +- Deduplicate (6)\n                  +- Project (5)\n                     +- Project (4)\n                        +- LogicalRelation (3)\n\n\n(1) Execute CreateViewCommand\nOutput: []\n\n(2) CreateViewCommand\nArguments: `carriers`, false, true, LocalTempView\n\n(3) LogicalRelation\nArguments: csv, [FL_DATE#18, OP_CARRIER#19, OP_CARRIER_FL_NUM#20, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_TIME#24, DEP_DELAY#25, TAXI_OUT#26, WHEELS_OFF#27, WHEELS_ON#28, TAXI_IN#29, CRS_ARR_TIME#30, ARR_TIME#31, ARR_DELAY#32, CANCELLED#33, CANCELLATION_CODE#34, DIVERTED#35, CRS_ELAPSED_TIME#36, ACTUAL_ELAPSED_TIME#37, AIR_TIME#38, DISTANCE#39, CARRIER_DELAY#40, WEATHER_DELAY#41, ... 4 more fields], false\n\n(4) Project\nArguments: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\n\n(5) Project\nArguments: [OP_CARRIER#19]\n\n(6) Deduplicate\nArguments: [OP_CARRIER#19]\n\n(7) Project\nArguments: [OP_CARRIER#19]\n\n(8) Window\nArguments: [OP_CARRIER#19, row_number() windowspecdefinition(OP_CARRIER#19 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#19 ASC NULLS FIRST]\n\n","sparkPlanInfo":{"nodeName":"Execute CreateViewCommand","simpleString":"Execute CreateViewCommand","children":[],"metadata":{},"metrics":[]},"time":1646432529422}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":5,"time":1646432529424,"errorMessage":""}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":6,"description":"\nimport tempfile\nfile = tempfile.NamedTemporary...","details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","physicalPlanDescription":"== Physical Plan ==\nAdaptiveSparkPlan (9)\n+- TakeOrderedAndProject (8)\n   +- RunningWindowFunction (7)\n      +- Sort (6)\n         +- Exchange (5)\n            +- HashAggregate (4)\n               +- Exchange (3)\n                  +- HashAggregate (2)\n                     +- Scan csv  (1)\n\n\n(1) Scan csv \nOutput [1]: [OP_CARRIER#19]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(2) HashAggregate\nInput [1]: [OP_CARRIER#19]\nKeys [1]: [OP_CARRIER#19]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#19]\n\n(3) Exchange\nInput [1]: [OP_CARRIER#19]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#161]\n\n(4) HashAggregate\nInput [1]: [OP_CARRIER#19]\nKeys [1]: [OP_CARRIER#19]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#19]\n\n(5) Exchange\nInput [1]: [OP_CARRIER#19]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#164]\n\n(6) Sort\nInput [1]: [OP_CARRIER#19]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(7) RunningWindowFunction\nInput [1]: [OP_CARRIER#19]\nArguments: [OP_CARRIER#19, row_number() windowspecdefinition(OP_CARRIER#19 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#19 ASC NULLS FIRST], false\n\n(8) TakeOrderedAndProject\nInput [2]: [OP_CARRIER#19, carrier_id#123]\nArguments: 21, [OP_CARRIER#19 ASC NULLS FIRST], [OP_CARRIER#19, cast(carrier_id#123 as string) AS carrier_id#130]\n\n(9) AdaptiveSparkPlan\nOutput [2]: [OP_CARRIER#19, carrier_id#130]\nArguments: isFinalPlan=false\n\n","sparkPlanInfo":{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"TakeOrderedAndProject","simpleString":"TakeOrderedAndProject(limit=21, orderBy=[OP_CARRIER#19 ASC NULLS FIRST], output=[OP_CARRIER#19,carrier_id#130])","children":[{"nodeName":"RunningWindowFunction","simpleString":"RunningWindowFunction [OP_CARRIER#19, row_number() windowspecdefinition(OP_CARRIER#19 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#19 ASC NULLS FIRST], false","children":[{"nodeName":"Sort","simpleString":"Sort [OP_CARRIER#19 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#164]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[OP_CARRIER#19], functions=[])","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#161]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[OP_CARRIER#19], functions=[])","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [OP_CARRIER#19] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<OP_CARRIER:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<OP_CARRIER:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]},"time":1646432529683}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":6,"physicalPlanDescription":"== Physical Plan ==\nAdaptiveSparkPlan (17)\n+- == Current Plan ==\n   TakeOrderedAndProject (9)\n   +- RunningWindowFunction (8)\n      +- Sort (7)\n         +- Exchange (6)\n            +- HashAggregate (5)\n               +- ShuffleQueryStage (4)\n                  +- Exchange (3)\n                     +- * HashAggregate (2)\n                        +- Scan csv  (1)\n+- == Initial Plan ==\n   TakeOrderedAndProject (16)\n   +- RunningWindowFunction (15)\n      +- Sort (14)\n         +- Exchange (13)\n            +- HashAggregate (12)\n               +- Exchange (11)\n                  +- HashAggregate (10)\n                     +- Scan csv  (1)\n\n\n(1) Scan csv \nOutput [1]: [OP_CARRIER#19]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(2) HashAggregate [codegen id : 1]\nInput [1]: [OP_CARRIER#19]\nKeys [1]: [OP_CARRIER#19]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#19]\n\n(3) Exchange\nInput [1]: [OP_CARRIER#19]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#223]\n\n(4) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#19]\nArguments: 0, Statistics(sizeInBytes=1816.0 MiB)\n\n(5) HashAggregate\nInput [1]: [OP_CARRIER#19]\nKeys [1]: [OP_CARRIER#19]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#19]\n\n(6) Exchange\nInput [1]: [OP_CARRIER#19]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#233]\n\n(7) Sort\nInput [1]: [OP_CARRIER#19]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(8) RunningWindowFunction\nInput [1]: [OP_CARRIER#19]\nArguments: [OP_CARRIER#19, row_number() windowspecdefinition(OP_CARRIER#19 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#19 ASC NULLS FIRST], false\n\n(9) TakeOrderedAndProject\nInput [2]: [OP_CARRIER#19, carrier_id#123]\nArguments: 21, [OP_CARRIER#19 ASC NULLS FIRST], [OP_CARRIER#19, cast(carrier_id#123 as string) AS carrier_id#130]\n\n(10) HashAggregate\nInput [1]: [OP_CARRIER#19]\nKeys [1]: [OP_CARRIER#19]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#19]\n\n(11) Exchange\nInput [1]: [OP_CARRIER#19]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#161]\n\n(12) HashAggregate\nInput [1]: [OP_CARRIER#19]\nKeys [1]: [OP_CARRIER#19]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#19]\n\n(13) Exchange\nInput [1]: [OP_CARRIER#19]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#164]\n\n(14) Sort\nInput [1]: [OP_CARRIER#19]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(15) RunningWindowFunction\nInput [1]: [OP_CARRIER#19]\nArguments: [OP_CARRIER#19, row_number() windowspecdefinition(OP_CARRIER#19 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#19 ASC NULLS FIRST], false\n\n(16) TakeOrderedAndProject\nInput [2]: [OP_CARRIER#19, carrier_id#123]\nArguments: 21, [OP_CARRIER#19 ASC NULLS FIRST], [OP_CARRIER#19, cast(carrier_id#123 as string) AS carrier_id#130]\n\n(17) AdaptiveSparkPlan\nOutput [2]: [OP_CARRIER#19, carrier_id#130]\nArguments: isFinalPlan=false\n\n","sparkPlanInfo":{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"TakeOrderedAndProject","simpleString":"TakeOrderedAndProject(limit=21, orderBy=[OP_CARRIER#19 ASC NULLS FIRST], output=[OP_CARRIER#19,carrier_id#130])","children":[{"nodeName":"RunningWindowFunction","simpleString":"RunningWindowFunction [OP_CARRIER#19, row_number() windowspecdefinition(OP_CARRIER#19 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#19 ASC NULLS FIRST], false","children":[{"nodeName":"Sort","simpleString":"Sort [OP_CARRIER#19 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#233]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[OP_CARRIER#19], functions=[])","children":[{"nodeName":"ShuffleQueryStage","simpleString":"ShuffleQueryStage 0, Statistics(sizeInBytes=1816.0 MiB)","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#223]","children":[{"nodeName":"WholeStageCodegen (1)","simpleString":"WholeStageCodegen","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[OP_CARRIER#19], functions=[])","children":[{"nodeName":"InputAdapter","simpleString":"InputAdapter","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [OP_CARRIER#19] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<OP_CARRIER:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<OP_CARRIER:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[{"name":"file sorting by size time","accumulatorId":308,"metricType":"timing"},{"name":"number of files read","accumulatorId":302,"metricType":"sum"},{"name":"filesystem read data size","accumulatorId":307,"metricType":"size"},{"name":"filesystem read data size (sampled)","accumulatorId":306,"metricType":"size"},{"name":"filesystem read time (sampled)","accumulatorId":305,"metricType":"timing"},{"name":"metadata time","accumulatorId":303,"metricType":"timing"},{"name":"size of files read","accumulatorId":304,"metricType":"size"},{"name":"number of output rows","accumulatorId":301,"metricType":"sum"}]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[{"name":"spill size","accumulatorId":323,"metricType":"size"},{"name":"time in aggregation build","accumulatorId":324,"metricType":"timing"},{"name":"peak memory","accumulatorId":322,"metricType":"size"},{"name":"passthrough output rows","accumulatorId":326,"metricType":"sum"},{"name":"number of output rows","accumulatorId":321,"metricType":"sum"},{"name":"avg hash probe bucket list iters","accumulatorId":325,"metricType":"average"}]}],"metadata":{},"metrics":[{"name":"duration","accumulatorId":320,"metricType":"timing"}]}],"metadata":{},"metrics":[{"name":"shuffle records written","accumulatorId":318,"metricType":"sum"},{"name":"shuffle write time","accumulatorId":319,"metricType":"nsTiming"},{"name":"records read","accumulatorId":316,"metricType":"sum"},{"name":"local bytes read","accumulatorId":314,"metricType":"size"},{"name":"fetch wait time","accumulatorId":315,"metricType":"timing"},{"name":"remote bytes read","accumulatorId":312,"metricType":"size"},{"name":"local blocks read","accumulatorId":311,"metricType":"sum"},{"name":"remote blocks read","accumulatorId":310,"metricType":"sum"},{"name":"data size","accumulatorId":309,"metricType":"size"},{"name":"remote bytes read to disk","accumulatorId":313,"metricType":"size"},{"name":"shuffle bytes written","accumulatorId":317,"metricType":"size"}]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerDriverAccumUpdates","executionId":6,"accumUpdates":[[308,0],[302,10],[303,0],[304,1904190334]]}
+{"Event":"SparkListenerJobStart","Job ID":5,"Submission Time":1646432529806,"Stage Infos":[{"Stage ID":6,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":10,"RDD Info":[{"RDD ID":21,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_223\",\"name\":\"Exchange\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[20],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":20,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_222\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[19],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":18,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_155\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":19,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_155\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[18],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Accumulables":[],"Resource Profile Id":0}],"Stage 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ID":18,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_155\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":19,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_155\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[18],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native 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+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":11,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":27,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"37\",\"name\":\"mapPartitionsInternal\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[26],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":26,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_303\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[25],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":25,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_1\",\"name\":\"ShuffleQueryStage\"}","Callsite":"inputRDDs at RunningWindowFunctionExec.scala:218","Parent IDs":[24],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[10],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432546702,"Completion Time":1646432547022,"Accumulables":[{"ID":358,"Name":"remote blocks read","Value":"1","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":360,"Name":"remote bytes read","Value":"264","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":363,"Name":"fetch wait time","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":364,"Name":"records read","Value":"23","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":416,"Name":"duration","Value":"73","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":417,"Name":"sort time","Value":"2","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":418,"Name":"peak memory","Value":"67174400","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":419,"Name":"spill size","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":420,"Name":"cumulative time","Value":"17","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":421,"Name":"spill write time","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":422,"Name":"num prefix comparisons","Value":"75","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":424,"Name":"internal.metrics.executorDeserializeTime","Value":93,"Internal":true,"Count Failed Values":true},{"ID":425,"Name":"internal.metrics.executorDeserializeCpuTime","Value":90819288,"Internal":true,"Count Failed Values":true},{"ID":426,"Name":"internal.metrics.executorRunTime","Value":179,"Internal":true,"Count Failed Values":true},{"ID":427,"Name":"internal.metrics.executorCpuTime","Value":145455219,"Internal":true,"Count Failed Values":true},{"ID":428,"Name":"internal.metrics.resultSize","Value":5115,"Internal":true,"Count Failed Values":true},{"ID":433,"Name":"internal.metrics.peakExecutionMemory","Value":67174400,"Internal":true,"Count Failed Values":true},{"ID":437,"Name":"internal.metrics.shuffle.read.remoteBlocksFetched","Value":1,"Internal":true,"Count Failed Values":true},{"ID":438,"Name":"internal.metrics.shuffle.read.localBlocksFetched","Value":0,"Internal":true,"Count Failed Values":true},{"ID":439,"Name":"internal.metrics.shuffle.read.remoteBytesRead","Value":264,"Internal":true,"Count Failed Values":true},{"ID":440,"Name":"internal.metrics.shuffle.read.remoteBytesReadToDisk","Value":0,"Internal":true,"Count Failed Values":true},{"ID":441,"Name":"internal.metrics.shuffle.read.localBytesRead","Value":0,"Internal":true,"Count Failed Values":true},{"ID":442,"Name":"internal.metrics.shuffle.read.fetchWaitTime","Value":0,"Internal":true,"Count Failed Values":true},{"ID":443,"Name":"internal.metrics.shuffle.read.recordsRead","Value":23,"Internal":true,"Count Failed Values":true}],"Resource Profile Id":0}}
+{"Event":"SparkListenerJobEnd","Job ID":7,"Completion Time":1646432547023,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":6,"time":1646432547086,"errorMessage":""}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":7,"description":"\nimport tempfile\nfile = tempfile.NamedTemporary...","details":"org.apache.spark.sql.Dataset.createOrReplaceTempView(Dataset.scala:3394)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","physicalPlanDescription":"== Physical Plan ==\nExecute CreateViewCommand (1)\n   +- CreateViewCommand (2)\n         +- Window (10)\n            +- Project (9)\n               +- Deduplicate (8)\n                  +- Union (7)\n                     :- Project (5)\n                     :  +- Project (4)\n                     :     +- LogicalRelation (3)\n                     +- Project (6)\n                        +- Project (4)\n                           +- LogicalRelation (3)\n\n\n(1) Execute CreateViewCommand\nOutput: []\n\n(2) CreateViewCommand\nArguments: `airports`, false, true, LocalTempView\n\n(3) LogicalRelation\nArguments: csv, [FL_DATE#18, OP_CARRIER#19, OP_CARRIER_FL_NUM#20, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_TIME#24, DEP_DELAY#25, TAXI_OUT#26, WHEELS_OFF#27, WHEELS_ON#28, TAXI_IN#29, CRS_ARR_TIME#30, ARR_TIME#31, ARR_DELAY#32, CANCELLED#33, CANCELLATION_CODE#34, DIVERTED#35, CRS_ELAPSED_TIME#36, ACTUAL_ELAPSED_TIME#37, AIR_TIME#38, DISTANCE#39, CARRIER_DELAY#40, WEATHER_DELAY#41, ... 4 more fields], false\n\n(4) Project\nArguments: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\n\n(5) Project\nArguments: [ORIGIN#21 AS airport#144]\n\n(6) Project\nArguments: [DEST#22 AS airport#146]\n\n(7) Union\nArguments: false, false\n\n(8) Deduplicate\nArguments: [airport#144]\n\n(9) Project\nArguments: [airport#144]\n\n(10) Window\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST]\n\n","sparkPlanInfo":{"nodeName":"Execute CreateViewCommand","simpleString":"Execute CreateViewCommand","children":[],"metadata":{},"metrics":[]},"time":1646432547180}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":7,"time":1646432547180,"errorMessage":""}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":8,"description":"\nimport tempfile\nfile = tempfile.NamedTemporary...","details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","physicalPlanDescription":"== Physical Plan ==\nAdaptiveSparkPlan (14)\n+- GlobalLimit (13)\n   +- LocalLimit (12)\n      +- Window (11)\n         +- Sort (10)\n            +- Exchange (9)\n               +- HashAggregate (8)\n                  +- Exchange (7)\n                     +- HashAggregate (6)\n                        +- Union (5)\n                           :- Project (2)\n                           :  +- Scan csv  (1)\n                           +- Project (4)\n                              +- Scan csv  (3)\n\n\n(1) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(2) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(3) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(4) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(5) Union\n\n(6) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(7) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#341]\n\n(8) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(9) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#344]\n\n(10) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(11) Window\nInput [1]: [airport#144]\nArguments: [airport#144, cast(row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) as string) AS airport_id#156], [airport#144 ASC NULLS FIRST], 21\n\n(12) LocalLimit\nInput [2]: [airport#144, airport_id#156]\nArguments: 21\n\n(13) GlobalLimit\nInput [2]: [airport#144, airport_id#156]\nArguments: 21\n\n(14) AdaptiveSparkPlan\nOutput [2]: [airport#144, airport_id#156]\nArguments: isFinalPlan=false\n\n","sparkPlanInfo":{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"GlobalLimit","simpleString":"GlobalLimit 21","children":[{"nodeName":"LocalLimit","simpleString":"LocalLimit 21","children":[{"nodeName":"Window","simpleString":"Window [airport#144, cast(row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) as string) AS airport_id#156], [airport#144 ASC NULLS FIRST], 21","children":[{"nodeName":"Sort","simpleString":"Sort [airport#144 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#344]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[airport#144], functions=[])","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#341]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[airport#144], functions=[])","children":[{"nodeName":"Union","simpleString":"Union","children":[{"nodeName":"Project","simpleString":"Project [ORIGIN#21 AS airport#144]","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [ORIGIN#21] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<ORIGIN:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<ORIGIN:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]},{"nodeName":"Project","simpleString":"Project [DEST#22 AS airport#146]","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [DEST#22] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<DEST:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<DEST:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]},"time":1646432547307}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":8,"physicalPlanDescription":"== Physical Plan ==\nAdaptiveSparkPlan (26)\n+- == Current Plan ==\n   GlobalLimit (14)\n   +- LocalLimit (13)\n      +- Window (12)\n         +- Sort (11)\n            +- Exchange (10)\n               +- HashAggregate (9)\n                  +- ShuffleQueryStage (8)\n                     +- Exchange (7)\n                        +- * HashAggregate (6)\n                           +- Union (5)\n                              :- * Project (2)\n                              :  +- Scan csv  (1)\n                              +- * Project (4)\n                                 +- Scan csv  (3)\n+- == Initial Plan ==\n   GlobalLimit (25)\n   +- LocalLimit (24)\n      +- Window (23)\n         +- Sort (22)\n            +- Exchange (21)\n               +- HashAggregate (20)\n                  +- Exchange (19)\n                     +- HashAggregate (18)\n                        +- Union (17)\n                           :- Project (15)\n                           :  +- Scan csv  (1)\n                           +- Project (16)\n                              +- Scan csv  (3)\n\n\n(1) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(2) Project [codegen id : 1]\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(3) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(4) Project [codegen id : 2]\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(5) Union\n\n(6) HashAggregate [codegen id : 3]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(7) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#438]\n\n(8) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 0, Statistics(sizeInBytes=3.5 GiB)\n\n(9) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(10) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#457]\n\n(11) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(12) Window\nInput [1]: [airport#144]\nArguments: [airport#144, cast(row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) as string) AS airport_id#156], [airport#144 ASC NULLS FIRST], 21\n\n(13) LocalLimit\nInput [2]: [airport#144, airport_id#156]\nArguments: 21\n\n(14) GlobalLimit\nInput [2]: [airport#144, airport_id#156]\nArguments: 21\n\n(15) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(16) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(17) Union\n\n(18) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(19) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#341]\n\n(20) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(21) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#344]\n\n(22) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(23) Window\nInput [1]: [airport#144]\nArguments: [airport#144, cast(row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) as string) AS airport_id#156], [airport#144 ASC NULLS FIRST], 21\n\n(24) LocalLimit\nInput [2]: [airport#144, airport_id#156]\nArguments: 21\n\n(25) GlobalLimit\nInput [2]: [airport#144, airport_id#156]\nArguments: 21\n\n(26) AdaptiveSparkPlan\nOutput [2]: [airport#144, airport_id#156]\nArguments: isFinalPlan=false\n\n","sparkPlanInfo":{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"GlobalLimit","simpleString":"GlobalLimit 21","children":[{"nodeName":"LocalLimit","simpleString":"LocalLimit 21","children":[{"nodeName":"Window","simpleString":"Window [airport#144, cast(row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) as string) AS airport_id#156], [airport#144 ASC NULLS FIRST], 21","children":[{"nodeName":"Sort","simpleString":"Sort [airport#144 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, 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ID":41,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_509\",\"name\":\"Sort\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[40],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":42,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_510\",\"name\":\"Window\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[41],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[16],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432564394,"Completion Time":1646432564721,"Accumulables":[{"ID":562,"Name":"local blocks read","Value":"1","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":565,"Name":"local bytes read","Value":"2249","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":566,"Name":"fetch wait time","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":567,"Name":"records read","Value":"381","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":609,"Name":"duration","Value":"2","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":610,"Name":"sort time","Value":"4","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":611,"Name":"peak memory","Value":"67174400","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":612,"Name":"spill size","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":613,"Name":"cumulative time","Value":"76","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":614,"Name":"spill write time","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":615,"Name":"num prefix comparisons","Value":"2773","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":617,"Name":"internal.metrics.executorDeserializeTime","Value":85,"Internal":true,"Count Failed Values":true},{"ID":618,"Name":"internal.metrics.executorDeserializeCpuTime","Value":84894808,"Internal":true,"Count Failed Values":true},{"ID":619,"Name":"internal.metrics.executorRunTime","Value":191,"Internal":true,"Count Failed Values":true},{"ID":620,"Name":"internal.metrics.executorCpuTime","Value":168798972,"Internal":true,"Count Failed Values":true},{"ID":621,"Name":"internal.metrics.resultSize","Value":4853,"Internal":true,"Count Failed Values":true},{"ID":626,"Name":"internal.metrics.peakExecutionMemory","Value":67174400,"Internal":true,"Count Failed Values":true},{"ID":630,"Name":"internal.metrics.shuffle.read.remoteBlocksFetched","Value":0,"Internal":true,"Count Failed Values":true},{"ID":631,"Name":"internal.metrics.shuffle.read.localBlocksFetched","Value":1,"Internal":true,"Count Failed Values":true},{"ID":632,"Name":"internal.metrics.shuffle.read.remoteBytesRead","Value":0,"Internal":true,"Count Failed Values":true},{"ID":633,"Name":"internal.metrics.shuffle.read.remoteBytesReadToDisk","Value":0,"Internal":true,"Count Failed Values":true},{"ID":634,"Name":"internal.metrics.shuffle.read.localBytesRead","Value":2249,"Internal":true,"Count Failed Values":true},{"ID":635,"Name":"internal.metrics.shuffle.read.fetchWaitTime","Value":0,"Internal":true,"Count Failed Values":true},{"ID":636,"Name":"internal.metrics.shuffle.read.recordsRead","Value":381,"Internal":true,"Count Failed Values":true}],"Resource Profile Id":0}}
+{"Event":"SparkListenerJobEnd","Job ID":10,"Completion Time":1646432564721,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionEnd","executionId":8,"time":1646432564761,"errorMessage":""}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLExecutionStart","executionId":9,"description":"\nimport tempfile\nfile = tempfile.NamedTemporary...","details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","physicalPlanDescription":"== Physical Plan ==\nCollectLimit (54)\n+- AdaptiveSparkPlan (53)\n   +- Project (52)\n      +- SortMergeJoin Inner (51)\n         :- Sort (40)\n         :  +- Exchange (39)\n         :     +- Project (38)\n         :        +- SortMergeJoin Inner (37)\n         :           :- Sort (22)\n         :           :  +- Exchange (21)\n         :           :     +- Project (20)\n         :           :        +- SortMergeJoin Inner (19)\n         :           :           :- Sort (4)\n         :           :           :  +- Exchange (3)\n         :           :           :     +- Filter (2)\n         :           :           :        +- Scan csv  (1)\n         :           :           +- Sort (18)\n         :           :              +- Exchange (17)\n         :           :                 +- Filter (16)\n         :           :                    +- RunningWindowFunction (15)\n         :           :                       +- Sort (14)\n         :           :                          +- Exchange (13)\n         :           :                             +- HashAggregate (12)\n         :           :                                +- Exchange (11)\n         :           :                                   +- HashAggregate (10)\n         :           :                                      +- Union (9)\n         :           :                                         :- Project (6)\n         :           :                                         :  +- Scan csv  (5)\n         :           :                                         +- Project (8)\n         :           :                                            +- Scan csv  (7)\n         :           +- Sort (36)\n         :              +- Exchange (35)\n         :                 +- Filter (34)\n         :                    +- RunningWindowFunction (33)\n         :                       +- Sort (32)\n         :                          +- Exchange (31)\n         :                             +- HashAggregate (30)\n         :                                +- Exchange (29)\n         :                                   +- HashAggregate (28)\n         :                                      +- Union (27)\n         :                                         :- Project (24)\n         :                                         :  +- Scan csv  (23)\n         :                                         +- Project (26)\n         :                                            +- Scan csv  (25)\n         +- Sort (50)\n            +- Exchange (49)\n               +- Filter (48)\n                  +- RunningWindowFunction (47)\n                     +- Sort (46)\n                        +- Exchange (45)\n                           +- HashAggregate (44)\n                              +- Exchange (43)\n                                 +- HashAggregate (42)\n                                    +- Scan csv  (41)\n\n\n(1) Scan csv \nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nPushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]\nReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>\n\n(2) Filter\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(3) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]\n\n(4) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(5) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(6) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(7) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(8) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(9) Union\n\n(10) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(11) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]\n\n(12) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(13) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#659]\n\n(14) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(15) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(16) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(17) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]\n\n(18) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(19) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(20) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(21) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]\n\n(22) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(23) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(24) Project\nOutput [1]: [ORIGIN#21 AS airport#178]\nInput [1]: [ORIGIN#21]\n\n(25) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(26) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(27) Union\n\n(28) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(29) Exchange\nInput [1]: [airport#178]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]\n\n(30) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(31) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#674]\n\n(32) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(33) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(34) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(35) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]\n\n(36) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(37) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(38) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(39) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]\n\n(40) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(41) Scan csv \nOutput [1]: [OP_CARRIER#180]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(42) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(43) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]\n\n(44) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(45) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#689]\n\n(46) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(47) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(48) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(49) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]\n\n(50) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(51) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(52) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(53) AdaptiveSparkPlan\nOutput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: isFinalPlan=false\n\n(54) CollectLimit\nInput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: 11\n\n","sparkPlanInfo":{"nodeName":"CollectLimit","simpleString":"CollectLimit 11","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"Project","simpleString":"Project [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]","children":[{"nodeName":"SortMergeJoin","simpleString":"SortMergeJoin [OP_CARRIER#19], [OP_CARRIER#180], Inner, AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)","children":[{"nodeName":"Sort","simpleString":"Sort [OP_CARRIER#19 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]","children":[{"nodeName":"SortMergeJoin","simpleString":"SortMergeJoin [DEST#22], [airport#178], Inner","children":[{"nodeName":"Sort","simpleString":"Sort [DEST#22 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]","children":[{"nodeName":"SortMergeJoin","simpleString":"SortMergeJoin [ORIGIN#21], [airport#144], Inner","children":[{"nodeName":"Sort","simpleString":"Sort [ORIGIN#21 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]","children":[{"nodeName":"Filter","simpleString":"Filter ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [FL_DATE#18,OP_CARRIER#19,ORIGIN#21,DEST#22,CRS_DEP_TIME#23,DEP_DELAY#25] Batched: false, DataFilters: [isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)], ReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:d...","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]","DataFilters":"[isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)]"},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]},{"nodeName":"Sort","simpleString":"Sort [airport#144 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]","children":[{"nodeName":"Filter","simpleString":"Filter isnotnull(airport#144)","children":[{"nodeName":"RunningWindowFunction","simpleString":"RunningWindowFunction [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false","children":[{"nodeName":"Sort","simpleString":"Sort [airport#144 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#659]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[airport#144], functions=[])","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[airport#144], functions=[])","children":[{"nodeName":"Union","simpleString":"Union","children":[{"nodeName":"Project","simpleString":"Project [ORIGIN#21 AS airport#144]","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [ORIGIN#21] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<ORIGIN:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<ORIGIN:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]},{"nodeName":"Project","simpleString":"Project [DEST#22 AS airport#146]","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [DEST#22] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<DEST:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<DEST:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]},{"nodeName":"Sort","simpleString":"Sort [airport#178 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]","children":[{"nodeName":"Filter","simpleString":"Filter isnotnull(airport#178)","children":[{"nodeName":"RunningWindowFunction","simpleString":"RunningWindowFunction [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false","children":[{"nodeName":"Sort","simpleString":"Sort [airport#178 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#674]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[airport#178], functions=[])","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[airport#178], functions=[])","children":[{"nodeName":"Union","simpleString":"Union","children":[{"nodeName":"Project","simpleString":"Project [ORIGIN#21 AS airport#178]","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [ORIGIN#21] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<ORIGIN:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<ORIGIN:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]},{"nodeName":"Project","simpleString":"Project [DEST#22 AS airport#146]","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [DEST#22] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<DEST:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<DEST:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]},{"nodeName":"Sort","simpleString":"Sort [OP_CARRIER#180 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]","children":[{"nodeName":"Filter","simpleString":"Filter isnotnull(OP_CARRIER#180)","children":[{"nodeName":"RunningWindowFunction","simpleString":"RunningWindowFunction [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false","children":[{"nodeName":"Sort","simpleString":"Sort [OP_CARRIER#180 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#689]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[OP_CARRIER#180], functions=[])","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[OP_CARRIER#180], functions=[])","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [OP_CARRIER#180] Batched: false, DataFilters: [], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<OP_CARRIER:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<OP_CARRIER:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[{"name":"shuffle records written","accumulatorId":655,"metricType":"sum"},{"name":"records read","accumulatorId":653,"metricType":"sum"},{"name":"local bytes read","accumulatorId":651,"metricType":"size"},{"name":"fetch wait time","accumulatorId":652,"metricType":"timing"},{"name":"remote bytes read","accumulatorId":649,"metricType":"size"},{"name":"local blocks read","accumulatorId":648,"metricType":"sum"},{"name":"remote blocks read","accumulatorId":647,"metricType":"sum"},{"name":"remote bytes read to disk","accumulatorId":650,"metricType":"size"},{"name":"shuffle bytes written","accumulatorId":654,"metricType":"size"}]},"time":1646432565262}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":9,"physicalPlanDescription":"== Physical Plan ==\nCollectLimit (100)\n+- AdaptiveSparkPlan (99)\n   +- == Current Plan ==\n      Project (50)\n      +- SortMergeJoin Inner (49)\n         :- Sort (37)\n         :  +- Exchange (36)\n         :     +- Project (35)\n         :        +- SortMergeJoin Inner (34)\n         :           :- Sort (24)\n         :           :  +- Exchange (23)\n         :           :     +- Project (22)\n         :           :        +- SortMergeJoin Inner (21)\n         :           :           :- Sort (5)\n         :           :           :  +- ShuffleQueryStage (4)\n         :           :           :     +- Exchange (3)\n         :           :           :        +- * Filter (2)\n         :           :           :           +- Scan csv  (1)\n         :           :           +- Sort (20)\n         :           :              +- Exchange (19)\n         :           :                 +- Filter (18)\n         :           :                    +- RunningWindowFunction (17)\n         :           :                       +- Sort (16)\n         :           :                          +- Exchange (15)\n         :           :                             +- HashAggregate (14)\n         :           :                                +- ShuffleQueryStage (13)\n         :           :                                   +- Exchange (12)\n         :           :                                      +- * HashAggregate (11)\n         :           :                                         +- Union (10)\n         :           :                                            :- * Project (7)\n         :           :                                            :  +- Scan csv  (6)\n         :           :                                            +- * Project (9)\n         :           :                                               +- Scan csv  (8)\n         :           +- Sort (33)\n         :              +- Exchange (32)\n         :                 +- Filter (31)\n         :                    +- RunningWindowFunction (30)\n         :                       +- Sort (29)\n         :                          +- Exchange (28)\n         :                             +- HashAggregate (27)\n         :                                +- ShuffleQueryStage (26)\n         :                                   +- ReusedExchange (25)\n         +- Sort (48)\n            +- Exchange (47)\n               +- Filter (46)\n                  +- RunningWindowFunction (45)\n                     +- Sort (44)\n                        +- Exchange (43)\n                           +- HashAggregate (42)\n                              +- ShuffleQueryStage (41)\n                                 +- Exchange (40)\n                                    +- * HashAggregate (39)\n                                       +- Scan csv  (38)\n   +- == Initial Plan ==\n      Project (98)\n      +- SortMergeJoin Inner (97)\n         :- Sort (87)\n         :  +- Exchange (86)\n         :     +- Project (85)\n         :        +- SortMergeJoin Inner (84)\n         :           :- Sort (69)\n         :           :  +- Exchange (68)\n         :           :     +- Project (67)\n         :           :        +- SortMergeJoin Inner (66)\n         :           :           :- Sort (53)\n         :           :           :  +- Exchange (52)\n         :           :           :     +- Filter (51)\n         :           :           :        +- Scan csv  (1)\n         :           :           +- Sort (65)\n         :           :              +- Exchange (64)\n         :           :                 +- Filter (63)\n         :           :                    +- RunningWindowFunction (62)\n         :           :                       +- Sort (61)\n         :           :                          +- Exchange (60)\n         :           :                             +- HashAggregate (59)\n         :           :                                +- Exchange (58)\n         :           :                                   +- HashAggregate (57)\n         :           :                                      +- Union (56)\n         :           :                                         :- Project (54)\n         :           :                                         :  +- Scan csv  (6)\n         :           :                                         +- Project (55)\n         :           :                                            +- Scan csv  (8)\n         :           +- Sort (83)\n         :              +- Exchange (82)\n         :                 +- Filter (81)\n         :                    +- RunningWindowFunction (80)\n         :                       +- Sort (79)\n         :                          +- Exchange (78)\n         :                             +- HashAggregate (77)\n         :                                +- Exchange (76)\n         :                                   +- HashAggregate (75)\n         :                                      +- Union (74)\n         :                                         :- Project (71)\n         :                                         :  +- Scan csv  (70)\n         :                                         +- Project (73)\n         :                                            +- Scan csv  (72)\n         +- Sort (96)\n            +- Exchange (95)\n               +- Filter (94)\n                  +- RunningWindowFunction (93)\n                     +- Sort (92)\n                        +- Exchange (91)\n                           +- HashAggregate (90)\n                              +- Exchange (89)\n                                 +- HashAggregate (88)\n                                    +- Scan csv  (38)\n\n\n(1) Scan csv \nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nPushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]\nReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>\n\n(2) Filter [codegen id : 1]\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(3) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]\n\n(4) ShuffleQueryStage\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: 0, Statistics(sizeInBytes=1816.0 MiB)\n\n(5) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(6) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(7) Project [codegen id : 2]\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(8) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(9) Project [codegen id : 3]\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(10) Union\n\n(11) HashAggregate [codegen id : 4]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(12) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1066]\n\n(13) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 1, Statistics(sizeInBytes=3.5 GiB)\n\n(14) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(15) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1086]\n\n(16) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(17) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(18) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(19) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1090]\n\n(20) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(21) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(22) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(23) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#1094]\n\n(24) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(25) ReusedExchange [Reuses operator id: 12]\nOutput [1]: [airport#178]\n\n(26) ShuffleQueryStage\nOutput [1]: [airport#178]\nArguments: 3, Statistics(sizeInBytes=3.5 GiB)\n\n(27) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(28) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1129]\n\n(29) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(30) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(31) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(32) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#1133]\n\n(33) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(34) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(35) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(36) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#1137]\n\n(37) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(38) Scan csv \nOutput [1]: [OP_CARRIER#180]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(39) HashAggregate [codegen id : 8]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(40) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#1142]\n\n(41) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 4, Statistics(sizeInBytes=1816.0 MiB)\n\n(42) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(43) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1153]\n\n(44) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(45) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(46) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(47) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#1157]\n\n(48) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(49) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(50) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(51) Filter\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(52) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]\n\n(53) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(54) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(55) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(56) Union\n\n(57) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(58) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]\n\n(59) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(60) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#659]\n\n(61) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(62) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(63) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(64) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]\n\n(65) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(66) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(67) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(68) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]\n\n(69) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(70) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(71) Project\nOutput [1]: [ORIGIN#21 AS airport#178]\nInput [1]: [ORIGIN#21]\n\n(72) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(73) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(74) Union\n\n(75) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(76) Exchange\nInput [1]: [airport#178]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]\n\n(77) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(78) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#674]\n\n(79) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(80) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(81) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(82) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]\n\n(83) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(84) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(85) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(86) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]\n\n(87) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(88) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(89) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]\n\n(90) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(91) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#689]\n\n(92) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(93) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(94) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(95) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]\n\n(96) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(97) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(98) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(99) AdaptiveSparkPlan\nOutput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: isFinalPlan=false\n\n(100) CollectLimit\nInput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: 11\n\n","sparkPlanInfo":{"nodeName":"CollectLimit","simpleString":"CollectLimit 11","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"Project","simpleString":"Project [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as 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tabricks.clusterUsageTags.clusterOwnerUserId":"6878104766663444","spark.hadoop.fs.s3a.fast.upload":"true","spark.databricks.clusterUsageTags.clusterLogDeliveryEnabled":"true","spark.databricks.clusterUsageTags.driverNodeType":"m5.xlarge","spark.speculation.quantile":"0.9","spark.databricks.cloudProvider":"AWS","spark.akka.frameSize":"256","spark.hadoop.parquet.page.write-checksum.enabled":"true","spark.eventLog.rolling.enabled":"false","spark.databricks.clusterUsageTags.clusterAllTags":"[{\"key\":\"Vendor\",\"value\":\"Databricks\"},{\"key\":\"Creator\",\"value\":\"arianna@synccomputing.com\"},{\"key\":\"ClusterName\",\"value\":\"job-703-run-31144\"},{\"key\":\"ClusterId\",\"value\":\"0304-221748-fngq99ev\"},{\"key\":\"JobId\",\"value\":\"703\"},{\"key\":\"RunName\",\"value\":\"AirlineDelayLR_gz\"},{\"key\":\"Name\",\"value\":\"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313-worker\"}]","spark.sql.hive.metastore.version":"0.13.0"}}
+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":18,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":10,"RDD Info":[{"RDD ID":47,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1046\",\"name\":\"Exchange\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[46],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":46,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1045\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[45],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":45,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_616\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[44],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":44,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_616\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432565356,"Accumulables":[],"Resource Profile 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build","Update":"18372","Value":"160021","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":989,"Name":"avg hash probe bucket list iters","Update":"1000","Value":"10000","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1051,"Name":"internal.metrics.executorDeserializeTime","Update":7,"Value":40,"Internal":true,"Count Failed Values":true},{"ID":1052,"Name":"internal.metrics.executorDeserializeCpuTime","Update":3296959,"Value":34812594,"Internal":true,"Count Failed Values":true},{"ID":1053,"Name":"internal.metrics.executorRunTime","Update":18411,"Value":160468,"Internal":true,"Count Failed Values":true},{"ID":1054,"Name":"internal.metrics.executorCpuTime","Update":18082267487,"Value":157403520339,"Internal":true,"Count Failed Values":true},{"ID":1055,"Name":"internal.metrics.resultSize","Update":2133,"Value":21328,"Internal":true,"Count Failed Values":true},{"ID":1056,"Name":"internal.metrics.jvmGCTime","Update":145,"Value":1710,"Internal":true,"Count Failed Values":true},{"ID":1060,"Name":"internal.metrics.peakExecutionMemory","Update":67371008,"Value":673710080,"Internal":true,"Count Failed Values":true},{"ID":1071,"Name":"internal.metrics.shuffle.write.bytesWritten","Update":1224,"Value":10292,"Internal":true,"Count Failed Values":true},{"ID":1072,"Name":"internal.metrics.shuffle.write.recordsWritten","Update":18,"Value":154,"Internal":true,"Count Failed Values":true},{"ID":1073,"Name":"internal.metrics.shuffle.write.writeTime","Update":3876166,"Value":44898301,"Internal":true,"Count Failed Values":true},{"ID":1074,"Name":"internal.metrics.input.bytesRead","Update":228606620,"Value":1904190334,"Internal":true,"Count Failed Values":true},{"ID":1075,"Name":"internal.metrics.input.recordsRead","Update":7213446,"Value":61556964,"Internal":true,"Count Failed Values":true},{"ID":1076,"Name":"internal.metrics.input.sampledTimeReadNano","Update":1817234545,"Value":15548455058,"Internal":true,"Count Failed Values":true},{"ID":1077,"Name":"internal.metrics.input.sampledBytesRead","Update":228606620,"Value":1904190334,"Internal":true,"Count Failed Values":true}]},"Task Executor Metrics":{"JVMHeapMemory":4655239904,"JVMOffHeapMemory":127611656,"OnHeapExecutionMemory":538968064,"OffHeapExecutionMemory":0,"OnHeapStorageMemory":4181542,"OffHeapStorageMemory":0,"OnHeapUnifiedMemory":543149606,"OffHeapUnifiedMemory":0,"DirectPoolMemory":1460414,"MappedPoolMemory":0,"ProcessTreeJVMVMemory":0,"ProcessTreeJVMRSSMemory":0,"ProcessTreePythonVMemory":0,"ProcessTreePythonRSSMemory":0,"ProcessTreeOtherVMemory":0,"ProcessTreeOtherRSSMemory":0,"MinorGCCount":60,"MinorGCTime":522,"MajorGCCount":5,"MajorGCTime":290},"Task Metrics":{"Executor Deserialize Time":7,"Executor Deserialize CPU Time":3296959,"Executor Run Time":18411,"Executor CPU Time":18082267487,"Peak Execution Memory":67371008,"Result Size":2133,"JVM GC Time":145,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":0,"Local Blocks Fetched":0,"Fetch Wait Time":0,"Remote Bytes Read":0,"Remote Bytes Read To Disk":0,"Local Bytes Read":0,"Total Records Read":0},"Shuffle Write Metrics":{"Shuffle Bytes Written":1224,"Shuffle Write Time":3876166,"Shuffle Records Written":18},"Input Metrics":{"Bytes Read":228606620,"Records Read":7213446},"Output Metrics":{"Bytes Written":0,"Records Written":0},"Updated Blocks":[]}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":20,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":10,"RDD Info":[{"RDD ID":60,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1142\",\"name\":\"Exchange\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[59],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":59,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1141\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[58],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":57,"Name":"FileScanRDD","Scope":"{\"id\":\"spark_plan_641\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":58,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_641\",\"name\":\"Scan csv \"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[57],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":10,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432565571,"Completion Time":1646432584044,"Accumulables":[{"ID":932,"Name":"number of output rows","Value":"61556964","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":936,"Name":"filesystem read time (sampled)","Value":"15544","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":937,"Name":"filesystem read data size (sampled)","Value":"1904190334","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":938,"Name":"filesystem read data size","Value":"1904190334","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":973,"Name":"data size","Value":"3696","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":981,"Name":"shuffle bytes written","Value":"10292","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":982,"Name":"shuffle records written","Value":"154","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":983,"Name":"shuffle write time","Value":"44898301","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":984,"Name":"duration","Value":"160136","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":985,"Name":"number of output rows","Value":"154","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":986,"Name":"peak memory","Value":"673710080","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":988,"Name":"time in aggregation build","Value":"160021","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":989,"Name":"avg hash probe bucket list iters","Value":"10000","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1051,"Name":"internal.metrics.executorDeserializeTime","Value":40,"Internal":true,"Count Failed Values":true},{"ID":1052,"Name":"internal.metrics.executorDeserializeCpuTime","Value":34812594,"Internal":true,"Count Failed Values":true},{"ID":1053,"Name":"internal.metrics.executorRunTime","Value":160468,"Internal":true,"Count Failed Values":true},{"ID":1054,"Name":"internal.metrics.executorCpuTime","Value":157403520339,"Internal":true,"Count Failed Values":true},{"ID":1055,"Name":"internal.metrics.resultSize","Value":21328,"Internal":true,"Count Failed Values":true},{"ID":1056,"Name":"internal.metrics.jvmGCTime","Value":1710,"Internal":true,"Count Failed Values":true},{"ID":1060,"Name":"internal.metrics.peakExecutionMemory","Value":673710080,"Internal":true,"Count Failed Values":true},{"ID":1071,"Name":"internal.metrics.shuffle.write.bytesWritten","Value":10292,"Internal":true,"Count Failed Values":true},{"ID":1072,"Name":"internal.metrics.shuffle.write.recordsWritten","Value":154,"Internal":true,"Count Failed Values":true},{"ID":1073,"Name":"internal.metrics.shuffle.write.writeTime","Value":44898301,"Internal":true,"Count Failed Values":true},{"ID":1074,"Name":"internal.metrics.input.bytesRead","Value":1904190334,"Internal":true,"Count Failed Values":true},{"ID":1075,"Name":"internal.metrics.input.recordsRead","Value":61556964,"Internal":true,"Count Failed Values":true},{"ID":1076,"Name":"internal.metrics.input.sampledTimeReadNano","Value":15548455058,"Internal":true,"Count Failed Values":true},{"ID":1077,"Name":"internal.metrics.input.sampledBytesRead","Value":1904190334,"Internal":true,"Count Failed Values":true}],"Resource Profile Id":0}}
+{"Event":"SparkListenerJobEnd","Job ID":13,"Completion Time":1646432584045,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":9,"physicalPlanDescription":"== Physical Plan ==\nCollectLimit (99)\n+- AdaptiveSparkPlan (98)\n   +- == Current Plan ==\n      Project (49)\n      +- BroadcastHashJoin Inner BuildRight (48)\n         :- Project (35)\n         :  +- SortMergeJoin Inner (34)\n         :     :- Sort (24)\n         :     :  +- Exchange (23)\n         :     :     +- Project (22)\n         :     :        +- SortMergeJoin Inner (21)\n         :     :           :- Sort (5)\n         :     :           :  +- ShuffleQueryStage (4)\n         :     :           :     +- Exchange (3)\n         :     :           :        +- * Filter (2)\n         :     :           :           +- Scan csv  (1)\n         :     :           +- Sort (20)\n         :     :              +- Exchange (19)\n         :     :                 +- Filter (18)\n         :     :                    +- RunningWindowFunction (17)\n         :     :                       +- Sort (16)\n         :     :                          +- Exchange (15)\n         :     :                             +- HashAggregate (14)\n         :     :                                +- ShuffleQueryStage (13)\n         :     :                                   +- Exchange (12)\n         :     :                                      +- * HashAggregate (11)\n         :     :                                         +- Union (10)\n         :     :                                            :- * Project (7)\n         :     :                                            :  +- Scan csv  (6)\n         :     :                                            +- * Project (9)\n         :     :                                               +- Scan csv  (8)\n         :     +- Sort (33)\n         :        +- Exchange (32)\n         :           +- Filter (31)\n         :              +- RunningWindowFunction (30)\n         :                 +- Sort (29)\n         :                    +- Exchange (28)\n         :                       +- HashAggregate (27)\n         :                          +- ShuffleQueryStage (26)\n         :                             +- ReusedExchange (25)\n         +- BroadcastExchange (47)\n            +- Filter (46)\n               +- RunningWindowFunction (45)\n                  +- Sort (44)\n                     +- ShuffleQueryStage (43)\n                        +- Exchange (42)\n                           +- * HashAggregate (41)\n                              +- CustomShuffleReader (40)\n                                 +- ShuffleQueryStage (39)\n                                    +- Exchange (38)\n                                       +- * HashAggregate (37)\n                                          +- Scan csv  (36)\n   +- == Initial Plan ==\n      Project (97)\n      +- SortMergeJoin Inner (96)\n         :- Sort (86)\n         :  +- Exchange (85)\n         :     +- Project (84)\n         :        +- SortMergeJoin Inner (83)\n         :           :- Sort (68)\n         :           :  +- Exchange (67)\n         :           :     +- Project (66)\n         :           :        +- SortMergeJoin Inner (65)\n         :           :           :- Sort (52)\n         :           :           :  +- Exchange (51)\n         :           :           :     +- Filter (50)\n         :           :           :        +- Scan csv  (1)\n         :           :           +- Sort (64)\n         :           :              +- Exchange (63)\n         :           :                 +- Filter (62)\n         :           :                    +- RunningWindowFunction (61)\n         :           :                       +- Sort (60)\n         :           :                          +- Exchange (59)\n         :           :                             +- HashAggregate (58)\n         :           :                                +- Exchange (57)\n         :           :                                   +- HashAggregate (56)\n         :           :                                      +- Union (55)\n         :           :                                         :- Project (53)\n         :           :                                         :  +- Scan csv  (6)\n         :           :                                         +- Project (54)\n         :           :                                            +- Scan csv  (8)\n         :           +- Sort (82)\n         :              +- Exchange (81)\n         :                 +- Filter (80)\n         :                    +- RunningWindowFunction (79)\n         :                       +- Sort (78)\n         :                          +- Exchange (77)\n         :                             +- HashAggregate (76)\n         :                                +- Exchange (75)\n         :                                   +- HashAggregate (74)\n         :                                      +- Union (73)\n         :                                         :- Project (70)\n         :                                         :  +- Scan csv  (69)\n         :                                         +- Project (72)\n         :                                            +- Scan csv  (71)\n         +- Sort (95)\n            +- Exchange (94)\n               +- Filter (93)\n                  +- RunningWindowFunction (92)\n                     +- Sort (91)\n                        +- Exchange (90)\n                           +- HashAggregate (89)\n                              +- Exchange (88)\n                                 +- HashAggregate (87)\n                                    +- Scan csv  (36)\n\n\n(1) Scan csv \nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nPushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]\nReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>\n\n(2) Filter [codegen id : 1]\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(3) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]\n\n(4) ShuffleQueryStage\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: 0, Statistics(sizeInBytes=1816.0 MiB)\n\n(5) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(6) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(7) Project [codegen id : 2]\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(8) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(9) Project [codegen id : 3]\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(10) Union\n\n(11) HashAggregate [codegen id : 4]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(12) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1066]\n\n(13) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 1, Statistics(sizeInBytes=3.5 GiB)\n\n(14) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(15) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1259]\n\n(16) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(17) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(18) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(19) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1265]\n\n(20) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(21) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(22) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(23) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#1276]\n\n(24) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(25) ReusedExchange [Reuses operator id: 12]\nOutput [1]: [airport#178]\n\n(26) ShuffleQueryStage\nOutput [1]: [airport#178]\nArguments: 3, Statistics(sizeInBytes=3.5 GiB)\n\n(27) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(28) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1270]\n\n(29) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(30) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(31) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(32) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#1277]\n\n(33) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(34) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(35) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(36) Scan csv \nOutput [1]: [OP_CARRIER#180]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(37) HashAggregate [codegen id : 8]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(38) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#1142]\n\n(39) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 4, Statistics(sizeInBytes=3.6 KiB, rowCount=154, isRuntime=true)\n\n(40) CustomShuffleReader\nInput [1]: [OP_CARRIER#180]\nArguments: coalesced\n\n(41) HashAggregate [codegen id : 9]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(42) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1458]\n\n(43) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 5, Statistics(sizeInBytes=4.2 KiB, rowCount=154, isRuntime=true)\n\n(44) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(45) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(46) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(47) BroadcastExchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#1469]\n\n(48) BroadcastHashJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(49) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(50) Filter\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(51) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]\n\n(52) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(53) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(54) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(55) Union\n\n(56) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(57) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]\n\n(58) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(59) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#659]\n\n(60) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(61) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(62) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(63) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]\n\n(64) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(65) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(66) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(67) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]\n\n(68) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(69) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(70) Project\nOutput [1]: [ORIGIN#21 AS airport#178]\nInput [1]: [ORIGIN#21]\n\n(71) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(72) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(73) Union\n\n(74) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(75) Exchange\nInput [1]: [airport#178]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]\n\n(76) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(77) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#674]\n\n(78) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(79) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(80) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(81) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]\n\n(82) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(83) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(84) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(85) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]\n\n(86) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(87) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(88) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]\n\n(89) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(90) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#689]\n\n(91) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(92) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(93) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(94) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]\n\n(95) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(96) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(97) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(98) AdaptiveSparkPlan\nOutput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: isFinalPlan=false\n\n(99) CollectLimit\nInput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: 11\n\n","sparkPlanInfo":{"nodeName":"CollectLimit","simpleString":"CollectLimit 11","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"Project","simpleString":"Project [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [OP_CARRIER#19], [OP_CARRIER#180], Inner, BuildRight, AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25), false","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]","children":[{"nodeName":"SortMergeJoin","simpleString":"SortMergeJoin [DEST#22], [airport#178], Inner","children":[{"nodeName":"Sort","simpleString":"Sort [DEST#22 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#1276]","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]","children":[{"nodeName":"SortMergeJoin","simpleString":"SortMergeJoin [ORIGIN#21], [airport#144], Inner","children":[{"nodeName":"Sort","simpleString":"Sort [ORIGIN#21 ASC NULLS FIRST], false, 0","children":[{"nodeName":"ShuffleQueryStage","simpleString":"ShuffleQueryStage 0, Statistics(sizeInBytes=1816.0 MiB)","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]","children":[{"nodeName":"WholeStageCodegen (1)","simpleString":"WholeStageCodegen","children":[{"nodeName":"Filter","simpleString":"Filter ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))","children":[{"nodeName":"InputAdapter","simpleString":"InputAdapter","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [FL_DATE#18,OP_CARRIER#19,ORIGIN#21,DEST#22,CRS_DEP_TIME#23,DEP_DELAY#25] Batched: false, DataFilters: [isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)], ReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:d...","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]","DataFilters":"[isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)]"},"metrics":[{"name":"file sorting by size time","accumulatorId":722,"metricType":"timing"},{"name":"number of files read","accumulatorId":716,"metricType":"sum"},{"name":"filesystem read data size","accumulatorId":721,"metricType":"size"},{"name":"filesystem read data size (sampled)","accumulatorId":720,"metricType":"size"},{"name":"filesystem read time 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read","accumulatorId":942,"metricType":"sum"},{"name":"remote blocks read","accumulatorId":941,"metricType":"sum"},{"name":"data size","accumulatorId":940,"metricType":"size"},{"name":"remote bytes read to disk","accumulatorId":944,"metricType":"size"},{"name":"shuffle bytes written","accumulatorId":948,"metricType":"size"}]}],"metadata":{},"metrics":[]}],"metadata":{},"metrics":[]},{"nodeName":"Sort","simpleString":"Sort [airport#144 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1265]","children":[{"nodeName":"Filter","simpleString":"Filter isnotnull(airport#144)","children":[{"nodeName":"RunningWindowFunction","simpleString":"RunningWindowFunction [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], 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struct<DEST:string>","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<DEST:string>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[]","DataFilters":"[]"},"metrics":[{"name":"file sorting by size time","accumulatorId":797,"metricType":"timing"},{"name":"number of files read","accumulatorId":791,"metricType":"sum"},{"name":"filesystem read data size","accumulatorId":796,"metricType":"size"},{"name":"filesystem read data size (sampled)","accumulatorId":795,"metricType":"size"},{"name":"filesystem read time (sampled)","accumulatorId":794,"metricType":"timing"},{"name":"metadata time","accumulatorId":792,"metricType":"timing"},{"name":"size of files read","accumulatorId":793,"metricType":"size"},{"name":"number of output 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[airport#178 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#1277]","children":[{"nodeName":"Filter","simpleString":"Filter isnotnull(airport#178)","children":[{"nodeName":"RunningWindowFunction","simpleString":"RunningWindowFunction [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false","children":[{"nodeName":"Sort","simpleString":"Sort [airport#178 ASC NULLS FIRST], false, 0","children":[{"nodeName":"Exchange","simpleString":"Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#1270]","children":[{"nodeName":"HashAggregate","simpleString":"HashAggregate(keys=[airport#178], functions=[])","children":[{"nodeName":"ShuffleQueryStage","simpleString":"ShuffleQueryStage 3, Statistics(sizeInBytes=3.5 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+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":22,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":63,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1458\",\"name\":\"Exchange\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[62],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":62,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1457\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[61],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":61,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_1452\",\"name\":\"CustomShuffleReader\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[60],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[21],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native 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Bytes Written":259,"Shuffle Write Time":262091,"Shuffle Records Written":23},"Input Metrics":{"Bytes Read":0,"Records Read":0},"Output Metrics":{"Bytes Written":0,"Records Written":0},"Updated Blocks":[]}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":22,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":63,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1458\",\"name\":\"Exchange\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[62],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":62,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1457\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[61],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":61,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_1452\",\"name\":\"CustomShuffleReader\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[60],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[21],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432584170,"Completion Time":1646432584333,"Accumulables":[{"ID":974,"Name":"remote blocks read","Value":"7","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":975,"Name":"local blocks read","Value":"3","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":976,"Name":"remote bytes read","Value":"7368","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":978,"Name":"local bytes read","Value":"2924","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":979,"Name":"fetch wait time","Value":"0","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":980,"Name":"records read","Value":"154","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1081,"Name":"data size","Value":"552","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1089,"Name":"shuffle bytes written","Value":"259","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1090,"Name":"shuffle records written","Value":"23","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1091,"Name":"shuffle write time","Value":"262091","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1092,"Name":"duration","Value":"15","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1093,"Name":"number of output rows","Value":"23","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1094,"Name":"peak memory","Value":"67371008","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1096,"Name":"time in aggregation build","Value":"12","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1097,"Name":"avg hash probe bucket list iters","Value":"1000","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":1100,"Name":"internal.metrics.executorDeserializeTime","Value":23,"Internal":true,"Count Failed Values":true},{"ID":1101,"Name":"internal.metrics.executorDeserializeCpuTime","Value":23095536,"Internal":true,"Count Failed Values":true},{"ID":1102,"Name":"internal.metrics.executorRunTime","Value":97,"Internal":true,"Count Failed Values":true},{"ID":1103,"Name":"internal.metrics.executorCpuTime","Value":86604772,"Internal":true,"Count Failed Values":true},{"ID":1104,"Name":"internal.metrics.resultSize","Value":3469,"Internal":true,"Count Failed Values":true},{"ID":1109,"Name":"internal.metrics.peakExecutionMemory","Value":67371008,"Internal":true,"Count Failed Values":true},{"ID":1113,"Name":"internal.metrics.shuffle.read.remoteBlocksFetched","Value":7,"Internal":true,"Count Failed Values":true},{"ID":1114,"Name":"internal.metrics.shuffle.read.localBlocksFetched","Value":3,"Internal":true,"Count Failed Values":true},{"ID":1115,"Name":"internal.metrics.shuffle.read.remoteBytesRead","Value":7368,"Internal":true,"Count Failed Values":true},{"ID":1116,"Name":"internal.metrics.shuffle.read.remoteBytesReadToDisk","Value":0,"Internal":true,"Count Failed Values":true},{"ID":1117,"Name":"internal.metrics.shuffle.read.localBytesRead","Value":2924,"Internal":true,"Count Failed Values":true},{"ID":1118,"Name":"internal.metrics.shuffle.read.fetchWaitTime","Value":0,"Internal":true,"Count Failed Values":true},{"ID":1119,"Name":"internal.metrics.shuffle.read.recordsRead","Value":154,"Internal":true,"Count Failed Values":true},{"ID":1120,"Name":"internal.metrics.shuffle.write.bytesWritten","Value":259,"Internal":true,"Count Failed Values":true},{"ID":1121,"Name":"internal.metrics.shuffle.write.recordsWritten","Value":23,"Internal":true,"Count Failed Values":true},{"ID":1122,"Name":"internal.metrics.shuffle.write.writeTime","Value":262091,"Internal":true,"Count Failed Values":true}],"Resource Profile Id":0}}
