text: # -*- coding: utf-8 -*-
  #
  # Licensed to the Apache Software Foundation (ASF) under one
  # or more contributor license agreements.  See the NOTICE file
  # distributed with this work for additional information
  # regarding copyright ownership.  The ASF licenses this file
  # to you under the Apache License, Version 2.0 (the
  # "License"); you may not use this file except in compliance
  # with the License.  You may obtain a copy of the License at
  #
  #   http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing,
  # software distributed under the License is distributed on an
  # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  # KIND, either express or implied.  See the License for the
  # specific language governing permissions and limitations
  # under the License.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  from future.standard_library import install_aliases
  
  from builtins import str, object, bytes, ImportError as BuiltinImportError
  import copy
  from collections import namedtuple, defaultdict, Hashable
  from datetime import timedelta
  
  import dill
  import functools
  import getpass
  import imp
  import importlib
  import itertools
  import zipfile
  import jinja2
  import json
  import logging
  import numbers
  import os
  import pickle
  import re
  import signal
  import sys
  import textwrap
  import traceback
  import warnings
  import hashlib
  
  import uuid
  from datetime import datetime
  from urllib.parse import urlparse, quote, parse_qsl
  
  from sqlalchemy import (
      Column, Integer, String, DateTime, Text, Boolean, ForeignKey, PickleType,
      Index, Float, LargeBinary, UniqueConstraint)
  from sqlalchemy import func, or_, and_, true as sqltrue
  from sqlalchemy.ext.declarative import declarative_base, declared_attr
  from sqlalchemy.orm import reconstructor, relationship, synonym
  from sqlalchemy_utc import UtcDateTime
  
  from croniter import croniter
  import six
  
  from airflow import settings, utils
  from airflow.executors import GetDefaultExecutor, LocalExecutor
  from airflow import configuration
  from airflow.exceptions import (
      AirflowDagCycleException, AirflowException, AirflowSkipException, AirflowTaskTimeout
  )
  from airflow.dag.base_dag import BaseDag, BaseDagBag
  from airflow.lineage import apply_lineage, prepare_lineage
  from airflow.ti_deps.deps.not_in_retry_period_dep import NotInRetryPeriodDep
  from airflow.ti_deps.deps.prev_dagrun_dep import PrevDagrunDep
  from airflow.ti_deps.deps.trigger_rule_dep import TriggerRuleDep
  
  from airflow.ti_deps.dep_context import DepContext, QUEUE_DEPS, RUN_DEPS
  from airflow.utils import timezone
  from airflow.utils.dag_processing import list_py_file_paths
  from airflow.utils.dates import cron_presets, date_range as utils_date_range
  from airflow.utils.db import provide_session
  from airflow.utils.decorators import apply_defaults
  from airflow.utils.email import send_email
  from airflow.utils.helpers import (
      as_tuple, is_container, validate_key, pprinttable)
  from airflow.utils.operator_resources import Resources
  from airflow.utils.state import State
  from airflow.utils.timeout import timeout
  from airflow.utils.trigger_rule import TriggerRule
  from airflow.utils.weight_rule import WeightRule
  from airflow.utils.net import get_hostname
  from airflow.utils.log.logging_mixin import LoggingMixin
  
  install_aliases()
  
  Base = declarative_base()
  ID_LEN = 250
  XCOM_RETURN_KEY = 'return_value'
  
  Stats = settings.Stats
  
  
  class InvalidFernetToken(Exception):
      # If Fernet isn't loaded we need a valid exception class to catch. If it is
      # loaded this will get reset to the actual class once get_fernet() is called
      pass
  
  
  class NullFernet(object):
      """
      A "Null" encryptor class that doesn't encrypt or decrypt but that presents
      a similar interface to Fernet.
  
      The purpose of this is to make the rest of the code not have to know the
      difference, and to only display the message once, not 20 times when
      `airflow initdb` is ran.
      """
      is_encrypted = False
  
      def decrpyt(self, b):
          return b
  
      def encrypt(self, b):
          return b
  
  
  _fernet = None
  
  
  def get_fernet():
      """
      Deferred load of Fernet key.
  
      This function could fail either because Cryptography is not installed
      or because the Fernet key is invalid.
  
      :return: Fernet object
      :raises: AirflowException if there's a problem trying to load Fernet
      """
      global _fernet
      if _fernet:
          return _fernet
      try:
          from cryptography.fernet import Fernet, InvalidToken
          global InvalidFernetToken
          InvalidFernetToken = InvalidToken
  
      except BuiltinImportError:
          LoggingMixin().log.warn("cryptography not found - values will not be stored "
                                  "encrypted.",
                                  exc_info=1)
          _fernet = NullFernet()
          return _fernet
  
      try:
          _fernet = Fernet(configuration.conf.get('core', 'FERNET_KEY').encode('utf-8'))
          _fernet.is_encrypted = True
          return _fernet
      except (ValueError, TypeError) as ve:
          raise AirflowException("Could not create Fernet object: {}".format(ve))
  
  
  # Used by DAG context_managers
  _CONTEXT_MANAGER_DAG = None
  
  
  def clear_task_instances(tis,
                           session,
                           activate_dag_runs=True,
                           dag=None,
                           ):
      """
      Clears a set of task instances, but makes sure the running ones
      get killed.
  
      :param tis: a list of task instances
      :param session: current session
      :param activate_dag_runs: flag to check for active dag run
      :param dag: DAG object
      """
      job_ids = []
      for ti in tis:
          if ti.state == State.RUNNING:
              if ti.job_id:
                  ti.state = State.SHUTDOWN
                  job_ids.append(ti.job_id)
          else:
              task_id = ti.task_id
              if dag and dag.has_task(task_id):
                  task = dag.get_task(task_id)
                  task_retries = task.retries
                  ti.max_tries = ti.try_number + task_retries - 1
              else:
                  # Ignore errors when updating max_tries if dag is None or
                  # task not found in dag since database records could be
                  # outdated. We make max_tries the maximum value of its
                  # original max_tries or the current task try number.
                  ti.max_tries = max(ti.max_tries, ti.try_number - 1)
              ti.state = State.NONE
              session.merge(ti)
  
      if job_ids:
          from airflow.jobs import BaseJob as BJ
          for job in session.query(BJ).filter(BJ.id.in_(job_ids)).all():
              job.state = State.SHUTDOWN
  
      if activate_dag_runs and tis:
          drs = session.query(DagRun).filter(
              DagRun.dag_id.in_({ti.dag_id for ti in tis}),
              DagRun.execution_date.in_({ti.execution_date for ti in tis}),
          ).all()
          for dr in drs:
              dr.state = State.RUNNING
              dr.start_date = timezone.utcnow()
  
  
  class DagBag(BaseDagBag, LoggingMixin):
      """
      A dagbag is a collection of dags, parsed out of a folder tree and has high
      level configuration settings, like what database to use as a backend and
      what executor to use to fire off tasks. This makes it easier to run
      distinct environments for say production and development, tests, or for
      different teams or security profiles. What would have been system level
      settings are now dagbag level so that one system can run multiple,
      independent settings sets.
  
      :param dag_folder: the folder to scan to find DAGs
      :type dag_folder: unicode
      :param executor: the executor to use when executing task instances
          in this DagBag
      :param include_examples: whether to include the examples that ship
          with airflow or not
      :type include_examples: bool
      :param has_logged: an instance boolean that gets flipped from False to True after a
          file has been skipped. This is to prevent overloading the user with logging
          messages about skipped files. Therefore only once per DagBag is a file logged
          being skipped.
      """
  
      # static class variables to detetct dag cycle
      CYCLE_NEW = 0
      CYCLE_IN_PROGRESS = 1
      CYCLE_DONE = 2
  
      def __init__(
              self,
              dag_folder=None,
              executor=None,
              include_examples=configuration.conf.getboolean('core', 'LOAD_EXAMPLES')):
  
          # do not use default arg in signature, to fix import cycle on plugin load
          if executor is None:
              executor = GetDefaultExecutor()
          dag_folder = dag_folder or settings.DAGS_FOLDER
          self.log.info("Filling up the DagBag from %s", dag_folder)
          self.dag_folder = dag_folder
          self.dags = {}
          # the file's last modified timestamp when we last read it
          self.file_last_changed = {}
          self.executor = executor
          self.import_errors = {}
          self.has_logged = False
  
          if include_examples:
              example_dag_folder = os.path.join(
                  os.path.dirname(__file__),
                  'example_dags')
              self.collect_dags(example_dag_folder)
          self.collect_dags(dag_folder)
  
      def size(self):
          """
          :return: the amount of dags contained in this dagbag
          """
          return len(self.dags)
  
      def get_dag(self, dag_id):
          """
          Gets the DAG out of the dictionary, and refreshes it if expired
          """
          # If asking for a known subdag, we want to refresh the parent
          root_dag_id = dag_id
          if dag_id in self.dags:
              dag = self.dags[dag_id]
              if dag.is_subdag:
                  root_dag_id = dag.parent_dag.dag_id
  
          # If the dag corresponding to root_dag_id is absent or expired
          orm_dag = DagModel.get_current(root_dag_id)
          if orm_dag and (
                  root_dag_id not in self.dags or
                  (
                      orm_dag.last_expired and
                      dag.last_loaded < orm_dag.last_expired
                  )
          ):
              # Reprocess source file
              found_dags = self.process_file(
                  filepath=orm_dag.fileloc, only_if_updated=False)
  
              # If the source file no longer exports `dag_id`, delete it from self.dags
              if found_dags and dag_id in [found_dag.dag_id for found_dag in found_dags]:
                  return self.dags[dag_id]
              elif dag_id in self.dags:
                  del self.dags[dag_id]
          return self.dags.get(dag_id)
  
      def process_file(self, filepath, only_if_updated=True, safe_mode=True):
          """
          Given a path to a python module or zip file, this method imports
          the module and look for dag objects within it.
          """
          found_dags = []
  
          # if the source file no longer exists in the DB or in the filesystem,
          # return an empty list
          # todo: raise exception?
          if filepath is None or not os.path.isfile(filepath):
              return found_dags
  
          try:
              # This failed before in what may have been a git sync
              # race condition
              file_last_changed_on_disk = datetime.fromtimestamp(os.path.getmtime(filepath))
              if only_if_updated \
                      and filepath in self.file_last_changed \
                      and file_last_changed_on_disk == self.file_last_changed[filepath]:
                  return found_dags
  
          except Exception as e:
              self.log.exception(e)
              return found_dags
  
          mods = []
          if not zipfile.is_zipfile(filepath):
              if safe_mode and os.path.isfile(filepath):
                  with open(filepath, 'rb') as f:
                      content = f.read()
                      if not all([s in content for s in (b'DAG', b'airflow')]):
                          self.file_last_changed[filepath] = file_last_changed_on_disk
                          # Don't want to spam user with skip messages
                          if not self.has_logged:
                              self.has_logged = True
                              self.log.info(
                                  "File %s assumed to contain no DAGs. Skipping.",
                                  filepath)
                          return found_dags
  
              self.log.debug("Importing %s", filepath)
              org_mod_name, _ = os.path.splitext(os.path.split(filepath)[-1])
              mod_name = ('unusual_prefix_' +
                          hashlib.sha1(filepath.encode('utf-8')).hexdigest() +
                          '_' + org_mod_name)
  
              if mod_name in sys.modules:
                  del sys.modules[mod_name]
  
              with timeout(configuration.conf.getint('core', "DAGBAG_IMPORT_TIMEOUT")):
                  try:
                      m = imp.load_source(mod_name, filepath)
                      mods.append(m)
                  except Exception as e:
                      self.log.exception("Failed to import: %s", filepath)
                      self.import_errors[filepath] = str(e)
                      self.file_last_changed[filepath] = file_last_changed_on_disk
  
