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models.py
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models.py
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from collections import OrderedDict
import json
import logging
from copy import deepcopy
from datetime import datetime, timedelta
from six import string_types
import requests
import sqlalchemy as sa
from sqlalchemy import (
Column, Integer, String, ForeignKey, Text, Boolean,
DateTime,
)
from sqlalchemy.orm import backref, relationship
from dateutil.parser import parse as dparse
from pydruid.client import PyDruid
from pydruid.utils.aggregators import count
from pydruid.utils.filters import Dimension, Filter
from pydruid.utils.postaggregator import (
Postaggregator, Quantile, Quantiles, Field, Const, HyperUniqueCardinality,
)
from pydruid.utils.having import Aggregation
from flask import Markup, escape
from flask_appbuilder.models.decorators import renders
from flask_appbuilder import Model
from flask_babel import lazy_gettext as _
from superset import conf, db, import_util, utils, sm, get_session
from superset.utils import (
flasher, MetricPermException, DimSelector, DTTM_ALIAS
)
from superset.connectors.base import BaseDatasource, BaseColumn, BaseMetric
from superset.models.helpers import AuditMixinNullable, QueryResult, set_perm
DRUID_TZ = conf.get("DRUID_TZ")
class JavascriptPostAggregator(Postaggregator):
def __init__(self, name, field_names, function):
self.post_aggregator = {
'type': 'javascript',
'fieldNames': field_names,
'name': name,
'function': function,
}
self.name = name
class DruidCluster(Model, AuditMixinNullable):
"""ORM object referencing the Druid clusters"""
__tablename__ = 'clusters'
type = "druid"
id = Column(Integer, primary_key=True)
verbose_name = Column(String(250), unique=True)
# short unique name, used in permissions
cluster_name = Column(String(250), unique=True)
coordinator_host = Column(String(255))
coordinator_port = Column(Integer)
coordinator_endpoint = Column(
String(255), default='druid/coordinator/v1/metadata')
broker_host = Column(String(255))
broker_port = Column(Integer)
broker_endpoint = Column(String(255), default='druid/v2')
metadata_last_refreshed = Column(DateTime)
cache_timeout = Column(Integer)
def __repr__(self):
return self.verbose_name if self.verbose_name else self.cluster_name
def get_pydruid_client(self):
cli = PyDruid(
"http://{0}:{1}/".format(self.broker_host, self.broker_port),
self.broker_endpoint)
return cli
def get_datasources(self):
endpoint = (
"http://{obj.coordinator_host}:{obj.coordinator_port}/"
"{obj.coordinator_endpoint}/datasources"
).format(obj=self)
return json.loads(requests.get(endpoint).text)
def get_druid_version(self):
endpoint = (
"http://{obj.coordinator_host}:{obj.coordinator_port}/status"
).format(obj=self)
return json.loads(requests.get(endpoint).text)['version']
def refresh_datasources(self, datasource_name=None, merge_flag=False):
"""Refresh metadata of all datasources in the cluster
If ``datasource_name`` is specified, only that datasource is updated
"""
self.druid_version = self.get_druid_version()
for datasource in self.get_datasources():
if datasource not in conf.get('DRUID_DATA_SOURCE_BLACKLIST', []):
if not datasource_name or datasource_name == datasource:
DruidDatasource.sync_to_db(datasource, self, merge_flag)
@property
def perm(self):
return "[{obj.cluster_name}].(id:{obj.id})".format(obj=self)
@property
def name(self):
return self.verbose_name if self.verbose_name else self.cluster_name
@property
def unique_name(self):
return self.verbose_name if self.verbose_name else self.cluster_name
class DruidColumn(Model, BaseColumn):
"""ORM model for storing Druid datasource column metadata"""
__tablename__ = 'columns'
datasource_name = Column(
String(255),
ForeignKey('datasources.datasource_name'))