+{"Event":"SparkListenerJobEnd","Job ID":14,"Completion Time":1646432584334,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":9,"physicalPlanDescription":"== Physical Plan ==\nCollectLimit (100)\n+- AdaptiveSparkPlan (99)\n   +- == Current Plan ==\n      Project (50)\n      +- BroadcastHashJoin Inner BuildRight (49)\n         :- Project (35)\n         :  +- SortMergeJoin Inner (34)\n         :     :- Sort (24)\n         :     :  +- Exchange (23)\n         :     :     +- Project (22)\n         :     :        +- SortMergeJoin Inner (21)\n         :     :           :- Sort (5)\n         :     :           :  +- ShuffleQueryStage (4)\n         :     :           :     +- Exchange (3)\n         :     :           :        +- * Filter (2)\n         :     :           :           +- Scan csv  (1)\n         :     :           +- Sort (20)\n         :     :              +- Exchange (19)\n         :     :                 +- Filter (18)\n         :     :                    +- RunningWindowFunction (17)\n         :     :                       +- Sort (16)\n         :     :                          +- Exchange (15)\n         :     :                             +- HashAggregate (14)\n         :     :                                +- ShuffleQueryStage (13)\n         :     :                                   +- Exchange (12)\n         :     :                                      +- * HashAggregate (11)\n         :     :                                         +- Union (10)\n         :     :                                            :- * Project (7)\n         :     :                                            :  +- Scan csv  (6)\n         :     :                                            +- * Project (9)\n         :     :                                               +- Scan csv  (8)\n         :     +- Sort (33)\n         :        +- Exchange (32)\n         :           +- Filter (31)\n         :              +- RunningWindowFunction (30)\n         :                 +- Sort (29)\n         :                    +- Exchange (28)\n         :                       +- HashAggregate (27)\n         :                          +- ShuffleQueryStage (26)\n         :                             +- ReusedExchange (25)\n         +- BroadcastQueryStage (48)\n            +- BroadcastExchange (47)\n               +- * Filter (46)\n                  +- * RunningWindowFunction (45)\n                     +- * Sort (44)\n                        +- ShuffleQueryStage (43)\n                           +- Exchange (42)\n                              +- * HashAggregate (41)\n                                 +- CustomShuffleReader (40)\n                                    +- ShuffleQueryStage (39)\n                                       +- Exchange (38)\n                                          +- * HashAggregate (37)\n                                             +- Scan csv  (36)\n   +- == Initial Plan ==\n      Project (98)\n      +- SortMergeJoin Inner (97)\n         :- Sort (87)\n         :  +- Exchange (86)\n         :     +- Project (85)\n         :        +- SortMergeJoin Inner (84)\n         :           :- Sort (69)\n         :           :  +- Exchange (68)\n         :           :     +- Project (67)\n         :           :        +- SortMergeJoin Inner (66)\n         :           :           :- Sort (53)\n         :           :           :  +- Exchange (52)\n         :           :           :     +- Filter (51)\n         :           :           :        +- Scan csv  (1)\n         :           :           +- Sort (65)\n         :           :              +- Exchange (64)\n         :           :                 +- Filter (63)\n         :           :                    +- RunningWindowFunction (62)\n         :           :                       +- Sort (61)\n         :           :                          +- Exchange (60)\n         :           :                             +- HashAggregate (59)\n         :           :                                +- Exchange (58)\n         :           :                                   +- HashAggregate (57)\n         :           :                                      +- Union (56)\n         :           :                                         :- Project (54)\n         :           :                                         :  +- Scan csv  (6)\n         :           :                                         +- Project (55)\n         :           :                                            +- Scan csv  (8)\n         :           +- Sort (83)\n         :              +- Exchange (82)\n         :                 +- Filter (81)\n         :                    +- RunningWindowFunction (80)\n         :                       +- Sort (79)\n         :                          +- Exchange (78)\n         :                             +- HashAggregate (77)\n         :                                +- Exchange (76)\n         :                                   +- HashAggregate (75)\n         :                                      +- Union (74)\n         :                                         :- Project (71)\n         :                                         :  +- Scan csv  (70)\n         :                                         +- Project (73)\n         :                                            +- Scan csv  (72)\n         +- Sort (96)\n            +- Exchange (95)\n               +- Filter (94)\n                  +- RunningWindowFunction (93)\n                     +- Sort (92)\n                        +- Exchange (91)\n                           +- HashAggregate (90)\n                              +- Exchange (89)\n                                 +- HashAggregate (88)\n                                    +- Scan csv  (36)\n\n\n(1) Scan csv \nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nPushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]\nReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>\n\n(2) Filter [codegen id : 1]\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(3) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]\n\n(4) ShuffleQueryStage\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: 0, Statistics(sizeInBytes=1816.0 MiB)\n\n(5) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(6) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(7) Project [codegen id : 2]\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(8) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(9) Project [codegen id : 3]\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(10) Union\n\n(11) HashAggregate [codegen id : 4]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(12) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1066]\n\n(13) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 1, Statistics(sizeInBytes=3.5 GiB)\n\n(14) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(15) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1259]\n\n(16) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(17) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(18) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(19) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1265]\n\n(20) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(21) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(22) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(23) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#1276]\n\n(24) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(25) ReusedExchange [Reuses operator id: 12]\nOutput [1]: [airport#178]\n\n(26) ShuffleQueryStage\nOutput [1]: [airport#178]\nArguments: 3, Statistics(sizeInBytes=3.5 GiB)\n\n(27) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(28) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1270]\n\n(29) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(30) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(31) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(32) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#1277]\n\n(33) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(34) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(35) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(36) Scan csv \nOutput [1]: [OP_CARRIER#180]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(37) HashAggregate [codegen id : 8]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(38) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#1142]\n\n(39) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 4, Statistics(sizeInBytes=3.6 KiB, rowCount=154, isRuntime=true)\n\n(40) CustomShuffleReader\nInput [1]: [OP_CARRIER#180]\nArguments: coalesced\n\n(41) HashAggregate [codegen id : 9]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(42) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1458]\n\n(43) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 5, Statistics(sizeInBytes=552.0 B, rowCount=23, isRuntime=true)\n\n(44) Sort [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(45) RunningWindowFunction [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(46) Filter [codegen id : 10]\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(47) BroadcastExchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#1676]\n\n(48) BroadcastQueryStage\nOutput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: 6, Statistics(sizeInBytes=630.0 B, isRuntime=true)\n\n(49) BroadcastHashJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(50) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(51) Filter\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(52) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]\n\n(53) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(54) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(55) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(56) Union\n\n(57) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(58) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]\n\n(59) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(60) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#659]\n\n(61) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(62) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(63) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(64) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]\n\n(65) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(66) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(67) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(68) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]\n\n(69) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(70) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(71) Project\nOutput [1]: [ORIGIN#21 AS airport#178]\nInput [1]: [ORIGIN#21]\n\n(72) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(73) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(74) Union\n\n(75) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(76) Exchange\nInput [1]: [airport#178]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]\n\n(77) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(78) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#674]\n\n(79) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(80) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(81) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(82) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]\n\n(83) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(84) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(85) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(86) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]\n\n(87) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(88) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(89) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]\n\n(90) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(91) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#689]\n\n(92) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(93) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(94) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(95) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]\n\n(96) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(97) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(98) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(99) AdaptiveSparkPlan\nOutput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: isFinalPlan=false\n\n(100) CollectLimit\nInput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: 11\n\n","sparkPlanInfo":{"nodeName":"CollectLimit","simpleString":"CollectLimit 11","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"Project","simpleString":"Project [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS 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+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":25,"Stage Attempt ID":0,"Stage Name":"$anonfun$withThreadLocalCaptured$1 at CompletableFuture.java:1604","Number of Tasks":1,"RDD Info":[{"RDD ID":65,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1675\",\"name\":\"WholeStageCodegen\"}","Callsite":"$anonfun$withThreadLocalCaptured$1 at CompletableFuture.java:1604","Parent IDs":[64],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":64,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_5\",\"name\":\"ShuffleQueryStage\"}","Callsite":"inputRDDs at RunningWindowFunctionExec.scala:218","Parent IDs":[63],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[24],"Details":"org.apache.spark.sql.execution.SQLExecution$.$anonfun$withThreadLocalCaptured$1(SQLExecution.scala:330)\njava.util.concurrent.CompletableFuture$AsyncSupply.run(CompletableFuture.java:1604)\norg.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.$anonfun$run$1(SparkThreadLocalForwardingThreadPoolExecutor.scala:104)\nscala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)\norg.apache.spark.util.threads.SparkThreadLocalCapturingHelper.runWithCaptured(SparkThreadLocalForwardingThreadPoolExecutor.scala:68)\norg.apache.spark.util.threads.SparkThreadLocalCapturingHelper.runWithCaptured$(SparkThreadLocalForwardingThreadPoolExecutor.scala:54)\norg.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.runWithCaptured(SparkThreadLocalForwardingThreadPoolExecutor.scala:101)\norg.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.run(SparkThreadLocalForwardingThreadPoolExecutor.scala:104)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432584462,"Accumulables":[],"Resource Profile 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fb098b05-da7e-4bc9-9864-594443088fd0)","spark.databricks.clusterUsageTags.clusterEbsVolumeType":"GENERAL_PURPOSE_SSD","spark.sql.streaming.stopTimeout":"15s","spark.app.startTime":"1646432441343","spark.executor.id":"driver","spark.databricks.job.runId":"31144","spark.hadoop.databricks.s3commit.client.sslTrustAll":"false","spark.hadoop.hive.server2.idle.session.timeout":"900000","spark.hadoop.fs.cpfs-abfss.impl.disable.cache":"true","spark.hadoop.fs.mcfs-s3a.impl":"com.databricks.sql.acl.fs.ManagedCatalogFileSystem","spark.databricks.clusterUsageTags.sparkEnvVarContainsNewline":"false","spark.databricks.clusterUsageTags.clusterPinned":"false","spark.hadoop.fs.fcfs-s3n.impl":"*********(redacted)","spark.hadoop.fs.mcfs-gs.impl":"com.databricks.sql.acl.fs.ManagedCatalogFileSystem","spark.databricks.replId":"ReplId-3fd17-60988-17274-b","spark.hadoop.hive.server2.thrift.http.port":"10000","spark.jobGroup.id":"fb098b05-da7e-4bc9-9864-594443088fd0","spark.databricks.clusterUsageTags.instanceWorkerEnvId":"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313","spark.databricks.io.directoryCommit.enableLogicalDelete":"false","spark.databricks.clusterUsageTags.enableJobsAutostart":"true","spark.databricks.clusterUsageTags.clusterLogDestination":"s3://job-bank-temp/databricks/9.1.x-scala2.12/AirlineDelayLR_gz/arianna","spark.hadoop.parquet.page.verify-checksum.enabled":"true","spark.databricks.clusterUsageTags.clusterResourceClass":"default","spark.shuffle.service.port":"4048","spark.databricks.passthrough.glue.executorServiceFactoryClassName":"*********(redacted)","spark.hadoop.fs.cpfs-s3n.impl":"*********(redacted)","spark.databricks.acl.client":"com.databricks.spark.sql.acl.client.SparkSqlAclClient","spark.sql.legacy.createHiveTableByDefault":"false","spark.databricks.clusterUsageTags.enableJdbcAutoStart":"true","spark.databricks.clusterUsageTags.sparkEnvVarContainsDollarSign":"false","spark.databricks.clusterUsageTags.clusterStateMessage":"Starting Spark","spark.hadoop.spark.sql.parquet.output.committer.class":"org.apache.spark.sql.parquet.DirectParquetOutputCommitter","spark.databricks.preemption.enabled":"true","spark.master":"spark://10.77.243.103:7077","spark.databricks.tahoe.logStore.aws.class":"com.databricks.tahoe.store.MultiClusterLogStore","spark.hadoop.fs.adl.impl.disable.cache":"true","spark.rpc.message.maxSize":"256","spark.hadoop.fs.gs.impl":"shaded.databricks.V2_1_4.com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem","spark.task.reaper.killTimeout":"60s","spark.hadoop.hive.server2.enable.doAs":"false","spark.hadoop.fs.fcfs-s3n.impl.disable.cache":"true","spark.hadoop.fs.s3n.impl":"shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem","spark.databricks.clusterUsageTags.enableLocalDiskEncryption":"false","spark.databricks.clusterUsageTags.numPerClusterInitScriptsV2":"0","spark.databricks.isolationID":"1efbc00e-cb29-4e80-b07b-fe3ae8b92b02","spark.hadoop.fs.azure.skip.metrics":"true","spark.databricks.clusterUsageTags.driverContainerId":"8a041ac64b95432ab1643fe44cf52169","spark.files.overwrite":"true","spark.databricks.clusterUsageTags.sparkEnvVarContainsSingleQuotes":"false","spark.hadoop.fs.s3a.threads.max":"136","spark.databricks.clusterUsageTags.clusterNumSshKeys":"0","spark.sparkr.use.daemon":"false","spark.hadoop.fs.mcfs-abfss.impl.disable.cache":"true","spark.streaming.driver.writeAheadLog.allowBatching":"true","spark.databricks.clusterUsageTags.clusterScalingType":"fixed_size","spark.databricks.workerNodeTypeId":"m5.4xlarge","spark.hadoop.fs.fcfs-s3.impl.disable.cache":"true","spark.sql.sources.commitProtocolClass":"com.databricks.sql.transaction.directory.DirectoryAtomicCommitProtocol","spark.databricks.clusterUsageTags.clusterWorkers":"4","spark.sql.hive.metastore.sharedPrefixes":"org.mariadb.jdbc,com.mysql.jdbc,org.postgresql,com.microsoft.sqlserver,microsoft.sql.DateTimeOffset,microsoft.sql.Types,com.databricks,com.codahale,com.fasterxml.jackson,shaded.databricks","spark.hadoop.fs.AbstractFileSystem.gs.impl":"shaded.databricks.V2_1_4.com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS","spark.executor.memory":"47016m","spark.hadoop.databricks.fs.perfMetrics.enable":"true","spark.databricks.inherited.credentials.keys.spark.databricks.api.token":"*********(redacted)","spark.databricks.clusterUsageTags.dataPlaneRegion":"us-east-1","spark.home":"/databricks/spark","spark.hadoop.fs.cpfs-s3a.impl":"*********(redacted)","spark.databricks.tahoe.logStore.azure.class":"com.databricks.tahoe.store.AzureLogStore","spark.hadoop.parquet.page.size.check.estimate":"false","spark.hadoop.fs.gs.impl.disable.cache":"true","spark.sql.execution.id":"9","spark.sql.catalogImplementation":"hive","spark.hadoop.fs.cpfs-adl.impl":"*********(redacted)","spark.databricks.passthrough.s3a.tokenProviderClassName":"*********(redacted)","spark.databricks.clusterUsageTags.isSingleUserCluster":"false","spark.databricks.cloudfetch.requesterClassName":"*********(redacted)","spark.databricks.clusterUsageTags.sparkEnvVarContainsEscape":"false","spark.databricks.clusterUsageTags.attribute_tag_dust_suite":"","spark.hadoop.hive.server2.keystore.path":"/databricks/keys/jetty-ssl-driver-keystore.jks","spark.hadoop.fs.wasb.impl.disable.cache":"true","spark.hadoop.hive.server2.transport.mode":"http","spark.hadoop.spark.databricks.io.parquet.verifyChecksumOnWrite.enabled":"true","spark.driver.allowMultipleContexts":"false","spark.databricks.clusterUsageTags.enableSqlAclsOnly":"false","spark.databricks.clusterUsageTags.clusterNodeType":"m5.4xlarge","spark.databricks.clusterUsageTags.instanceProfileArn":"arn:aws:iam::471881062455:instance-profile/databricks-workspace-stack-access-data-buckets","spark.databricks.clusterUsageTags.containerType":"LXC","spark.databricks.managedCatalog.adls.gen2.tokenProviderClassName":"*********(redacted)","spark.files.useFetchCache":"false","spark.sql.allowMultipleContexts":"false","spark.databricks.clusterUsageTags.enableDfAcls":"false","spark.databricks.clusterUsageTags.clusterPythonVersion":"3","spark.databricks.cloudfetch.hasRegionSupport":"true","spark.databricks.job.type":"python","spark.hadoop.fs.s3a.impl":"shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem","spark.shuffle.memoryFraction":"0.2","spark.hadoop.fs.mcfs-s3.impl":"com.databricks.sql.acl.fs.ManagedCatalogFileSystem","spark.sql.sources.default":"delta","spark.databricks.clusterUsageTags.attribute_tag_dust_maintainer":"","spark.databricks.clusterUsageTags.clusterAvailability":"SPOT_WITH_FALLBACK","spark.sql.parquet.compression.codec":"snappy","spark.hadoop.mapred.output.committer.class":"com.databricks.backend.daemon.data.client.DirectOutputCommitter","spark.hadoop.fs.s3a.fast.upload.active.blocks":"32","spark.databricks.token":"*********(redacted)","spark.hadoop.spark.sql.sources.outputCommitterClass":"com.databricks.backend.daemon.data.client.MapReduceDirectOutputCommitter","spark.hadoop.fs.cpfs-adl.impl.disable.cache":"true","spark.app.id":"app-20220304222044-0000","spark.databricks.clusterUsageTags.clusterOwnerUserId":"6878104766663444","spark.hadoop.fs.s3a.fast.upload":"true","spark.databricks.clusterUsageTags.clusterLogDeliveryEnabled":"true","spark.databricks.clusterUsageTags.driverNodeType":"m5.xlarge","spark.speculation.quantile":"0.9","spark.databricks.cloudProvider":"AWS","spark.akka.frameSize":"256","spark.hadoop.parquet.page.write-checksum.enabled":"true","spark.eventLog.rolling.enabled":"false","spark.databricks.clusterUsageTags.clusterAllTags":"[{\"key\":\"Vendor\",\"value\":\"Databricks\"},{\"key\":\"Creator\",\"value\":\"arianna@synccomputing.com\"},{\"key\":\"ClusterName\",\"value\":\"job-703-run-31144\"},{\"key\":\"ClusterId\",\"value\":\"0304-221748-fngq99ev\"},{\"key\":\"JobId\",\"value\":\"703\"},{\"key\":\"RunName\",\"value\":\"AirlineDelayLR_gz\"},{\"key\":\"Name\",\"value\":\"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313-worker\"}]","spark.sql.hive.metastore.version":"0.13.0"}}
+{"Event":"SparkListenerTaskStart","Stage ID":25,"Stage Attempt ID":0,"Task Info":{"Task ID":98,"Index":0,"Attempt":0,"Launch Time":1646432584511,"Executor ID":"2","Host":"10.77.253.227","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":0,"Failed":false,"Killed":false,"Accumulables":[]}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":9,"physicalPlanDescription":"== Physical Plan ==\nCollectLimit (95)\n+- AdaptiveSparkPlan (94)\n   +- == Current Plan ==\n      Project (45)\n      +- BroadcastHashJoin Inner BuildRight (44)\n         :- Project (30)\n         :  +- BroadcastHashJoin Inner BuildRight (29)\n         :     :- Project (22)\n         :     :  +- BroadcastHashJoin Inner BuildRight (21)\n         :     :     :- ShuffleQueryStage (4)\n         :     :     :  +- Exchange (3)\n         :     :     :     +- * Filter (2)\n         :     :     :        +- Scan csv  (1)\n         :     :     +- BroadcastExchange (20)\n         :     :        +- Filter (19)\n         :     :           +- RunningWindowFunction (18)\n         :     :              +- Sort (17)\n         :     :                 +- ShuffleQueryStage (16)\n         :     :                    +- Exchange (15)\n         :     :                       +- * HashAggregate (14)\n         :     :                          +- CustomShuffleReader (13)\n         :     :                             +- ShuffleQueryStage (12)\n         :     :                                +- Exchange (11)\n         :     :                                   +- * HashAggregate (10)\n         :     :                                      +- Union (9)\n         :     :                                         :- * Project (6)\n         :     :                                         :  +- Scan csv  (5)\n         :     :                                         +- * Project (8)\n         :     :                                            +- Scan csv  (7)\n         :     +- BroadcastExchange (28)\n         :        +- Filter (27)\n         :           +- RunningWindowFunction (26)\n         :              +- Sort (25)\n         :                 +- ShuffleQueryStage (24)\n         :                    +- ReusedExchange (23)\n         +- BroadcastQueryStage (43)\n            +- BroadcastExchange (42)\n               +- * Filter (41)\n                  +- * RunningWindowFunction (40)\n                     +- * Sort (39)\n                        +- ShuffleQueryStage (38)\n                           +- Exchange (37)\n                              +- * HashAggregate (36)\n                                 +- CustomShuffleReader (35)\n                                    +- ShuffleQueryStage (34)\n                                       +- Exchange (33)\n                                          +- * HashAggregate (32)\n                                             +- Scan csv  (31)\n   +- == Initial Plan ==\n      Project (93)\n      +- SortMergeJoin Inner (92)\n         :- Sort (82)\n         :  +- Exchange (81)\n         :     +- Project (80)\n         :        +- SortMergeJoin Inner (79)\n         :           :- Sort (64)\n         :           :  +- Exchange (63)\n         :           :     +- Project (62)\n         :           :        +- SortMergeJoin Inner (61)\n         :           :           :- Sort (48)\n         :           :           :  +- Exchange (47)\n         :           :           :     +- Filter (46)\n         :           :           :        +- Scan csv  (1)\n         :           :           +- Sort (60)\n         :           :              +- Exchange (59)\n         :           :                 +- Filter (58)\n         :           :                    +- RunningWindowFunction (57)\n         :           :                       +- Sort (56)\n         :           :                          +- Exchange (55)\n         :           :                             +- HashAggregate (54)\n         :           :                                +- Exchange (53)\n         :           :                                   +- HashAggregate (52)\n         :           :                                      +- Union (51)\n         :           :                                         :- Project (49)\n         :           :                                         :  +- Scan csv  (5)\n         :           :                                         +- Project (50)\n         :           :                                            +- Scan csv  (7)\n         :           +- Sort (78)\n         :              +- Exchange (77)\n         :                 +- Filter (76)\n         :                    +- RunningWindowFunction (75)\n         :                       +- Sort (74)\n         :                          +- Exchange (73)\n         :                             +- HashAggregate (72)\n         :                                +- Exchange (71)\n         :                                   +- HashAggregate (70)\n         :                                      +- Union (69)\n         :                                         :- Project (66)\n         :                                         :  +- Scan csv  (65)\n         :                                         +- Project (68)\n         :                                            +- Scan csv  (67)\n         +- Sort (91)\n            +- Exchange (90)\n               +- Filter (89)\n                  +- RunningWindowFunction (88)\n                     +- Sort (87)\n                        +- Exchange (86)\n                           +- HashAggregate (85)\n                              +- Exchange (84)\n                                 +- HashAggregate (83)\n                                    +- Scan csv  (31)\n\n\n(1) Scan csv \nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nPushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]\nReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>\n\n(2) Filter [codegen id : 1]\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(3) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]\n\n(4) ShuffleQueryStage\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: 0, Statistics(sizeInBytes=1816.0 MiB)\n\n(5) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(6) Project [codegen id : 2]\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(7) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(8) Project [codegen id : 3]\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(9) Union\n\n(10) HashAggregate [codegen id : 4]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(11) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1066]\n\n(12) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 1, Statistics(sizeInBytes=148.5 KiB, rowCount=6.34E+3, isRuntime=true)\n\n(13) CustomShuffleReader\nInput [1]: [airport#144]\nArguments: coalesced\n\n(14) HashAggregate [codegen id : 11]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(15) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1834]\n\n(16) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 7, Statistics(sizeInBytes=173.3 KiB, rowCount=6.34E+3, isRuntime=true)\n\n(17) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(18) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(19) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(20) BroadcastExchange\nInput [2]: [airport#144, airport_id#149]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#1845]\n\n(21) BroadcastHashJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(22) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(23) ReusedExchange [Reuses operator id: 15]\nOutput [1]: [airport#178]\n\n(24) ShuffleQueryStage\nOutput [1]: [airport#178]\nArguments: 9, Statistics(sizeInBytes=173.3 KiB, rowCount=6.34E+3, isRuntime=true)\n\n(25) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(26) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(27) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(28) BroadcastExchange\nInput [2]: [airport#178, airport_id#177]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#1875]\n\n(29) BroadcastHashJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(30) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(31) Scan csv \nOutput [1]: [OP_CARRIER#180]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(32) HashAggregate [codegen id : 8]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(33) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#1142]\n\n(34) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 4, Statistics(sizeInBytes=3.6 KiB, rowCount=154, isRuntime=true)\n\n(35) CustomShuffleReader\nInput [1]: [OP_CARRIER#180]\nArguments: coalesced\n\n(36) HashAggregate [codegen id : 9]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(37) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1458]\n\n(38) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 5, Statistics(sizeInBytes=552.0 B, rowCount=23, isRuntime=true)\n\n(39) Sort [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(40) RunningWindowFunction [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(41) Filter [codegen id : 10]\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(42) BroadcastExchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#1676]\n\n(43) BroadcastQueryStage\nOutput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: 6, Statistics(sizeInBytes=630.0 B, isRuntime=true)\n\n(44) BroadcastHashJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(45) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(46) Filter\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(47) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]\n\n(48) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(49) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(50) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(51) Union\n\n(52) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(53) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]\n\n(54) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(55) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#659]\n\n(56) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(57) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(58) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(59) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]\n\n(60) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(61) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(62) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(63) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]\n\n(64) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(65) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(66) Project\nOutput [1]: [ORIGIN#21 AS airport#178]\nInput [1]: [ORIGIN#21]\n\n(67) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(68) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(69) Union\n\n(70) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(71) Exchange\nInput [1]: [airport#178]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]\n\n(72) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(73) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#674]\n\n(74) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(75) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(76) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(77) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]\n\n(78) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(79) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(80) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(81) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]\n\n(82) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(83) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(84) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]\n\n(85) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(86) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#689]\n\n(87) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(88) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(89) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(90) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]\n\n(91) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(92) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(93) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(94) AdaptiveSparkPlan\nOutput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: isFinalPlan=false\n\n(95) CollectLimit\nInput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: 11\n\n","sparkPlanInfo":{"nodeName":"CollectLimit","simpleString":"CollectLimit 11","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"Project","simpleString":"Project [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [OP_CARRIER#19], [OP_CARRIER#180], Inner, BuildRight, AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25), false","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [DEST#22], [airport#178], Inner, BuildRight, false","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, 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+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":27,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":68,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1834\",\"name\":\"Exchange\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[67],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":66,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_1828\",\"name\":\"CustomShuffleReader\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[56],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":67,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1833\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[66],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[26],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native 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Metrics":{"JVMHeapMemory":0,"JVMOffHeapMemory":0,"OnHeapExecutionMemory":0,"OffHeapExecutionMemory":0,"OnHeapStorageMemory":0,"OffHeapStorageMemory":0,"OnHeapUnifiedMemory":0,"OffHeapUnifiedMemory":0,"DirectPoolMemory":0,"MappedPoolMemory":0,"ProcessTreeJVMVMemory":0,"ProcessTreeJVMRSSMemory":0,"ProcessTreePythonVMemory":0,"ProcessTreePythonRSSMemory":0,"ProcessTreeOtherVMemory":0,"ProcessTreeOtherRSSMemory":0,"MinorGCCount":0,"MinorGCTime":0,"MajorGCCount":0,"MajorGCTime":0},"Task Metrics":{"Executor Deserialize Time":4,"Executor Deserialize CPU Time":3423409,"Executor Run Time":51,"Executor CPU Time":44922225,"Peak Execution Memory":67371008,"Result Size":3899,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":15,"Local Blocks Fetched":5,"Fetch Wait Time":0,"Remote Bytes Read":178398,"Remote Bytes Read To Disk":0,"Local Bytes Read":60083,"Total Records Read":6337},"Shuffle Write Metrics":{"Shuffle Bytes Written":2252,"Shuffle Write Time":384395,"Shuffle Records Written":381},"Input Metrics":{"Bytes Read":0,"Records Read":0},"Output Metrics":{"Bytes Written":0,"Records Written":0},"Updated Blocks":[]}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":27,"Stage Attempt ID":0,"Stage Name":"showString at NativeMethodAccessorImpl.java:0","Number of Tasks":1,"RDD Info":[{"RDD ID":68,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1834\",\"name\":\"Exchange\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[67],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":66,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_1828\",\"name\":\"CustomShuffleReader\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[56],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":67,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_1833\",\"name\":\"WholeStageCodegen\"}","Callsite":"showString at NativeMethodAccessorImpl.java:0","Parent IDs":[66],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[26],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432584550,"Completion Time":1646432584657,"Accumulables":[{"ID":954,"Name":"remote blocks read","Value":"15","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":955,"Name":"local blocks read","Value":"5","Internal":true,"Count Failed Values":true,"Metadata":"sql"},{"ID":956,"Name":"remote bytes read","Value":"178398","Internal":true,"Count Failed 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+{"Event":"SparkListenerJobEnd","Job ID":16,"Completion Time":1646432584668,"Job Result":{"Result":"JobSucceeded"}}