          else:
              zip_file = zipfile.ZipFile(filepath)
              for mod in zip_file.infolist():
                  head, _ = os.path.split(mod.filename)
                  mod_name, ext = os.path.splitext(mod.filename)
                  if not head and (ext == '.py' or ext == '.pyc'):
                      if mod_name == '__init__':
                          self.log.warning("Found __init__.%s at root of %s", ext, filepath)
                      if safe_mode:
                          with zip_file.open(mod.filename) as zf:
                              self.log.debug("Reading %s from %s", mod.filename, filepath)
                              content = zf.read()
                              if not all([s in content for s in (b'DAG', b'airflow')]):
                                  self.file_last_changed[filepath] = (
                                      file_last_changed_on_disk)
                                  # todo: create ignore list
                                  # Don't want to spam user with skip messages
                                  if not self.has_logged:
                                      self.has_logged = True
                                      self.log.info(
                                          "File %s assumed to contain no DAGs. Skipping.",
                                          filepath)
  
                      if mod_name in sys.modules:
                          del sys.modules[mod_name]
  
                      try:
                          sys.path.insert(0, filepath)
                          m = importlib.import_module(mod_name)
                          mods.append(m)
                      except Exception as e:
                          self.log.exception("Failed to import: %s", filepath)
                          self.import_errors[filepath] = str(e)
                          self.file_last_changed[filepath] = file_last_changed_on_disk
  
          for m in mods:
              for dag in list(m.__dict__.values()):
                  if isinstance(dag, DAG):
                      if not dag.full_filepath:
                          dag.full_filepath = filepath
                          if dag.fileloc != filepath:
                              dag.fileloc = filepath
                      try:
                          dag.is_subdag = False
                          self.bag_dag(dag, parent_dag=dag, root_dag=dag)
                          found_dags.append(dag)
                          found_dags += dag.subdags
                      except AirflowDagCycleException as cycle_exception:
                          self.log.exception("Failed to bag_dag: %s", dag.full_filepath)
                          self.import_errors[dag.full_filepath] = str(cycle_exception)
                          self.file_last_changed[dag.full_filepath] = \
                              file_last_changed_on_disk
  
          self.file_last_changed[filepath] = file_last_changed_on_disk
          return found_dags
  
      @provide_session
      def kill_zombies(self, session=None):
          """
          Fails tasks that haven't had a heartbeat in too long
          """
          from airflow.jobs import LocalTaskJob as LJ
          self.log.info("Finding 'running' jobs without a recent heartbeat")
          TI = TaskInstance
          secs = configuration.conf.getint('scheduler', 'scheduler_zombie_task_threshold')
          limit_dttm = timezone.utcnow() - timedelta(seconds=secs)
          self.log.info("Failing jobs without heartbeat after %s", limit_dttm)
  
          tis = (
              session.query(TI)
              .join(LJ, TI.job_id == LJ.id)
              .filter(TI.state == State.RUNNING)
              .filter(
                  or_(
                      LJ.state != State.RUNNING,
                      LJ.latest_heartbeat < limit_dttm,
                  ))
              .all()
          )
  
          for ti in tis:
              if ti and ti.dag_id in self.dags:
                  dag = self.dags[ti.dag_id]
                  if ti.task_id in dag.task_ids:
                      task = dag.get_task(ti.task_id)
  
                      # now set non db backed vars on ti
                      ti.task = task
                      ti.test_mode = configuration.getboolean('core', 'unit_test_mode')
  
                      ti.handle_failure("{} detected as zombie".format(ti),
                                        ti.test_mode, ti.get_template_context())
                      self.log.info(
                          'Marked zombie job %s as %s', ti, ti.state)
                      Stats.incr('zombies_killed')
          session.commit()
  
      def bag_dag(self, dag, parent_dag, root_dag):
          """
          Adds the DAG into the bag, recurses into sub dags.
          Throws AirflowDagCycleException if a cycle is detected in this dag or its subdags
          """
  
          dag.test_cycle()  # throws if a task cycle is found
  
          dag.resolve_template_files()
          dag.last_loaded = timezone.utcnow()
  
          for task in dag.tasks:
              settings.policy(task)
  
          subdags = dag.subdags
  
          try:
              for subdag in subdags:
                  subdag.full_filepath = dag.full_filepath
                  subdag.parent_dag = dag
                  subdag.is_subdag = True
                  self.bag_dag(subdag, parent_dag=dag, root_dag=root_dag)
  
              self.dags[dag.dag_id] = dag
              self.log.debug('Loaded DAG {dag}'.format(**locals()))
          except AirflowDagCycleException as cycle_exception:
              # There was an error in bagging the dag. Remove it from the list of dags
              self.log.exception('Exception bagging dag: {dag.dag_id}'.format(**locals()))
              # Only necessary at the root level since DAG.subdags automatically
              # performs DFS to search through all subdags
              if dag == root_dag:
                  for subdag in subdags:
                      if subdag.dag_id in self.dags:
                          del self.dags[subdag.dag_id]
              raise cycle_exception
  
      def collect_dags(
              self,
              dag_folder=None,
              only_if_updated=True):
          """
          Given a file path or a folder, this method looks for python modules,
          imports them and adds them to the dagbag collection.
  
          Note that if a .airflowignore file is found while processing,
          the directory, it will behaves much like a .gitignore does,
          ignoring files that match any of the regex patterns specified
          in the file. **Note**: The patterns in .airflowignore are treated as
          un-anchored regexes, not shell-like glob patterns.
          """
          start_dttm = timezone.utcnow()
          dag_folder = dag_folder or self.dag_folder
  
          # Used to store stats around DagBag processing
          stats = []
          FileLoadStat = namedtuple(
              'FileLoadStat', "file duration dag_num task_num dags")
          for filepath in list_py_file_paths(dag_folder):
              try:
                  ts = timezone.utcnow()
                  found_dags = self.process_file(
                      filepath, only_if_updated=only_if_updated)
  
                  td = timezone.utcnow() - ts
                  td = td.total_seconds() + (
                      float(td.microseconds) / 1000000)
                  stats.append(FileLoadStat(
                      filepath.replace(dag_folder, ''),
                      td,
                      len(found_dags),
                      sum([len(dag.tasks) for dag in found_dags]),
                      str([dag.dag_id for dag in found_dags]),
                  ))
              except Exception as e:
                  self.log.exception(e)
          Stats.gauge(
              'collect_dags', (timezone.utcnow() - start_dttm).total_seconds(), 1)
          Stats.gauge(
              'dagbag_size', len(self.dags), 1)
          Stats.gauge(
              'dagbag_import_errors', len(self.import_errors), 1)
          self.dagbag_stats = sorted(
              stats, key=lambda x: x.duration, reverse=True)
  
      def dagbag_report(self):
          """Prints a report around DagBag loading stats"""
          report = textwrap.dedent("""\n
          -------------------------------------------------------------------
          DagBag loading stats for {dag_folder}
          -------------------------------------------------------------------
          Number of DAGs: {dag_num}
          Total task number: {task_num}
          DagBag parsing time: {duration}
          {table}
          """)
          stats = self.dagbag_stats
          return report.format(
              dag_folder=self.dag_folder,
              duration=sum([o.duration for o in stats]),
              dag_num=sum([o.dag_num for o in stats]),
              task_num=sum([o.task_num for o in stats]),
              table=pprinttable(stats),
          )
  
      @provide_session
      def deactivate_inactive_dags(self, session=None):
          active_dag_ids = [dag.dag_id for dag in list(self.dags.values())]
          for dag in session.query(
                  DagModel).filter(~DagModel.dag_id.in_(active_dag_ids)).all():
              dag.is_active = False
              session.merge(dag)
          session.commit()
  
      @provide_session
      def paused_dags(self, session=None):
          dag_ids = [dp.dag_id for dp in session.query(DagModel).filter(
              DagModel.is_paused.__eq__(True))]
          return dag_ids
  
  
  class User(Base):
      __tablename__ = "users"
  
      id = Column(Integer, primary_key=True)
      username = Column(String(ID_LEN), unique=True)
      email = Column(String(500))
      superuser = False
  
      def __repr__(self):
          return self.username
  
      def get_id(self):
          return str(self.id)
  
      def is_superuser(self):
          return self.superuser
  
  
  class Connection(Base, LoggingMixin):
      """
      Placeholder to store information about different database instances
      connection information. The idea here is that scripts use references to
      database instances (conn_id) instead of hard coding hostname, logins and
      passwords when using operators or hooks.
      """
      __tablename__ = "connection"
  
      id = Column(Integer(), primary_key=True)
      conn_id = Column(String(ID_LEN))
      conn_type = Column(String(500))
      host = Column(String(500))
      schema = Column(String(500))
      login = Column(String(500))
      _password = Column('password', String(5000))
      port = Column(Integer())
      is_encrypted = Column(Boolean, unique=False, default=False)
      is_extra_encrypted = Column(Boolean, unique=False, default=False)
      _extra = Column('extra', String(5000))
  
      _types = [
          ('docker', 'Docker Registry',),
          ('fs', 'File (path)'),
          ('ftp', 'FTP',),
          ('google_cloud_platform', 'Google Cloud Platform'),
          ('hdfs', 'HDFS',),
          ('http', 'HTTP',),
          ('hive_cli', 'Hive Client Wrapper',),
          ('hive_metastore', 'Hive Metastore Thrift',),
          ('hiveserver2', 'Hive Server 2 Thrift',),
          ('jdbc', 'Jdbc Connection',),
          ('jenkins', 'Jenkins'),
          ('mysql', 'MySQL',),
          ('postgres', 'Postgres',),
          ('oracle', 'Oracle',),
          ('vertica', 'Vertica',),
          ('presto', 'Presto',),
          ('s3', 'S3',),
          ('samba', 'Samba',),
          ('sqlite', 'Sqlite',),
          ('ssh', 'SSH',),
          ('cloudant', 'IBM Cloudant',),
          ('mssql', 'Microsoft SQL Server'),
          ('mesos_framework-id', 'Mesos Framework ID'),
          ('jira', 'JIRA',),
          ('redis', 'Redis',),
          ('wasb', 'Azure Blob Storage'),
          ('databricks', 'Databricks',),
          ('aws', 'Amazon Web Services',),
          ('emr', 'Elastic MapReduce',),
          ('snowflake', 'Snowflake',),
          ('segment', 'Segment',),
          ('azure_data_lake', 'Azure Data Lake'),
          ('cassandra', 'Cassandra',),
      ]
  
      def __init__(
              self, conn_id=None, conn_type=None,
              host=None, login=None, password=None,
              schema=None, port=None, extra=None,
              uri=None):
          self.conn_id = conn_id
          if uri:
              self.parse_from_uri(uri)
          else:
              self.conn_type = conn_type
              self.host = host
              self.login = login
              self.password = password
              self.schema = schema
              self.port = port
              self.extra = extra
  
      def parse_from_uri(self, uri):
          temp_uri = urlparse(uri)
          hostname = temp_uri.hostname or ''
          if '%2f' in hostname:
              hostname = hostname.replace('%2f', '/').replace('%2F', '/')
          conn_type = temp_uri.scheme
          if conn_type == 'postgresql':
              conn_type = 'postgres'
          self.conn_type = conn_type
          self.host = hostname
          self.schema = temp_uri.path[1:]
          self.login = temp_uri.username
          self.password = temp_uri.password
          self.port = temp_uri.port
          if temp_uri.query:
              self.extra = json.dumps(dict(parse_qsl(temp_uri.query)))
  
      def get_password(self):
          if self._password and self.is_encrypted:
              fernet = get_fernet()
              if not fernet.is_encrypted:
                  raise AirflowException(
                      "Can't decrypt encrypted password for login={}, \
                      FERNET_KEY configuration is missing".format(self.login))
              return fernet.decrypt(bytes(self._password, 'utf-8')).decode()
          else:
              return self._password
  
      def set_password(self, value):
          if value:
              fernet = get_fernet()
              self._password = fernet.encrypt(bytes(value, 'utf-8')).decode()
              self.is_encrypted = fernet.is_encrypted
  
