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550 lines
18 KiB
Python
550 lines
18 KiB
Python
from flask.ext.appbuilder import Model
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from datetime import timedelta
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from flask.ext.appbuilder.models.mixins import AuditMixin
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from sqlalchemy import Column, Integer, String, ForeignKey, Text, Boolean, DateTime
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from sqlalchemy import create_engine, MetaData
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from sqlalchemy import Table as sqlaTable
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from sqlalchemy.orm import relationship
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from dateutil.parser import parse
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from pydruid import client
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from pydruid.utils.filters import Dimension, Filter
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from copy import deepcopy, copy
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import logging
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import json
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import requests
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from app import db, get_session
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class Queryable(object):
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@property
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def column_names(self):
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return sorted([c.column_name for c in self.columns])
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@property
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def groupby_column_names(self):
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return sorted([c.column_name for c in self.columns if c.groupby])
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@property
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def filterable_column_names(self):
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return sorted([c.column_name for c in self.columns if c.filterable])
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class Database(Model, AuditMixin):
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__tablename__ = 'databases'
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id = Column(Integer, primary_key=True)
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database_name = Column(String(256), unique=True)
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sqlalchemy_uri = Column(String(1024))
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def __repr__(self):
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return self.database_name
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def get_sqla_engine(self):
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return create_engine(self.sqlalchemy_uri)
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def get_table(self, table_name):
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meta = MetaData()
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return sqlaTable(
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table_name, meta,
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autoload=True,
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autoload_with=self.get_sqla_engine())
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class Table(Model, AuditMixin, Queryable):
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__tablename__ = 'tables'
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id = Column(Integer, primary_key=True)
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table_name = Column(String(256), unique=True)
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default_endpoint = Column(Text)
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database_id = Column(
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String(256), ForeignKey('databases.id'))
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database = relationship(
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'Database', backref='tables', foreign_keys=[database_id])
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@property
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def name(self):
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return self.table_name
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@property
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def table_link(self):
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url = "/panoramix/table/{}/".format(self.id)
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return '<a href="{url}">{self.table_name}</a>'.format(**locals())
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@property
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def metrics_combo(self):
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return sorted(
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[
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(m.metric_name, m.verbose_name)
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for m in self.metrics],
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key=lambda x: x[1])
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def query(
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self, groupby, metrics,
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granularity,
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from_dttm, to_dttm,
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limit_spec=None,
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filter=None,
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is_timeseries=True,
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timeseries_limit=15, row_limit=None):
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from pandas import read_sql_query
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metrics_exprs = [
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"{} AS {}".format(m.expression, m.metric_name)
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for m in self.metrics if m.metric_name in metrics]
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from_dttm_iso = from_dttm.isoformat()
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to_dttm_iso = to_dttm.isoformat()
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if metrics:
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main_metric_expr = [m.expression for m in self.metrics if m.metric_name == metrics[0]][0]
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else:
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main_metric_expr = "COUNT(*)"
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select_exprs = []
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groupby_exprs = []
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if groupby:
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select_exprs = copy(groupby)
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groupby_exprs = [s for s in groupby]
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inner_groupby_exprs = [s for s in groupby]
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select_exprs += metrics_exprs
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if granularity != "all":
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select_exprs += ['ds as timestamp']
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groupby_exprs += ['ds']
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select_exprs = ",\n".join(select_exprs)
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groupby_exprs = ",\n".join(groupby_exprs)
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where_clause = [
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"ds >= '{from_dttm_iso}'",
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"ds < '{to_dttm_iso}'"
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]
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for col, op, eq in filter:
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if op in ('in', 'not in'):
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l = ["'{}'".format(s) for s in eq.split(",")]
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l = ", ".join(l)
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op = op.upper()
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where_clause.append(
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"{col} {op} ({l})".format(**locals())
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)
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where_clause = " AND\n".join(where_clause).format(**locals())
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on_clause = " AND ".join(["{g} = __{g}".format(g=g) for g in groupby])
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limiting_join = ""
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if timeseries_limit and groupby:
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inner_select = ", ".join(["{g} as __{g}".format(g=g) for g in inner_groupby_exprs])
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inner_groupby_exprs = ", ".join(inner_groupby_exprs)
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limiting_join = """
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JOIN (