+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":9,"physicalPlanDescription":"== Physical Plan ==\nCollectLimit (92)\n+- AdaptiveSparkPlan (91)\n   +- == Current Plan ==\n      Project (42)\n      +- BroadcastHashJoin Inner BuildRight (41)\n         :- Project (27)\n         :  +- BroadcastHashJoin Inner BuildRight (26)\n         :     :- Project (23)\n         :     :  +- BroadcastHashJoin Inner BuildRight (22)\n         :     :     :- ShuffleQueryStage (4)\n         :     :     :  +- Exchange (3)\n         :     :     :     +- * Filter (2)\n         :     :     :        +- Scan csv  (1)\n         :     :     +- BroadcastQueryStage (21)\n         :     :        +- BroadcastExchange (20)\n         :     :           +- * Filter (19)\n         :     :              +- * RunningWindowFunction (18)\n         :     :                 +- * Sort (17)\n         :     :                    +- ShuffleQueryStage (16)\n         :     :                       +- Exchange (15)\n         :     :                          +- * HashAggregate (14)\n         :     :                             +- CustomShuffleReader (13)\n         :     :                                +- ShuffleQueryStage (12)\n         :     :                                   +- Exchange (11)\n         :     :                                      +- * HashAggregate (10)\n         :     :                                         +- Union (9)\n         :     :                                            :- * Project (6)\n         :     :                                            :  +- Scan csv  (5)\n         :     :                                            +- * Project (8)\n         :     :                                               +- Scan csv  (7)\n         :     +- BroadcastQueryStage (25)\n         :        +- ReusedExchange (24)\n         +- BroadcastQueryStage (40)\n            +- BroadcastExchange (39)\n               +- * Filter (38)\n                  +- * RunningWindowFunction (37)\n                     +- * Sort (36)\n                        +- ShuffleQueryStage (35)\n                           +- Exchange (34)\n                              +- * HashAggregate (33)\n                                 +- CustomShuffleReader (32)\n                                    +- ShuffleQueryStage (31)\n                                       +- Exchange (30)\n                                          +- * HashAggregate (29)\n                                             +- Scan csv  (28)\n   +- == Initial Plan ==\n      Project (90)\n      +- SortMergeJoin Inner (89)\n         :- Sort (79)\n         :  +- Exchange (78)\n         :     +- Project (77)\n         :        +- SortMergeJoin Inner (76)\n         :           :- Sort (61)\n         :           :  +- Exchange (60)\n         :           :     +- Project (59)\n         :           :        +- SortMergeJoin Inner (58)\n         :           :           :- Sort (45)\n         :           :           :  +- Exchange (44)\n         :           :           :     +- Filter (43)\n         :           :           :        +- Scan csv  (1)\n         :           :           +- Sort (57)\n         :           :              +- Exchange (56)\n         :           :                 +- Filter (55)\n         :           :                    +- RunningWindowFunction (54)\n         :           :                       +- Sort (53)\n         :           :                          +- Exchange (52)\n         :           :                             +- HashAggregate (51)\n         :           :                                +- Exchange (50)\n         :           :                                   +- HashAggregate (49)\n         :           :                                      +- Union (48)\n         :           :                                         :- Project (46)\n         :           :                                         :  +- Scan csv  (5)\n         :           :                                         +- Project (47)\n         :           :                                            +- Scan csv  (7)\n         :           +- Sort (75)\n         :              +- Exchange (74)\n         :                 +- Filter (73)\n         :                    +- RunningWindowFunction (72)\n         :                       +- Sort (71)\n         :                          +- Exchange (70)\n         :                             +- HashAggregate (69)\n         :                                +- Exchange (68)\n         :                                   +- HashAggregate (67)\n         :                                      +- Union (66)\n         :                                         :- Project (63)\n         :                                         :  +- Scan csv  (62)\n         :                                         +- Project (65)\n         :                                            +- Scan csv  (64)\n         +- Sort (88)\n            +- Exchange (87)\n               +- Filter (86)\n                  +- RunningWindowFunction (85)\n                     +- Sort (84)\n                        +- Exchange (83)\n                           +- HashAggregate (82)\n                              +- Exchange (81)\n                                 +- HashAggregate (80)\n                                    +- Scan csv  (28)\n\n\n(1) Scan csv \nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nPushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]\nReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>\n\n(2) Filter [codegen id : 1]\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(3) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]\n\n(4) ShuffleQueryStage\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: 0, Statistics(sizeInBytes=1816.0 MiB)\n\n(5) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(6) Project [codegen id : 2]\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(7) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(8) Project [codegen id : 3]\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(9) Union\n\n(10) HashAggregate [codegen id : 4]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(11) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1066]\n\n(12) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 1, Statistics(sizeInBytes=148.5 KiB, rowCount=6.34E+3, isRuntime=true)\n\n(13) CustomShuffleReader\nInput [1]: [airport#144]\nArguments: coalesced\n\n(14) HashAggregate [codegen id : 11]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(15) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1834]\n\n(16) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 7, Statistics(sizeInBytes=8.9 KiB, rowCount=381, isRuntime=true)\n\n(17) Sort [codegen id : 13]\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(18) RunningWindowFunction [codegen id : 13]\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(19) Filter [codegen id : 13]\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(20) BroadcastExchange\nInput [2]: [airport#144, airport_id#149]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#2105]\n\n(21) BroadcastQueryStage\nOutput [2]: [airport#144, airport_id#149]\nArguments: 10, Statistics(sizeInBytes=10.2 KiB, isRuntime=true)\n\n(22) BroadcastHashJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(23) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(24) ReusedExchange [Reuses operator id: 20]\nOutput [2]: [airport#178, airport_id#177]\n\n(25) BroadcastQueryStage\nOutput [2]: [airport#178, airport_id#177]\nArguments: 12, Statistics(sizeInBytes=10.2 KiB, isRuntime=true)\n\n(26) BroadcastHashJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(27) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(28) Scan csv \nOutput [1]: [OP_CARRIER#180]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(29) HashAggregate [codegen id : 8]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(30) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#1142]\n\n(31) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 4, Statistics(sizeInBytes=3.6 KiB, rowCount=154, isRuntime=true)\n\n(32) CustomShuffleReader\nInput [1]: [OP_CARRIER#180]\nArguments: coalesced\n\n(33) HashAggregate [codegen id : 9]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(34) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1458]\n\n(35) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 5, Statistics(sizeInBytes=552.0 B, rowCount=23, isRuntime=true)\n\n(36) Sort [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(37) RunningWindowFunction [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(38) Filter [codegen id : 10]\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(39) BroadcastExchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#1676]\n\n(40) BroadcastQueryStage\nOutput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: 6, Statistics(sizeInBytes=4.0 MiB, rowCount=23, isRuntime=true)\n\n(41) BroadcastHashJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(42) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(43) Filter\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(44) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]\n\n(45) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(46) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(47) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(48) Union\n\n(49) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(50) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]\n\n(51) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(52) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#659]\n\n(53) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(54) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(55) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(56) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]\n\n(57) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(58) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(59) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(60) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]\n\n(61) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(62) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(63) Project\nOutput [1]: [ORIGIN#21 AS airport#178]\nInput [1]: [ORIGIN#21]\n\n(64) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(65) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(66) Union\n\n(67) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(68) Exchange\nInput [1]: [airport#178]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]\n\n(69) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(70) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#674]\n\n(71) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(72) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(73) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(74) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]\n\n(75) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(76) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(77) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(78) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]\n\n(79) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(80) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(81) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]\n\n(82) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(83) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#689]\n\n(84) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(85) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(86) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(87) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]\n\n(88) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(89) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(90) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(91) AdaptiveSparkPlan\nOutput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: isFinalPlan=false\n\n(92) CollectLimit\nInput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: 11\n\n","sparkPlanInfo":{"nodeName":"CollectLimit","simpleString":"CollectLimit 11","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=false","children":[{"nodeName":"Project","simpleString":"Project [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [OP_CARRIER#19], [OP_CARRIER#180], Inner, BuildRight, AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25), false","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [DEST#22], [airport#178], Inner, BuildRight, false","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [ORIGIN#21], [airport#144], Inner, BuildRight, false","children":[{"nodeName":"ShuffleQueryStage","simpleString":"ShuffleQueryStage 0, Statistics(sizeInBytes=1816.0 MiB)","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]","children":[{"nodeName":"WholeStageCodegen (1)","simpleString":"WholeStageCodegen","children":[{"nodeName":"Filter","simpleString":"Filter ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))","children":[{"nodeName":"InputAdapter","simpleString":"InputAdapter","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [FL_DATE#18,OP_CARRIER#19,ORIGIN#21,DEST#22,CRS_DEP_TIME#23,DEP_DELAY#25] Batched: false, DataFilters: [isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)], ReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:d...","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]","DataFilters":"[isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)]"},"metrics":[{"name":"file sorting by size time","accumulatorId":722,"metricType":"timing"},{"name":"number of files read","accumulatorId":716,"metricType":"sum"},{"name":"filesystem read data size","accumulatorId":721,"metricType":"size"},{"name":"filesystem read data size (sampled)","accumulatorId":720,"metricType":"size"},{"name":"filesystem read time (sampled)","accumulatorId":719,"metricType":"timing"},{"name":"metadata 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+{"Event":"SparkListenerStageSubmitted","Stage Info":{"Stage ID":30,"Stage Attempt ID":0,"Stage Name":"$anonfun$withThreadLocalCaptured$1 at CompletableFuture.java:1604","Number of Tasks":1,"RDD Info":[{"RDD ID":70,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"spark_plan_2104\",\"name\":\"WholeStageCodegen\"}","Callsite":"$anonfun$withThreadLocalCaptured$1 at CompletableFuture.java:1604","Parent IDs":[69],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":69,"Name":"ShuffledRowRDD","Scope":"{\"id\":\"spark_plan_7\",\"name\":\"ShuffleQueryStage\"}","Callsite":"inputRDDs at RunningWindowFunctionExec.scala:218","Parent IDs":[68],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":1,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[29],"Details":"org.apache.spark.sql.execution.SQLExecution$.$anonfun$withThreadLocalCaptured$1(SQLExecution.scala:330)\njava.util.concurrent.CompletableFuture$AsyncSupply.run(CompletableFuture.java:1604)\norg.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.$anonfun$run$1(SparkThreadLocalForwardingThreadPoolExecutor.scala:104)\nscala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)\norg.apache.spark.util.threads.SparkThreadLocalCapturingHelper.runWithCaptured(SparkThreadLocalForwardingThreadPoolExecutor.scala:68)\norg.apache.spark.util.threads.SparkThreadLocalCapturingHelper.runWithCaptured$(SparkThreadLocalForwardingThreadPoolExecutor.scala:54)\norg.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.runWithCaptured(SparkThreadLocalForwardingThreadPoolExecutor.scala:101)\norg.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.run(SparkThreadLocalForwardingThreadPoolExecutor.scala:104)\njava.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)\njava.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432584758,"Accumulables":[],"Resource Profile 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+{"Event":"org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate","executionId":9,"physicalPlanDescription":"== Physical Plan ==\nCollectLimit (93)\n+- AdaptiveSparkPlan (92)\n   +- == Final Plan ==\n      * Project (43)\n      +- * BroadcastHashJoin Inner BuildRight (42)\n         :- * Project (28)\n         :  +- * BroadcastHashJoin Inner BuildRight (27)\n         :     :- * Project (24)\n         :     :  +- * BroadcastHashJoin Inner BuildRight (23)\n         :     :     :- CustomShuffleReader (5)\n         :     :     :  +- ShuffleQueryStage (4)\n         :     :     :     +- Exchange (3)\n         :     :     :        +- * Filter (2)\n         :     :     :           +- Scan csv  (1)\n         :     :     +- BroadcastQueryStage (22)\n         :     :        +- BroadcastExchange (21)\n         :     :           +- * Filter (20)\n         :     :              +- * RunningWindowFunction (19)\n         :     :                 +- * Sort (18)\n         :     :                    +- ShuffleQueryStage (17)\n         :     :                       +- Exchange (16)\n         :     :                          +- * HashAggregate (15)\n         :     :                             +- CustomShuffleReader (14)\n         :     :                                +- ShuffleQueryStage (13)\n         :     :                                   +- Exchange (12)\n         :     :                                      +- * HashAggregate (11)\n         :     :                                         +- Union (10)\n         :     :                                            :- * Project (7)\n         :     :                                            :  +- Scan csv  (6)\n         :     :                                            +- * Project (9)\n         :     :                                               +- Scan csv  (8)\n         :     +- BroadcastQueryStage (26)\n         :        +- ReusedExchange (25)\n         +- BroadcastQueryStage (41)\n            +- BroadcastExchange (40)\n               +- * Filter (39)\n                  +- * RunningWindowFunction (38)\n                     +- * Sort (37)\n                        +- ShuffleQueryStage (36)\n                           +- Exchange (35)\n                              +- * HashAggregate (34)\n                                 +- CustomShuffleReader (33)\n                                    +- ShuffleQueryStage (32)\n                                       +- Exchange (31)\n                                          +- * HashAggregate (30)\n                                             +- Scan csv  (29)\n   +- == Initial Plan ==\n      Project (91)\n      +- SortMergeJoin Inner (90)\n         :- Sort (80)\n         :  +- Exchange (79)\n         :     +- Project (78)\n         :        +- SortMergeJoin Inner (77)\n         :           :- Sort (62)\n         :           :  +- Exchange (61)\n         :           :     +- Project (60)\n         :           :        +- SortMergeJoin Inner (59)\n         :           :           :- Sort (46)\n         :           :           :  +- Exchange (45)\n         :           :           :     +- Filter (44)\n         :           :           :        +- Scan csv  (1)\n         :           :           +- Sort (58)\n         :           :              +- Exchange (57)\n         :           :                 +- Filter (56)\n         :           :                    +- RunningWindowFunction (55)\n         :           :                       +- Sort (54)\n         :           :                          +- Exchange (53)\n         :           :                             +- HashAggregate (52)\n         :           :                                +- Exchange (51)\n         :           :                                   +- HashAggregate (50)\n         :           :                                      +- Union (49)\n         :           :                                         :- Project (47)\n         :           :                                         :  +- Scan csv  (6)\n         :           :                                         +- Project (48)\n         :           :                                            +- Scan csv  (8)\n         :           +- Sort (76)\n         :              +- Exchange (75)\n         :                 +- Filter (74)\n         :                    +- RunningWindowFunction (73)\n         :                       +- Sort (72)\n         :                          +- Exchange (71)\n         :                             +- HashAggregate (70)\n         :                                +- Exchange (69)\n         :                                   +- HashAggregate (68)\n         :                                      +- Union (67)\n         :                                         :- Project (64)\n         :                                         :  +- Scan csv  (63)\n         :                                         +- Project (66)\n         :                                            +- Scan csv  (65)\n         +- Sort (89)\n            +- Exchange (88)\n               +- Filter (87)\n                  +- RunningWindowFunction (86)\n                     +- Sort (85)\n                        +- Exchange (84)\n                           +- HashAggregate (83)\n                              +- Exchange (82)\n                                 +- HashAggregate (81)\n                                    +- Scan csv  (29)\n\n\n(1) Scan csv \nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nPushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]\nReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>\n\n(2) Filter [codegen id : 1]\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(3) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]\n\n(4) ShuffleQueryStage\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: 0, Statistics(sizeInBytes=5.5 GiB, rowCount=6.16E+7, isRuntime=true)\n\n(5) CustomShuffleReader\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: local\n\n(6) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(7) Project [codegen id : 2]\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(8) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(9) Project [codegen id : 3]\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(10) Union\n\n(11) HashAggregate [codegen id : 4]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(12) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#1066]\n\n(13) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 1, Statistics(sizeInBytes=148.5 KiB, rowCount=6.34E+3, isRuntime=true)\n\n(14) CustomShuffleReader\nInput [1]: [airport#144]\nArguments: coalesced\n\n(15) HashAggregate [codegen id : 11]\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(16) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1834]\n\n(17) ShuffleQueryStage\nOutput [1]: [airport#144]\nArguments: 7, Statistics(sizeInBytes=8.9 KiB, rowCount=381, isRuntime=true)\n\n(18) Sort [codegen id : 13]\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(19) RunningWindowFunction [codegen id : 13]\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(20) Filter [codegen id : 13]\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(21) BroadcastExchange\nInput [2]: [airport#144, airport_id#149]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#2105]\n\n(22) BroadcastQueryStage\nOutput [2]: [airport#144, airport_id#149]\nArguments: 10, Statistics(sizeInBytes=4.0 MiB, rowCount=381, isRuntime=true)\n\n(23) BroadcastHashJoin [codegen id : 15]\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(24) Project [codegen id : 15]\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(25) ReusedExchange [Reuses operator id: 21]\nOutput [2]: [airport#178, airport_id#177]\n\n(26) BroadcastQueryStage\nOutput [2]: [airport#178, airport_id#177]\nArguments: 12, Statistics(sizeInBytes=4.0 MiB, rowCount=381, isRuntime=true)\n\n(27) BroadcastHashJoin [codegen id : 15]\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(28) Project [codegen id : 15]\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(29) Scan csv \nOutput [1]: [OP_CARRIER#180]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<OP_CARRIER:string>\n\n(30) HashAggregate [codegen id : 8]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(31) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#1142]\n\n(32) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 4, Statistics(sizeInBytes=3.6 KiB, rowCount=154, isRuntime=true)\n\n(33) CustomShuffleReader\nInput [1]: [OP_CARRIER#180]\nArguments: coalesced\n\n(34) HashAggregate [codegen id : 9]\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(35) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#1458]\n\n(36) ShuffleQueryStage\nOutput [1]: [OP_CARRIER#180]\nArguments: 5, Statistics(sizeInBytes=552.0 B, rowCount=23, isRuntime=true)\n\n(37) Sort [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(38) RunningWindowFunction [codegen id : 10]\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(39) Filter [codegen id : 10]\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(40) BroadcastExchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: HashedRelationBroadcastMode(ArrayBuffer(input[0, string, false]),false), [id=#1676]\n\n(41) BroadcastQueryStage\nOutput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: 6, Statistics(sizeInBytes=4.0 MiB, rowCount=23, isRuntime=true)\n\n(42) BroadcastHashJoin [codegen id : 15]\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(43) Project [codegen id : 15]\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(44) Filter\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nCondition : ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))\n\n(45) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#665]\n\n(46) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25]\nArguments: [ORIGIN#21 ASC NULLS FIRST], false, 0\n\n(47) Project\nOutput [1]: [ORIGIN#21 AS airport#144]\nInput [1]: [ORIGIN#21]\n\n(48) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(49) Union\n\n(50) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(51) Exchange\nInput [1]: [airport#144]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#656]\n\n(52) HashAggregate\nInput [1]: [airport#144]\nKeys [1]: [airport#144]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#144]\n\n(53) Exchange\nInput [1]: [airport#144]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#659]\n\n(54) Sort\nInput [1]: [airport#144]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(55) RunningWindowFunction\nInput [1]: [airport#144]\nArguments: [airport#144, row_number() windowspecdefinition(airport#144 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#149], [airport#144 ASC NULLS FIRST], false\n\n(56) Filter\nInput [2]: [airport#144, airport_id#149]\nCondition : isnotnull(airport#144)\n\n(57) Exchange\nInput [2]: [airport#144, airport_id#149]\nArguments: hashpartitioning(airport#144, 200), ENSURE_REQUIREMENTS, [id=#666]\n\n(58) Sort\nInput [2]: [airport#144, airport_id#149]\nArguments: [airport#144 ASC NULLS FIRST], false, 0\n\n(59) SortMergeJoin\nLeft keys [1]: [ORIGIN#21]\nRight keys [1]: [airport#144]\nJoin condition: None\n\n(60) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, ORIGIN#21, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport#144, airport_id#149]\n\n(61) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: hashpartitioning(DEST#22, 200), ENSURE_REQUIREMENTS, [id=#680]\n\n(62) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]\nArguments: [DEST#22 ASC NULLS FIRST], false, 0\n\n(63) Scan csv \nOutput [1]: [ORIGIN#21]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<ORIGIN:string>\n\n(64) Project\nOutput [1]: [ORIGIN#21 AS airport#178]\nInput [1]: [ORIGIN#21]\n\n(65) Scan csv \nOutput [1]: [DEST#22]\nBatched: false\nLocation: InMemoryFileIndex [s3://job-bank/ml-rec/input-data/airlinedelay/gz]\nReadSchema: struct<DEST:string>\n\n(66) Project\nOutput [1]: [DEST#22 AS airport#146]\nInput [1]: [DEST#22]\n\n(67) Union\n\n(68) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(69) Exchange\nInput [1]: [airport#178]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#671]\n\n(70) HashAggregate\nInput [1]: [airport#178]\nKeys [1]: [airport#178]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [airport#178]\n\n(71) Exchange\nInput [1]: [airport#178]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#674]\n\n(72) Sort\nInput [1]: [airport#178]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(73) RunningWindowFunction\nInput [1]: [airport#178]\nArguments: [airport#178, row_number() windowspecdefinition(airport#178 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS airport_id#177], [airport#178 ASC NULLS FIRST], false\n\n(74) Filter\nInput [2]: [airport#178, airport_id#177]\nCondition : isnotnull(airport#178)\n\n(75) Exchange\nInput [2]: [airport#178, airport_id#177]\nArguments: hashpartitioning(airport#178, 200), ENSURE_REQUIREMENTS, [id=#681]\n\n(76) Sort\nInput [2]: [airport#178, airport_id#177]\nArguments: [airport#178 ASC NULLS FIRST], false, 0\n\n(77) SortMergeJoin\nLeft keys [1]: [DEST#22]\nRight keys [1]: [airport#178]\nJoin condition: None\n\n(78) Project\nOutput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport#178, airport_id#177]\n\n(79) Exchange\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: hashpartitioning(OP_CARRIER#19, 200), ENSURE_REQUIREMENTS, [id=#695]\n\n(80) Sort\nInput [6]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]\nArguments: [OP_CARRIER#19 ASC NULLS FIRST], false, 0\n\n(81) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(82) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#686]\n\n(83) HashAggregate\nInput [1]: [OP_CARRIER#180]\nKeys [1]: [OP_CARRIER#180]\nFunctions: []\nAggregate Attributes: []\nResults [1]: [OP_CARRIER#180]\n\n(84) Exchange\nInput [1]: [OP_CARRIER#180]\nArguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#689]\n\n(85) Sort\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(86) RunningWindowFunction\nInput [1]: [OP_CARRIER#180]\nArguments: [OP_CARRIER#180, row_number() windowspecdefinition(OP_CARRIER#180 ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS carrier_id#123], [OP_CARRIER#180 ASC NULLS FIRST], false\n\n(87) Filter\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nCondition : isnotnull(OP_CARRIER#180)\n\n(88) Exchange\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: hashpartitioning(OP_CARRIER#180, 200), ENSURE_REQUIREMENTS, [id=#696]\n\n(89) Sort\nInput [2]: [OP_CARRIER#180, carrier_id#123]\nArguments: [OP_CARRIER#180 ASC NULLS FIRST], false, 0\n\n(90) SortMergeJoin\nLeft keys [1]: [OP_CARRIER#19]\nRight keys [1]: [OP_CARRIER#180]\nJoin condition: AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25)\n\n(91) Project\nOutput [8]: [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]\nInput [8]: [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177, OP_CARRIER#180, carrier_id#123]\n\n(92) AdaptiveSparkPlan\nOutput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: isFinalPlan=true\n\n(93) CollectLimit\nInput [8]: [month#231, day_of_month#232, day_of_week#233, OP_CARRIER#234, ORIGIN#235, DEST#236, CRS_DEP_TIME#237, DEP_DELAY#238]\nArguments: 11\n\n","sparkPlanInfo":{"nodeName":"CollectLimit","simpleString":"CollectLimit 11","children":[{"nodeName":"AdaptiveSparkPlan","simpleString":"AdaptiveSparkPlan isFinalPlan=true","children":[{"nodeName":"WholeStageCodegen (15)","simpleString":"WholeStageCodegen","children":[{"nodeName":"Project","simpleString":"Project [cast(month(cast(FL_DATE#18 as date)) as string) AS month#231, cast(dayofmonth(cast(FL_DATE#18 as date)) as string) AS day_of_month#232, cast(dayofweek(cast(FL_DATE#18 as date)) as string) AS day_of_week#233, cast(carrier_id#123 as string) AS OP_CARRIER#234, cast(airport_id#149 as string) AS ORIGIN#235, cast(airport_id#177 as string) AS DEST#236, cast(CRS_DEP_TIME#23 as string) AS CRS_DEP_TIME#237, cast(DEP_DELAY#25 as string) AS DEP_DELAY#238]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [OP_CARRIER#19], [OP_CARRIER#180], Inner, BuildRight, AtLeastNNulls(n, month(cast(FL_DATE#18 as date)),dayofmonth(cast(FL_DATE#18 as date)),dayofweek(cast(FL_DATE#18 as date)),carrier_id#123,airport_id#149,airport_id#177,CRS_DEP_TIME#23,DEP_DELAY#25), false","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149, airport_id#177]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [DEST#22], [airport#178], Inner, BuildRight, false","children":[{"nodeName":"Project","simpleString":"Project [FL_DATE#18, OP_CARRIER#19, DEST#22, CRS_DEP_TIME#23, DEP_DELAY#25, airport_id#149]","children":[{"nodeName":"BroadcastHashJoin","simpleString":"BroadcastHashJoin [ORIGIN#21], [airport#144], Inner, BuildRight, false","children":[{"nodeName":"InputAdapter","simpleString":"InputAdapter","children":[{"nodeName":"CustomShuffleReader","simpleString":"CustomShuffleReader local","children":[{"nodeName":"ShuffleQueryStage","simpleString":"ShuffleQueryStage 0, Statistics(sizeInBytes=5.5 GiB, rowCount=6.16E+7, isRuntime=true)","children":[{"nodeName":"Exchange","simpleString":"Exchange hashpartitioning(ORIGIN#21, 200), ENSURE_REQUIREMENTS, [id=#1046]","children":[{"nodeName":"WholeStageCodegen (1)","simpleString":"WholeStageCodegen","children":[{"nodeName":"Filter","simpleString":"Filter ((isnotnull(ORIGIN#21) AND isnotnull(DEST#22)) AND isnotnull(OP_CARRIER#19))","children":[{"nodeName":"InputAdapter","simpleString":"InputAdapter","children":[{"nodeName":"Scan csv ","simpleString":"FileScan csv [FL_DATE#18,OP_CARRIER#19,ORIGIN#21,DEST#22,CRS_DEP_TIME#23,DEP_DELAY#25] Batched: false, DataFilters: [isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)], Format: CSV, Location: InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz], PartitionFilters: [], PushedFilters: [IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)], ReadSchema: struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:d...","children":[],"metadata":{"Location":"InMemoryFileIndex[s3://job-bank/ml-rec/input-data/airlinedelay/gz]","ReadSchema":"struct<FL_DATE:string,OP_CARRIER:string,ORIGIN:string,DEST:string,CRS_DEP_TIME:double,DEP_DELAY:double>","Format":"CSV","Batched":"false","PartitionFilters":"[]","PushedFilters":"[IsNotNull(ORIGIN), IsNotNull(DEST), IsNotNull(OP_CARRIER)]","DataFilters":"[isnotnull(ORIGIN#21), isnotnull(DEST#22), isnotnull(OP_CARRIER#19)]"},"metrics":[{"name":"file sorting by size 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IDs":[31],"Details":"org.apache.spark.sql.Dataset.showString(Dataset.scala:354)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432591763,"Accumulables":[],"Resource Profile 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+{"Event":"SparkListenerTaskEnd","Stage ID":93,"Stage Attempt ID":0,"Task Type":"ResultTask","Task End Reason":{"Reason":"Success"},"Task Info":{"Task ID":442,"Index":2,"Attempt":0,"Launch Time":1646432743176,"Executor ID":"1","Host":"10.77.224.250","Locality":"PROCESS_LOCAL","Speculative":false,"Getting Result Time":0,"Finish Time":1646432743218,"Failed":false,"Killed":false,"Accumulables":[{"ID":2955,"Name":"internal.metrics.executorDeserializeTime","Update":7,"Value":41,"Internal":true,"Count Failed Values":true},{"ID":2956,"Name":"internal.metrics.executorDeserializeCpuTime","Update":7647709,"Value":42872946,"Internal":true,"Count Failed Values":true},{"ID":2957,"Name":"internal.metrics.executorRunTime","Update":16,"Value":66,"Internal":true,"Count Failed Values":true},{"ID":2958,"Name":"internal.metrics.executorCpuTime","Update":7103925,"Value":33381375,"Internal":true,"Count Failed Values":true},{"ID":2959,"Name":"internal.metrics.resultSize","Update":9231,"Value":55386,"Internal":true,"Count Failed Values":true},{"ID":2962,"Name":"internal.metrics.memoryBytesSpilled","Update":0,"Value":0,"Internal":true,"Count Failed Values":true},{"ID":2963,"Name":"internal.metrics.diskBytesSpilled","Update":0,"Value":0,"Internal":true,"Count Failed Values":true},{"ID":2964,"Name":"internal.metrics.peakExecutionMemory","Update":2352,"Value":14112,"Internal":true,"Count Failed Values":true},{"ID":2968,"Name":"internal.metrics.shuffle.read.remoteBlocksFetched","Update":6,"Value":40,"Internal":true,"Count Failed Values":true},{"ID":2969,"Name":"internal.metrics.shuffle.read.localBlocksFetched","Update":2,"Value":10,"Internal":true,"Count Failed Values":true},{"ID":2970,"Name":"internal.metrics.shuffle.read.remoteBytesRead","Update":4944,"Value":32904,"Internal":true,"Count Failed Values":true},{"ID":2971,"Name":"internal.metrics.shuffle.read.remoteBytesReadToDisk","Update":0,"Value":0,"Internal":true,"Count Failed Values":true},{"ID":2972,"Name":"internal.metrics.shuffle.read.localBytesRead","Update":1644,"Value":8217,"Internal":true,"Count Failed Values":true},{"ID":2973,"Name":"internal.metrics.shuffle.read.fetchWaitTime","Update":0,"Value":6,"Internal":true,"Count Failed Values":true},{"ID":2974,"Name":"internal.metrics.shuffle.read.recordsRead","Update":8,"Value":50,"Internal":true,"Count Failed Values":true}]},"Task Executor Metrics":{"JVMHeapMemory":0,"JVMOffHeapMemory":0,"OnHeapExecutionMemory":0,"OffHeapExecutionMemory":0,"OnHeapStorageMemory":0,"OffHeapStorageMemory":0,"OnHeapUnifiedMemory":0,"OffHeapUnifiedMemory":0,"DirectPoolMemory":0,"MappedPoolMemory":0,"ProcessTreeJVMVMemory":0,"ProcessTreeJVMRSSMemory":0,"ProcessTreePythonVMemory":0,"ProcessTreePythonRSSMemory":0,"ProcessTreeOtherVMemory":0,"ProcessTreeOtherRSSMemory":0,"MinorGCCount":0,"MinorGCTime":0,"MajorGCCount":0,"MajorGCTime":0},"Task Metrics":{"Executor Deserialize Time":7,"Executor Deserialize CPU Time":7647709,"Executor Run Time":16,"Executor CPU Time":7103925,"Peak Execution Memory":2352,"Result Size":9231,"JVM GC Time":0,"Result Serialization Time":0,"Memory Bytes Spilled":0,"Disk Bytes Spilled":0,"Shuffle Read Metrics":{"Remote Blocks Fetched":6,"Local Blocks Fetched":2,"Fetch Wait Time":0,"Remote Bytes Read":4944,"Remote Bytes Read To Disk":0,"Local Bytes Read":1644,"Total Records Read":8},"Shuffle Write Metrics":{"Shuffle Bytes Written":0,"Shuffle Write Time":0,"Shuffle Records Written":0},"Input Metrics":{"Bytes Read":0,"Records Read":0},"Output Metrics":{"Bytes Written":0,"Records Written":0},"Updated Blocks":[]}}
+{"Event":"SparkListenerStageCompleted","Stage Info":{"Stage ID":93,"Stage Attempt ID":0,"Stage Name":"treeAggregate at Statistics.scala:58","Number of Tasks":6,"RDD Info":[{"RDD ID":217,"Name":"MapPartitionsRDD","Scope":"{\"id\":\"354\",\"name\":\"treeAggregate\"}","Callsite":"treeAggregate at Statistics.scala:58","Parent IDs":[216],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":6,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0},{"RDD ID":216,"Name":"ShuffledRDD","Scope":"{\"id\":\"354\",\"name\":\"treeAggregate\"}","Callsite":"treeAggregate at Statistics.scala:58","Parent IDs":[215],"Storage Level":{"Use Disk":false,"Use Memory":false,"Deserialized":false,"Replication":1},"Barrier":false,"Number of Partitions":6,"Number of Cached Partitions":0,"Memory Size":0,"Disk Size":0}],"Parent IDs":[92],"Details":"org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1232)\norg.apache.spark.mllib.stat.Statistics$.colStats(Statistics.scala:58)\norg.apache.spark.mllib.evaluation.RegressionMetrics.summary$lzycompute(RegressionMetrics.scala:70)\norg.apache.spark.mllib.evaluation.RegressionMetrics.summary(RegressionMetrics.scala:62)\norg.apache.spark.mllib.evaluation.RegressionMetrics.SSreg$lzycompute(RegressionMetrics.scala:76)\norg.apache.spark.mllib.evaluation.RegressionMetrics.SSreg(RegressionMetrics.scala:77)\norg.apache.spark.mllib.evaluation.RegressionMetrics.explainedVariance(RegressionMetrics.scala:88)\norg.apache.spark.ml.regression.LinearRegressionSummary.<init>(LinearRegression.scala:925)\norg.apache.spark.ml.regression.LinearRegressionModel.evaluate(LinearRegression.scala:718)\nsun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\nsun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\nsun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\njava.lang.reflect.Method.invoke(Method.java:498)\npy4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\npy4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)\npy4j.Gateway.invoke(Gateway.java:295)\npy4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\npy4j.commands.CallCommand.execute(CallCommand.java:79)\npy4j.GatewayConnection.run(GatewayConnection.java:251)\njava.lang.Thread.run(Thread.java:748)","Submission Time":1646432743169,"Completion Time":1646432743219,"Accumulables":[{"ID":2955,"Name":"internal.metrics.executorDeserializeTime","Value":41,"Internal":true,"Count Failed Values":true},{"ID":2956,"Name":"internal.metrics.executorDeserializeCpuTime","Value":42872946,"Internal":true,"Count Failed Values":true},{"ID":2957,"Name":"internal.metrics.executorRunTime","Value":66,"Internal":true,"Count Failed Values":true},{"ID":2958,"Name":"internal.metrics.executorCpuTime","Value":33381375,"Internal":true,"Count Failed Values":true},{"ID":2959,"Name":"internal.metrics.resultSize","Value":55386,"Internal":true,"Count Failed Values":true},{"ID":2962,"Name":"internal.metrics.memoryBytesSpilled","Value":0,"Internal":true,"Count Failed Values":true},{"ID":2963,"Name":"internal.metrics.diskBytesSpilled","Value":0,"Internal":true,"Count Failed Values":true},{"ID":2964,"Name":"internal.metrics.peakExecutionMemory","Value":14112,"Internal":true,"Count Failed Values":true},{"ID":2968,"Name":"internal.metrics.shuffle.read.remoteBlocksFetched","Value":40,"Internal":true,"Count Failed Values":true},{"ID":2969,"Name":"internal.metrics.shuffle.read.localBlocksFetched","Value":10,"Internal":true,"Count Failed Values":true},{"ID":2970,"Name":"internal.metrics.shuffle.read.remoteBytesRead","Value":32904,"Internal":true,"Count Failed Values":true},{"ID":2971,"Name":"internal.metrics.shuffle.read.remoteBytesReadToDisk","Value":0,"Internal":true,"Count Failed Values":true},{"ID":2972,"Name":"internal.metrics.shuffle.read.localBytesRead","Value":8217,"Internal":true,"Count Failed Values":true},{"ID":2973,"Name":"internal.metrics.shuffle.read.fetchWaitTime","Value":6,"Internal":true,"Count Failed Values":true},{"ID":2974,"Name":"internal.metrics.shuffle.read.recordsRead","Value":50,"Internal":true,"Count Failed Values":true}],"Resource Profile Id":0}}
+{"Event":"SparkListenerJobEnd","Job ID":49,"Completion Time":1646432743219,"Job Result":{"Result":"JobSucceeded"}}
diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
index a791d20..00f7c54 100644
--- a/tests/test_eventlog.py
+++ b/tests/test_eventlog.py
@@ -5,8 +5,8 @@
 from spark_log_parser import eventlog
 