# Setting enable_typechecks=False disables polymorphic inheritance.
datasource = relationship(
'DruidDatasource',
backref=backref('columns', cascade='all, delete-orphan'),
enable_typechecks=False)
dimension_spec_json = Column(Text)
export_fields = (
'datasource_name', 'column_name', 'is_active', 'type', 'groupby',
'count_distinct', 'sum', 'avg', 'max', 'min', 'filterable',
'description', 'dimension_spec_json'
)
def __repr__(self):
return self.column_name
@property
def dimension_spec(self):
if self.dimension_spec_json:
return json.loads(self.dimension_spec_json)
def generate_metrics(self):
"""Generate metrics based on the column metadata"""
M = DruidMetric # noqa
metrics = []
metrics.append(DruidMetric(
metric_name='count',
verbose_name='COUNT(*)',
metric_type='count',
json=json.dumps({'type': 'count', 'name': 'count'})
))
# Somehow we need to reassign this for UDAFs
if self.type in ('DOUBLE', 'FLOAT'):
corrected_type = 'DOUBLE'
else:
corrected_type = self.type
if self.sum and self.is_num:
mt = corrected_type.lower() + 'Sum'
name = 'sum__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
metric_type='sum',
verbose_name='SUM({})'.format(self.column_name),
json=json.dumps({
'type': mt, 'name': name, 'fieldName': self.column_name})
))
if self.avg and self.is_num:
mt = corrected_type.lower() + 'Avg'
name = 'avg__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
metric_type='avg',
verbose_name='AVG({})'.format(self.column_name),
json=json.dumps({
'type': mt, 'name': name, 'fieldName': self.column_name})
))
if self.min and self.is_num:
mt = corrected_type.lower() + 'Min'
name = 'min__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
metric_type='min',
verbose_name='MIN({})'.format(self.column_name),
json=json.dumps({
'type': mt, 'name': name, 'fieldName': self.column_name})
))
if self.max and self.is_num:
mt = corrected_type.lower() + 'Max'
name = 'max__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
metric_type='max',
verbose_name='MAX({})'.format(self.column_name),
json=json.dumps({
'type': mt, 'name': name, 'fieldName': self.column_name})
))
if self.count_distinct:
name = 'count_distinct__' + self.column_name
if self.type == 'hyperUnique' or self.type == 'thetaSketch':
metrics.append(DruidMetric(
metric_name=name,
verbose_name='COUNT(DISTINCT {})'.format(self.column_name),
metric_type=self.type,
json=json.dumps({
'type': self.type,
'name': name,
'fieldName': self.column_name
})
))
else:
mt = 'count_distinct'
metrics.append(DruidMetric(
metric_name=name,
verbose_name='COUNT(DISTINCT {})'.format(self.column_name),
metric_type='count_distinct',
json=json.dumps({
'type': 'cardinality',
'name': name,
'fieldNames': [self.column_name]})
))
session = get_session()
new_metrics = []
for metric in metrics:
m = (
session.query(M)
.filter(M.metric_name == metric.metric_name)
.filter(M.datasource_name == self.datasource_name)
.filter(DruidCluster.cluster_name == self.datasource.cluster_name)
.first()
)
metric.datasource_name = self.datasource_name
if not m:
new_metrics.append(metric)
session.add(metric)
session.flush()
@classmethod
def import_obj(cls, i_column):
def lookup_obj(lookup_column):
return db.session.query(DruidColumn).filter(
DruidColumn.datasource_name == lookup_column.datasource_name,
DruidColumn.column_name == lookup_column.column_name).first()
return import_util.import_simple_obj(db.session, i_column, lookup_obj)
class DruidMetric(Model, BaseMetric):
"""ORM object referencing Druid metrics for a datasource"""
__tablename__ = 'metrics'
datasource_name = Column(
String(255),
ForeignKey('datasources.datasource_name'))