      @declared_attr
      def password(cls):
          return synonym('_password',
                         descriptor=property(cls.get_password, cls.set_password))
  
      def get_extra(self):
          if self._extra and self.is_extra_encrypted:
              fernet = get_fernet()
              if not fernet.is_encrypted:
                  raise AirflowException(
                      "Can't decrypt `extra` params for login={},\
                      FERNET_KEY configuration is missing".format(self.login))
              return fernet.decrypt(bytes(self._extra, 'utf-8')).decode()
          else:
              return self._extra
  
      def set_extra(self, value):
          if value:
              fernet = get_fernet()
              self._extra = fernet.encrypt(bytes(value, 'utf-8')).decode()
              self.is_extra_encrypted = fernet.is_encrypted
          else:
              self._extra = value
              self.is_extra_encrypted = False
  
      @declared_attr
      def extra(cls):
          return synonym('_extra',
                         descriptor=property(cls.get_extra, cls.set_extra))
  
      def get_hook(self):
          try:
              if self.conn_type == 'mysql':
                  from airflow.hooks.mysql_hook import MySqlHook
                  return MySqlHook(mysql_conn_id=self.conn_id)
              elif self.conn_type == 'google_cloud_platform':
                  from airflow.contrib.hooks.bigquery_hook import BigQueryHook
                  return BigQueryHook(bigquery_conn_id=self.conn_id)
              elif self.conn_type == 'postgres':
                  from airflow.hooks.postgres_hook import PostgresHook
                  return PostgresHook(postgres_conn_id=self.conn_id)
              elif self.conn_type == 'hive_cli':
                  from airflow.hooks.hive_hooks import HiveCliHook
                  return HiveCliHook(hive_cli_conn_id=self.conn_id)
              elif self.conn_type == 'presto':
                  from airflow.hooks.presto_hook import PrestoHook
                  return PrestoHook(presto_conn_id=self.conn_id)
              elif self.conn_type == 'hiveserver2':
                  from airflow.hooks.hive_hooks import HiveServer2Hook
                  return HiveServer2Hook(hiveserver2_conn_id=self.conn_id)
              elif self.conn_type == 'sqlite':
                  from airflow.hooks.sqlite_hook import SqliteHook
                  return SqliteHook(sqlite_conn_id=self.conn_id)
              elif self.conn_type == 'jdbc':
                  from airflow.hooks.jdbc_hook import JdbcHook
                  return JdbcHook(jdbc_conn_id=self.conn_id)
              elif self.conn_type == 'mssql':
                  from airflow.hooks.mssql_hook import MsSqlHook
                  return MsSqlHook(mssql_conn_id=self.conn_id)
              elif self.conn_type == 'oracle':
                  from airflow.hooks.oracle_hook import OracleHook
                  return OracleHook(oracle_conn_id=self.conn_id)
              elif self.conn_type == 'vertica':
                  from airflow.contrib.hooks.vertica_hook import VerticaHook
                  return VerticaHook(vertica_conn_id=self.conn_id)
              elif self.conn_type == 'cloudant':
                  from airflow.contrib.hooks.cloudant_hook import CloudantHook
                  return CloudantHook(cloudant_conn_id=self.conn_id)
              elif self.conn_type == 'jira':
                  from airflow.contrib.hooks.jira_hook import JiraHook
                  return JiraHook(jira_conn_id=self.conn_id)
              elif self.conn_type == 'redis':
                  from airflow.contrib.hooks.redis_hook import RedisHook
                  return RedisHook(redis_conn_id=self.conn_id)
              elif self.conn_type == 'wasb':
                  from airflow.contrib.hooks.wasb_hook import WasbHook
                  return WasbHook(wasb_conn_id=self.conn_id)
              elif self.conn_type == 'docker':
                  from airflow.hooks.docker_hook import DockerHook
                  return DockerHook(docker_conn_id=self.conn_id)
              elif self.conn_type == 'azure_data_lake':
                  from airflow.contrib.hooks.azure_data_lake_hook import AzureDataLakeHook
                  return AzureDataLakeHook(azure_data_lake_conn_id=self.conn_id)
              elif self.conn_type == 'cassandra':
                  from airflow.contrib.hooks.cassandra_hook import CassandraHook
                  return CassandraHook(cassandra_conn_id=self.conn_id)
          except Exception:
              pass
  
      def __repr__(self):
          return self.conn_id
  
      @property
      def extra_dejson(self):
          """Returns the extra property by deserializing json."""
          obj = {}
          if self.extra:
              try:
                  obj = json.loads(self.extra)
              except Exception as e:
                  self.log.exception(e)
                  self.log.error("Failed parsing the json for conn_id %s", self.conn_id)
  
          return obj
  
  
  class DagPickle(Base):
      """
      Dags can originate from different places (user repos, master repo, ...)
      and also get executed in different places (different executors). This
      object represents a version of a DAG and becomes a source of truth for
      a BackfillJob execution. A pickle is a native python serialized object,
      and in this case gets stored in the database for the duration of the job.
  
      The executors pick up the DagPickle id and read the dag definition from
      the database.
      """
      id = Column(Integer, primary_key=True)
      pickle = Column(PickleType(pickler=dill))
      created_dttm = Column(UtcDateTime, default=timezone.utcnow)
      pickle_hash = Column(Text)
  
      __tablename__ = "dag_pickle"
  
      def __init__(self, dag):
          self.dag_id = dag.dag_id
          if hasattr(dag, 'template_env'):
              dag.template_env = None
          self.pickle_hash = hash(dag)
          self.pickle = dag
  
  
  class TaskInstance(Base, LoggingMixin):
      """
      Task instances store the state of a task instance. This table is the
      authority and single source of truth around what tasks have run and the
      state they are in.
  
      The SqlAlchemy model doesn't have a SqlAlchemy foreign key to the task or
      dag model deliberately to have more control over transactions.
  
      Database transactions on this table should insure double triggers and
      any confusion around what task instances are or aren't ready to run
      even while multiple schedulers may be firing task instances.
      """
  
      __tablename__ = "task_instance"
  
      task_id = Column(String(ID_LEN), primary_key=True)
      dag_id = Column(String(ID_LEN), primary_key=True)
      execution_date = Column(UtcDateTime, primary_key=True)
      start_date = Column(UtcDateTime)
      end_date = Column(UtcDateTime)
      duration = Column(Float)
      state = Column(String(20))
      _try_number = Column('try_number', Integer, default=0)
      max_tries = Column(Integer)
      hostname = Column(String(1000))
      unixname = Column(String(1000))
      job_id = Column(Integer)
      pool = Column(String(50))
      queue = Column(String(50))
      priority_weight = Column(Integer)
      operator = Column(String(1000))
      queued_dttm = Column(UtcDateTime)
      pid = Column(Integer)
      executor_config = Column(PickleType(pickler=dill))
  
      __table_args__ = (
          Index('ti_dag_state', dag_id, state),
          Index('ti_state', state),
          Index('ti_state_lkp', dag_id, task_id, execution_date, state),
          Index('ti_pool', pool, state, priority_weight),
          Index('ti_job_id', job_id),
      )
  
      def __init__(self, task, execution_date, state=None):
          self.dag_id = task.dag_id
          self.task_id = task.task_id
          self.task = task
          self._log = logging.getLogger("airflow.task")
  
          # make sure we have a localized execution_date stored in UTC
          if execution_date and not timezone.is_localized(execution_date):
              self.log.warning("execution date %s has no timezone information. Using "
                               "default from dag or system", execution_date)
              if self.task.has_dag():
                  execution_date = timezone.make_aware(execution_date,
                                                       self.task.dag.timezone)
              else:
                  execution_date = timezone.make_aware(execution_date)
  
              execution_date = timezone.convert_to_utc(execution_date)
  
          self.execution_date = execution_date
  
          self.queue = task.queue
          self.pool = task.pool
          self.priority_weight = task.priority_weight_total
          self.try_number = 0
          self.max_tries = self.task.retries
          self.unixname = getpass.getuser()
          self.run_as_user = task.run_as_user
          if state:
              self.state = state
          self.hostname = ''
          self.executor_config = task.executor_config
          self.init_on_load()
          # Is this TaskInstance being currently running within `airflow run --raw`.
          # Not persisted to the database so only valid for the current process
          self.raw = False
  
      @reconstructor
      def init_on_load(self):
          """ Initialize the attributes that aren't stored in the DB. """
          self.test_mode = False  # can be changed when calling 'run'
  
      @property
      def try_number(self):
          """
          Return the try number that this task number will be when it is acutally
          run.
  
          If the TI is currently running, this will match the column in the
          databse, in all othercases this will be incremenetd
          """
          # This is designed so that task logs end up in the right file.
          if self.state == State.RUNNING:
              return self._try_number
          return self._try_number + 1
  
      @try_number.setter
      def try_number(self, value):
          self._try_number = value
  
      @property
      def next_try_number(self):
          return self._try_number + 1
  
      def command(
              self,
              mark_success=False,
              ignore_all_deps=False,
              ignore_depends_on_past=False,
              ignore_task_deps=False,
              ignore_ti_state=False,
              local=False,
              pickle_id=None,
              raw=False,
              job_id=None,
              pool=None,
              cfg_path=None):
          """
          Returns a command that can be executed anywhere where airflow is
          installed. This command is part of the message sent to executors by
          the orchestrator.
          """
          return " ".join(self.command_as_list(
              mark_success=mark_success,
              ignore_all_deps=ignore_all_deps,
              ignore_depends_on_past=ignore_depends_on_past,
              ignore_task_deps=ignore_task_deps,
              ignore_ti_state=ignore_ti_state,
              local=local,
              pickle_id=pickle_id,
              raw=raw,
              job_id=job_id,
              pool=pool,
              cfg_path=cfg_path))
  
      def command_as_list(
              self,
              mark_success=False,
              ignore_all_deps=False,
              ignore_task_deps=False,
              ignore_depends_on_past=False,
              ignore_ti_state=False,
              local=False,
              pickle_id=None,
              raw=False,
              job_id=None,
              pool=None,
              cfg_path=None):
          """
          Returns a command that can be executed anywhere where airflow is
          installed. This command is part of the message sent to executors by
          the orchestrator.
          """
          dag = self.task.dag
  
          should_pass_filepath = not pickle_id and dag
          if should_pass_filepath and dag.full_filepath != dag.filepath:
              path = "DAGS_FOLDER/{}".format(dag.filepath)
          elif should_pass_filepath and dag.full_filepath:
              path = dag.full_filepath
          else:
              path = None
  
          return TaskInstance.generate_command(
              self.dag_id,
              self.task_id,
              self.execution_date,
              mark_success=mark_success,
              ignore_all_deps=ignore_all_deps,
              ignore_task_deps=ignore_task_deps,
              ignore_depends_on_past=ignore_depends_on_past,
              ignore_ti_state=ignore_ti_state,
              local=local,
              pickle_id=pickle_id,
              file_path=path,
              raw=raw,
              job_id=job_id,
              pool=pool,
              cfg_path=cfg_path)
  
      @staticmethod
      def generate_command(dag_id,
                           task_id,
                           execution_date,
                           mark_success=False,
                           ignore_all_deps=False,
                           ignore_depends_on_past=False,
                           ignore_task_deps=False,
                           ignore_ti_state=False,
                           local=False,
                           pickle_id=None,
                           file_path=None,
                           raw=False,
                           job_id=None,
                           pool=None,
                           cfg_path=None
                           ):
          """
          Generates the shell command required to execute this task instance.
  