 
-def test_simple_databricks_log():
-    event_log = Path("tests", "logs", "databricks.zip").resolve()
+def test_simple_emr_log():
+    event_log = Path("tests", "logs", "emr.zip").resolve()
 
     with tempfile.TemporaryDirectory(
         prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
@@ -16,18 +16,15 @@ def test_simple_databricks_log():
         with open(log.event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
 
-    assert log.file_total == 1, "One file in log archive"
+    assert not log.is_parsed, "Log is correctly identified as being not parsed"
 
-    assert all(
-        key in event
-        for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]
-    ), "All keys are present"
+    assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
 
-    assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
+    assert event["Event"] == "SparkListenerLogStart", "Expected first event is present"
 
 
-def test_simple_emr_log():
-    event_log = Path("tests", "logs", "emr.zip").resolve()
+def test_simple_databricks_log():
+    event_log = Path("tests", "logs", "databricks.zip").resolve()
 
     with tempfile.TemporaryDirectory(
         prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
@@ -37,11 +34,14 @@ def test_simple_emr_log():
         with open(log.event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
 
-    assert log.file_total == 1, "One file in log archive"
+    assert not log.is_parsed, "Log is correctly identified as being not parsed"
 
-    assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
+    assert all(
+        key in event
+        for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]
+    ), "All keys are present"
 
-    assert event["Event"] == "SparkListenerLogStart", "Expected first event is present"
+    assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
 
 
 def test_databricks_rollover_log():
@@ -78,8 +78,6 @@ def test_databricks_rollover_log():
             assert rollover_count == 3, "All log parts are present"
             assert i + 1 == 16945, "All events are present"
 
-    assert log.file_total == 4, "4 files in the log archive - 3 event log parts and 1 pricing file"
-
 
 def test_databricks_messy_rollover_log_archive():
     event_log = Path("tests", "logs", "databricks-rollover-messy.zip").resolve()
@@ -115,4 +113,39 @@ def test_databricks_messy_rollover_log_archive():
             assert rollover_count == 3, "All log parts are present"
             assert i + 1 == 16945, "All events are present"
 
-    assert log.file_total == 4, "4 files in the log archive - 3 event log parts and 1 pricing file"
+
+def test_raw_databricks_log():
+    event_log = Path("tests", "logs", "databricks.json").resolve()
+
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
+    ) as temp_dir:
+        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+
+        with open(log.event_log) as log_fobj:
+            event = json.loads(log_fobj.readline())
+
+    assert all(
+        key in event
+        for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]
+    ), "All keys are present"
+
+    assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
+
+
+def test_parsed_log():
+    parsed_path = Path("tests", "logs", "databricks-parsed.json").resolve()
+
+    with tempfile.TemporaryDirectory(
+        prefix="eventlog-%s-" % parsed_path.name[: -len("".join(parsed_path.suffixes))]
+    ) as temp_dir:
+        log = eventlog.EventLog(source_url=parsed_path.as_uri(), work_dir=temp_dir)
+
+        with open(log.event_log) as log_fobj:
+            parsed = json.loads(log_fobj.readline())
+
+    assert log.is_parsed, "Parsed flag is set"
+
+    assert all(
+        key in parsed for key in ["jobData", "stageData", "taskData"]
+    ), "All keys are present"

From 340dc972500af5eca5c3250941047aaeae493469 Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Fri, 29 Jul 2022 12:00:31 -0700
Subject: [PATCH 08/14] [PROD-325] Don't build the log if it's already there

---
 spark_log_parser/eventlog.py | 25 +++++++++++++++----------
 1 file changed, 15 insertions(+), 10 deletions(-)

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index 45149a0..c7660bd 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -9,7 +9,7 @@
 import boto3 as boto
 import pandas as pd
 import requests
-from pydantic import BaseModel, root_validator, stricturl
+from pydantic import BaseModel, ValidationError, root_validator, stricturl
 
 THRESHOLD_ENTRIES = 100
 THRESHOLD_SIZE = 5000000000
@@ -21,19 +21,24 @@
 
 
 class EventLog(BaseModel):
-    source_url: AllowedURL
-    work_dir: Path
-    event_log: Path | None
+    source_url: AllowedURL = None
+    work_dir: Path = None
+    event_log: Path = None
     is_parsed = False
 
     @root_validator()
     def validate_event_log(cls, values):
-        return vars(
-            EventLogBuilder(
-                values["source_url"],
-                values["work_dir"],
-            ).build()
-        )
+        if not values["event_log"]:
+            if values["source_url"] and values["work_dir"]:
+                return vars(
+                    EventLogBuilder(
+                        values["source_url"],
+                        values["work_dir"],
+                    ).build()
+                )
+            raise ValidationError("source_url and work_dir must be set if event_log isn't")
+
+        return values
 
 
 class EventLogBuilder:

From e5f19748b1c1370f744a21919e1f8e661a877088 Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Sun, 31 Jul 2022 00:50:05 -0700
Subject: [PATCH 09/14] [PROD-325] Extract extraction code, remove support for
 parsed log

---
 spark_log_parser/__main__.py      |  10 +-
 spark_log_parser/eventlog.py      | 183 ++-----------------------
 spark_log_parser/extractor.py     | 213 ++++++++++++++++++++++++++++++
 tests/logs/databricks-parsed.json |   1 -
 tests/test_eventlog.py            | 140 +++++++-------------
 5 files changed, 277 insertions(+), 270 deletions(-)
 create mode 100644 spark_log_parser/extractor.py
 delete mode 100644 tests/logs/databricks-parsed.json

diff --git a/spark_log_parser/__main__.py b/spark_log_parser/__main__.py
index c99e3dc..9036051 100644
--- a/spark_log_parser/__main__.py
+++ b/spark_log_parser/__main__.py
@@ -30,9 +30,13 @@
     event_log = EventLog(source_url=args.log_file.resolve().as_uri(), work_dir=work_dir)
     app = sparkApplication(eventlog=str(event_log.event_log))
 
-result_path = os.path.join(
-    args.result_dir, "parsed-" + args.log_file.name[: -len("".join(args.log_file.suffixes))]
-)
+if args.log_file.suffixes:
+    result_path = os.path.join(
+        args.result_dir, "parsed-" + args.log_file.name[: -len("".join(args.log_file.suffixes))]
+    )
+else:
+    result_path = os.path.join(args.result_dir, "parsed-" + args.log_file.name)
+
 app.save(result_path)
 
 print(f"--Result saved to: {result_path}.json")
diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index c7660bd..c0584a5 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -1,19 +1,11 @@
-import gzip
 import json
-import shutil
-import struct
-import tarfile
-import zipfile
 from pathlib import Path
+from typing import Optional
 
-import boto3 as boto
 import pandas as pd
-import requests
 from pydantic import BaseModel, ValidationError, root_validator, stricturl
 
-THRESHOLD_ENTRIES = 100
-THRESHOLD_SIZE = 5000000000
-THRESHOLD_RATIO = 100
+from spark_log_parser.extractor import Extractor
 
 AllowedURL = stricturl(
     host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"}
@@ -21,10 +13,9 @@
 
 
 class EventLog(BaseModel):
-    source_url: AllowedURL = None
-    work_dir: Path = None
-    event_log: Path = None
-    is_parsed = False
+    source_url: Optional[AllowedURL] = None
+    work_dir: Optional[Path] = None
+    event_log: Optional[Path] = None
 
     @root_validator()
     def validate_event_log(cls, values):
@@ -42,60 +33,26 @@ def validate_event_log(cls, values):
 
 
 class EventLogBuilder:
-    def __init__(self, source_url, work_dir):
+    def __init__(self, source_url: AllowedURL, work_dir: Path):
         self.source_url = source_url
         self.work_dir = work_dir
         self.file_total = 0
         self.size_total = 0
 
     def build(self) -> "EventLogBuilder":
-        if self.source_url.scheme in {"https", "s3"}:
-            local_path = self.work_dir.joinpath(self._basename())
-            self._download(local_path)
-        else:
-            local_path = Path(self.source_url.path)
-
-        event_logs = self._extract_archive(local_path, self.work_dir)
+        event_logs = Extractor(self.source_url, self.work_dir).extract()
 
-        # Remove the archive if we downloaded it to free up space
-        if local_path not in event_logs and self.source_url.scheme in {"https", "s3"}:
-            local_path.unlink()
+        name = Path(self._basename())
 
         self.event_log = self._concat(
             event_logs,
             self.work_dir.joinpath(
-                local_path.name[: -len("".join(local_path.suffixes))] + "-concatenated.json"
+                name.name[: -len("".join(name.suffixes))] + "-concatenated.json"
             ),
         )
 
-        self.is_parsed = self._is_parsed(self.event_log)
-
         return self
 
-    def _extract_archive(self, event_log: Path, extract_dir: Path | None = None):
-        if not extract_dir:
-            extract_dir = event_log.parent
-
-        extension = "".join(event_log.suffixes)
-        if extension.endswith(".tar.gz"):
-            return self._extract_tgz(
-                event_log, extract_dir.joinpath(event_log.name[: -len(".tar.gz")])
-            )
-        if extension.endswith(".tgz"):
-            return self._extract_tgz(
-                event_log, extract_dir.joinpath(event_log.name[: -len(".tgz")])
-            )
-        if extension.endswith(".zip"):
-            return self._extract_zip(
-                event_log, extract_dir.joinpath(event_log.name[: -len(".zip")])
-            )
-        if extension.endswith(".gz"):
-            return self._extract_gz(event_log, extract_dir.joinpath(event_log.name[: -len(".gz")]))
-        if extension in {".json", ".log", ""}:
-            return [event_log]
-
-        raise ValueError("Unsupported extension found in the archive")
-
     def _concat(self, event_logs: list[Path], event_log: Path):
         if len(event_logs) == 1:
             return event_logs[0]
@@ -138,128 +95,6 @@ def _validate_rollover_logs(self, df: pd.DataFrame):
         if any(diffs < 1):
             raise ValueError("Duplicate rollover file detected")
 