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SELECT {inner_select}
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FROM {self.table_name}
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WHERE
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{where_clause}
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GROUP BY {inner_groupby_exprs}
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ORDER BY {main_metric_expr} DESC
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LIMIT {timeseries_limit}
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) z ON {on_clause}
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""".format(**locals())
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sql = """
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SELECT
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{select_exprs}
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FROM {self.table_name}
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{limiting_join}
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WHERE
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{where_clause}
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GROUP BY
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{groupby_exprs}
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""".format(**locals())
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df = read_sql_query(
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sql=sql,
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con=self.database.get_sqla_engine()
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)
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return df
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def fetch_metadata(self):
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table = self.database.get_table(self.table_name)
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TC = TableColumn
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for col in table.columns:
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dbcol = (
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db.session
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.query(TC)
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.filter(TC.table==self)
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.filter(TC.column_name==col.name)
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.first()
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)
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db.session.flush()
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if not dbcol:
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dbcol = TableColumn(column_name=col.name)
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if str(col.type) in ('VARCHAR', 'STRING'):
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dbcol.groupby = True
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dbcol.filterable = True
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self.columns.append(dbcol)
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dbcol.type = str(col.type)
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db.session.commit()
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class SqlMetric(Model):
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__tablename__ = 'sql_metrics'
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id = Column(Integer, primary_key=True)
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metric_name = Column(String(512))
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verbose_name = Column(String(1024))
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metric_type = Column(String(32))
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table_id = Column(
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String(256),
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ForeignKey('tables.id'))
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table = relationship(
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'Table', backref='metrics', foreign_keys=[table_id])
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expression = Column(Text)
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description = Column(Text)
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class TableColumn(Model, AuditMixin):
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__tablename__ = 'table_columns'
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id = Column(Integer, primary_key=True)
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table_id = Column(
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String(256),
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ForeignKey('tables.id'))
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table = relationship('Table', backref='columns', foreign_keys=[table_id])
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column_name = Column(String(256))
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is_dttm = Column(Boolean, default=True)
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is_active = Column(Boolean, default=True)
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type = Column(String(32), default='')
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groupby = Column(Boolean, default=False)
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count_distinct = Column(Boolean, default=False)
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sum = Column(Boolean, default=False)
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max = Column(Boolean, default=False)
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min = Column(Boolean, default=False)
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filterable = Column(Boolean, default=False)
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description = Column(Text, default='')
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class Cluster(Model, AuditMixin):
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__tablename__ = 'clusters'
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id = Column(Integer, primary_key=True)
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cluster_name = Column(String(256), unique=True)
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coordinator_host = Column(String(256))
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coordinator_port = Column(Integer)
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coordinator_endpoint = Column(String(256))
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broker_host = Column(String(256))
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broker_port = Column(Integer)
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broker_endpoint = Column(String(256))
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metadata_last_refreshed = Column(DateTime)
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def __repr__(self):
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return self.cluster_name
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def get_pydruid_client(self):
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cli = client.PyDruid(
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"http://{0}:{1}/".format(self.broker_host, self.broker_port),
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self.broker_endpoint)
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return cli
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def refresh_datasources(self):
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endpoint = (
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"http://{self.coordinator_host}:{self.coordinator_port}/"
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"{self.coordinator_endpoint}/datasources"
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).format(self=self)
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datasources = json.loads(requests.get(endpoint).text)
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for datasource in datasources:
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Datasource.sync_to_db(datasource, self)
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class Datasource(Model, AuditMixin, Queryable):
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__tablename__ = 'datasources'
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id = Column(Integer, primary_key=True)
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datasource_name = Column(String(256), unique=True)
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is_featured = Column(Boolean, default=False)
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is_hidden = Column(Boolean, default=False)
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description = Column(Text)
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default_endpoint = Column(Text)
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user_id = Column(Integer,
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ForeignKey('ab_user.id'))
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owner = relationship('User', backref='datasources', foreign_keys=[user_id])
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cluster_name = Column(Integer,
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ForeignKey('clusters.cluster_name'))
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cluster = relationship('Cluster', backref='datasources', foreign_keys=[cluster_name])
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@property