          :param dag_id: DAG ID
          :type dag_id: unicode
          :param task_id: Task ID
          :type task_id: unicode
          :param execution_date: Execution date for the task
          :type execution_date: datetime
          :param mark_success: Whether to mark the task as successful
          :type mark_success: bool
          :param ignore_all_deps: Ignore all ignorable dependencies.
              Overrides the other ignore_* parameters.
          :type ignore_all_deps: boolean
          :param ignore_depends_on_past: Ignore depends_on_past parameter of DAGs
              (e.g. for Backfills)
          :type ignore_depends_on_past: boolean
          :param ignore_task_deps: Ignore task-specific dependencies such as depends_on_past
              and trigger rule
          :type ignore_task_deps: boolean
          :param ignore_ti_state: Ignore the task instance's previous failure/success
          :type ignore_ti_state: boolean
          :param local: Whether to run the task locally
          :type local: bool
          :param pickle_id: If the DAG was serialized to the DB, the ID
              associated with the pickled DAG
          :type pickle_id: unicode
          :param file_path: path to the file containing the DAG definition
          :param raw: raw mode (needs more details)
          :param job_id: job ID (needs more details)
          :param pool: the Airflow pool that the task should run in
          :type pool: unicode
          :param cfg_path: the Path to the configuration file
          :type cfg_path: basestring
          :return: shell command that can be used to run the task instance
          """
          iso = execution_date.isoformat()
          cmd = ["airflow", "run", str(dag_id), str(task_id), str(iso)]
          cmd.extend(["--mark_success"]) if mark_success else None
          cmd.extend(["--pickle", str(pickle_id)]) if pickle_id else None
          cmd.extend(["--job_id", str(job_id)]) if job_id else None
          cmd.extend(["-A"]) if ignore_all_deps else None
          cmd.extend(["-i"]) if ignore_task_deps else None
          cmd.extend(["-I"]) if ignore_depends_on_past else None
          cmd.extend(["--force"]) if ignore_ti_state else None
          cmd.extend(["--local"]) if local else None
          cmd.extend(["--pool", pool]) if pool else None
          cmd.extend(["--raw"]) if raw else None
          cmd.extend(["-sd", file_path]) if file_path else None
          cmd.extend(["--cfg_path", cfg_path]) if cfg_path else None
          return cmd
  
      @property
      def log_filepath(self):
          iso = self.execution_date.isoformat()
          log = os.path.expanduser(configuration.conf.get('core', 'BASE_LOG_FOLDER'))
          return (
              "{log}/{self.dag_id}/{self.task_id}/{iso}.log".format(**locals()))
  
      @property
      def log_url(self):
          iso = quote(self.execution_date.isoformat())
          BASE_URL = configuration.conf.get('webserver', 'BASE_URL')
          if settings.RBAC:
              return BASE_URL + (
                  "/log/list/"
                  "?_flt_3_dag_id={self.dag_id}"
                  "&_flt_3_task_id={self.task_id}"
                  "&_flt_3_execution_date={iso}"
              ).format(**locals())
          else:
              return BASE_URL + (
                  "/admin/airflow/log"
                  "?dag_id={self.dag_id}"
                  "&task_id={self.task_id}"
                  "&execution_date={iso}"
              ).format(**locals())
  
      @property
      def mark_success_url(self):
          iso = quote(self.execution_date.isoformat())
          BASE_URL = configuration.conf.get('webserver', 'BASE_URL')
          if settings.RBAC:
              return BASE_URL + (
                  "/success"
                  "?task_id={self.task_id}"
                  "&dag_id={self.dag_id}"
                  "&execution_date={iso}"
                  "&upstream=false"
                  "&downstream=false"
              ).format(**locals())
          else:
              return BASE_URL + (
                  "/admin/airflow/success"
                  "?task_id={self.task_id}"
                  "&dag_id={self.dag_id}"
                  "&execution_date={iso}"
                  "&upstream=false"
                  "&downstream=false"
              ).format(**locals())
  
      @provide_session
      def current_state(self, session=None):
          """
          Get the very latest state from the database, if a session is passed,
          we use and looking up the state becomes part of the session, otherwise
          a new session is used.
          """
          TI = TaskInstance
          ti = session.query(TI).filter(
              TI.dag_id == self.dag_id,
              TI.task_id == self.task_id,
              TI.execution_date == self.execution_date,
          ).all()
          if ti:
              state = ti[0].state
          else:
              state = None
          return state
  
      @provide_session
      def error(self, session=None):
          """
          Forces the task instance's state to FAILED in the database.
          """
          self.log.error("Recording the task instance as FAILED")
          self.state = State.FAILED
          session.merge(self)
          session.commit()
  
      @provide_session
      def refresh_from_db(self, session=None, lock_for_update=False):
          """
          Refreshes the task instance from the database based on the primary key
  
          :param lock_for_update: if True, indicates that the database should
              lock the TaskInstance (issuing a FOR UPDATE clause) until the
              session is committed.
          """
          TI = TaskInstance
  
          qry = session.query(TI).filter(
              TI.dag_id == self.dag_id,
              TI.task_id == self.task_id,
              TI.execution_date == self.execution_date)
  
          if lock_for_update:
              ti = qry.with_for_update().first()
          else:
              ti = qry.first()
          if ti:
              self.state = ti.state
              self.start_date = ti.start_date
              self.end_date = ti.end_date
              # Get the raw value of try_number column, don't read through the
              # accessor here otherwise it will be incremeneted by one already.
              self.try_number = ti._try_number
              self.max_tries = ti.max_tries
              self.hostname = ti.hostname
              self.pid = ti.pid
              self.executor_config = ti.executor_config
          else:
              self.state = None
  
      @provide_session
      def clear_xcom_data(self, session=None):
          """
          Clears all XCom data from the database for the task instance
          """
          session.query(XCom).filter(
              XCom.dag_id == self.dag_id,
              XCom.task_id == self.task_id,
              XCom.execution_date == self.execution_date
          ).delete()
          session.commit()
  
      @property
      def key(self):
          """
          Returns a tuple that identifies the task instance uniquely
          """
          return self.dag_id, self.task_id, self.execution_date
  
      @provide_session
      def set_state(self, state, session=None):
          self.state = state
          self.start_date = timezone.utcnow()
          self.end_date = timezone.utcnow()
          session.merge(self)
          session.commit()
  
      @property
      def is_premature(self):
          """
          Returns whether a task is in UP_FOR_RETRY state and its retry interval
          has elapsed.
          """
          # is the task still in the retry waiting period?
          return self.state == State.UP_FOR_RETRY and not self.ready_for_retry()
  
      @provide_session
      def are_dependents_done(self, session=None):
          """
          Checks whether the dependents of this task instance have all succeeded.
          This is meant to be used by wait_for_downstream.
  
          This is useful when you do not want to start processing the next
          schedule of a task until the dependents are done. For instance,
          if the task DROPs and recreates a table.
          """
          task = self.task
  
          if not task.downstream_task_ids:
              return True
  
          ti = session.query(func.count(TaskInstance.task_id)).filter(
              TaskInstance.dag_id == self.dag_id,
              TaskInstance.task_id.in_(task.downstream_task_ids),
              TaskInstance.execution_date == self.execution_date,
              TaskInstance.state == State.SUCCESS,
          )
          count = ti[0][0]
          return count == len(task.downstream_task_ids)
  
      @property
      @provide_session
      def previous_ti(self, session=None):
          """ The task instance for the task that ran before this task instance """
  
          dag = self.task.dag
          if dag:
              dr = self.get_dagrun(session=session)
  
              # LEGACY: most likely running from unit tests
              if not dr:
                  # Means that this TI is NOT being run from a DR, but from a catchup
                  previous_scheduled_date = dag.previous_schedule(self.execution_date)
                  if not previous_scheduled_date:
                      return None
  
                  return TaskInstance(task=self.task,
                                      execution_date=previous_scheduled_date)
  
              dr.dag = dag
              if dag.catchup:
                  last_dagrun = dr.get_previous_scheduled_dagrun(session=session)
              else:
                  last_dagrun = dr.get_previous_dagrun(session=session)
  
              if last_dagrun:
                  return last_dagrun.get_task_instance(self.task_id, session=session)
  
          return None
  
      @provide_session
      def are_dependencies_met(
              self,
              dep_context=None,
              session=None,
              verbose=False):
          """
          Returns whether or not all the conditions are met for this task instance to be run
          given the context for the dependencies (e.g. a task instance being force run from
          the UI will ignore some dependencies).
  
          :param dep_context: The execution context that determines the dependencies that
              should be evaluated.
          :type dep_context: DepContext
          :param session: database session
          :type session: Session
          :param verbose: whether log details on failed dependencies on
              info or debug log level
          :type verbose: boolean
          """
          dep_context = dep_context or DepContext()
          failed = False
          verbose_aware_logger = self.log.info if verbose else self.log.debug
          for dep_status in self.get_failed_dep_statuses(
                  dep_context=dep_context,
                  session=session):
              failed = True
  
              verbose_aware_logger(
                  "Dependencies not met for %s, dependency '%s' FAILED: %s",
                  self, dep_status.dep_name, dep_status.reason
              )
  
          if failed:
              return False
  
          verbose_aware_logger("Dependencies all met for %s", self)
          return True
  
      @provide_session
      def get_failed_dep_statuses(
              self,
              dep_context=None,
              session=None):
          dep_context = dep_context or DepContext()
          for dep in dep_context.deps | self.task.deps:
              for dep_status in dep.get_dep_statuses(
                      self,
                      session,
                      dep_context):
  
                  self.log.debug(
                      "%s dependency '%s' PASSED: %s, %s",
                      self, dep_status.dep_name, dep_status.passed, dep_status.reason
                  )
  
                  if not dep_status.passed:
                      yield dep_status
  
      def __repr__(self):
          return (
              "<TaskInstance: {ti.dag_id}.{ti.task_id} "
              "{ti.execution_date} [{ti.state}]>"
          ).format(ti=self)
  
      def next_retry_datetime(self):
          """
          Get datetime of the next retry if the task instance fails. For exponential
          backoff, retry_delay is used as base and will be converted to seconds.
          """
          delay = self.task.retry_delay
          if self.task.retry_exponential_backoff:
              min_backoff = int(delay.total_seconds() * (2 ** (self.try_number - 2)))
              # deterministic per task instance
              hash = int(hashlib.sha1("{}#{}#{}#{}".format(self.dag_id,
                                                           self.task_id,
                                                           self.execution_date,
                                                           self.try_number)
                                      .encode('utf-8')).hexdigest(), 16)
              # between 0.5 * delay * (2^retry_number) and 1.0 * delay * (2^retry_number)
              modded_hash = min_backoff + hash % min_backoff
              # timedelta has a maximum representable value. The exponentiation
              # here means this value can be exceeded after a certain number
              # of tries (around 50 if the initial delay is 1s, even fewer if
              # the delay is larger). Cap the value here before creating a
              # timedelta object so the operation doesn't fail.
              delay_backoff_in_seconds = min(
                  modded_hash,
                  timedelta.max.total_seconds() - 1
              )
              delay = timedelta(seconds=delay_backoff_in_seconds)
              if self.task.max_retry_delay:
                  delay = min(self.task.max_retry_delay, delay)
          return self.end_date + delay
  
      def ready_for_retry(self):
          """
          Checks on whether the task instance is in the right state and timeframe
          to be retried.
          """
          return (self.state == State.UP_FOR_RETRY and
                  self.next_retry_datetime() < timezone.utcnow())
  
      @provide_session
      def pool_full(self, session):
          """
          Returns a boolean as to whether the slot pool has room for this
          task to run
          """
          if not self.task.pool:
              return False
  
          pool = (
              session
              .query(Pool)
              .filter(Pool.pool == self.task.pool)
              .first()
          )
          if not pool:
              return False
          open_slots = pool.open_slots(session=session)
  
          return open_slots <= 0
  
      @provide_session
      def get_dagrun(self, session):
          """
          Returns the DagRun for this TaskInstance
  