-    def _extract_tgz(self, archive_path: Path, extract_dir: Path):
-        paths = []
-
-        with tarfile.open(archive_path) as tar_file:
-            for tf in tar_file:
-                if self._should_skip_file(tf.name):
-                    continue
-                if tf.isdir():
-                    continue
-
-                self.file_total += 1
-                self.size_total += tf.size
-                self._safety_check(tf.size)
-
-                extract_path = extract_dir.joinpath(tf.name)
-                extract_path.parent.mkdir(parents=True, exist_ok=True)
-                with tar_file.extractfile(tf.name) as source, open(extract_path, "wb") as target:
-                    shutil.copyfileobj(source, target)
-
-                # Remove the archive if it was extracted to free up space
-                sub_paths = self._extract_archive(extract_path)
-                if extract_path not in sub_paths:
-                    self.file_total -= 1
-                    self.size_total -= tf.size
-                    extract_path.unlink()
-
-                paths += sub_paths
-
-        return paths
-
-    def _extract_zip(self, archive_path: Path, extract_dir: Path):
-        paths = []
-
-        with zipfile.ZipFile(archive_path) as zfile:
-            for zinfo in zfile.infolist():
-                if self._should_skip_file(zinfo.filename):
-                    continue
-                if zinfo.is_dir():
-                    continue
-
-                self.file_total += 1
-                self.size_total += zinfo.file_size
-                self._safety_check(zinfo.file_size)
-
-                extract_path = extract_dir.joinpath(zinfo.filename)
-                extract_path.parent.mkdir(parents=True, exist_ok=True)
-                with zfile.open(zinfo.filename) as source, open(extract_path, "wb") as target:
-                    shutil.copyfileobj(source, target)
-
-                # Remove the archive if it was extracted to free up space
-                sub_paths = self._extract_archive(extract_path)
-                if extract_path not in sub_paths:
-                    self.file_total -= 1
-                    self.size_total -= zinfo.file_size
-                    extract_path.unlink()
-
-                paths += sub_paths
-
-        return paths
-
-    def _extract_gz(self, archive_path: Path, extract_path: Path):
-        with open(archive_path, "rb") as fobj:
-            fobj.seek(-4, 2)
-            size = struct.unpack("I", fobj.read(4))[0]
-
-        self.file_total += 1
-        self.size_total += size
-        self._safety_check(size)
-
-        with gzip.open(archive_path, "rb") as source, open(extract_path, "wb") as target:
-            shutil.copyfileobj(source, target)
-
-        # Remove the archive if it was extracted to free up space
-        sub_paths = self._extract_archive(extract_path)
-        if extract_path not in sub_paths:
-            self.file_total -= 1
-            self.size_total -= size
-            extract_path.unlink()
-
-        return sub_paths
-
-    FILE_SKIP_PATTERNS = [".DS_Store".lower(), "__MACOSX".lower(), "/."]
-
-    def _should_skip_file(self, filename: str):
-        if filename.startswith("."):
-            return True
-
-        filename = filename.lower()
-        for name in EventLogBuilder.FILE_SKIP_PATTERNS:
-            if name in filename:
-                return True
-
-        return False
-
-    def _safety_check(self, size):
-        ratio = size / self.size_total
-        if ratio > THRESHOLD_RATIO:
-            raise AssertionError("Encountered suspicious compression ratio in the archive")
-
-        if self.size_total > THRESHOLD_SIZE:
-            raise AssertionError("The archive is too big")
-
-        if self.file_total > THRESHOLD_ENTRIES:
-            raise AssertionError("Too many files in the archive")
-
-    def _download(self, path):
-        with open(path, "wb") as fobj:
-            if self.source_url.scheme == "https":
-                response = requests.get(self.source_url, stream=True)
-                for chunk in response.iter_content():
-                    fobj.write(chunk)
-            elif self.source_url.scheme == "s3":
-                s3 = boto.client("s3")
-                s3.download_fileobj(self.source_url.host, self.source_url.path, fobj)
-
-        return path
-
-    def _is_parsed(self, log_path: Path):
-        with open(log_path) as log:
-            entry = json.loads(log.readline())
-            return "jobData" in entry and "stageData" in entry and "taskData" in entry
-
     def _basename(self):
         if self.source_url.scheme in {"https", "s3"}:
             return self.source_url.path.split("/")[-1]
diff --git a/spark_log_parser/extractor.py b/spark_log_parser/extractor.py
new file mode 100644
index 0000000..1abdd14
--- /dev/null
+++ b/spark_log_parser/extractor.py
@@ -0,0 +1,213 @@
+import os
+import boto3 as boto
+import gzip
+from pathlib import Path
+import shutil
+import struct
+import tarfile
+import zipfile
+
+import requests
+
+
+THRESHOLD_ENTRIES = 100
+THRESHOLD_SIZE = 5000000000
+THRESHOLD_RATIO = 100
+
+
+class Extractor:
+    """
+    Expands archive to a "work" directory and provides a list of extracted files
+    """
+
+    def __init__(self, source_url, work_dir):
+        self.source_url = source_url
+        self.work_dir = work_dir
+        self.file_total = 0
+        self.size_total = 0
+
+    def extract(self) -> list[Path]:
+        if self.source_url.scheme in {"https", "s3"}:
+            local_path = self.work_dir.joinpath(self.source_url.path.split("/")[-1])
+            self._download(local_path)
+        else:
+            local_path = Path(self.source_url.path)
+
+        files = self._extract(local_path, self.work_dir)
+
+        # Remove the archive if we downloaded it to free up space
+        if local_path not in files and self.source_url.scheme in {"https", "s3"}:
+            local_path.unlink()
+
+        return files
+
+    def _extract(self, event_log: Path, extract_dir: Path | None = None) -> list[Path]:
+        """ returns list of uncompressed files"""
+        if not extract_dir:
+            extract_dir = event_log.parent
+
+        if event_log.is_dir():
+            return self._extract_dir(event_log, extract_dir.joinpath(event_log.name))
+
+        extension = "".join(event_log.suffixes)
+        if extension.endswith(".tar.gz"):
+            return self._extract_tgz(
+                event_log, extract_dir.joinpath(event_log.name[: -len(".tar.gz")])
+            )
+        if extension.endswith(".tgz"):
+            return self._extract_tgz(
+                event_log, extract_dir.joinpath(event_log.name[: -len(".tgz")])
+            )
+        if extension.endswith(".zip"):
+            return self._extract_zip(
+                event_log, extract_dir.joinpath(event_log.name[: -len(".zip")])
+            )
+        if extension.endswith(".gz"):
+            return self._extract_gz(event_log, extract_dir.joinpath(event_log.name[: -len(".gz")]))
+        if extension in {".json", ".log", ""}:
+            return [event_log]
+
+        raise ValueError("Unsupported extension found in the archive")
+
+
+    def _extract_dir(self, log_dir: Path, extract_dir: Path) -> list[Path]:
+        paths = []
+
+        source_root = Path(self.source_url.path)
+
+        for root, _, files in os.walk(log_dir):
+            for file in files:
+                if self._should_skip_file(file):
+                    continue
+
+                path = Path(root, file)
+
+                self._add_to_stats_and_verify(path.stat().st_size)
+
+                # Remove the archive if it was extracted to free up space
+                sub_paths = self._extract(path, Path(extract_dir, path.parent.relative_to(source_root)))
+                if path not in sub_paths:
+                    self._remove_from_stats(path.stat().st_size)
+
+                paths += sub_paths
+
+        return paths
+
+
+    def _extract_tgz(self, archive_path: Path, extract_dir: Path):
+        paths = []
+
+        with tarfile.open(archive_path) as tar_file:
+            for tf in tar_file:
+                if self._should_skip_file(tf.name):
+                    continue
+                if tf.isdir():
+                    continue
+
+                self._add_to_stats_and_verify(tf.size)
+
+                extract_path = extract_dir.joinpath(tf.name)
+                extract_path.parent.mkdir(parents=True, exist_ok=True)
+                with tar_file.extractfile(tf.name) as source, open(extract_path, "wb") as target:
+                    shutil.copyfileobj(source, target)
+
+                # Remove the archive if it was extracted to free up space
+                sub_paths = self._extract(extract_path)
+                if extract_path not in sub_paths:
+                    self._remove_from_stats(tf.size)
+                    extract_path.unlink()
+
+                paths += sub_paths
+
+        return paths
+
+    def _extract_zip(self, archive_path: Path, extract_dir: Path):
+        paths = []
+
+        with zipfile.ZipFile(archive_path) as zfile:
+            for zinfo in zfile.infolist():
+                if self._should_skip_file(zinfo.filename):
+                    continue
+                if zinfo.is_dir():
+                    continue
+
+                self._add_to_stats_and_verify(zinfo.file_size)
+
+                extract_path = extract_dir.joinpath(zinfo.filename)
+                extract_path.parent.mkdir(parents=True, exist_ok=True)
+                with zfile.open(zinfo.filename) as source, open(extract_path, "wb") as target:
+                    shutil.copyfileobj(source, target)
+
+                # Remove the archive if it was extracted to free up space
+                sub_paths = self._extract(extract_path)
+                if extract_path not in sub_paths:
+                    self._remove_from_stats(zinfo.file_size)
+                    extract_path.unlink()
+
+                paths += sub_paths
+
+        return paths
+
+    def _extract_gz(self, archive_path: Path, extract_path: Path):
+        with open(archive_path, "rb") as fobj:
+            fobj.seek(-4, 2)
+            size = struct.unpack("I", fobj.read(4))[0]
+
+        self._add_to_stats_and_verify(size)
+
+        extract_path.parent.mkdir(parents=True, exist_ok=True)
+        with gzip.open(archive_path, "rb") as source, open(extract_path, "wb") as target:
+            shutil.copyfileobj(source, target)
+
+        # Remove the archive if it was extracted to free up space
+        sub_paths = self._extract(extract_path)
+        if extract_path not in sub_paths:
+            self._remove_from_stats(size)
+            extract_path.unlink()
+
+        return sub_paths
+
+    FILE_SKIP_PATTERNS = [".DS_Store".lower(), "__MACOSX".lower(), "/."]
+
+    def _should_skip_file(self, filename: str):
+        if filename.startswith("."):
+            return True
+
+        filename = filename.lower()
+        for name in Extractor.FILE_SKIP_PATTERNS:
+            if name in filename:
+                return True
+
+        return False
+
+    def _add_to_stats_and_verify(self, size, count=1):
+        self.size_total += size
+        self.file_total += count
+
+        ratio = size / self.size_total
+        if ratio > THRESHOLD_RATIO:
+            raise AssertionError("Encountered suspicious compression ratio in the archive")
+
+        if self.size_total > THRESHOLD_SIZE:
+            raise AssertionError("The archive is too big")
+
+        if self.file_total > THRESHOLD_ENTRIES:
+            raise AssertionError("Too many files in the archive")
+
+
+    def _remove_from_stats(self, size, count=1):
+        self.size_total -= size
+        self.file_total -= count
+
+
+    def _download(self, path):
+        with open(path, "wb") as fobj:
+            if self.source_url.scheme == "https":
+                response = requests.get(self.source_url, stream=True)
+                for chunk in response.iter_content():
+                    fobj.write(chunk)
+            elif self.source_url.scheme == "s3":
+                s3 = boto.client("s3")
+                s3.download_fileobj(self.source_url.host, self.source_url.path, fobj)
+
+        return path
\ No newline at end of file
diff --git a/tests/logs/databricks-parsed.json b/tests/logs/databricks-parsed.json
deleted file mode 100644
index 273cedf..0000000
--- a/tests/logs/databricks-parsed.json
+++ /dev/null
@@ -1 +0,0 @@
-{"jobData": {"job_id": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], "sql_id": [0, null, 3, 3, 4, 6, 6, 6, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null], "stage_ids": [[0], [1], [2], [4], [5], [6], [8], [11], [12], [14], [17], [18], [19], [20], [22], [25], [27], [30], [32], [33], [34], [35], [37], [40], [42], [45], [46], [47], [48], [50], [53], [55], [58], [60, 61], [62], [63], [64], [66], [69], [71], [74], [76, 77], [78], [79], [80], [82], [85], [87], [90], [92, 93]], "submission_time": [37.59400010108948, 41.884000062942505, 78.25200009346008, 87.43200016021729, 87.72000002861023, 88.46300005912781, 105.00600004196167, 105.35500001907349, 106.0640001296997, 122.86600017547607, 123.04800009727478, 124.0090000629425, 124.11199998855591, 124.22399997711182, 142.82299995422363, 143.11800003051758, 143.20500016212463, 143.41300010681152, 150.41300010681152, 151.4670000076294, 151.54700016975403, 151.59899997711182, 169.65100002288818, 169.80800008773804, 170.40499997138977, 170.53600001335144, 177.70300006866455, 177.7850000858307, 177.85800004005432, 196.58000016212463, 196.72099995613098, 199.45900011062622, 199.56900000572205, 205.0220000743866, 221.0090000629425, 221.143000125885, 221.239000082016, 239.31100010871887, 239.43799996376038, 240.02600002288818, 240.17799997329712, 245.96099996566772, 261.2640001773834, 261.3250000476837, 261.3800001144409, 279.6410000324249, 279.8220000267029, 281.6160001754761, 281.71200013160706, 286.74000000953674], "completion_time": [41.21300005912781, 77.58700013160706, 87.36500000953674, 87.6010000705719, 87.91600012779236, 104.94000005722046, 105.23099994659424, 105.6800000667572, 122.8270001411438, 123.00600004196167, 123.37800002098083, 150.29100012779236, 143.06900000572205, 142.7020001411438, 142.99100017547607, 143.26200008392334, 143.32500004768372, 143.52999997138977, 150.5329999923706, 177.32800006866455, 170.36300015449524, 169.56299996376038, 169.75, 169.84800004959106, 170.49200010299683, 170.60300016403198, 204.6690001487732, 199.4190001487732, 196.53900003433228, 196.6600000858307, 196.75699996948242, 199.5090000629425, 199.61100006103516, 220.55400013923645, 245.7720000743866, 239.24500012397766, 239.9980001449585, 239.3710000514984, 239.50200009346008, 240.08700013160706, 240.32800006866455, 261.01200008392334, 286.63100004196167, 281.5859999656677, 279.55400013923645, 279.7720000743866, 279.8489999771118, 281.6579999923706, 281.7430000305176, 301.87600016593933], "duration": [3.61899995803833, 35.70300006866455, 9.11299991607666, 0.16899991035461426, 0.1960000991821289, 16.47699999809265, 0.22499990463256836, 0.3250000476837158, 16.763000011444092, 0.1399998664855957, 0.3299999237060547, 26.282000064849854, 18.957000017166138, 18.478000164031982, 0.1680002212524414, 0.14400005340576172, 0.11999988555908203, 0.11699986457824707, 0.11999988555908203, 25.861000061035156, 18.81599998474121, 17.96399998664856, 0.0989999771118164, 0.039999961853027344, 0.08700013160705566, 0.06700015068054199, 26.966000080108643, 21.634000062942505, 18.680999994277954, 0.07999992370605469, 0.03600001335144043, 0.04999995231628418, 0.04200005531311035, 15.532000064849854, 24.763000011444092, 18.10199999809265, 18.759000062942505, 0.059999942779541016, 0.06400012969970703, 0.06100010871887207, 0.15000009536743164, 15.051000118255615, 25.366999864578247, 20.26099991798401, 18.174000024795532, 0.13100004196166992, 0.026999950408935547, 0.04199981689453125, 0.03099989891052246, 15.136000156402588], "submission_timestamp": [1646432478.937, 1646432483.227, 1646432519.595, 1646432528.775, 1646432529.063, 1646432529.806, 1646432546.349, 1646432546.698, 1646432547.407, 1646432564.209, 1646432564.391, 1646432565.352, 1646432565.455, 1646432565.567, 1646432584.166, 1646432584.461, 1646432584.548, 1646432584.756, 1646432591.756, 1646432592.81, 1646432592.89, 1646432592.942, 1646432610.994, 1646432611.151, 1646432611.748, 1646432611.879, 1646432619.046, 1646432619.128, 1646432619.201, 1646432637.923, 1646432638.064, 1646432640.802, 1646432640.912, 1646432646.365, 1646432662.352, 1646432662.486, 1646432662.582, 1646432680.654, 1646432680.781, 1646432681.369, 1646432681.521, 1646432687.304, 1646432702.607, 1646432702.668, 1646432702.723, 1646432720.984, 1646432721.165, 1646432722.959, 1646432723.055, 1646432728.083], "completion_timestamp": [1646432482.556, 1646432518.93, 1646432528.708, 1646432528.944, 1646432529.259, 1646432546.283, 1646432546.574, 1646432547.023, 1646432564.17, 1646432564.349, 1646432564.721, 1646432591.634, 1646432584.412, 1646432584.045, 1646432584.334, 1646432584.605, 1646432584.668, 1646432584.873, 1646432591.876, 1646432618.671, 1646432611.706, 1646432610.906, 1646432611.093, 1646432611.191, 1646432611.835, 1646432611.946, 1646432646.012, 1646432640.762, 1646432637.882, 1646432638.003, 1646432638.1, 1646432640.852, 1646432640.954, 1646432661.897, 1646432687.115, 1646432680.588, 1646432681.341, 1646432680.714, 1646432680.845, 1646432681.43, 1646432681.671, 1646432702.355, 1646432727.974, 1646432722.929, 1646432720.897, 1646432721.115, 1646432721.192, 1646432723.001, 1646432723.086, 1646432743.219]}, "stageData": {"stage_id": [0, 1, 2, 4, 5, 6, 8, 11, 12, 14, 17, 18, 19, 20, 22, 25, 27, 30, 32, 33, 34, 35, 37, 40, 42, 45, 46, 47, 48, 50, 53, 55, 58, 60, 61, 62, 63, 64, 66, 69, 71, 74, 76, 77, 78, 79, 80, 82, 85, 87, 90, 92, 93], "query_id": [0, [], 3, 3, 4, 6, 6, 6, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []], "job_id": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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"true", "spark.databricks.clusterUsageTags.dataPlaneRegion": "us-east-1", "spark.home": "/databricks/spark", "spark.hadoop.fs.cpfs-s3a.impl": "*********(redacted)", "spark.databricks.tahoe.logStore.azure.class": "com.databricks.tahoe.store.AzureLogStore", "spark.hadoop.parquet.page.size.check.estimate": "false", "spark.executor.extraClassPath": 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-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--native_system-java--com.github.fommil.netlib__native_system-java__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--native_system-java-natives--com.github.fommil.netlib__native_system-java-natives__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--netlib-native_ref-linux-x86_64-natives--com.github.fommil.netlib__netlib-native_ref-linux-x86_64-natives__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.fommil.netlib--netlib-native_system-linux-x86_64-natives--com.github.fommil.netlib__netlib-native_system-linux-x86_64-natives__1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.joshelser--dropwizard-metrics-hadoop-metrics2-reporter--com.github.joshelser__dropwizard-metrics-hadoop-metrics2-reporter__0.1.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.luben--zstd-jni--com.github.luben__zstd-jni__1.4.8-1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.github.wendykierp--JTransforms--com.github.wendykierp__JTransforms__3.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.code.findbugs--jsr305--com.google.code.findbugs__jsr305__3.0.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.code.gson--gson--com.google.code.gson__gson__2.2.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.crypto.tink--tink--com.google.crypto.tink__tink__1.6.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.flatbuffers--flatbuffers-java--com.google.flatbuffers__flatbuffers-java__1.9.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.guava--guava--com.google.guava__guava__15.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.google.protobuf--protobuf-java--com.google.protobuf__protobuf-java__2.6.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.h2database--h2--com.h2database__h2__1.4.195.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.helger--profiler--com.helger__profiler__1.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.jcraft--jsch--com.jcraft__jsch__0.1.50.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.jolbox--bonecp--com.jolbox__bonecp__0.8.0.RELEASE.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.lihaoyi--sourcecode_2.12--com.lihaoyi__sourcecode_2.12__0.1.9.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.microsoft.azure--azure-data-lake-store-sdk--com.microsoft.azure__azure-data-lake-store-sdk__2.3.9.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.ning--compress-lzf--com.ning__compress-lzf__1.0.3.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.sun.mail--javax.mail--com.sun.mail__javax.mail__1.5.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.tdunning--json--com.tdunning__json__1.8.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.thoughtworks.paranamer--paranamer--com.thoughtworks.paranamer__paranamer__2.8.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.trueaccord.lenses--lenses_2.12--com.trueaccord.lenses__lenses_2.12__0.4.12.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--chill-java--com.twitter__chill-java__0.9.5.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--chill_2.12--com.twitter__chill_2.12__0.9.5.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-app_2.12--com.twitter__util-app_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-core_2.12--com.twitter__util-core_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-function_2.12--com.twitter__util-function_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-jvm_2.12--com.twitter__util-jvm_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-lint_2.12--com.twitter__util-lint_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-registry_2.12--com.twitter__util-registry_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.twitter--util-stats_2.12--com.twitter__util-stats_2.12__7.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.typesafe--config--com.typesafe__config__1.2.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.typesafe.scala-logging--scala-logging_2.12--com.typesafe.scala-logging__scala-logging_2.12__3.7.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.univocity--univocity-parsers--com.univocity__univocity-parsers__2.9.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--com.zaxxer--HikariCP--com.zaxxer__HikariCP__3.1.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-beanutils--commons-beanutils--commons-beanutils__commons-beanutils__1.9.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-cli--commons-cli--commons-cli__commons-cli__1.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-codec--commons-codec--commons-codec__commons-codec__1.10.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-collections--commons-collections--commons-collections__commons-collections__3.2.2.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-configuration--commons-configuration--commons-configuration__commons-configuration__1.6.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-dbcp--commons-dbcp--commons-dbcp__commons-dbcp__1.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-digester--commons-digester--commons-digester__commons-digester__1.8.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-fileupload--commons-fileupload--commons-fileupload__commons-fileupload__1.3.3.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-httpclient--commons-httpclient--commons-httpclient__commons-httpclient__3.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-io--commons-io--commons-io__commons-io__2.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-lang--commons-lang--commons-lang__commons-lang__2.6.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-logging--commons-logging--commons-logging__commons-logging__1.1.3.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-net--commons-net--commons-net__commons-net__3.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--commons-pool--commons-pool--commons-pool__commons-pool__1.5.4.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--info.ganglia.gmetric4j--gmetric4j--info.ganglia.gmetric4j__gmetric4j__1.0.10.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.airlift--aircompressor--io.airlift__aircompressor__0.10.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.delta--delta-sharing-spark_2.12--io.delta__delta-sharing-spark_2.12__0.2.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-core--io.dropwizard.metrics__metrics-core__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-graphite--io.dropwizard.metrics__metrics-graphite__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-healthchecks--io.dropwizard.metrics__metrics-healthchecks__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-jetty9--io.dropwizard.metrics__metrics-jetty9__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-jmx--io.dropwizard.metrics__metrics-jmx__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-json--io.dropwizard.metrics__metrics-json__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-jvm--io.dropwizard.metrics__metrics-jvm__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.dropwizard.metrics--metrics-servlets--io.dropwizard.metrics__metrics-servlets__4.1.1.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.netty--netty-all--io.netty__netty-all__4.1.51.Final.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.prometheus--simpleclient--io.prometheus__simpleclient__0.7.0.jar:/databricks/jars/----workspace_spark_3_1--maven-trees--hive-2.3__hadoop-2.7--io.prometheus--simpleclient_common--io.prometheus__simpleclie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pc-api_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-auth_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-context_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-core_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-grpclb_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-netty-shaded_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-protobuf-lite_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-protobuf_shaded.jar:/databricks/jars/third_party--gcp-java--grpc-stub_shaded.jar:/databricks/jars/third_party--gcp-java--gson_shaded.jar:/databricks/jars/third_party--gcp-java--guava_shaded.jar:/databricks/jars/third_party--gcp-java--httpclient_shaded.jar:/databricks/jars/third_party--gcp-java--httpcore_shaded.jar:/databricks/jars/third_party--gcp-java--j2objc-annotations_shaded.jar:/databricks/jars/third_party--gcp-java--jackson-core_shaded.jar:/databricks/jars/third_party--gcp-java--javax.annotation-api_shaded.jar:/databricks/jars/third_party--gcp-java--jsr305_shaded.jar:/databricks/jars/third_party--gcp-java--listenablefuture_shaded.jar:/databricks/jars/third_party--gcp-java--opencensus-api_shaded.jar:/databricks/jars/third_party--gcp-java--opencensus-contrib-http-util_shaded.jar:/databricks/jars/third_party--gcp-java--perfmark-api_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-cloud-pubsub-v1_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-common-protos_shaded.jar:/databricks/jars/third_party--gcp-java--proto-google-iam-v1_shaded.jar:/databricks/jars/third_party--gcp-java--protobuf-java-util_shaded.jar:/databricks/jars/third_party--gcp-java--protobuf-java_shaded.jar:/databricks/jars/third_party--gcp-java--threetenbp_shaded.jar:/databricks/jars/third_party--gcs-private--animal-sniffer-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--annotations_shaded.jar:/databricks/jars/third_party--gcs-private--auto-value-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--checker-compat-qual_shaded.jar:/databricks/jars/third_party--gcs-private--checker-qual_shaded.jar:/databricks/jars/third_party--gcs-private--commons-codec_shaded.jar:/databricks/jars/third_party--gcs-private--commons-logging_shaded.jar:/databricks/jars/third_party--gcs-private--conscrypt-openjdk-uber_shaded.jar:/databricks/jars/third_party--gcs-private--error_prone_annotations_shaded.jar:/databricks/jars/third_party--gcs-private--failureaccess_shaded.jar:/databricks/jars/third_party--gcs-private--flogger-system-backend_shaded.jar:/databricks/jars/third_party--gcs-private--flogger_shaded.jar:/databricks/jars/third_party--gcs-private--gcs-connector-hadoop2_shaded.jar:/databricks/jars/third_party--gcs-private--gcs-shaded-jetty9-hadoop1_2.12_deploy.jar:/databricks/jars/third_party--gcs-private--gcs-shaded-spark_3.1_2.12_deploy.jar:/databricks/jars/third_party--gcs-private--gcsio-hadoop2_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-client-jackson2_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-client_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-services-iamcredentials_shaded.jar:/databricks/jars/third_party--gcs-private--google-api-services-storage_shaded.jar:/databricks/jars/third_party--gcs-private--google-auth-library-credentials_shaded.jar:/databricks/jars/third_party--gcs-private--google-auth-library-oauth2-http_shaded.jar:/databricks/jars/third_party--gcs-private--google-extensions_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client-apache-v2_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client-gson_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client-jackson2_shaded.jar:/databricks/jars/third_party--gcs-private--google-http-client_shaded.jar:/databricks/jars/third_party--gcs-private--google-oauth-client_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-alts_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-api_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-auth_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-context_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-core_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-netty-shaded_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-protobuf-lite_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-protobuf_shaded.jar:/databricks/jars/third_party--gcs-private--grpc-stub_shaded.jar:/databricks/jars/third_party--gcs-private--gson_shaded.jar:/databricks/jars/third_party--gcs-private--guava_shaded.jar:/databricks/jars/third_party--gcs-private--httpclient_shaded.jar:/databricks/jars/third_party--gcs-private--httpcore_shaded.jar:/databricks/jars/third_party--gcs-private--j2objc-annotations_shaded.jar:/databricks/jars/third_party--gcs-private--jackson-core_shaded.jar:/databricks/jars/third_party--gcs-private--libmeta-services.jar:/databricks/jars/third_party--gcs-private--listenablefuture_shaded.jar:/databricks/jars/third_party--gcs-private--opencensus-api_shaded.jar:/databricks/jars/third_party--gcs-private--opencensus-contrib-http-util_shaded.jar:/databricks/jars/third_party--gcs-private--perfmark-api_shaded.jar:/databricks/jars/third_party--gcs-private--proto-google-common-protos_shaded.jar:/databricks/jars/third_party--gcs-private--proto-google-iam-v1_shaded.jar:/databricks/jars/third_party--gcs-private--protobuf-java-util_shaded.jar:/databricks/jars/third_party--gcs-private--protobuf-java_shaded.jar:/databricks/jars/third_party--gcs-private--util-hadoop-hadoop2_shaded.jar:/databricks/jars/third_party--gcs-private--util-hadoop2_shaded.jar:/databricks/jars/third_party--hadoop--hadoop-tools--hadoop-aws--lib-spark_3.1_2.12_deploy_shaded.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--aopalliance__aopalliance__1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-annotations__2.7.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-core__2.7.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-core__2.7.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.core__jackson-databind__2.7.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.fasterxml.jackson.datatype__jackson-datatype-joda__2.7.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.code.findbugs__jsr305__1.3.9_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.guava__guava__11.0.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.guava__guava__16.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.google.inject__guice__3.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-annotations__1.2.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-keyvault-core__1.0.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-keyvault-core__1.0.0_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-storage__7.0.0_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.azure__azure-storage__8.6.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.microsoft.rest__client-runtime__1.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okhttp3__logging-interceptor__3.3.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okhttp3__okhttp-urlconnection__3.3.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okhttp3__okhttp__3.3.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.okio__okio__1.8.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.retrofit2__adapter-rxjava__2.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.retrofit2__converter-jackson__2.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.squareup.retrofit2__retrofit__2.1.0_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--com.sun.xml.bind__jaxb-impl__2.2.3-1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-codec__commons-codec__1.9_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-codec__commons-codec__1.9_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-logging__commons-logging__1.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--commons-logging__commons-logging__1.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--hadoop-azure-2.7.3-abfs-external-20180625_3682417-spark_3.1_2.12_deploy_shaded.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--hadoop-azure-2.7.3-abfs-external-20180920_b33d810-spark_3.1_2.12_deploy_shaded.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--io.netty__netty-all__4.0.52.Final_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--io.reactivex__rxjava__1.2.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.activation__activation__1.1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.inject__javax.inject__1_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.xml.bind__jaxb-api__2.2.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--javax.xml.stream__stax-api__1.0-2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--joda-time__joda-time__2.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.htrace__htrace-core__3.1.0-incubating_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpclient__4.5.2_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpclient__4.5.2_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpcore__4.4.4_2.12_shaded_20180625_3682417_spark_3.1.jar:/databricks/jars/third_party--hadoop-azure-2.7.3-abfs--org.apache.httpcomponents__httpcore__4.4.4_2.12_shaded_20180920_b33d810_spark_3.1.jar:/databricks/jars/third_party--hadoop_azure_abfs--hadoop-tools--hadoop-azure--lib-spark_3.1_2.12_deploy.jar_shaded.jar:/databricks/jars/third_party--jackson--guava_only_shaded.jar:/databricks/jars/third_party--jackson--jackson-module-scala-shaded_2.12_deploy.jar:/databricks/jars/third_party--jackson--jsr305_only_shaded.jar:/databricks/jars/third_party--jackson--paranamer_only_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-client_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-http_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-io_shaded.jar:/databricks/jars/third_party--jetty-client--jetty-util_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jackson-annotations_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jackson-core_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jackson-databind_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jjwt-api_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jjwt-impl_shaded.jar:/databricks/jars/third_party--jsonwebtoken--jjwt-jackson_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.code.findbugs__jsr305__3.0.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.code.gson__gson__2.8.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.errorprone__error_prone_annotations__2.1.3_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.guava__guava__26.0-android_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.google.j2objc__j2objc-annotations__1.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.lmax__disruptor__3.4.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.squareup.okhttp3__okhttp__3.9.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--com.squareup.okio__okio__1.13.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--commons-codec__commons-codec__1.9_shaded.jar:/databricks/jars/third_party--opencensus-shaded--commons-logging__commons-logging__1.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.grpc__grpc-context__1.19.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-client__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-core__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-thrift__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.jaegertracing__jaeger-tracerresolver__0.33.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-api__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-exporter-trace-jaeger__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-exporter-trace-util__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-impl-core__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opencensus__opencensus-impl__0.22.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing.contrib__opentracing-tracerresolver__0.1.5_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing__opentracing-api__0.31.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing__opentracing-noop__0.31.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--io.opentracing__opentracing-util__0.31.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.apache.httpcomponents__httpclient__4.4.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.apache.httpcomponents__httpcore__4.4.1_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.apache.thrift__libthrift__0.11.0_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.checkerframework__checker-compat-qual__2.5.2_shaded.jar:/databricks/jars/third_party--opencensus-shaded--org.codehaus.mojo__animal-sniffer-annotations__1.14_shaded.jar:/databricks/jars/third_party--zeromq--jeromq_shaded.jar:/databricks/jars/third_party--zeromq--jnacl_shaded.jar:/databricks/jars/utils--process_utils-spark_3.1_2.12_deploy.jar:/databricks/jars/workflow--workflow-spark_3.1_2.12_deploy.jar", "spark.hadoop.fs.gs.impl.disable.cache": "true", "spark.hadoop.fs.cpfs-adl.impl": "*********(redacted)", "spark.databricks.passthrough.s3a.tokenProviderClassName": "*********(redacted)", "spark.databricks.clusterUsageTags.isSingleUserCluster": "false", "spark.databricks.cloudfetch.requesterClassName": "*********(redacted)", "spark.databricks.clusterUsageTags.sparkEnvVarContainsEscape": "false", "spark.databricks.clusterUsageTags.attribute_tag_dust_suite": "", "spark.hadoop.hive.server2.keystore.path": "/databricks/keys/jetty-ssl-driver-keystore.jks", "spark.hadoop.fs.wasb.impl.disable.cache": "true", "spark.hadoop.hive.server2.transport.mode": "http", "spark.hadoop.spark.databricks.io.parquet.verifyChecksumOnWrite.enabled": "true", "spark.driver.allowMultipleContexts": "false", "spark.databricks.clusterUsageTags.enableSqlAclsOnly": "false", "spark.databricks.clusterUsageTags.clusterNodeType": "m5.4xlarge", "spark.databricks.clusterUsageTags.instanceProfileArn": "arn:aws:iam::471881062455:instance-profile/databricks-workspace-stack-access-data-buckets", "spark.databricks.clusterUsageTags.containerType": "LXC", "spark.databricks.managedCatalog.adls.gen2.tokenProviderClassName": "*********(redacted)", "spark.files.useFetchCache": "false", "spark.sql.allowMultipleContexts": "false", "spark.databricks.clusterUsageTags.enableDfAcls": "false", "spark.databricks.clusterUsageTags.clusterPythonVersion": "3", "spark.databricks.cloudfetch.hasRegionSupport": "true", "spark.hadoop.fs.s3a.impl": "shaded.databricks.org.apache.hadoop.fs.s3a.S3AFileSystem", "spark.shuffle.memoryFraction": "0.2", "spark.hadoop.fs.mcfs-s3.impl": "com.databricks.sql.acl.fs.ManagedCatalogFileSystem", "spark.sql.sources.default": "delta", "spark.databricks.clusterUsageTags.attribute_tag_dust_maintainer": "", "spark.databricks.clusterUsageTags.clusterAvailability": "SPOT_WITH_FALLBACK", "spark.sql.parquet.compression.codec": "snappy", "spark.hadoop.mapred.output.committer.class": "com.databricks.backend.daemon.data.client.DirectOutputCommitter", "spark.hadoop.fs.s3a.fast.upload.active.blocks": "32", "spark.hadoop.spark.sql.sources.outputCommitterClass": "com.databricks.backend.daemon.data.client.MapReduceDirectOutputCommitter", "spark.hadoop.fs.cpfs-adl.impl.disable.cache": "true", "spark.app.id": "app-20220304222044-0000", "spark.databricks.clusterUsageTags.clusterOwnerUserId": "6878104766663444", "spark.hadoop.fs.s3a.fast.upload": "true", "spark.databricks.clusterUsageTags.clusterLogDeliveryEnabled": "true", "spark.databricks.clusterUsageTags.driverNodeType": "m5.xlarge", "spark.speculation.quantile": "0.9", "spark.databricks.cloudProvider": "AWS", "spark.akka.frameSize": "256", "spark.hadoop.parquet.page.write-checksum.enabled": "true", "spark.eventLog.rolling.enabled": "false", "spark.databricks.clusterUsageTags.clusterAllTags": "[{\"key\":\"Vendor\",\"value\":\"Databricks\"},{\"key\":\"Creator\",\"value\":\"arianna@synccomputing.com\"},{\"key\":\"ClusterName\",\"value\":\"job-703-run-31144\"},{\"key\":\"ClusterId\",\"value\":\"0304-221748-fngq99ev\"},{\"key\":\"JobId\",\"value\":\"703\"},{\"key\":\"RunName\",\"value\":\"AirlineDelayLR_gz\"},{\"key\":\"Name\",\"value\":\"workerenv-656201176161048-20de29ca-1c16-42b0-a91e-4f6a30f57313-worker\"}]", "spark.sql.hive.metastore.version": "0.13.0"}, "existsSQL": true, "existsExecutors": true}}
\ No newline at end of file
diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
index 00f7c54..8b88392 100644
--- a/tests/test_eventlog.py
+++ b/tests/test_eventlog.py
@@ -1,5 +1,7 @@
 import json
 import tempfile
+import unittest
+import zipfile
 from pathlib import Path
 