# Setting enable_typechecks=False disables polymorphic inheritance.
datasource = relationship(
'DruidDatasource',
backref=backref('metrics', cascade='all, delete-orphan'),
enable_typechecks=False)
json = Column(Text)
def refresh_datasources(self, datasource_name=None, merge_flag=False):
"""Refresh metadata of all datasources in the cluster
If ``datasource_name`` is specified, only that datasource is updated
"""
self.druid_version = self.get_druid_version()
for datasource in self.get_datasources():
if datasource not in conf.get('DRUID_DATA_SOURCE_BLACKLIST'):
if not datasource_name or datasource_name == datasource:
DruidDatasource.sync_to_db(datasource, self, merge_flag)
export_fields = (
'metric_name', 'verbose_name', 'metric_type', 'datasource_name',
'json', 'description', 'is_restricted', 'd3format'
)
@property
def json_obj(self):
try:
obj = json.loads(self.json)
except Exception:
obj = {}
return obj
@property
def perm(self):
return (
"{parent_name}.[{obj.metric_name}](id:{obj.id})"
).format(obj=self,
parent_name=self.datasource.full_name
) if self.datasource else None
@classmethod
def import_obj(cls, i_metric):
def lookup_obj(lookup_metric):
return db.session.query(DruidMetric).filter(
DruidMetric.datasource_name == lookup_metric.datasource_name,
DruidMetric.metric_name == lookup_metric.metric_name).first()
return import_util.import_simple_obj(db.session, i_metric, lookup_obj)
class DruidDatasource(Model, BaseDatasource):
"""ORM object referencing Druid datasources (tables)"""
type = "druid"
query_langtage = "json"
metric_class = DruidMetric
cluster_class = DruidCluster
baselink = "druiddatasourcemodelview"
__tablename__ = 'datasources'
id = Column(Integer, primary_key=True)
datasource_name = Column(String(255), unique=True)
is_featured = Column(Boolean, default=False)
is_hidden = Column(Boolean, default=False)
filter_select_enabled = Column(Boolean, default=False)
description = Column(Text)
fetch_values_from = Column(String(100))
default_endpoint = Column(Text)
user_id = Column(Integer, ForeignKey('ab_user.id'))
owner = relationship(
'User',
backref=backref('datasources', cascade='all, delete-orphan'),
foreign_keys=[user_id])
cluster_name = Column(
String(250), ForeignKey('clusters.cluster_name'))
cluster = relationship(
'DruidCluster', backref='datasources', foreign_keys=[cluster_name])
offset = Column(Integer, default=0)
cache_timeout = Column(Integer)
params = Column(String(1000))
perm = Column(String(1000))
metric_cls = DruidMetric
column_cls = DruidColumn
export_fields = (
'datasource_name', 'is_hidden', 'description', 'default_endpoint',
'cluster_name', 'is_featured', 'offset', 'cache_timeout', 'params'
)
@property
def metrics_combo(self):
return sorted(
[(m.metric_name, m.verbose_name) for m in self.metrics],
key=lambda x: x[1])
@property
def database(self):
return self.cluster
@property
def num_cols(self):
return [c.column_name for c in self.columns if c.is_num]
@property
def name(self):
return self.datasource_name
@property
def schema(self):
ds_name = self.datasource_name or ''
name_pieces = ds_name.split('.')
if len(name_pieces) > 1:
return name_pieces[0]
else:
return None
@property
def schema_perm(self):
"""Returns schema permission if present, cluster one otherwise."""
return utils.get_schema_perm(self.cluster, self.schema)
def get_perm(self):
return (
"[{obj.cluster_name}].[{obj.datasource_name}]"
"(id:{obj.id})").format(obj=self)
@property
def link(self):
name = escape(self.datasource_name)
return Markup('<a href="{self.url}">{name}</a>').format(**locals())
@property
def full_name(self):
return utils.get_datasource_full_name(
self.cluster_name, self.datasource_name)
@property
def time_column_grains(self):
return {
"time_columns": [
'all', '5 seconds', '30 seconds', '1 minute',
'5 minutes', '1 hour', '6 hour', '1 day', '7 days',
'week', 'week_starting_sunday', 'week_ending_saturday',
'month',
],
"time_grains": ['now']
}
def __repr__(self):
return self.datasource_name
@renders('datasource_name')
def datasource_link(self):
url = "/superset/explore/{obj.type}/{obj.id}/".format(obj=self)
name = escape(self.datasource_name)
return Markup('<a href="{url}">{name}</a>'.format(**locals()))
def get_metric_obj(self, metric_name):
return [
m.json_obj for m in self.metrics
if m.metric_name == metric_name
][0]
@classmethod
def import_obj(cls, i_datasource, import_time=None):
"""Imports the datasource from the object to the database.
Metrics and columns and datasource will be overridden if exists.
This function can be used to import/export dashboards between multiple
superset instances. Audit metadata isn't copies over.