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def metrics_combo(self):
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return sorted(
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[(m.metric_name, m.verbose_name) for m in self.metrics],
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key=lambda x: x[1])
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@property
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def name(self):
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return self.datasource_name
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def __repr__(self):
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return self.datasource_name
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@property
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def datasource_link(self):
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url = "/panoramix/datasource/{}/".format(self.datasource_name)
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return '<a href="{url}">{self.datasource_name}</a>'.format(**locals())
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def get_metric_obj(self, metric_name):
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return [
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m.json_obj for m in self.metrics
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if m.metric_name == metric_name
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][0]
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def latest_metadata(self):
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client = self.cluster.get_pydruid_client()
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results = client.time_boundary(datasource=self.datasource_name)
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max_time = results[0]['result']['minTime']
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max_time = parse(max_time)
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intervals = (max_time - timedelta(seconds=1)).isoformat() + '/'
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intervals += (max_time + timedelta(seconds=1)).isoformat()
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segment_metadata = client.segment_metadata(
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datasource=self.datasource_name,
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intervals=intervals)
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if segment_metadata:
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return segment_metadata[-1]['columns']
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def generate_metrics(self):
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for col in self.columns:
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col.generate_metrics()
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@classmethod
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def sync_to_db(cls, name, cluster):
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session = get_session()
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datasource = session.query(cls).filter_by(datasource_name=name).first()
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if not datasource:
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datasource = cls(datasource_name=name)
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session.add(datasource)
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datasource.cluster = cluster
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cols = datasource.latest_metadata()
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if not cols:
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return
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for col in cols:
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col_obj = (
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session
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.query(Column)
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.filter_by(datasource_name=name, column_name=col)
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.first()
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)
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datatype = cols[col]['type']
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if not col_obj:
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col_obj = Column(datasource_name=name, column_name=col)
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session.add(col_obj)
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if datatype == "STRING":
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col_obj.groupby = True
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col_obj.filterable = True
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if col_obj:
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col_obj.type = cols[col]['type']
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col_obj.datasource = datasource
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col_obj.generate_metrics()
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#session.commit()
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def query(
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self, groupby, metrics,
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granularity,
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from_dttm, to_dttm,
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limit_spec=None,
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filter=None,
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is_timeseries=True,
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timeseries_limit=15, row_limit=None):
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aggregations = {
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m.metric_name: m.json_obj
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for m in self.metrics if m.metric_name in metrics
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}
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if not isinstance(granularity, basestring):
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granularity = {"type": "duration", "duration": granularity}
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qry = dict(
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datasource=self.datasource_name,
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dimensions=groupby,
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aggregations=aggregations,
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granularity=granularity,
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intervals= from_dttm.isoformat() + '/' + to_dttm.isoformat(),
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)
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filters = None
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for col, op, eq in filter:
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cond = None
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if op == '==':
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cond = Dimension(col)==eq
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elif op == '!=':
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cond = ~(Dimension(col)==eq)
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elif op in ('in', 'not in'):
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fields = []
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splitted = eq.split(',')
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if len(splitted) > 1:
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for s in eq.split(','):
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s = s.strip()
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fields.append(Filter.build_filter(Dimension(col)==s))
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cond = Filter(type="or", fields=fields)
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else:
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cond = Dimension(col)==eq
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if op == 'not in':
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cond = ~cond
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if filters:
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filters = Filter(type="and", fields=[
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Filter.build_filter(cond),
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Filter.build_filter(filters)
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])
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else:
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filters = cond
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if filters:
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qry['filter'] = filters
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client = self.cluster.get_pydruid_client()
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orig_filters = filters
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if timeseries_limit:
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# Limit on the number of timeseries, doing a two-phases query
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pre_qry = deepcopy(qry)
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pre_qry['granularity'] = "all"
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pre_qry['limit_spec'] = {
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"type": "default",