 from spark_log_parser import eventlog
@@ -16,8 +18,6 @@ def test_simple_emr_log():
         with open(log.event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
 
-    assert not log.is_parsed, "Log is correctly identified as being not parsed"
-
     assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
 
     assert event["Event"] == "SparkListenerLogStart", "Expected first event is present"
@@ -34,85 +34,11 @@ def test_simple_databricks_log():
         with open(log.event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
 
-    assert not log.is_parsed, "Log is correctly identified as being not parsed"
-
     assert all(
         key in event
         for key in ["Event", "Spark Version", "Timestamp", "Rollover Number", "SparkContext Id"]
     ), "All keys are present"
 
-    assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
-
-
-def test_databricks_rollover_log():
-    event_log = Path("tests", "logs", "databricks-rollover.zip").resolve()
-
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
-
-        with open(log.event_log) as log_fobj:
-            event = json.loads(log_fobj.readline())
-            log_fobj.seek(0)
-
-            rollover_count = 0
-            for i, event_str in enumerate(log_fobj):
-                event = json.loads(event_str)
-                if "Rollover Number" in event:
-                    assert all(
-                        key in event
-                        for key in [
-                            "Event",
-                            "Spark Version",
-                            "Timestamp",
-                            "Rollover Number",
-                            "SparkContext Id",
-                        ]
-                    ), "All keys are present"
-                    assert (
-                        rollover_count == event["Rollover Number"]
-                    ), "Contiguous monotonically increasing IDs"
-                    rollover_count += 1
-
-            assert rollover_count == 3, "All log parts are present"
-            assert i + 1 == 16945, "All events are present"
-
-
-def test_databricks_messy_rollover_log_archive():
-    event_log = Path("tests", "logs", "databricks-rollover-messy.zip").resolve()
-
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
-
-        with open(log.event_log) as log_fobj:
-            event = json.loads(log_fobj.readline())
-            log_fobj.seek(0)
-
-            rollover_count = 0
-            for i, event_str in enumerate(log_fobj):
-                event = json.loads(event_str)
-                if "Rollover Number" in event:
-                    assert all(
-                        key in event
-                        for key in [
-                            "Event",
-                            "Spark Version",
-                            "Timestamp",
-                            "Rollover Number",
-                            "SparkContext Id",
-                        ]
-                    ), "All keys are present"
-                    assert (
-                        rollover_count == event["Rollover Number"]
-                    ), "Contiguous monotonically increasing IDs"
-                    rollover_count += 1
-
-            assert rollover_count == 3, "All log parts are present"
-            assert i + 1 == 16945, "All events are present"
-
 
 def test_raw_databricks_log():
     event_log = Path("tests", "logs", "databricks.json").resolve()
@@ -131,21 +57,51 @@ def test_raw_databricks_log():
     ), "All keys are present"
 
     assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
+    assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
 
 
-def test_parsed_log():
-    parsed_path = Path("tests", "logs", "databricks-parsed.json").resolve()
-
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % parsed_path.name[: -len("".join(parsed_path.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=parsed_path.as_uri(), work_dir=temp_dir)
-
-        with open(log.event_log) as log_fobj:
-            parsed = json.loads(log_fobj.readline())
-
-    assert log.is_parsed, "Parsed flag is set"
-
-    assert all(
-        key in parsed for key in ["jobData", "stageData", "taskData"]
-    ), "All keys are present"
+class RolloverLog(unittest.TestCase):
+    def test_databricks_rollover_log(self):
+        self.validate_log(Path("tests", "logs", "databricks-rollover.zip").resolve(), 3, 16945)
+
+    def test_databricks_messy_rollover_log_archive(self):
+        self.validate_log(
+            Path("tests", "logs", "databricks-rollover-messy.zip").resolve(), 3, 16945
+        )
+
+    def test_databricks_messy_rollover_log_dir(self):
+        with tempfile.TemporaryDirectory() as temp_dir:
+            with zipfile.ZipFile(Path("tests", "logs", "databricks-rollover-messy.zip")) as zinfo:
+                temp_dir_path = Path(temp_dir)
+                zinfo.extractall(temp_dir_path)
+                self.validate_log(temp_dir_path, 3, 16945)
+
+    def validate_log(self, event_log: Path, log_file_total: int, log_entry_total: int):
+        with tempfile.TemporaryDirectory() as temp_dir:
+            log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+
+            with open(log.event_log) as log_fobj:
+                event = json.loads(log_fobj.readline())
+                log_fobj.seek(0)
+
+                rollover_count = 0
+                for i, event_str in enumerate(log_fobj):
+                    event = json.loads(event_str)
+                    if "Rollover Number" in event:
+                        assert all(
+                            key in event
+                            for key in [
+                                "Event",
+                                "Spark Version",
+                                "Timestamp",
+                                "Rollover Number",
+                                "SparkContext Id",
+                            ]
+                        ), "All keys are present"
+                        assert (
+                            rollover_count == event["Rollover Number"]
+                        ), "Contiguous monotonically increasing IDs"
+                        rollover_count += 1
+
+                assert rollover_count == log_file_total, "All log parts are present"
+                assert i + 1 == log_entry_total, "All events are present"

From 38cc094826276cd25101be4794532d71752b8f4e Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Tue, 2 Aug 2022 09:44:38 -0700
Subject: [PATCH 10/14] [PROD-325] Refactor to remove pydantic model that
 wasn't doing much, and add support for s3 directories

---
 parse.sh                      |   6 +-
 spark_log_parser/__main__.py  |   6 +-
 spark_log_parser/eventlog.py  |  62 ++++++++--------
 spark_log_parser/extractor.py | 129 ++++++++++++++++++++++------------
 tests/test_bad_eventlog.py    |  26 +++----
 tests/test_eventlog.py        |  41 +++++------
 tests/test_eventlog_s3.py     | 114 ++++++++++++++++++++++++++++++
 tests/test_parse.py           |  20 +++---
 8 files changed, 264 insertions(+), 140 deletions(-)
 create mode 100644 tests/test_eventlog_s3.py

diff --git a/parse.sh b/parse.sh
index ca52f5e..9355963 100755
--- a/parse.sh
+++ b/parse.sh
@@ -13,13 +13,11 @@ function print_usage() {
 }
 
 results_dir="$HOME/results"
-args=()
 
 while getopts l:r:h name; do
   case $name in
     l)
       event_log="$OPTARG"
-      args=("${args[@]}" -l "$OPTARG")
       ;;
     r)
       results_dir="$OPTARG"
@@ -41,6 +39,4 @@ if [[ -z $event_log ]]; then
   exit 1
 fi
 
-args=("${args[@]}" -r "$results_dir")
-
-exec python3 -m spark_log_parser "${args[@]}"
+exec python3 -m spark_log_parser -l "$event_log" -r "$results_dir"
diff --git a/spark_log_parser/__main__.py b/spark_log_parser/__main__.py
index 9036051..1f6d748 100644
--- a/spark_log_parser/__main__.py
+++ b/spark_log_parser/__main__.py
@@ -7,7 +7,7 @@
 
 logging.captureWarnings(True)
 
-from spark_log_parser.eventlog import EventLog  # noqa: E402
+from spark_log_parser.eventlog import EventLogBuilder  # noqa: E402
 from spark_log_parser.parsing_models.application_model_v2 import sparkApplication  # noqa: E402
 
 logger = logging.getLogger("spark_log_parser")
@@ -27,8 +27,8 @@
 print("--Processing log file: " + str(args.log_file))
 
 with tempfile.TemporaryDirectory() as work_dir:
-    event_log = EventLog(source_url=args.log_file.resolve().as_uri(), work_dir=work_dir)
-    app = sparkApplication(eventlog=str(event_log.event_log))
+    event_log = EventLogBuilder(args.log_file.resolve().as_uri(), work_dir).build()
+    app = sparkApplication(eventlog=str(event_log))
 
 if args.log_file.suffixes:
     result_path = os.path.join(
diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index c0584a5..43940a6 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -1,59 +1,53 @@
 import json
+import tempfile
 from pathlib import Path
-from typing import Optional
+from urllib.parse import urlparse
 
 import pandas as pd
-from pydantic import BaseModel, ValidationError, root_validator, stricturl
 
 from spark_log_parser.extractor import Extractor
 
-AllowedURL = stricturl(
-    host_required=False, tld_required=False, allowed_schemes={"https", "s3", "file"}
-)
 
+class EventLogBuilder:
+    ALLOWED_SCHEMES = {"https", "s3", "file"}
 
-class EventLog(BaseModel):
-    source_url: Optional[AllowedURL] = None
-    work_dir: Optional[Path] = None
-    event_log: Optional[Path] = None
+    def __init__(self, source_url: str, work_dir: Path | str, s3_client=None):
+        self.source_url = self._validate_url(source_url)
+        self.work_dir = self._validate_work_dir(work_dir)
+        self.s3_client = s3_client
+        self.file_total = 0
+        self.size_total = 0
 
-    @root_validator()
-    def validate_event_log(cls, values):
-        if not values["event_log"]:
-            if values["source_url"] and values["work_dir"]:
-                return vars(
-                    EventLogBuilder(
-                        values["source_url"],
-                        values["work_dir"],
-                    ).build()
-                )
-            raise ValidationError("source_url and work_dir must be set if event_log isn't")
+    def _validate_url(self, url: str):
+        parsed_url = urlparse(url)
+        if parsed_url.scheme not in self.ALLOWED_SCHEMES:
+            raise ValueError(
+                "URL scheme '%s' is not one of {'%s'}"
+                % (parsed_url.scheme, "', '".join(self.ALLOWED_SCHEMES))
+            )
 
-        return values
+        return parsed_url
 
+    def _validate_work_dir(self, work_dir: Path):
+        work_dir_path = work_dir if isinstance(work_dir, Path) else Path(work_dir)
+        if not work_dir_path.is_dir():
+            raise ValueError("Path is not a directory")
 
-class EventLogBuilder:
-    def __init__(self, source_url: AllowedURL, work_dir: Path):
-        self.source_url = source_url
-        self.work_dir = work_dir
-        self.file_total = 0
-        self.size_total = 0
+        return work_dir_path
 
     def build(self) -> "EventLogBuilder":
-        event_logs = Extractor(self.source_url, self.work_dir).extract()
+        event_logs = Extractor(self.source_url, self.work_dir, self.s3_client).extract()
 
-        name = Path(self._basename())
+        # name = Path(self._basename())
 
         self.event_log = self._concat(
             event_logs,
-            self.work_dir.joinpath(
-                name.name[: -len("".join(name.suffixes))] + "-concatenated.json"
-            ),
+            Path(tempfile.mkstemp(suffix="-concatenated.json", dir=str(self.work_dir))[1]),
         )
 
-        return self
+        return self.event_log
 
-    def _concat(self, event_logs: list[Path], event_log: Path):
+    def _concat(self, event_logs: list[Path], event_log: Path) -> Path:
         if len(event_logs) == 1:
             return event_logs[0]
 
diff --git a/spark_log_parser/extractor.py b/spark_log_parser/extractor.py
index 1abdd14..bc0d9d4 100644
--- a/spark_log_parser/extractor.py
+++ b/spark_log_parser/extractor.py
@@ -1,53 +1,75 @@
-import os
-import boto3 as boto
 import gzip
-from pathlib import Path
+import os
 import shutil
 import struct
 import tarfile
 import zipfile
+from pathlib import Path
+from urllib.parse import ParseResult, urlparse
 
 import requests
 
 
-THRESHOLD_ENTRIES = 100
-THRESHOLD_SIZE = 5000000000
-THRESHOLD_RATIO = 100
-
-
 class Extractor:
     """
     Expands archive to a "work" directory and provides a list of extracted files
     """
 
-    def __init__(self, source_url, work_dir):
-        self.source_url = source_url
-        self.work_dir = work_dir
+    ALLOWED_SCHEMES = {"https", "s3", "file"}
+    FILE_SKIP_PATTERNS = [".DS_Store".lower(), "__MACOSX".lower(), "/."]
+
+    THRESHOLD_ENTRIES = 100
+    THRESHOLD_SIZE = 5000000000
+    THRESHOLD_RATIO = 100
+
+    def __init__(self, source_url: ParseResult | str, work_dir: Path | str, s3_client=None):
+        self.source_url = self._validate_url(source_url)
+        self.work_dir = self._validate_work_dir(work_dir)
+        self.s3_client = self._validate_s3_client(s3_client)
         self.file_total = 0
         self.size_total = 0
 
+    def _validate_url(self, url: ParseResult | str):
+        parsed_url = url if isinstance(url, ParseResult) else urlparse(url)
+        if parsed_url.scheme not in self.ALLOWED_SCHEMES:
+            raise ValueError(
+                "URL scheme '%s' is not one of {'%s'}"
+                % (parsed_url.scheme, "', '".join(self.ALLOWED_SCHEMES))
+            )
+
+        return parsed_url
+
+    def _validate_work_dir(self, work_dir: Path | str):
+        work_dir_path = work_dir if isinstance(work_dir, Path) else Path(work_dir)
+        if not work_dir_path.is_dir():
+            raise ValueError("Path is not a directory")
+
+        return work_dir_path
+
+    def _validate_s3_client(self, s3_client):
+        if self.source_url.scheme == "s3" and s3_client is None:
+            raise ValueError("An S3 client is needed for S3 URLs")
+
+        return s3_client
+
     def extract(self) -> list[Path]:
         if self.source_url.scheme in {"https", "s3"}:
-            local_path = self.work_dir.joinpath(self.source_url.path.split("/")[-1])
-            self._download(local_path)
+            self._download()
+            local_path = self.work_dir
         else:
             local_path = Path(self.source_url.path)
 
-        files = self._extract(local_path, self.work_dir)
-
-        # Remove the archive if we downloaded it to free up space
-        if local_path not in files and self.source_url.scheme in {"https", "s3"}:
-            local_path.unlink()
+        result_files = self._extract(local_path, self.work_dir)
 
-        return files
+        return result_files
 
     def _extract(self, event_log: Path, extract_dir: Path | None = None) -> list[Path]:
-        """ returns list of uncompressed files"""
+        """returns list of uncompressed files"""
         if not extract_dir:
             extract_dir = event_log.parent
 
         if event_log.is_dir():
-            return self._extract_dir(event_log, extract_dir.joinpath(event_log.name))
+            return self._extract_dir(event_log, extract_dir)
 
         extension = "".join(event_log.suffixes)
         if extension.endswith(".tar.gz"):
@@ -69,31 +91,30 @@ def _extract(self, event_log: Path, extract_dir: Path | None = None) -> list[Pat
 
         raise ValueError("Unsupported extension found in the archive")
 
-
     def _extract_dir(self, log_dir: Path, extract_dir: Path) -> list[Path]:
         paths = []
 
-        source_root = Path(self.source_url.path)
-
         for root, _, files in os.walk(log_dir):
             for file in files:
+                path = Path(root, file)
                 if self._should_skip_file(file):
+                    if path.is_relative_to(self.work_dir):
+                        path.unlink()
                     continue
 
-                path = Path(root, file)
-
                 self._add_to_stats_and_verify(path.stat().st_size)
 
-                # Remove the archive if it was extracted to free up space
-                sub_paths = self._extract(path, Path(extract_dir, path.parent.relative_to(source_root)))
+                sub_paths = self._extract(path, Path(extract_dir, path.parent.relative_to(log_dir)))
                 if path not in sub_paths:
                     self._remove_from_stats(path.stat().st_size)
+                    # Remove the archive if it was extracted to free up space
+                    if path.is_relative_to(self.work_dir):
+                        path.unlink()
 
                 paths += sub_paths
 
         return paths
 
-
     def _extract_tgz(self, archive_path: Path, extract_dir: Path):
         paths = []
 
@@ -167,14 +188,12 @@ def _extract_gz(self, archive_path: Path, extract_path: Path):
 
         return sub_paths
 
-    FILE_SKIP_PATTERNS = [".DS_Store".lower(), "__MACOSX".lower(), "/."]
-
     def _should_skip_file(self, filename: str):
         if filename.startswith("."):
             return True
 
         filename = filename.lower()
-        for name in Extractor.FILE_SKIP_PATTERNS:
+        for name in self.FILE_SKIP_PATTERNS:
             if name in filename:
                 return True
 
@@ -185,29 +204,47 @@ def _add_to_stats_and_verify(self, size, count=1):
         self.file_total += count
 
         ratio = size / self.size_total
-        if ratio > THRESHOLD_RATIO:
+        if ratio > self.THRESHOLD_RATIO:
             raise AssertionError("Encountered suspicious compression ratio in the archive")
 
-        if self.size_total > THRESHOLD_SIZE:
+        if self.size_total > self.THRESHOLD_SIZE:
             raise AssertionError("The archive is too big")
 
-        if self.file_total > THRESHOLD_ENTRIES:
+        if self.file_total > self.THRESHOLD_ENTRIES:
             raise AssertionError("Too many files in the archive")
 
-
     def _remove_from_stats(self, size, count=1):
         self.size_total -= size
         self.file_total -= count
 
-
-    def _download(self, path):
-        with open(path, "wb") as fobj:
-            if self.source_url.scheme == "https":
-                response = requests.get(self.source_url, stream=True)
+    def _download(self):
+        if self.source_url.scheme == "https":
+            response = requests.get(self.source_url, stream=True)
+            target_path = self.work_dir.joinpath(self.source_url.path.split("/")[-1])
+            with open(target_path, "wb") as fobj:
                 for chunk in response.iter_content():
                     fobj.write(chunk)
-            elif self.source_url.scheme == "s3":
-                s3 = boto.client("s3")
-                s3.download_fileobj(self.source_url.host, self.source_url.path, fobj)
-
-        return path
\ No newline at end of file
+        elif self.source_url.scheme == "s3":
+            source_key = self.source_url.path.lstrip("/")
+            source_bucket = self.source_url.netloc
+
+            s3_content_count = 0
+            s3_content_size = 0
+            result = self.s3_client.list_objects_v2(Bucket=source_bucket, Prefix=source_key)
+            if "Contents" in result:
+                relative_index = source_key.rfind("/") + 1
+                for content in result["Contents"]:
+                    s3_content_count += 1
+                    s3_content_size += content["Size"]
+                    if s3_content_count > self.THRESHOLD_ENTRIES:
+                        raise AssertionError("Too many objects at %s" % self.source_url)
+                    if s3_content_size > self.THRESHOLD_SIZE:
+                        raise AssertionError(
+                            "Size limit exceeded while downloading from %s" % self.source_url
+                        )
+
+                    target_path = self.work_dir.joinpath(
+                        *content["Key"][relative_index:].split("/")
+                    )
+                    target_path.parent.mkdir(parents=True, exist_ok=True)
+                    self.s3_client.download_file(source_bucket, content["Key"], str(target_path))
diff --git a/tests/test_bad_eventlog.py b/tests/test_bad_eventlog.py
index 5ea928f..a429c29 100644
--- a/tests/test_bad_eventlog.py
+++ b/tests/test_bad_eventlog.py
@@ -2,44 +2,36 @@
 import unittest
 from pathlib import Path
 
-from spark_log_parser import eventlog
+from spark_log_parser.eventlog import EventLogBuilder
 
 
 class BadEventLog(unittest.TestCase):
     def test_multiple_context_ids(self):
         event_log = Path("tests", "logs", "bad", "non-unique-context-id.zip").resolve()
 
-        with tempfile.TemporaryDirectory(
-            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-        ) as temp_dir:
+        with tempfile.TemporaryDirectory() as temp_dir:
             with self.assertRaises(
                 ValueError, msg="Not all rollover files have the same Spark context ID"
             ):
-                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+                EventLogBuilder(event_log.as_uri(), temp_dir).build()
 
     def test_missing_dbc_event(self):
         event_log = Path("tests", "logs", "bad", "missing-dbc-event.zip").resolve()
 
-        with tempfile.TemporaryDirectory(
-            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-        ) as temp_dir:
+        with tempfile.TemporaryDirectory() as temp_dir:
             with self.assertRaises(ValueError, msg="Expected DBC event not found"):
-                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+                EventLogBuilder(event_log.as_uri(), temp_dir).build()
 
     def test_duplicate_log_part(self):
         event_log = Path("tests", "logs", "bad", "duplicate-part.tgz").resolve()
 
-        with tempfile.TemporaryDirectory(
-            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-        ) as temp_dir:
+        with tempfile.TemporaryDirectory() as temp_dir:
             with self.assertRaises(ValueError, msg="Duplicate rollover file detected"):
-                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+                EventLogBuilder(event_log.as_uri(), temp_dir).build()
 
     def test_missing_log_part(self):
         event_log = Path("tests", "logs", "bad", "missing-part.zip").resolve()
 
-        with tempfile.TemporaryDirectory(
-            prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-        ) as temp_dir:
+        with tempfile.TemporaryDirectory() as temp_dir:
             with self.assertRaises(ValueError, msg="Rollover file appears to be missing"):
-                eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+                EventLogBuilder(event_log.as_uri(), temp_dir).build()
diff --git a/tests/test_eventlog.py b/tests/test_eventlog.py
index 8b88392..9691ea9 100644
--- a/tests/test_eventlog.py
+++ b/tests/test_eventlog.py
@@ -8,14 +8,14 @@
 
 
 def test_simple_emr_log():
-    event_log = Path("tests", "logs", "emr.zip").resolve()
+    event_log_path = Path("tests", "logs", "emr.zip").resolve()
 
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+    with tempfile.TemporaryDirectory() as temp_dir:
+        event_log = eventlog.EventLogBuilder(
+            source_url=event_log_path.as_uri(), work_dir=temp_dir
+        ).build()
 
-        with open(log.event_log) as log_fobj:
+        with open(event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
 
     assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
@@ -24,14 +24,12 @@ def test_simple_emr_log():
 
 
 def test_simple_databricks_log():
-    event_log = Path("tests", "logs", "databricks.zip").resolve()
+    event_log_path = Path("tests", "logs", "databricks.zip").resolve()
 
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+    with tempfile.TemporaryDirectory() as temp_dir:
+        event_log = eventlog.EventLogBuilder(event_log_path.as_uri(), temp_dir).build()
 
-        with open(log.event_log) as log_fobj:
+        with open(event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
 
     assert all(
@@ -41,14 +39,12 @@ def test_simple_databricks_log():
 
 
 def test_raw_databricks_log():
-    event_log = Path("tests", "logs", "databricks.json").resolve()
+    event_log_path = Path("tests", "logs", "databricks.json").resolve()
 
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+    with tempfile.TemporaryDirectory() as temp_dir:
+        event_log = eventlog.EventLogBuilder(event_log_path.as_uri(), temp_dir).build()
 
-        with open(log.event_log) as log_fobj:
+        with open(event_log) as log_fobj:
             event = json.loads(log_fobj.readline())
 
     assert all(
@@ -57,7 +53,6 @@ def test_raw_databricks_log():
     ), "All keys are present"
 
     assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
-    assert event["Event"] == "DBCEventLoggingListenerMetadata", "Expected first event is present"
 
 
 class RolloverLog(unittest.TestCase):
@@ -71,16 +66,16 @@ def test_databricks_messy_rollover_log_archive(self):
 
     def test_databricks_messy_rollover_log_dir(self):
         with tempfile.TemporaryDirectory() as temp_dir:
-            with zipfile.ZipFile(Path("tests", "logs", "databricks-rollover-messy.zip")) as zinfo:
+            with zipfile.ZipFile(Path("tests", "logs", "databricks-rollover-messy.zip")) as zfile:
                 temp_dir_path = Path(temp_dir)
-                zinfo.extractall(temp_dir_path)
+                zfile.extractall(temp_dir_path)
                 self.validate_log(temp_dir_path, 3, 16945)
 
     def validate_log(self, event_log: Path, log_file_total: int, log_entry_total: int):
         with tempfile.TemporaryDirectory() as temp_dir:
-            log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+            event_log = eventlog.EventLogBuilder(event_log.as_uri(), temp_dir).build()
 