          :param session:
          :return: DagRun
          """
          dr = session.query(DagRun).filter(
              DagRun.dag_id == self.dag_id,
              DagRun.execution_date == self.execution_date
          ).first()
  
          return dr
  
      @provide_session
      def _check_and_change_state_before_execution(
              self,
              verbose=True,
              ignore_all_deps=False,
              ignore_depends_on_past=False,
              ignore_task_deps=False,
              ignore_ti_state=False,
              mark_success=False,
              test_mode=False,
              job_id=None,
              pool=None,
              session=None):
          """
          Checks dependencies and then sets state to RUNNING if they are met. Returns
          True if and only if state is set to RUNNING, which implies that task should be
          executed, in preparation for _run_raw_task
  
          :param verbose: whether to turn on more verbose logging
          :type verbose: boolean
          :param ignore_all_deps: Ignore all of the non-critical dependencies, just runs
          :type ignore_all_deps: boolean
          :param ignore_depends_on_past: Ignore depends_on_past DAG attribute
          :type ignore_depends_on_past: boolean
          :param ignore_task_deps: Don't check the dependencies of this TI's task
          :type ignore_task_deps: boolean
          :param ignore_ti_state: Disregards previous task instance state
          :type ignore_ti_state: boolean
          :param mark_success: Don't run the task, mark its state as success
          :type mark_success: boolean
          :param test_mode: Doesn't record success or failure in the DB
          :type test_mode: boolean
          :param pool: specifies the pool to use to run the task instance
          :type pool: str
          :return: whether the state was changed to running or not
          :rtype: bool
          """
          task = self.task
          self.pool = pool or task.pool
          self.test_mode = test_mode
          self.refresh_from_db(session=session, lock_for_update=True)
          self.job_id = job_id
          self.hostname = get_hostname()
          self.operator = task.__class__.__name__
  
          if not ignore_all_deps and not ignore_ti_state and self.state == State.SUCCESS:
              Stats.incr('previously_succeeded', 1, 1)
  
          queue_dep_context = DepContext(
              deps=QUEUE_DEPS,
              ignore_all_deps=ignore_all_deps,
              ignore_ti_state=ignore_ti_state,
              ignore_depends_on_past=ignore_depends_on_past,
              ignore_task_deps=ignore_task_deps)
          if not self.are_dependencies_met(
                  dep_context=queue_dep_context,
                  session=session,
                  verbose=True):
              session.commit()
              return False
  
          # TODO: Logging needs cleanup, not clear what is being printed
          hr = "\n" + ("-" * 80) + "\n"  # Line break
  
          # For reporting purposes, we report based on 1-indexed,
          # not 0-indexed lists (i.e. Attempt 1 instead of
          # Attempt 0 for the first attempt).
          msg = "Starting attempt {attempt} of {total}".format(
              attempt=self.try_number,
              total=self.max_tries + 1)
          self.start_date = timezone.utcnow()
  
          dep_context = DepContext(
              deps=RUN_DEPS - QUEUE_DEPS,
              ignore_all_deps=ignore_all_deps,
              ignore_depends_on_past=ignore_depends_on_past,
              ignore_task_deps=ignore_task_deps,
              ignore_ti_state=ignore_ti_state)
          runnable = self.are_dependencies_met(
              dep_context=dep_context,
              session=session,
              verbose=True)
  
          if not runnable and not mark_success:
              # FIXME: we might have hit concurrency limits, which means we probably
              # have been running prematurely. This should be handled in the
              # scheduling mechanism.
              self.state = State.NONE
              msg = ("FIXME: Rescheduling due to concurrency limits reached at task "
                     "runtime. Attempt {attempt} of {total}. State set to NONE.").format(
                  attempt=self.try_number,
                  total=self.max_tries + 1)
              self.log.warning(hr + msg + hr)
  
              self.queued_dttm = timezone.utcnow()
              self.log.info("Queuing into pool %s", self.pool)
              session.merge(self)
              session.commit()
              return False
  
          # Another worker might have started running this task instance while
          # the current worker process was blocked on refresh_from_db
          if self.state == State.RUNNING:
              msg = "Task Instance already running {}".format(self)
              self.log.warning(msg)
              session.commit()
              return False
  
          # print status message
          self.log.info(hr + msg + hr)
          self._try_number += 1
  
          if not test_mode:
              session.add(Log(State.RUNNING, self))
          self.state = State.RUNNING
          self.pid = os.getpid()
          self.end_date = None
          if not test_mode:
              session.merge(self)
          session.commit()
  
          # Closing all pooled connections to prevent
          # "max number of connections reached"
          settings.engine.dispose()
          if verbose:
              if mark_success:
                  msg = "Marking success for {} on {}".format(self.task,
                                                              self.execution_date)
                  self.log.info(msg)
              else:
                  msg = "Executing {} on {}".format(self.task, self.execution_date)
                  self.log.info(msg)
          return True
  
      @provide_session
      def _run_raw_task(
              self,
              mark_success=False,
              test_mode=False,
              job_id=None,
              pool=None,
              session=None):
          """
          Immediately runs the task (without checking or changing db state
          before execution) and then sets the appropriate final state after
          completion and runs any post-execute callbacks. Meant to be called
          only after another function changes the state to running.
  
          :param mark_success: Don't run the task, mark its state as success
          :type mark_success: boolean
          :param test_mode: Doesn't record success or failure in the DB
          :type test_mode: boolean
          :param pool: specifies the pool to use to run the task instance
          :type pool: str
          """
          task = self.task
          self.pool = pool or task.pool
          self.test_mode = test_mode
          self.refresh_from_db(session=session)
          self.job_id = job_id
          self.hostname = get_hostname()
          self.operator = task.__class__.__name__
  
          context = {}
          try:
              if not mark_success:
                  context = self.get_template_context()
  
                  task_copy = copy.copy(task)
                  self.task = task_copy
  
                  def signal_handler(signum, frame):
                      self.log.error("Received SIGTERM. Terminating subprocesses.")
                      task_copy.on_kill()
                      raise AirflowException("Task received SIGTERM signal")
                  signal.signal(signal.SIGTERM, signal_handler)
  
                  # Don't clear Xcom until the task is certain to execute
                  self.clear_xcom_data()
  
                  self.render_templates()
                  task_copy.pre_execute(context=context)
  
                  # If a timeout is specified for the task, make it fail
                  # if it goes beyond
                  result = None
                  if task_copy.execution_timeout:
                      try:
                          with timeout(int(
                                  task_copy.execution_timeout.total_seconds())):
                              result = task_copy.execute(context=context)
                      except AirflowTaskTimeout:
                          task_copy.on_kill()
                          raise
                  else:
                      result = task_copy.execute(context=context)
  
                  # If the task returns a result, push an XCom containing it
                  if result is not None:
                      self.xcom_push(key=XCOM_RETURN_KEY, value=result)
  
                  # TODO remove deprecated behavior in Airflow 2.0
                  try:
                      task_copy.post_execute(context=context, result=result)
                  except TypeError as e:
                      if 'unexpected keyword argument' in str(e):
                          warnings.warn(
                              'BaseOperator.post_execute() now takes two '
                              'arguments, `context` and `result`, but "{}" only '
                              'expected one. This behavior is deprecated and '
                              'will be removed in a future version of '
                              'Airflow.'.format(self.task_id),
                              category=DeprecationWarning)
                          task_copy.post_execute(context=context)
                      else:
                          raise
  
                  Stats.incr('operator_successes_{}'.format(
                      self.task.__class__.__name__), 1, 1)
                  Stats.incr('ti_successes')
              self.refresh_from_db(lock_for_update=True)
              self.state = State.SUCCESS
          except AirflowSkipException:
              self.refresh_from_db(lock_for_update=True)
              self.state = State.SKIPPED
          except AirflowException as e:
              self.refresh_from_db()
              # for case when task is marked as success/failed externally
              # current behavior doesn't hit the success callback
              if self.state in {State.SUCCESS, State.FAILED}:
                  return
              else:
                  self.handle_failure(e, test_mode, context)
                  raise
          except (Exception, KeyboardInterrupt) as e:
              self.handle_failure(e, test_mode, context)
              raise
  
          # Recording SUCCESS
          self.end_date = timezone.utcnow()
          self.set_duration()
          if not test_mode:
              session.add(Log(self.state, self))
              session.merge(self)
          session.commit()
  
          # Success callback
          try:
              if task.on_success_callback:
                  task.on_success_callback(context)
          except Exception as e3:
              self.log.error("Failed when executing success callback")
              self.log.exception(e3)
  
          session.commit()
  
      @provide_session
      def run(
              self,
              verbose=True,
              ignore_all_deps=False,
              ignore_depends_on_past=False,
              ignore_task_deps=False,
              ignore_ti_state=False,
              mark_success=False,
              test_mode=False,
              job_id=None,
              pool=None,
              session=None):
          res = self._check_and_change_state_before_execution(
              verbose=verbose,
              ignore_all_deps=ignore_all_deps,
              ignore_depends_on_past=ignore_depends_on_past,
              ignore_task_deps=ignore_task_deps,
              ignore_ti_state=ignore_ti_state,
              mark_success=mark_success,
              test_mode=test_mode,
              job_id=job_id,
              pool=pool,
              session=session)
          if res:
              self._run_raw_task(
                  mark_success=mark_success,
                  test_mode=test_mode,
                  job_id=job_id,
                  pool=pool,
                  session=session)
  
      def dry_run(self):
          task = self.task
          task_copy = copy.copy(task)
          self.task = task_copy
  
          self.render_templates()
          task_copy.dry_run()
  
      @provide_session
      def handle_failure(self, error, test_mode=False, context=None, session=None):
          self.log.exception(error)
          task = self.task
          self.end_date = timezone.utcnow()
          self.set_duration()
          Stats.incr('operator_failures_{}'.format(task.__class__.__name__), 1, 1)
          Stats.incr('ti_failures')
          if not test_mode:
              session.add(Log(State.FAILED, self))
  
          # Log failure duration
          session.add(TaskFail(task, self.execution_date, self.start_date, self.end_date))
  
          # Let's go deeper
          try:
              # Since this function is called only when the TI state is running,
              # try_number contains the current try_number (not the next). We
              # only mark task instance as FAILED if the next task instance
              # try_number exceeds the max_tries.
              if self.is_eligible_to_retry():
                  self.state = State.UP_FOR_RETRY
                  self.log.info('Marking task as UP_FOR_RETRY')
                  if task.email_on_retry and task.email:
                      self.email_alert(error)
              else:
                  self.state = State.FAILED
                  if task.retries:
                      self.log.info('All retries failed; marking task as FAILED')
                  else:
                      self.log.info('Marking task as FAILED.')
                  if task.email_on_failure and task.email:
                      self.email_alert(error)
          except Exception as e2:
              self.log.error('Failed to send email to: %s', task.email)
              self.log.exception(e2)
  
          # Handling callbacks pessimistically
          try:
              if self.state == State.UP_FOR_RETRY and task.on_retry_callback:
                  task.on_retry_callback(context)
              if self.state == State.FAILED and task.on_failure_callback:
                  task.on_failure_callback(context)
          except Exception as e3:
              self.log.error("Failed at executing callback")
              self.log.exception(e3)
  
          if not test_mode:
              session.merge(self)
          session.commit()
  
      def is_eligible_to_retry(self):
          """Is task instance is eligible for retry"""
          return self.task.retries and self.try_number <= self.max_tries
  