"""
def lookup_datasource(d):
return db.session.query(DruidDatasource).join(DruidCluster).filter(
DruidDatasource.datasource_name == d.datasource_name,
DruidCluster.cluster_name == d.cluster_name,
).first()
def lookup_cluster(d):
return db.session.query(DruidCluster).filter_by(
cluster_name=d.cluster_name).one()
return import_util.import_datasource(
db.session, i_datasource, lookup_cluster, lookup_datasource,
import_time)
@staticmethod
def version_higher(v1, v2):
"""is v1 higher than v2
>>> DruidDatasource.version_higher('0.8.2', '0.9.1')
False
>>> DruidDatasource.version_higher('0.8.2', '0.6.1')
True
>>> DruidDatasource.version_higher('0.8.2', '0.8.2')
False
>>> DruidDatasource.version_higher('0.8.2', '0.9.BETA')
False
>>> DruidDatasource.version_higher('0.8.2', '0.9')
False
"""
def int_or_0(v):
try:
v = int(v)
except (TypeError, ValueError):
v = 0
return v
v1nums = [int_or_0(n) for n in v1.split('.')]
v2nums = [int_or_0(n) for n in v2.split('.')]
v1nums = (v1nums + [0, 0, 0])[:3]
v2nums = (v2nums + [0, 0, 0])[:3]
return v1nums[0] > v2nums[0] or \
(v1nums[0] == v2nums[0] and v1nums[1] > v2nums[1]) or \
(v1nums[0] == v2nums[0] and v1nums[1] == v2nums[1] and v1nums[2] > v2nums[2])
def latest_metadata(self):
"""Returns segment metadata from the latest segment"""
client = self.cluster.get_pydruid_client()
results = client.time_boundary(datasource=self.datasource_name)
if not results:
return
max_time = results[0]['result']['maxTime']
max_time = dparse(max_time)
# Query segmentMetadata for 7 days back. However, due to a bug,
# we need to set this interval to more than 1 day ago to exclude
# realtime segments, which triggered a bug (fixed in druid 0.8.2).
# https://groups.google.com/forum/#!topic/druid-user/gVCqqspHqOQ
lbound = (max_time - timedelta(days=7)).isoformat()
rbound = max_time.isoformat()
if not self.version_higher(self.cluster.druid_version, '0.8.2'):
rbound = (max_time - timedelta(1)).isoformat()
segment_metadata = None
try:
segment_metadata = client.segment_metadata(
datasource=self.datasource_name,
intervals=lbound + '/' + rbound,
merge=self.merge_flag,
analysisTypes=conf.get('DRUID_ANALYSIS_TYPES'))
except Exception as e:
logging.warning("Failed first attempt to get latest segment")
logging.exception(e)
if not segment_metadata:
# if no segments in the past 7 days, look at all segments
lbound = datetime(1901, 1, 1).isoformat()[:10]
rbound = datetime(2050, 1, 1).isoformat()[:10]
if not self.version_higher(self.cluster.druid_version, '0.8.2'):
rbound = datetime.now().isoformat()[:10]
try:
segment_metadata = client.segment_metadata(
datasource=self.datasource_name,
intervals=lbound + '/' + rbound,
merge=self.merge_flag,
analysisTypes=conf.get('DRUID_ANALYSIS_TYPES'))
except Exception as e:
logging.warning("Failed 2nd attempt to get latest segment")
logging.exception(e)
if segment_metadata:
return segment_metadata[-1]['columns']
def generate_metrics(self):
for col in self.columns:
col.generate_metrics()
@classmethod
def sync_to_db_from_config(cls, druid_config, user, cluster):
"""Merges the ds config from druid_config into one stored in the db."""
session = db.session()
datasource = (
session.query(cls)
.filter_by(
datasource_name=druid_config['name'])
.first()
)
# Create a new datasource.
if not datasource:
datasource = cls(
datasource_name=druid_config['name'],
cluster=cluster,
owner=user,
changed_by_fk=user.id,
created_by_fk=user.id,
)
session.add(datasource)
dimensions = druid_config['dimensions']
for dim in dimensions:
col_obj = (
session.query(DruidColumn)
.filter_by(
datasource_name=druid_config['name'],
column_name=dim)
.first()
)
if not col_obj:
col_obj = DruidColumn(
datasource_name=druid_config['name'],
column_name=dim,
groupby=True,
filterable=True,
# TODO: fetch type from Hive.
type="STRING",
datasource=datasource,
)
session.add(col_obj)
# Import Druid metrics
for metric_spec in druid_config["metrics_spec"]:
metric_name = metric_spec["name"]
metric_type = metric_spec["type"]
metric_json = json.dumps(metric_spec)
if metric_type == "count":
metric_type = "longSum"
metric_json = json.dumps({
"type": "longSum",
"name": metric_name,
"fieldName": metric_name,
})
metric_obj = (
session.query(DruidMetric)
.filter_by(
datasource_name=druid_config['name'],
metric_name=metric_name)
).first()
if not metric_obj:
metric_obj = DruidMetric(
metric_name=metric_name,
metric_type=metric_type,
verbose_name="%s(%s)" % (metric_type, metric_name),
datasource=datasource,
json=metric_json,
description=(
"Imported from the airolap config dir for %s" %
druid_config['name']),
)
session.add(metric_obj)
session.commit()
@classmethod
def sync_to_db(cls, name, cluster, merge):
"""Fetches metadata for that datasource and merges the Superset db"""
logging.info("Syncing Druid datasource [{}]".format(name))
session = get_session()
datasource = session.query(cls).filter_by(datasource_name=name).first()
if not datasource:
datasource = cls(datasource_name=name)
session.add(datasource)
flasher("Adding new datasource [{}]".format(name), "success")
else:
flasher("Refreshing datasource [{}]".format(name), "info")
session.flush()
datasource.cluster = cluster
datasource.merge_flag = merge
session.flush()
cols = datasource.latest_metadata()
if not cols:
logging.error("Failed at fetching the latest segment")
return
for col in cols:
col_obj = (
session
.query(DruidColumn)
.filter_by(datasource_name=name, column_name=col)
.first()
)
datatype = cols[col]['type']
if not col_obj:
col_obj = DruidColumn(datasource_name=name, column_name=col)
session.add(col_obj)
if datatype == "STRING":
col_obj.groupby = True
col_obj.filterable = True
if datatype == "hyperUnique" or datatype == "thetaSketch":
col_obj.count_distinct = True
if col_obj:
col_obj.type = cols[col]['type']
session.flush()
col_obj.datasource = datasource
col_obj.generate_metrics()
session.flush()
@staticmethod
def time_offset(granularity):
if granularity == 'week_ending_saturday':
return 6 * 24 * 3600 * 1000 # 6 days
return 0
# uses https://en.wikipedia.org/wiki/ISO_8601
# http://druid.io/docs/0.8.0/querying/granularities.html
# TODO: pass origin from the UI
@staticmethod
def granularity(period_name, timezone=None, origin=None):
if not period_name or period_name == 'all':
return 'all'
iso_8601_dict = {
'5 seconds': 'PT5S',
'30 seconds': 'PT30S',
'1 minute': 'PT1M',
'5 minutes': 'PT5M',
'1 hour': 'PT1H',
'6 hour': 'PT6H',
'one day': 'P1D',
'1 day': 'P1D',
'7 days': 'P7D',
'week': 'P1W',
'week_starting_sunday': 'P1W',
'week_ending_saturday': 'P1W',
'month': 'P1M',
}
granularity = {'type': 'period'}
if timezone:
granularity['timeZone'] = timezone
if origin:
dttm = utils.parse_human_datetime(origin)
granularity['origin'] = dttm.isoformat()
if period_name in iso_8601_dict:
granularity['period'] = iso_8601_dict[period_name]
if period_name in ('week_ending_saturday', 'week_starting_sunday'):
# use Sunday as start of the week
granularity['origin'] = '2016-01-03T00:00:00'
elif not isinstance(period_name, string_types):
granularity['type'] = 'duration'
granularity['duration'] = period_name
elif period_name.startswith('P'):
# identify if the string is the iso_8601 period
granularity['period'] = period_name
else:
granularity['type'] = 'duration'
granularity['duration'] = utils.parse_human_timedelta(
period_name).total_seconds() * 1000
return granularity
def values_for_column(self,
column_name,
limit=10000):
"""Retrieve some values for the given column"""
# TODO: Use Lexicographic TopNMetricSpec once supported by PyDruid
if self.fetch_values_from:
from_dttm = utils.parse_human_datetime(self.fetch_values_from)
else:
from_dttm = datetime(1970, 1, 1)
qry = dict(
datasource=self.datasource_name,
granularity="all",
intervals=from_dttm.isoformat() + '/' + datetime.now().isoformat(),
aggregations=dict(count=count("count")),
dimension=column_name,
metric="count",
threshold=limit,
)
client = self.cluster.get_pydruid_client()
client.topn(**qry)
df = client.export_pandas()
return [row[0] for row in df.to_records(index=False)]
def get_query_str( # noqa / druid
self,
groupby, metrics,
granularity,
from_dttm, to_dttm,
filter=None, # noqa
is_timeseries=True,
timeseries_limit=None,
timeseries_limit_metric=None,
row_limit=None,
inner_from_dttm=None, inner_to_dttm=None,
orderby=None,
extras=None, # noqa
select=None, # noqa
columns=None, phase=2):
"""Runs a query against Druid and returns a dataframe.