-            with open(log.event_log) as log_fobj:
+            with open(event_log) as log_fobj:
                 event = json.loads(log_fobj.readline())
                 log_fobj.seek(0)
 
diff --git a/tests/test_eventlog_s3.py b/tests/test_eventlog_s3.py
new file mode 100644
index 0000000..fa3a9b8
--- /dev/null
+++ b/tests/test_eventlog_s3.py
@@ -0,0 +1,114 @@
+import io
+import json
+import os
+from pathlib import Path
+from zipfile import ZipFile
+from botocore.stub import Stubber, ANY
+import boto3 as boto
+import tempfile
+from urllib.parse import urlparse
+
+import pytest
+
+from spark_log_parser import eventlog
+
+
+@pytest.mark.parametrize("event_log_url", ["s3://sync-test-artifacts/airlinedelay/jb-42K1E16/emr.zip", "s3://sync-test-artifacts/airlinedelay/jb-42K1E16/"])
+@pytest.mark.parametrize("event_log_file_archive,event_log_s3_dir", [(Path("tests", "logs", "emr.zip"), "airlinedelay/jb-42K1E16/")])
+def test_emr_log_from_s3(event_log_url, event_log_file_archive, event_log_s3_dir):
+    parsed_event_log_url = urlparse(event_log_url)
+    s3_bucket = parsed_event_log_url.netloc
+    s3_prefix = parsed_event_log_url.path.lstrip("/")
+
+    s3 = boto.client("s3")
+    stubber = Stubber(s3)
+    s3_key = event_log_s3_dir + event_log_file_archive.name
+    s3_object_size = event_log_file_archive.stat().st_size
+    stubber.add_response("list_objects_v2", {"Contents": [{"Key": s3_key, "Size": s3_object_size}]}, {"Bucket": s3_bucket, "Prefix": s3_prefix})
+    stubber.add_response("head_object", {"ContentLength": s3_object_size, "ContentType": "application/octet-stream"}, {"Bucket": s3_bucket, "Key": s3_key})
+
+    with open(event_log_file_archive, "rb") as fobj:
+        chunk_size =boto.s3.transfer.TransferConfig().multipart_chunksize
+        chunk = fobj.read(chunk_size)
+        while chunk:
+            if s3_object_size > chunk_size:
+                expected_params = {"Bucket": s3_bucket, "Key": s3_key, "Range": ANY}
+            else:
+                expected_params = {"Bucket": s3_bucket, "Key": s3_key}
+            stubber.add_response("get_object", {"ContentLength": len(chunk), "ContentType": "application/octet-stream", "Body": io.BytesIO(chunk)}, expected_params)
+
+            chunk = fobj.read(chunk_size)
+
+    with tempfile.TemporaryDirectory() as temp_dir, stubber:
+        event_log = eventlog.EventLogBuilder(event_log_url, temp_dir, s3).build()
+
+        with open(event_log) as log_fobj:
+            event = json.loads(log_fobj.readline())
+
+    assert all(key in event for key in ["Event", "Spark Version"]), "All keys are present"
+
+    assert event["Event"] == "SparkListenerLogStart", "Expected first event is present"
+
+
+@pytest.mark.parametrize("event_log_url", ["s3://sync-test-artifacts/airlinedelay/jb-42K1E16/", "s3://sync-test-artifacts/airlinedelay"])
+@pytest.mark.parametrize("event_log_file_archive,event_log_s3_dir", [(Path("tests", "logs", "databricks-rollover-messy.zip"), "airlinedelay/jb-42K1E16/")])
+def test_databricks_log_from_s3_dir(event_log_url, event_log_file_archive, event_log_s3_dir):
+    parsed_event_log_url = urlparse(event_log_url)
+    s3_bucket = parsed_event_log_url.netloc
+    s3_prefix = parsed_event_log_url.path.lstrip("/")
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        s3 = boto.client("s3")
+        stubber = Stubber(s3)
+        with ZipFile(event_log_file_archive) as zfile:
+            contents = [(event_log_s3_dir + "/".join(zinfo.filename.split(os.sep)), zinfo) for zinfo in zfile.infolist() if not zinfo.is_dir()]
+            stubber.add_response("list_objects_v2", {"Contents": [{"Key": key, "Size": zinfo.file_size} for key, zinfo in contents]}, {"Bucket": s3_bucket, "Prefix": s3_prefix})
+
+            for s3_key, zinfo in contents:
+                with zfile.open(zinfo) as zobj:
+                    stubber.add_response("head_object", {"ContentLength": zinfo.file_size, "ContentType": "application/octet-stream"}, {"Bucket": s3_bucket, "Key": s3_key})
+
+                    chunk_size =boto.s3.transfer.TransferConfig().multipart_chunksize
+                    chunk = zobj.read(chunk_size)
+                    while chunk:
+                        if zinfo.file_size > chunk_size:
+                            expected_params = {"Bucket": s3_bucket, "Key": s3_key, "Range": ANY}
+                        else:
+                            expected_params = {"Bucket": s3_bucket, "Key": s3_key}
+                        stubber.add_response("get_object", {"ContentLength": len(chunk), "ContentType": "application/octet-stream", "Body": io.BytesIO(chunk)}, expected_params)
+
+                        chunk = zobj.read(chunk_size)
+
+        with stubber:
+            event_log = eventlog.EventLogBuilder(event_log_url, temp_dir, s3).build()
+
+        stubber.assert_no_pending_responses()
+
+        with open(event_log) as log_fobj:
+                event = json.loads(log_fobj.readline())
+                log_fobj.seek(0)
+
+                rollover_count = 0
+                for i, event_str in enumerate(log_fobj):
+                    try:
+                        event = json.loads(event_str)
+                        if "Rollover Number" in event:
+                            assert all(
+                                key in event
+                                for key in [
+                                    "Event",
+                                    "Spark Version",
+                                    "Timestamp",
+                                    "Rollover Number",
+                                    "SparkContext Id",
+                                ]
+                            ), "All keys are present"
+                            assert (
+                                rollover_count == event["Rollover Number"]
+                            ), "Contiguous monotonically increasing IDs"
+                            rollover_count += 1
+                    except Exception as exc:
+                        raise ValueError("Problem with line %d: %s" % (i, event_str), exc)
+
+                assert rollover_count == 3, "All log parts are present"
+                assert i + 1 == 16945, "All events are present"
diff --git a/tests/test_parse.py b/tests/test_parse.py
index 08aeaea..99a5c01 100644
--- a/tests/test_parse.py
+++ b/tests/test_parse.py
@@ -7,15 +7,13 @@
 
 
 def test_simple_databricks_log():
-    event_log = Path("tests", "logs", "databricks.zip").resolve()
+    event_log_path = Path("tests", "logs", "databricks.zip").resolve()
 
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+    with tempfile.TemporaryDirectory() as temp_dir:
+        event_log = eventlog.EventLogBuilder(event_log_path.as_uri(), temp_dir).build()
 
         result_path = str(Path(temp_dir, "result"))
-        sparkApplication(eventlog=str(log.event_log)).save(result_path)
+        sparkApplication(eventlog=str(event_log)).save(result_path)
 
         with open(result_path + ".json") as result_fobj:
             parsed = json.load(result_fobj)
@@ -39,15 +37,13 @@ def test_simple_databricks_log():
 
 
 def test_simple_emr_log():
-    event_log = Path("tests", "logs", "emr.zip").resolve()
+    event_log_path = Path("tests", "logs", "emr.zip").resolve()
 
-    with tempfile.TemporaryDirectory(
-        prefix="eventlog-%s-" % event_log.name[: -len("".join(event_log.suffixes))]
-    ) as temp_dir:
-        log = eventlog.EventLog(source_url=event_log.as_uri(), work_dir=temp_dir)
+    with tempfile.TemporaryDirectory() as temp_dir:
+        event_log = eventlog.EventLogBuilder(event_log_path.as_uri(), temp_dir).build()
 
         result_path = str(Path(temp_dir, "result"))
-        sparkApplication(eventlog=str(log.event_log)).save(result_path)
+        sparkApplication(eventlog=str(event_log)).save(result_path)
 
         with open(str(result_path) + ".json") as result_fobj:
             parsed = json.load(result_fobj)

From de97dcea16e8a546c86d6a1807ecd90eda40c00d Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Tue, 2 Aug 2022 09:57:53 -0700
Subject: [PATCH 11/14] [PROD-325] Small clean-up

---
 spark_log_parser/eventlog.py  |  20 ++---
 spark_log_parser/extractor.py |  12 +--
 tests/test_eventlog_s3.py     | 144 +++++++++++++++++++++++-----------
 3 files changed, 111 insertions(+), 65 deletions(-)

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index 43940a6..de6b2a9 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -1,7 +1,7 @@
 import json
 import tempfile
 from pathlib import Path
-from urllib.parse import urlparse
+from urllib.parse import ParseResult, urlparse
 
 import pandas as pd
 
@@ -11,15 +11,15 @@
 class EventLogBuilder:
     ALLOWED_SCHEMES = {"https", "s3", "file"}
 
-    def __init__(self, source_url: str, work_dir: Path | str, s3_client=None):
+    def __init__(self, source_url: ParseResult | str, work_dir: Path | str, s3_client=None):
         self.source_url = self._validate_url(source_url)
         self.work_dir = self._validate_work_dir(work_dir)
         self.s3_client = s3_client
         self.file_total = 0
         self.size_total = 0
 
-    def _validate_url(self, url: str):
-        parsed_url = urlparse(url)
+    def _validate_url(self, url: ParseResult | str) -> ParseResult:
+        parsed_url = url if isinstance(url, ParseResult) else urlparse(url)
         if parsed_url.scheme not in self.ALLOWED_SCHEMES:
             raise ValueError(
                 "URL scheme '%s' is not one of {'%s'}"
@@ -28,18 +28,16 @@ def _validate_url(self, url: str):
 
         return parsed_url
 
-    def _validate_work_dir(self, work_dir: Path):
+    def _validate_work_dir(self, work_dir: Path | str) -> Path:
         work_dir_path = work_dir if isinstance(work_dir, Path) else Path(work_dir)
         if not work_dir_path.is_dir():
             raise ValueError("Path is not a directory")
 
         return work_dir_path
 
-    def build(self) -> "EventLogBuilder":
+    def build(self) -> Path:
         event_logs = Extractor(self.source_url, self.work_dir, self.s3_client).extract()
 
-        # name = Path(self._basename())
-
         self.event_log = self._concat(
             event_logs,
             Path(tempfile.mkstemp(suffix="-concatenated.json", dir=str(self.work_dir))[1]),
@@ -88,9 +86,3 @@ def _validate_rollover_logs(self, df: pd.DataFrame):
 
         if any(diffs < 1):
             raise ValueError("Duplicate rollover file detected")
-
-    def _basename(self):
-        if self.source_url.scheme in {"https", "s3"}:
-            return self.source_url.path.split("/")[-1]
-        elif self.source_url.scheme == "file":
-            return Path(self.source_url.path).name
diff --git a/spark_log_parser/extractor.py b/spark_log_parser/extractor.py
index bc0d9d4..afce1a7 100644
--- a/spark_log_parser/extractor.py
+++ b/spark_log_parser/extractor.py
@@ -29,7 +29,7 @@ def __init__(self, source_url: ParseResult | str, work_dir: Path | str, s3_clien
         self.file_total = 0
         self.size_total = 0
 
-    def _validate_url(self, url: ParseResult | str):
+    def _validate_url(self, url: ParseResult | str) -> ParseResult:
         parsed_url = url if isinstance(url, ParseResult) else urlparse(url)
         if parsed_url.scheme not in self.ALLOWED_SCHEMES:
             raise ValueError(
@@ -39,7 +39,7 @@ def _validate_url(self, url: ParseResult | str):
 
         return parsed_url
 
-    def _validate_work_dir(self, work_dir: Path | str):
+    def _validate_work_dir(self, work_dir: Path | str) -> Path:
         work_dir_path = work_dir if isinstance(work_dir, Path) else Path(work_dir)
         if not work_dir_path.is_dir():
             raise ValueError("Path is not a directory")
@@ -115,7 +115,7 @@ def _extract_dir(self, log_dir: Path, extract_dir: Path) -> list[Path]:
 
         return paths
 
-    def _extract_tgz(self, archive_path: Path, extract_dir: Path):
+    def _extract_tgz(self, archive_path: Path, extract_dir: Path) -> list[Path]:
         paths = []
 
         with tarfile.open(archive_path) as tar_file:
@@ -142,7 +142,7 @@ def _extract_tgz(self, archive_path: Path, extract_dir: Path):
 
         return paths
 
-    def _extract_zip(self, archive_path: Path, extract_dir: Path):
+    def _extract_zip(self, archive_path: Path, extract_dir: Path) -> list[Path]:
         paths = []
 
         with zipfile.ZipFile(archive_path) as zfile:
@@ -169,7 +169,7 @@ def _extract_zip(self, archive_path: Path, extract_dir: Path):
 
         return paths
 
-    def _extract_gz(self, archive_path: Path, extract_path: Path):
+    def _extract_gz(self, archive_path: Path, extract_path: Path) -> list[Path]:
         with open(archive_path, "rb") as fobj:
             fobj.seek(-4, 2)
             size = struct.unpack("I", fobj.read(4))[0]
@@ -188,7 +188,7 @@ def _extract_gz(self, archive_path: Path, extract_path: Path):
 
         return sub_paths
 
-    def _should_skip_file(self, filename: str):
+    def _should_skip_file(self, filename: str) -> bool:
         if filename.startswith("."):
             return True
 
diff --git a/tests/test_eventlog_s3.py b/tests/test_eventlog_s3.py
index fa3a9b8..ff67e85 100644
--- a/tests/test_eventlog_s3.py
+++ b/tests/test_eventlog_s3.py
@@ -1,20 +1,29 @@
 import io
 import json
 import os
-from pathlib import Path
-from zipfile import ZipFile
-from botocore.stub import Stubber, ANY
-import boto3 as boto
 import tempfile
+from pathlib import Path
 from urllib.parse import urlparse
+from zipfile import ZipFile
 
+import boto3 as boto
 import pytest
+from botocore.stub import ANY, Stubber
 
 from spark_log_parser import eventlog
 
 
-@pytest.mark.parametrize("event_log_url", ["s3://sync-test-artifacts/airlinedelay/jb-42K1E16/emr.zip", "s3://sync-test-artifacts/airlinedelay/jb-42K1E16/"])
-@pytest.mark.parametrize("event_log_file_archive,event_log_s3_dir", [(Path("tests", "logs", "emr.zip"), "airlinedelay/jb-42K1E16/")])
+@pytest.mark.parametrize(
+    "event_log_url",
+    [
+        "s3://sync-test-artifacts/airlinedelay/jb-42K1E16/emr.zip",
+        "s3://sync-test-artifacts/airlinedelay/jb-42K1E16/",
+    ],
+)
+@pytest.mark.parametrize(
+    "event_log_file_archive,event_log_s3_dir",
+    [(Path("tests", "logs", "emr.zip"), "airlinedelay/jb-42K1E16/")],
+)
 def test_emr_log_from_s3(event_log_url, event_log_file_archive, event_log_s3_dir):
     parsed_event_log_url = urlparse(event_log_url)
     s3_bucket = parsed_event_log_url.netloc
@@ -24,18 +33,34 @@ def test_emr_log_from_s3(event_log_url, event_log_file_archive, event_log_s3_dir
     stubber = Stubber(s3)
     s3_key = event_log_s3_dir + event_log_file_archive.name
     s3_object_size = event_log_file_archive.stat().st_size
-    stubber.add_response("list_objects_v2", {"Contents": [{"Key": s3_key, "Size": s3_object_size}]}, {"Bucket": s3_bucket, "Prefix": s3_prefix})
-    stubber.add_response("head_object", {"ContentLength": s3_object_size, "ContentType": "application/octet-stream"}, {"Bucket": s3_bucket, "Key": s3_key})
+    stubber.add_response(
+        "list_objects_v2",
+        {"Contents": [{"Key": s3_key, "Size": s3_object_size}]},
+        {"Bucket": s3_bucket, "Prefix": s3_prefix},
+    )
+    stubber.add_response(
+        "head_object",
+        {"ContentLength": s3_object_size, "ContentType": "application/octet-stream"},
+        {"Bucket": s3_bucket, "Key": s3_key},
+    )
 
     with open(event_log_file_archive, "rb") as fobj:
-        chunk_size =boto.s3.transfer.TransferConfig().multipart_chunksize
+        chunk_size = boto.s3.transfer.TransferConfig().multipart_chunksize
         chunk = fobj.read(chunk_size)
         while chunk:
             if s3_object_size > chunk_size:
                 expected_params = {"Bucket": s3_bucket, "Key": s3_key, "Range": ANY}
             else:
                 expected_params = {"Bucket": s3_bucket, "Key": s3_key}
-            stubber.add_response("get_object", {"ContentLength": len(chunk), "ContentType": "application/octet-stream", "Body": io.BytesIO(chunk)}, expected_params)
+            stubber.add_response(
+                "get_object",
+                {
+                    "ContentLength": len(chunk),
+                    "ContentType": "application/octet-stream",
+                    "Body": io.BytesIO(chunk),
+                },
+                expected_params,
+            )
 
             chunk = fobj.read(chunk_size)
 
@@ -50,8 +75,14 @@ def test_emr_log_from_s3(event_log_url, event_log_file_archive, event_log_s3_dir
     assert event["Event"] == "SparkListenerLogStart", "Expected first event is present"
 
 
-@pytest.mark.parametrize("event_log_url", ["s3://sync-test-artifacts/airlinedelay/jb-42K1E16/", "s3://sync-test-artifacts/airlinedelay"])
-@pytest.mark.parametrize("event_log_file_archive,event_log_s3_dir", [(Path("tests", "logs", "databricks-rollover-messy.zip"), "airlinedelay/jb-42K1E16/")])
+@pytest.mark.parametrize(
+    "event_log_url",
+    ["s3://sync-test-artifacts/airlinedelay/jb-42K1E16/", "s3://sync-test-artifacts/airlinedelay"],
+)
+@pytest.mark.parametrize(
+    "event_log_file_archive,event_log_s3_dir",
+    [(Path("tests", "logs", "databricks-rollover-messy.zip"), "airlinedelay/jb-42K1E16/")],
+)
 def test_databricks_log_from_s3_dir(event_log_url, event_log_file_archive, event_log_s3_dir):
     parsed_event_log_url = urlparse(event_log_url)
     s3_bucket = parsed_event_log_url.netloc
@@ -61,21 +92,44 @@ def test_databricks_log_from_s3_dir(event_log_url, event_log_file_archive, event
         s3 = boto.client("s3")
         stubber = Stubber(s3)
         with ZipFile(event_log_file_archive) as zfile:
-            contents = [(event_log_s3_dir + "/".join(zinfo.filename.split(os.sep)), zinfo) for zinfo in zfile.infolist() if not zinfo.is_dir()]
-            stubber.add_response("list_objects_v2", {"Contents": [{"Key": key, "Size": zinfo.file_size} for key, zinfo in contents]}, {"Bucket": s3_bucket, "Prefix": s3_prefix})
+            contents = [
+                (event_log_s3_dir + "/".join(zinfo.filename.split(os.sep)), zinfo)
+                for zinfo in zfile.infolist()
+                if not zinfo.is_dir()
+            ]
+            stubber.add_response(
+                "list_objects_v2",
+                {"Contents": [{"Key": key, "Size": zinfo.file_size} for key, zinfo in contents]},
+                {"Bucket": s3_bucket, "Prefix": s3_prefix},
+            )
 
             for s3_key, zinfo in contents:
                 with zfile.open(zinfo) as zobj:
-                    stubber.add_response("head_object", {"ContentLength": zinfo.file_size, "ContentType": "application/octet-stream"}, {"Bucket": s3_bucket, "Key": s3_key})
-
-                    chunk_size =boto.s3.transfer.TransferConfig().multipart_chunksize
+                    stubber.add_response(
+                        "head_object",
+                        {
+                            "ContentLength": zinfo.file_size,
+                            "ContentType": "application/octet-stream",
+                        },
+                        {"Bucket": s3_bucket, "Key": s3_key},
+                    )
+
+                    chunk_size = boto.s3.transfer.TransferConfig().multipart_chunksize
                     chunk = zobj.read(chunk_size)
                     while chunk:
                         if zinfo.file_size > chunk_size:
                             expected_params = {"Bucket": s3_bucket, "Key": s3_key, "Range": ANY}
                         else:
                             expected_params = {"Bucket": s3_bucket, "Key": s3_key}
-                        stubber.add_response("get_object", {"ContentLength": len(chunk), "ContentType": "application/octet-stream", "Body": io.BytesIO(chunk)}, expected_params)
+                        stubber.add_response(
+                            "get_object",
+                            {
+                                "ContentLength": len(chunk),
+                                "ContentType": "application/octet-stream",
+                                "Body": io.BytesIO(chunk),
+                            },
+                            expected_params,
+                        )
 
                         chunk = zobj.read(chunk_size)
 
@@ -85,30 +139,30 @@ def test_databricks_log_from_s3_dir(event_log_url, event_log_file_archive, event
         stubber.assert_no_pending_responses()
 
         with open(event_log) as log_fobj:
-                event = json.loads(log_fobj.readline())
-                log_fobj.seek(0)
-
-                rollover_count = 0
-                for i, event_str in enumerate(log_fobj):
-                    try:
-                        event = json.loads(event_str)
-                        if "Rollover Number" in event:
-                            assert all(
-                                key in event
-                                for key in [
-                                    "Event",
-                                    "Spark Version",
-                                    "Timestamp",
-                                    "Rollover Number",
-                                    "SparkContext Id",
-                                ]
-                            ), "All keys are present"
-                            assert (
-                                rollover_count == event["Rollover Number"]
-                            ), "Contiguous monotonically increasing IDs"
-                            rollover_count += 1
-                    except Exception as exc:
-                        raise ValueError("Problem with line %d: %s" % (i, event_str), exc)
-
-                assert rollover_count == 3, "All log parts are present"
-                assert i + 1 == 16945, "All events are present"
+            event = json.loads(log_fobj.readline())
+            log_fobj.seek(0)
+
+            rollover_count = 0
+            for i, event_str in enumerate(log_fobj):
+                try:
+                    event = json.loads(event_str)
+                    if "Rollover Number" in event:
+                        assert all(
+                            key in event
+                            for key in [
+                                "Event",
+                                "Spark Version",
+                                "Timestamp",
+                                "Rollover Number",
+                                "SparkContext Id",
+                            ]
+                        ), "All keys are present"
+                        assert (
+                            rollover_count == event["Rollover Number"]
+                        ), "Contiguous monotonically increasing IDs"
+                        rollover_count += 1
+                except Exception as exc:
+                    raise ValueError("Problem with line %d: %s" % (i, event_str), exc)
+
+            assert rollover_count == 3, "All log parts are present"
+            assert i + 1 == 16945, "All events are present"

From 3e664cb193206c598ab3cf34a30f9335e585570c Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Tue, 2 Aug 2022 10:31:08 -0700
Subject: [PATCH 12/14] [PROD-325] Handle corner case

---
 spark_log_parser/eventlog.py | 2 +-
 tests/test_bad_eventlog.py   | 7 +++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index de6b2a9..d0d42de 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -81,7 +81,7 @@ def _validate_rollover_logs(self, df: pd.DataFrame):
 
         diffs = df.rollover_index.diff()[1:]
 
-        if any(diffs > 1):
+        if any(diffs > 1) or df.rollover_index[0] > 0:
             raise ValueError("Rollover file appears to be missing")
 
         if any(diffs < 1):
diff --git a/tests/test_bad_eventlog.py b/tests/test_bad_eventlog.py
index a429c29..236db54 100644
--- a/tests/test_bad_eventlog.py
+++ b/tests/test_bad_eventlog.py
@@ -35,3 +35,10 @@ def test_missing_log_part(self):
         with tempfile.TemporaryDirectory() as temp_dir:
             with self.assertRaises(ValueError, msg="Rollover file appears to be missing"):
                 EventLogBuilder(event_log.as_uri(), temp_dir).build()
+
+    def test_missing_first_part(self):
+        event_log = Path("tests", "logs", "bad", "missing-first-part.zip").resolve()
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            with self.assertRaises(ValueError, msg="Rollover file appears to be missing"):
+                EventLogBuilder(event_log.as_uri(), temp_dir).build()

From bf12e1a05a68ad1ce0e2d45cbd4c7feb0c023c55 Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Tue, 2 Aug 2022 12:00:58 -0700
Subject: [PATCH 13/14] [PROD-325] Tiny clean-up

---
 spark_log_parser/eventlog.py | 27 +++++++--------------------
 1 file changed, 7 insertions(+), 20 deletions(-)

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index d0d42de..93dc4e4 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -1,7 +1,7 @@
 import json
 import tempfile
 from pathlib import Path
-from urllib.parse import ParseResult, urlparse
+from urllib.parse import ParseResult
 
 import pandas as pd
 
@@ -12,21 +12,10 @@ class EventLogBuilder:
     ALLOWED_SCHEMES = {"https", "s3", "file"}
 
     def __init__(self, source_url: ParseResult | str, work_dir: Path | str, s3_client=None):
-        self.source_url = self._validate_url(source_url)
+        self.source_url = source_url
         self.work_dir = self._validate_work_dir(work_dir)
         self.s3_client = s3_client
-        self.file_total = 0
-        self.size_total = 0
-
-    def _validate_url(self, url: ParseResult | str) -> ParseResult:
-        parsed_url = url if isinstance(url, ParseResult) else urlparse(url)
-        if parsed_url.scheme not in self.ALLOWED_SCHEMES:
-            raise ValueError(
-                "URL scheme '%s' is not one of {'%s'}"
-                % (parsed_url.scheme, "', '".join(self.ALLOWED_SCHEMES))
-            )
-
-        return parsed_url
+        self.extractor = Extractor(self.source_url, self.work_dir, self.s3_client)
 
     def _validate_work_dir(self, work_dir: Path | str) -> Path:
         work_dir_path = work_dir if isinstance(work_dir, Path) else Path(work_dir)
@@ -36,16 +25,13 @@ def _validate_work_dir(self, work_dir: Path | str) -> Path:
         return work_dir_path
 
     def build(self) -> Path:
-        event_logs = Extractor(self.source_url, self.work_dir, self.s3_client).extract()
+        event_logs = self.extractor.extract()
 
-        self.event_log = self._concat(
-            event_logs,
-            Path(tempfile.mkstemp(suffix="-concatenated.json", dir=str(self.work_dir))[1]),
-        )
+        self.event_log = self._concat(event_logs)
 
         return self.event_log
 
-    def _concat(self, event_logs: list[Path], event_log: Path) -> Path:
+    def _concat(self, event_logs: list[Path]) -> Path:
         if len(event_logs) == 1:
             return event_logs[0]
 
@@ -67,6 +53,7 @@ def _concat(self, event_logs: list[Path], event_log: Path) -> Path:
 
         self._validate_rollover_logs(df)
 
+        event_log = Path(tempfile.mkstemp(suffix="-concatenated.json", dir=str(self.work_dir))[1])
         with open(event_log, "w") as fobj:
             for path in df.path:
                 with open(path) as part_fobj:

From 80814e8cb66bdf1c1a96178d154414afbd82a21c Mon Sep 17 00:00:00 2001
From: Scott Romney <scott@synccomputing.com>
Date: Tue, 2 Aug 2022 12:11:06 -0700
Subject: [PATCH 14/14] [PROD-325] Removed one line. I'll stop

---
 spark_log_parser/eventlog.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/spark_log_parser/eventlog.py b/spark_log_parser/eventlog.py
index 93dc4e4..926fe28 100644
--- a/spark_log_parser/eventlog.py
+++ b/spark_log_parser/eventlog.py
@@ -9,8 +9,6 @@
 
 
 class EventLogBuilder:
-    ALLOWED_SCHEMES = {"https", "s3", "file"}
-
     def __init__(self, source_url: ParseResult | str, work_dir: Path | str, s3_client=None):
         self.source_url = source_url
         self.work_dir = self._validate_work_dir(work_dir)