      @provide_session
      def get_template_context(self, session=None):
          task = self.task
          from airflow import macros
          tables = None
          if 'tables' in task.params:
              tables = task.params['tables']
  
          ds = self.execution_date.strftime('%Y-%m-%d')
          ts = self.execution_date.isoformat()
          yesterday_ds = (self.execution_date - timedelta(1)).strftime('%Y-%m-%d')
          tomorrow_ds = (self.execution_date + timedelta(1)).strftime('%Y-%m-%d')
  
          prev_execution_date = task.dag.previous_schedule(self.execution_date)
          next_execution_date = task.dag.following_schedule(self.execution_date)
  
          next_ds = None
          if next_execution_date:
              next_ds = next_execution_date.strftime('%Y-%m-%d')
  
          prev_ds = None
          if prev_execution_date:
              prev_ds = prev_execution_date.strftime('%Y-%m-%d')
  
          ds_nodash = ds.replace('-', '')
          ts_nodash = ts.replace('-', '').replace(':', '')
          yesterday_ds_nodash = yesterday_ds.replace('-', '')
          tomorrow_ds_nodash = tomorrow_ds.replace('-', '')
  
          ti_key_str = "{task.dag_id}__{task.task_id}__{ds_nodash}"
          ti_key_str = ti_key_str.format(**locals())
  
          params = {}
          run_id = ''
          dag_run = None
          if hasattr(task, 'dag'):
              if task.dag.params:
                  params.update(task.dag.params)
              dag_run = (
                  session.query(DagRun)
                  .filter_by(
                      dag_id=task.dag.dag_id,
                      execution_date=self.execution_date)
                  .first()
              )
              run_id = dag_run.run_id if dag_run else None
              session.expunge_all()
              session.commit()
  
          if task.params:
              params.update(task.params)
  
          if configuration.getboolean('core', 'dag_run_conf_overrides_params'):
              self.overwrite_params_with_dag_run_conf(params=params, dag_run=dag_run)
  
          class VariableAccessor:
              """
              Wrapper around Variable. This way you can get variables in templates by using
              {var.value.your_variable_name}.
              """
              def __init__(self):
                  self.var = None
  
              def __getattr__(self, item):
                  self.var = Variable.get(item)
                  return self.var
  
              def __repr__(self):
                  return str(self.var)
  
          class VariableJsonAccessor:
              """
              Wrapper around deserialized Variables. This way you can get variables
              in templates by using {var.json.your_variable_name}.
              """
              def __init__(self):
                  self.var = None
  
              def __getattr__(self, item):
                  self.var = Variable.get(item, deserialize_json=True)
                  return self.var
  
              def __repr__(self):
                  return str(self.var)
  
          return {
              'dag': task.dag,
              'ds': ds,
              'next_ds': next_ds,
              'prev_ds': prev_ds,
              'ds_nodash': ds_nodash,
              'ts': ts,
              'ts_nodash': ts_nodash,
              'yesterday_ds': yesterday_ds,
              'yesterday_ds_nodash': yesterday_ds_nodash,
              'tomorrow_ds': tomorrow_ds,
              'tomorrow_ds_nodash': tomorrow_ds_nodash,
              'END_DATE': ds,
              'end_date': ds,
              'dag_run': dag_run,
              'run_id': run_id,
              'execution_date': self.execution_date,
              'prev_execution_date': prev_execution_date,
              'next_execution_date': next_execution_date,
              'latest_date': ds,
              'macros': macros,
              'params': params,
              'tables': tables,
              'task': task,
              'task_instance': self,
              'ti': self,
              'task_instance_key_str': ti_key_str,
              'conf': configuration,
              'test_mode': self.test_mode,
              'var': {
                  'value': VariableAccessor(),
                  'json': VariableJsonAccessor()
              },
              'inlets': task.inlets,
              'outlets': task.outlets,
          }
  
      def overwrite_params_with_dag_run_conf(self, params, dag_run):
          if dag_run and dag_run.conf:
              params.update(dag_run.conf)
  
      def render_templates(self):
          task = self.task
          jinja_context = self.get_template_context()
          if hasattr(self, 'task') and hasattr(self.task, 'dag'):
              if self.task.dag.user_defined_macros:
                  jinja_context.update(
                      self.task.dag.user_defined_macros)
  
          rt = self.task.render_template  # shortcut to method
          for attr in task.__class__.template_fields:
              content = getattr(task, attr)
              if content:
                  rendered_content = rt(attr, content, jinja_context)
                  setattr(task, attr, rendered_content)
  
      def email_alert(self, exception):
          task = self.task
          title = "Airflow alert: {self}".format(**locals())
          exception = str(exception).replace('\n', '<br>')
          # For reporting purposes, we report based on 1-indexed,
          # not 0-indexed lists (i.e. Try 1 instead of
          # Try 0 for the first attempt).
          body = (
              "Try {try_number} out of {max_tries}<br>"
              "Exception:<br>{exception}<br>"
              "Log: <a href='{self.log_url}'>Link</a><br>"
              "Host: {self.hostname}<br>"
              "Log file: {self.log_filepath}<br>"
              "Mark success: <a href='{self.mark_success_url}'>Link</a><br>"
          ).format(try_number=self.try_number, max_tries=self.max_tries + 1, **locals())
          send_email(task.email, title, body)
  
      def set_duration(self):
          if self.end_date and self.start_date:
              self.duration = (self.end_date - self.start_date).total_seconds()
          else:
              self.duration = None
  
      def xcom_push(
              self,
              key,
              value,
              execution_date=None):
          """
          Make an XCom available for tasks to pull.
  
          :param key: A key for the XCom
          :type key: string
          :param value: A value for the XCom. The value is pickled and stored
              in the database.
          :type value: any pickleable object
          :param execution_date: if provided, the XCom will not be visible until
              this date. This can be used, for example, to send a message to a
              task on a future date without it being immediately visible.
          :type execution_date: datetime
          """
  
          if execution_date and execution_date < self.execution_date:
              raise ValueError(
                  'execution_date can not be in the past (current '
                  'execution_date is {}; received {})'.format(
                      self.execution_date, execution_date))
  
          XCom.set(
              key=key,
              value=value,
              task_id=self.task_id,
              dag_id=self.dag_id,
              execution_date=execution_date or self.execution_date)
  
      def xcom_pull(
              self,
              task_ids=None,
              dag_id=None,
              key=XCOM_RETURN_KEY,
              include_prior_dates=False):
          """
          Pull XComs that optionally meet certain criteria.
  
          The default value for `key` limits the search to XComs
          that were returned by other tasks (as opposed to those that were pushed
          manually). To remove this filter, pass key=None (or any desired value).
  
          If a single task_id string is provided, the result is the value of the
          most recent matching XCom from that task_id. If multiple task_ids are
          provided, a tuple of matching values is returned. None is returned
          whenever no matches are found.
  
          :param key: A key for the XCom. If provided, only XComs with matching
              keys will be returned. The default key is 'return_value', also
              available as a constant XCOM_RETURN_KEY. This key is automatically
              given to XComs returned by tasks (as opposed to being pushed
              manually). To remove the filter, pass key=None.
          :type key: string
          :param task_ids: Only XComs from tasks with matching ids will be
              pulled. Can pass None to remove the filter.
          :type task_ids: string or iterable of strings (representing task_ids)
          :param dag_id: If provided, only pulls XComs from this DAG.
              If None (default), the DAG of the calling task is used.
          :type dag_id: string
          :param include_prior_dates: If False, only XComs from the current
              execution_date are returned. If True, XComs from previous dates
              are returned as well.
          :type include_prior_dates: bool
          """
  
          if dag_id is None:
              dag_id = self.dag_id
  
          pull_fn = functools.partial(
              XCom.get_one,
              execution_date=self.execution_date,
              key=key,
              dag_id=dag_id,
              include_prior_dates=include_prior_dates)
  
          if is_container(task_ids):
              return tuple(pull_fn(task_id=t) for t in task_ids)
          else:
              return pull_fn(task_id=task_ids)
  
      @provide_session
      def get_num_running_task_instances(self, session):
          TI = TaskInstance
          return session.query(TI).filter(
              TI.dag_id == self.dag_id,
              TI.task_id == self.task_id,
              TI.state == State.RUNNING
          ).count()
  
      def init_run_context(self, raw=False):
          """
          Sets the log context.
          """
          self.raw = raw
          self._set_context(self)
  
  
  class TaskFail(Base):
      """
      TaskFail tracks the failed run durations of each task instance.
      """
  
      __tablename__ = "task_fail"
  
      id = Column(Integer, primary_key=True)
      task_id = Column(String(ID_LEN), nullable=False)
      dag_id = Column(String(ID_LEN), nullable=False)
      execution_date = Column(UtcDateTime, nullable=False)
      start_date = Column(UtcDateTime)
      end_date = Column(UtcDateTime)
      duration = Column(Integer)
  
      __table_args__ = (
          Index('idx_task_fail_dag_task_date', dag_id, task_id, execution_date,
                unique=False),
      )
  
      def __init__(self, task, execution_date, start_date, end_date):
          self.dag_id = task.dag_id
          self.task_id = task.task_id
          self.execution_date = execution_date
          self.start_date = start_date
          self.end_date = end_date
          if self.end_date and self.start_date:
              self.duration = (self.end_date - self.start_date).total_seconds()
          else:
              self.duration = None
  
  
  class Log(Base):
      """
      Used to actively log events to the database
      """
  
      __tablename__ = "log"
  
      id = Column(Integer, primary_key=True)
      dttm = Column(UtcDateTime)
      dag_id = Column(String(ID_LEN))
      task_id = Column(String(ID_LEN))
      event = Column(String(30))
      execution_date = Column(UtcDateTime)
      owner = Column(String(500))
      extra = Column(Text)
  
      def __init__(self, event, task_instance, owner=None, extra=None, **kwargs):
          self.dttm = timezone.utcnow()
          self.event = event
          self.extra = extra
  
          task_owner = None
  
          if task_instance:
              self.dag_id = task_instance.dag_id
              self.task_id = task_instance.task_id
              self.execution_date = task_instance.execution_date
              task_owner = task_instance.task.owner
  
          if 'task_id' in kwargs:
              self.task_id = kwargs['task_id']
          if 'dag_id' in kwargs:
              self.dag_id = kwargs['dag_id']
          if 'execution_date' in kwargs:
              if kwargs['execution_date']:
                  self.execution_date = kwargs['execution_date']
  
          self.owner = owner or task_owner
  
  
  class SkipMixin(LoggingMixin):
      @provide_session
      def skip(self, dag_run, execution_date, tasks, session=None):
          """
          Sets tasks instances to skipped from the same dag run.
  
          :param dag_run: the DagRun for which to set the tasks to skipped
          :param execution_date: execution_date
          :param tasks: tasks to skip (not task_ids)
          :param session: db session to use
          """
          if not tasks:
              return
  
          task_ids = [d.task_id for d in tasks]
          now = timezone.utcnow()
  
          if dag_run:
              session.query(TaskInstance).filter(
                  TaskInstance.dag_id == dag_run.dag_id,
                  TaskInstance.execution_date == dag_run.execution_date,
                  TaskInstance.task_id.in_(task_ids)
              ).update({TaskInstance.state: State.SKIPPED,
                        TaskInstance.start_date: now,
                        TaskInstance.end_date: now},
                       synchronize_session=False)
              session.commit()
          else:
              assert execution_date is not None, "Execution date is None and no dag run"
  
              self.log.warning("No DAG RUN present this should not happen")
              # this is defensive against dag runs that are not complete
              for task in tasks:
                  ti = TaskInstance(task, execution_date=execution_date)
                  ti.state = State.SKIPPED
                  ti.start_date = now
                  ti.end_date = now
                  session.merge(ti)
  
              session.commit()
  
  
  @functools.total_ordering
  class BaseOperator(LoggingMixin):
      """
      Abstract base class for all operators. Since operators create objects that
      become nodes in the dag, BaseOperator contains many recursive methods for
      dag crawling behavior. To derive this class, you are expected to override
      the constructor as well as the 'execute' method.
  
      Operators derived from this class should perform or trigger certain tasks
      synchronously (wait for completion). Example of operators could be an
      operator that runs a Pig job (PigOperator), a sensor operator that
      waits for a partition to land in Hive (HiveSensorOperator), or one that
      moves data from Hive to MySQL (Hive2MySqlOperator). Instances of these
      operators (tasks) target specific operations, running specific scripts,
      functions or data transfers.
  