This query interface is common to SqlAlchemy and Druid
"""
# TODO refactor into using a TBD Query object
client = self.cluster.get_pydruid_client()
if not is_timeseries:
granularity = 'all'
inner_from_dttm = inner_from_dttm or from_dttm
inner_to_dttm = inner_to_dttm or to_dttm
# add tzinfo to native datetime with config
from_dttm = from_dttm.replace(tzinfo=DRUID_TZ)
to_dttm = to_dttm.replace(tzinfo=DRUID_TZ)
timezone = from_dttm.tzname()
query_str = ""
metrics_dict = {m.metric_name: m for m in self.metrics}
all_metrics = []
post_aggs = {}
columns_dict = {c.column_name: c for c in self.columns}
def recursive_get_fields(_conf):
_fields = _conf.get('fields', [])
field_names = []
for _f in _fields:
_type = _f.get('type')
if _type in ['fieldAccess', 'hyperUniqueCardinality']:
field_names.append(_f.get('fieldName'))
elif _type == 'arithmetic':
field_names += recursive_get_fields(_f)
return list(set(field_names))
for metric_name in metrics:
metric = metrics_dict[metric_name]
if metric.metric_type != 'postagg':
all_metrics.append(metric_name)
else:
mconf = metric.json_obj
all_metrics += recursive_get_fields(mconf)
all_metrics += mconf.get('fieldNames', [])
if mconf.get('type') == 'javascript':
post_aggs[metric_name] = JavascriptPostAggregator(
name=mconf.get('name', ''),
field_names=mconf.get('fieldNames', []),
function=mconf.get('function', ''))
elif mconf.get('type') == 'quantile':
post_aggs[metric_name] = Quantile(
mconf.get('name', ''),
mconf.get('probability', ''),
)
elif mconf.get('type') == 'quantiles':
post_aggs[metric_name] = Quantiles(
mconf.get('name', ''),
mconf.get('probabilities', ''),
)
elif mconf.get('type') == 'fieldAccess':
post_aggs[metric_name] = Field(mconf.get('name'), '')
elif mconf.get('type') == 'constant':
post_aggs[metric_name] = Const(
mconf.get('value'),
output_name=mconf.get('name', '')
)
elif mconf.get('type') == 'hyperUniqueCardinality':
post_aggs[metric_name] = HyperUniqueCardinality(
mconf.get('name'), ''
)
else:
post_aggs[metric_name] = Postaggregator(
mconf.get('fn', "/"),
mconf.get('fields', []),
mconf.get('name', ''))
aggregations = OrderedDict()
for m in self.metrics:
if m.metric_name in all_metrics:
aggregations[m.metric_name] = m.json_obj
rejected_metrics = [
m.metric_name for m in self.metrics
if m.is_restricted and
m.metric_name in aggregations.keys() and
not sm.has_access('metric_access', m.perm)
]
if rejected_metrics:
raise MetricPermException(
"Access to the metrics denied: " + ', '.join(rejected_metrics)
)
# the dimensions list with dimensionSpecs expanded
dimensions = []
groupby = [gb for gb in groupby if gb in columns_dict]
for column_name in groupby:
col = columns_dict.get(column_name)
dim_spec = col.dimension_spec
if dim_spec:
dimensions.append(dim_spec)
else:
dimensions.append(column_name)
qry = dict(
datasource=self.datasource_name,
dimensions=dimensions,
aggregations=aggregations,
granularity=DruidDatasource.granularity(
granularity,
timezone=timezone,
origin=extras.get('druid_time_origin'),
),
post_aggregations=post_aggs,
intervals=from_dttm.isoformat() + '/' + to_dttm.isoformat(),
)
filters = self.get_filters(filter)
if filters:
qry['filter'] = filters
having_filters = self.get_having_filters(extras.get('having_druid'))
if having_filters:
qry['having'] = having_filters
orig_filters = filters
if len(groupby) == 0:
del qry['dimensions']
client.timeseries(**qry)
if not having_filters and len(groupby) == 1:
qry['threshold'] = timeseries_limit or 1000
if row_limit and granularity == 'all':