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"limit": timeseries_limit,
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"columns": [{
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"dimension": metrics[0] if metrics else self.metrics[0],
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"direction": "descending",
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}],
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}
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client.groupby(**pre_qry)
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df = client.export_pandas()
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if not df is None and not df.empty:
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dims = qry['dimensions']
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filters = []
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for index, row in df.iterrows():
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fields = []
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for dim in dims:
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f = Filter.build_filter(Dimension(dim) == row[dim])
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fields.append(f)
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if len(fields) > 1:
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filt = Filter(type="and", fields=fields)
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filters.append(Filter.build_filter(filt))
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elif fields:
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filters.append(fields[0])
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if filters:
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ff = Filter(type="or", fields=filters)
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if not orig_filters:
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qry['filter'] = ff
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else:
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qry['filter'] = Filter(type="and", fields=[
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Filter.build_filter(ff),
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Filter.build_filter(orig_filters)])
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qry['limit_spec'] = None
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client.groupby(**qry)
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df = client.export_pandas()
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return df
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class Metric(Model):
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__tablename__ = 'metrics'
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id = Column(Integer, primary_key=True)
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metric_name = Column(String(512))
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verbose_name = Column(String(1024))
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metric_type = Column(String(32))
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datasource_name = Column(
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String(256),
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ForeignKey('datasources.datasource_name'))
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datasource = relationship('Datasource', backref='metrics')
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json = Column(Text)
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description = Column(Text)
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@property
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def json_obj(self):
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try:
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obj = json.loads(self.json)
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except Exception as e:
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obj = {}
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return obj
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class Column(Model, AuditMixin):
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__tablename__ = 'columns'
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id = Column(Integer, primary_key=True)
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datasource_name = Column(
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String(256),
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ForeignKey('datasources.datasource_name'))
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datasource = relationship('Datasource', backref='columns')
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column_name = Column(String(256))
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is_active = Column(Boolean, default=True)
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type = Column(String(32))
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groupby = Column(Boolean, default=False)
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count_distinct = Column(Boolean, default=False)
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sum = Column(Boolean, default=False)
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max = Column(Boolean, default=False)
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min = Column(Boolean, default=False)
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filterable = Column(Boolean, default=False)
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description = Column(Text)
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def __repr__(self):
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return self.column_name
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@property
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def isnum(self):
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return self.type in ('LONG', 'DOUBLE', 'FLOAT')
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def generate_metrics(self):
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M = Metric
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metrics = []
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metrics.append(Metric(
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metric_name='count',
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verbose_name='COUNT(*)',
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metric_type='count',
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json=json.dumps({'type': 'count', 'name': 'count'})
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))
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# Somehow we need to reassign this for UDAFs
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corrected_type = 'DOUBLE' if self.type in ('DOUBLE', 'FLOAT') else self.type
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if self.sum and self.isnum:
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mt = corrected_type.lower() + 'Sum'
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name='sum__' + self.column_name
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metrics.append(Metric(
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metric_name=name,
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metric_type='sum',
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verbose_name='SUM({})'.format(self.column_name),
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json=json.dumps({
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'type': mt, 'name': name, 'fieldName': self.column_name})
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))
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if self.min and self.isnum:
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mt = corrected_type.lower() + 'Min'
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name='min__' + self.column_name
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metrics.append(Metric(
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metric_name=name,
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metric_type='min',
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verbose_name='MIN({})'.format(self.column_name),
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json=json.dumps({
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'type': mt, 'name': name, 'fieldName': self.column_name})
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))
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if self.max and self.isnum:
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mt = corrected_type.lower() + 'Max'
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name='max__' + self.column_name
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metrics.append(Metric(
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metric_name=name,
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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:
|
|
mt = 'count_distinct'
|
|
name='count_distinct__' + self.column_name
|
|
metrics.append(Metric(
|
|
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()
|
|
for metric in metrics:
|
|
m = (
|
|
session.query(M)
|
|
.filter(M.metric_name==metric.metric_name)
|
|
.filter(M.datasource_name==self.datasource_name)
|
|
.filter(Cluster.cluster_name==self.datasource.cluster_name)
|
|
.first()
|
|
)
|
|
metric.datasource_name = self.datasource_name
|
|
if not m:
|
|
session.add(metric)
|
|
session.commit()
|