      This class is abstract and shouldn't be instantiated. Instantiating a
      class derived from this one results in the creation of a task object,
      which ultimately becomes a node in DAG objects. Task dependencies should
      be set by using the set_upstream and/or set_downstream methods.
  
      :param task_id: a unique, meaningful id for the task
      :type task_id: string
      :param owner: the owner of the task, using the unix username is recommended
      :type owner: string
      :param retries: the number of retries that should be performed before
          failing the task
      :type retries: int
      :param retry_delay: delay between retries
      :type retry_delay: timedelta
      :param retry_exponential_backoff: allow progressive longer waits between
          retries by using exponential backoff algorithm on retry delay (delay
          will be converted into seconds)
      :type retry_exponential_backoff: bool
      :param max_retry_delay: maximum delay interval between retries
      :type max_retry_delay: timedelta
      :param start_date: The ``start_date`` for the task, determines
          the ``execution_date`` for the first task instance. The best practice
          is to have the start_date rounded
          to your DAG's ``schedule_interval``. Daily jobs have their start_date
          some day at 00:00:00, hourly jobs have their start_date at 00:00
          of a specific hour. Note that Airflow simply looks at the latest
          ``execution_date`` and adds the ``schedule_interval`` to determine
          the next ``execution_date``. It is also very important
          to note that different tasks' dependencies
          need to line up in time. If task A depends on task B and their
          start_date are offset in a way that their execution_date don't line
          up, A's dependencies will never be met. If you are looking to delay
          a task, for example running a daily task at 2AM, look into the
          ``TimeSensor`` and ``TimeDeltaSensor``. We advise against using
          dynamic ``start_date`` and recommend using fixed ones. Read the
          FAQ entry about start_date for more information.
      :type start_date: datetime
      :param end_date: if specified, the scheduler won't go beyond this date
      :type end_date: datetime
      :param depends_on_past: when set to true, task instances will run
          sequentially while relying on the previous task's schedule to
          succeed. The task instance for the start_date is allowed to run.
      :type depends_on_past: bool
      :param wait_for_downstream: when set to true, an instance of task
          X will wait for tasks immediately downstream of the previous instance
          of task X to finish successfully before it runs. This is useful if the
          different instances of a task X alter the same asset, and this asset
          is used by tasks downstream of task X. Note that depends_on_past
          is forced to True wherever wait_for_downstream is used.
      :type wait_for_downstream: bool
      :param queue: which queue to target when running this job. Not
          all executors implement queue management, the CeleryExecutor
          does support targeting specific queues.
      :type queue: str
      :param dag: a reference to the dag the task is attached to (if any)
      :type dag: DAG
      :param priority_weight: priority weight of this task against other task.
          This allows the executor to trigger higher priority tasks before
          others when things get backed up.
      :type priority_weight: int
      :param weight_rule: weighting method used for the effective total
          priority weight of the task. Options are:
          ``{ downstream | upstream | absolute }`` default is ``downstream``
          When set to ``downstream`` the effective weight of the task is the
          aggregate sum of all downstream descendants. As a result, upstream
          tasks will have higher weight and will be scheduled more aggressively
          when using positive weight values. This is useful when you have
          multiple dag run instances and desire to have all upstream tasks to
          complete for all runs before each dag can continue processing
          downstream tasks. When set to ``upstream`` the effective weight is the
          aggregate sum of all upstream ancestors. This is the opposite where
          downtream tasks have higher weight and will be scheduled more
          aggressively when using positive weight values. This is useful when you
          have multiple dag run instances and prefer to have each dag complete
          before starting upstream tasks of other dags.  When set to
          ``absolute``, the effective weight is the exact ``priority_weight``
          specified without additional weighting. You may want to do this when
          you know exactly what priority weight each task should have.
          Additionally, when set to ``absolute``, there is bonus effect of
          significantly speeding up the task creation process as for very large
          DAGS. Options can be set as string or using the constants defined in
          the static class ``airflow.utils.WeightRule``
      :type weight_rule: str
      :param pool: the slot pool this task should run in, slot pools are a
          way to limit concurrency for certain tasks
      :type pool: str
      :param sla: time by which the job is expected to succeed. Note that
          this represents the ``timedelta`` after the period is closed. For
          example if you set an SLA of 1 hour, the scheduler would send an email
          soon after 1:00AM on the ``2016-01-02`` if the ``2016-01-01`` instance
          has not succeeded yet.
          The scheduler pays special attention for jobs with an SLA and
          sends alert
          emails for sla misses. SLA misses are also recorded in the database
          for future reference. All tasks that share the same SLA time
          get bundled in a single email, sent soon after that time. SLA
          notification are sent once and only once for each task instance.
      :type sla: datetime.timedelta
      :param execution_timeout: max time allowed for the execution of
          this task instance, if it goes beyond it will raise and fail.
      :type execution_timeout: datetime.timedelta
      :param on_failure_callback: a function to be called when a task instance
          of this task fails. a context dictionary is passed as a single
          parameter to this function. Context contains references to related
          objects to the task instance and is documented under the macros
          section of the API.
      :type on_failure_callback: callable
      :param on_retry_callback: much like the ``on_failure_callback`` except
          that it is executed when retries occur.
      :param on_success_callback: much like the ``on_failure_callback`` except
          that it is executed when the task succeeds.
      :type on_success_callback: callable
      :param trigger_rule: defines the rule by which dependencies are applied
          for the task to get triggered. Options are:
          ``{ all_success | all_failed | all_done | one_success |
          one_failed | dummy}``
          default is ``all_success``. Options can be set as string or
          using the constants defined in the static class
          ``airflow.utils.TriggerRule``
      :type trigger_rule: str
      :param resources: A map of resource parameter names (the argument names of the
          Resources constructor) to their values.
      :type resources: dict
      :param run_as_user: unix username to impersonate while running the task
      :type run_as_user: str
      :param task_concurrency: When set, a task will be able to limit the concurrent
          runs across execution_dates
      :type task_concurrency: int
      :param executor_config: Additional task-level configuration parameters that are
          interpreted by a specific executor. Parameters are namespaced by the name of
          executor.
          ``example: to run this task in a specific docker container through
          the KubernetesExecutor
          MyOperator(...,
              executor_config={
              "KubernetesExecutor":
                  {"image": "myCustomDockerImage"}
                  }
          )``
      :type executor_config: dict
      """
  
      # For derived classes to define which fields will get jinjaified
      template_fields = []
      # Defines which files extensions to look for in the templated fields
      template_ext = []
      # Defines the color in the UI
      ui_color = '#fff'
      ui_fgcolor = '#000'
  
      # base list which includes all the attrs that don't need deep copy.
      _base_operator_shallow_copy_attrs = ('user_defined_macros',
                                           'user_defined_filters',
                                           'params',
                                           '_log',)
  
      # each operator should override this class attr for shallow copy attrs.
      shallow_copy_attrs = ()
  
      @apply_defaults
      def __init__(
              self,
              task_id,
              owner=configuration.conf.get('operators', 'DEFAULT_OWNER'),
              email=None,
              email_on_retry=True,
              email_on_failure=True,
              retries=0,
              retry_delay=timedelta(seconds=300),
              retry_exponential_backoff=False,
              max_retry_delay=None,
              start_date=None,
              end_date=None,
              schedule_interval=None,  # not hooked as of now
              depends_on_past=False,
              wait_for_downstream=False,
              dag=None,
              params=None,
              default_args=None,
              adhoc=False,
              priority_weight=1,
              weight_rule=WeightRule.DOWNSTREAM,
              queue=configuration.conf.get('celery', 'default_queue'),
              pool=None,
              sla=None,
              execution_timeout=None,
              on_failure_callback=None,
              on_success_callback=None,
              on_retry_callback=None,
              trigger_rule=TriggerRule.ALL_SUCCESS,
              resources=None,
              run_as_user=None,
              task_concurrency=None,
              executor_config=None,
              inlets=None,
              outlets=None,
              *args,
              **kwargs):
  
          if args or kwargs:
              # TODO remove *args and **kwargs in Airflow 2.0
              warnings.warn(
                  'Invalid arguments were passed to {c}. Support for '
                  'passing such arguments will be dropped in Airflow 2.0. '
                  'Invalid arguments were:'
                  '\n*args: {a}\n**kwargs: {k}'.format(
                      c=self.__class__.__name__, a=args, k=kwargs),
                  category=PendingDeprecationWarning
              )
  
          validate_key(task_id)
          self.task_id = task_id
          self.owner = owner
          self.email = email
          self.email_on_retry = email_on_retry
          self.email_on_failure = email_on_failure
          self.start_date = start_date
          if start_date and not isinstance(start_date, datetime):
              self.log.warning("start_date for %s isn't datetime.datetime", self)
          self.end_date = end_date
          if not TriggerRule.is_valid(trigger_rule):
              raise AirflowException(
                  "The trigger_rule must be one of {all_triggers},"
                  "'{d}.{t}'; received '{tr}'."
                  .format(all_triggers=TriggerRule.all_triggers,
                          d=dag.dag_id if dag else "", t=task_id, tr=trigger_rule))
  
          self.trigger_rule = trigger_rule
          self.depends_on_past = depends_on_past
          self.wait_for_downstream = wait_for_downstream
          if wait_for_downstream:
              self.depends_on_past = True
  
          if schedule_interval:
              self.log.warning(
                  "schedule_interval is used for %s, though it has "
                  "been deprecated as a task parameter, you need to "
                  "specify it as a DAG parameter instead",
                  self
              )
          self._schedule_interval = schedule_interval
          self.retries = retries
          self.queue = queue
          self.pool = pool
          self.sla = sla
          self.execution_timeout = execution_timeout
          self.on_failure_callback = on_failure_callback
          self.on_success_callback = on_success_callback
          self.on_retry_callback = on_retry_callback
          if isinstance(retry_delay, timedelta):
              self.retry_delay = retry_delay
          else:
              self.log.debug("Retry_delay isn't timedelta object, assuming secs")
              self.retry_delay = timedelta(seconds=retry_delay)
          self.retry_exponential_backoff = retry_exponential_backoff
          self.max_retry_delay = max_retry_delay
          self.params = params or {}  # Available in templates!
          self.adhoc = adhoc
          self.priority_weight = priority_weight
          if not WeightRule.is_valid(weight_rule):
              raise AirflowException(
                  "The weight_rule must be one of {all_weight_rules},"
                  "'{d}.{t}'; received '{tr}'."
                  .format(all_weight_rules=WeightRule.all_weight_rules,
                          d=dag.dag_id if dag else "", t=task_id, tr=weight_rule))
          self.weight_rule = weight_rule
  
          self.resources = Resources(**(resources or {}))
          self.run_as_user = run_as_user
          self.task_concurrency = task_concurrency
          self.executor_config = executor_config or {}
  
          # Private attributes
          self._upstream_task_ids = set()
          self._downstream_task_ids = set()
  
          if not dag and _CONTEXT_MANAGER_DAG:
              dag = _CONTEXT_MANAGER_DAG
          if dag:
              self.dag = dag
  
          self._log = logging.getLogger("airflow.task.operators")
  
          # lineage
          self.inlets = []
          self.outlets = []
          self.lineage_data = None
  
          self._inlets = {
              "auto": False,
              "task_ids": [],
              "datasets": [],
          }
  
          self._outlets = {
              "datasets": [],
          }
  
          if inlets:
              self._inlets.update(inlets)
  
          if outlets:
              self._outlets.update(outlets)
  
          self._comps = {
              'task_id',
              'dag_id',
              'owner',
              'email',
              'email_on_retry',
              'retry_delay',
              'retry_exponential_backoff',
              'max_retry_delay',
              'start_date',
              'schedule_interval',
              'depends_on_past',
              'wait_for_downstream',
              'adhoc',
              'priority_weight',
              'sla',
              'execution_timeout',
              'on_failure_callback',
              'on_success_callback',
              'on_retry_callback',
          }
  
      def __eq__(self, other):
          return (
              type(self) == type(other) and
              all(self.__dict__.get(c, None) == other.__dict__.get(c, None)
                  for c in self._comps))
  
      def __ne__(self, other):
          return not self == other
  
      def __lt__(self, other):
          return self.task_id < other.task_id
  
      def __hash__(self):
          hash_components = [type(self)]
          for c in self._comps:
              val = getattr(self, c, None)
              try:
                  hash(val)
                  hash_components.append(val)
              except TypeError:
                  hash_components.append(repr(val))
          return hash(tuple(hash_components))
  