qry['threshold'] = row_limit
qry['dimension'] = list(qry.get('dimensions'))[0]
del qry['dimensions']
qry['metric'] = list(qry['aggregations'].keys())[0]
client.topn(**qry)
elif len(groupby) > 1 or having_filters:
# If grouping on multiple fields or using a having filter
# we have to force a groupby query
if timeseries_limit and is_timeseries:
order_by = metrics[0] if metrics else self.metrics[0]
if timeseries_limit_metric:
order_by = timeseries_limit_metric
# Limit on the number of timeseries, doing a two-phases query
pre_qry = deepcopy(qry)
pre_qry['granularity'] = "all"
pre_qry['limit_spec'] = {
"type": "default",
"limit": timeseries_limit,
'intervals': (
inner_from_dttm.isoformat() + '/' +
inner_to_dttm.isoformat()),
"columns": [{
"dimension": order_by,
"direction": "descending",
}],
}
client.groupby(**pre_qry)
query_str += "// Two phase query\n// Phase 1\n"
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
query_str += "\n"
if phase == 1:
return query_str
query_str += (
"//\nPhase 2 (built based on phase one's results)\n")
df = client.export_pandas()
if df is not None and not df.empty:
dims = qry['dimensions']
filters = []
for unused, row in df.iterrows():
fields = []
for dim in dims:
f = Dimension(dim) == row[dim]
fields.append(f)
if len(fields) > 1:
filt = Filter(type="and", fields=fields)
filters.append(filt)
elif fields:
filters.append(fields[0])
if filters:
ff = Filter(type="or", fields=filters)
if not orig_filters:
qry['filter'] = ff
else:
qry['filter'] = Filter(type="and", fields=[
ff,
orig_filters])
qry['limit_spec'] = None
if row_limit:
qry['limit_spec'] = {
"type": "default",
"limit": row_limit,
"columns": [{
"dimension": (
metrics[0] if metrics else self.metrics[0]),
"direction": "descending",
}],
}
client.groupby(**qry)
query_str += json.dumps(
client.query_builder.last_query.query_dict, indent=2)
return query_str
def query(self, query_obj):
qry_start_dttm = datetime.now()
client = self.cluster.get_pydruid_client()
query_str = self.get_query_str(**query_obj)
df = client.export_pandas()
if df is None or df.size == 0:
raise Exception(_("No data was returned."))
df.columns = [
DTTM_ALIAS if c == 'timestamp' else c for c in df.columns]
is_timeseries = query_obj['is_timeseries'] \
if 'is_timeseries' in query_obj else True
if (
not is_timeseries and
query_obj['granularity'] == "all" and
DTTM_ALIAS in df.columns):
del df[DTTM_ALIAS]
# Reordering columns
cols = []
if DTTM_ALIAS in df.columns:
cols += [DTTM_ALIAS]
cols += [col for col in query_obj['groupby'] if col in df.columns]
cols += [col for col in query_obj['metrics'] if col in df.columns]
df = df[cols]
time_offset = DruidDatasource.time_offset(query_obj['granularity'])
def increment_timestamp(ts):
dt = utils.parse_human_datetime(ts).replace(
tzinfo=DRUID_TZ)
return dt + timedelta(milliseconds=time_offset)
if DTTM_ALIAS in df.columns and time_offset:
df[DTTM_ALIAS] = df[DTTM_ALIAS].apply(increment_timestamp)
return QueryResult(
df=df,
query=query_str,
duration=datetime.now() - qry_start_dttm)
def get_filters(self, raw_filters): # noqa
filters = None
for flt in raw_filters:
if not all(f in flt for f in ['col', 'op', 'val']):
continue
col = flt['col']
op = flt['op']
eq = flt['val']
cond = None
if op in ('in', 'not in'):
eq = [types.replace("'", '').strip() for types in eq]
elif not isinstance(flt['val'], basestring):
eq = eq[0] if len(eq) > 0 else ''
if col in self.num_cols:
if op in ('in', 'not in'):
eq = [utils.js_string_to_num(v) for v in eq]
else:
eq = utils.js_string_to_num(eq)