      # Composing Operators -----------------------------------------------
  
      def __rshift__(self, other):
          """
          Implements Self >> Other == self.set_downstream(other)
  
          If "Other" is a DAG, the DAG is assigned to the Operator.
          """
          if isinstance(other, DAG):
              # if this dag is already assigned, do nothing
              # otherwise, do normal dag assignment
              if not (self.has_dag() and self.dag is other):
                  self.dag = other
          else:
              self.set_downstream(other)
          return other
  
      def __lshift__(self, other):
          """
          Implements Self << Other == self.set_upstream(other)
  
          If "Other" is a DAG, the DAG is assigned to the Operator.
          """
          if isinstance(other, DAG):
              # if this dag is already assigned, do nothing
              # otherwise, do normal dag assignment
              if not (self.has_dag() and self.dag is other):
                  self.dag = other
          else:
              self.set_upstream(other)
          return other
  
      def __rrshift__(self, other):
          """
          Called for [DAG] >> [Operator] because DAGs don't have
          __rshift__ operators.
          """
          self.__lshift__(other)
          return self
  
      def __rlshift__(self, other):
          """
          Called for [DAG] << [Operator] because DAGs don't have
          __lshift__ operators.
          """
          self.__rshift__(other)
          return self
  
      # /Composing Operators ---------------------------------------------
  
      @property
      def dag(self):
          """
          Returns the Operator's DAG if set, otherwise raises an error
          """
          if self.has_dag():
              return self._dag
          else:
              raise AirflowException(
                  'Operator {} has not been assigned to a DAG yet'.format(self))
  
      @dag.setter
      def dag(self, dag):
          """
          Operators can be assigned to one DAG, one time. Repeat assignments to
          that same DAG are ok.
          """
          if not isinstance(dag, DAG):
              raise TypeError(
                  'Expected DAG; received {}'.format(dag.__class__.__name__))
          elif self.has_dag() and self.dag is not dag:
              raise AirflowException(
                  "The DAG assigned to {} can not be changed.".format(self))
          elif self.task_id not in dag.task_dict:
              dag.add_task(self)
  
          self._dag = dag
  
      def has_dag(self):
          """
          Returns True if the Operator has been assigned to a DAG.
          """
          return getattr(self, '_dag', None) is not None
  
      @property
      def dag_id(self):
          if self.has_dag():
              return self.dag.dag_id
          else:
              return 'adhoc_' + self.owner
  
      @property
      def deps(self):
          """
          Returns the list of dependencies for the operator. These differ from execution
          context dependencies in that they are specific to tasks and can be
          extended/overridden by subclasses.
          """
          return {
              NotInRetryPeriodDep(),
              PrevDagrunDep(),
              TriggerRuleDep(),
          }
  
      @property
      def schedule_interval(self):
          """
          The schedule interval of the DAG always wins over individual tasks so
          that tasks within a DAG always line up. The task still needs a
          schedule_interval as it may not be attached to a DAG.
          """
          if self.has_dag():
              return self.dag._schedule_interval
          else:
              return self._schedule_interval
  
      @property
      def priority_weight_total(self):
          if self.weight_rule == WeightRule.ABSOLUTE:
              return self.priority_weight
          elif self.weight_rule == WeightRule.DOWNSTREAM:
              upstream = False
          elif self.weight_rule == WeightRule.UPSTREAM:
              upstream = True
          else:
              upstream = False
  
          return self.priority_weight + sum(
              map(lambda task_id: self._dag.task_dict[task_id].priority_weight,
                  self.get_flat_relative_ids(upstream=upstream))
          )
  
      @prepare_lineage
      def pre_execute(self, context):
          """
          This hook is triggered right before self.execute() is called.
          """
          pass
  
      def execute(self, context):
          """
          This is the main method to derive when creating an operator.
          Context is the same dictionary used as when rendering jinja templates.
  
          Refer to get_template_context for more context.
          """
          raise NotImplementedError()
  
      @apply_lineage
      def post_execute(self, context, result=None):
          """
          This hook is triggered right after self.execute() is called.
          It is passed the execution context and any results returned by the
          operator.
          """
          pass
  
      def on_kill(self):
          """
          Override this method to cleanup subprocesses when a task instance
          gets killed. Any use of the threading, subprocess or multiprocessing
          module within an operator needs to be cleaned up or it will leave
          ghost processes behind.
          """
          pass
  
      def __deepcopy__(self, memo):
          """
          Hack sorting double chained task lists by task_id to avoid hitting
          max_depth on deepcopy operations.
          """
          sys.setrecursionlimit(5000)  # TODO fix this in a better way
          cls = self.__class__
          result = cls.__new__(cls)
          memo[id(self)] = result
  
          shallow_copy = cls.shallow_copy_attrs + cls._base_operator_shallow_copy_attrs
  
          for k, v in list(self.__dict__.items()):
              if k not in shallow_copy:
                  setattr(result, k, copy.deepcopy(v, memo))
              else:
                  setattr(result, k, copy.copy(v))
          return result
  
      def __getstate__(self):
          state = dict(self.__dict__)
          del state['_log']
  
          return state
  
      def __setstate__(self, state):
          self.__dict__ = state
          self._log = logging.getLogger("airflow.task.operators")
  
      def render_template_from_field(self, attr, content, context, jinja_env):
          """
          Renders a template from a field. If the field is a string, it will
          simply render the string and return the result. If it is a collection or
          nested set of collections, it will traverse the structure and render
          all strings in it.
          """
          rt = self.render_template
          if isinstance(content, six.string_types):
              result = jinja_env.from_string(content).render(**context)
          elif isinstance(content, (list, tuple)):
              result = [rt(attr, e, context) for e in content]
          elif isinstance(content, numbers.Number):
              result = content
          elif isinstance(content, dict):
              result = {
                  k: rt("{}[{}]".format(attr, k), v, context)
                  for k, v in list(content.items())}
          else:
              param_type = type(content)
              msg = (
                  "Type '{param_type}' used for parameter '{attr}' is "
                  "not supported for templating").format(**locals())
              raise AirflowException(msg)
          return result
  
      def render_template(self, attr, content, context):
          """
          Renders a template either from a file or directly in a field, and returns
          the rendered result.
          """
          jinja_env = self.dag.get_template_env() \
              if hasattr(self, 'dag') \
              else jinja2.Environment(cache_size=0)
  
          exts = self.__class__.template_ext
          if (
                  isinstance(content, six.string_types) and
                  any([content.endswith(ext) for ext in exts])):
              return jinja_env.get_template(content).render(**context)
          else:
              return self.render_template_from_field(attr, content, context, jinja_env)
  
      def prepare_template(self):
          """
          Hook that is triggered after the templated fields get replaced
          by their content. If you need your operator to alter the
          content of the file before the template is rendered,
          it should override this method to do so.
          """
          pass
  
      def resolve_template_files(self):
          # Getting the content of files for template_field / template_ext
          for attr in self.template_fields:
              content = getattr(self, attr)
              if content is not None and \
                      isinstance(content, six.string_types) and \
                      any([content.endswith(ext) for ext in self.template_ext]):
                  env = self.dag.get_template_env()
                  try:
                      setattr(self, attr, env.loader.get_source(env, content)[0])
                  except Exception as e:
                      self.log.exception(e)
          self.prepare_template()
  
      @property
      def upstream_list(self):
          """@property: list of tasks directly upstream"""
          return [self.dag.get_task(tid) for tid in self._upstream_task_ids]
  
      @property
      def upstream_task_ids(self):
          return self._upstream_task_ids
  
      @property
      def downstream_list(self):
          """@property: list of tasks directly downstream"""
          return [self.dag.get_task(tid) for tid in self._downstream_task_ids]
  
      @property
      def downstream_task_ids(self):
          return self._downstream_task_ids
  
      @provide_session
      def clear(self,
                start_date=None,
                end_date=None,
                upstream=False,
                downstream=False,
                session=None):
          """
          Clears the state of task instances associated with the task, following
          the parameters specified.
          """
          TI = TaskInstance
          qry = session.query(TI).filter(TI.dag_id == self.dag_id)
  
          if start_date:
              qry = qry.filter(TI.execution_date >= start_date)
          if end_date:
              qry = qry.filter(TI.execution_date <= end_date)
  
          tasks = [self.task_id]
  
          if upstream:
              tasks += [
                  t.task_id for t in self.get_flat_relatives(upstream=True)]
  
          if downstream:
              tasks += [
                  t.task_id for t in self.get_flat_relatives(upstream=False)]
  
          qry = qry.filter(TI.task_id.in_(tasks))
  
          count = qry.count()
  
          clear_task_instances(qry.all(), session, dag=self.dag)
  
          session.commit()
  
          return count
  
      def get_task_instances(self, session, start_date=None, end_date=None):
          """
          Get a set of task instance related to this task for a specific date
          range.
          """
          TI = TaskInstance
          end_date = end_date or timezone.utcnow()
          return session.query(TI).filter(
              TI.dag_id == self.dag_id,
              TI.task_id == self.task_id,
              TI.execution_date >= start_date,
              TI.execution_date <= end_date,
          ).order_by(TI.execution_date).all()
  
      def get_flat_relative_ids(self, upstream=False, found_descendants=None):
          """
          Get a flat list of relatives' ids, either upstream or downstream.
          """
  
          if not found_descendants:
              found_descendants = set()
          relative_ids = self.get_direct_relative_ids(upstream)
  
          for relative_id in relative_ids:
              if relative_id not in found_descendants:
                  found_descendants.add(relative_id)
                  relative_task = self._dag.task_dict[relative_id]
                  relative_task.get_flat_relative_ids(upstream,
                                                      found_descendants)
  
          return found_descendants
  
      def get_flat_relatives(self, upstream=False):
          """
          Get a flat list of relatives, either upstream or downstream.
          """
          return list(map(lambda task_id: self._dag.task_dict[task_id],
                          self.get_flat_relative_ids(upstream)))
  
      def run(
              self,
              start_date=None,
              end_date=None,
              ignore_first_depends_on_past=False,
              ignore_ti_state=False,
              mark_success=False):
          """
          Run a set of task instances for a date range.
          """
          start_date = start_date or self.start_date
          end_date = end_date or self.end_date or timezone.utcnow()
  
          for dt in self.dag.date_range(start_date, end_date=end_date):
              TaskInstance(self, dt).run(
                  mark_success=mark_success,
                  ignore_depends_on_past=(
                      dt == start_date and ignore_first_depends_on_past),
                  ignore_ti_state=ignore_ti_state)
  
      def dry_run(self):
          self.log.info('Dry run')
          for attr in self.template_fields:
              content = getattr(self, attr)
              if content and isinstance(content, six.string_types):
                  self.log.info('Rendering template for %s', attr)
                  self.log.info(content)
  
      def get_direct_relative_ids(self, upstream=False):
          """
          Get the direct relative ids to the current task, upstream or
          downstream.
          """
          if upstream:
              return self._upstream_task_ids
          else:
              return self._downstream_task_ids
  
      def get_direct_relatives(self, upstream=False):
          """
          Get the direct relatives to the current task, upstream or
          downstream.
          """
          if upstream:
              return self.upstream_list
          else:
              return self.downstream_list
  
      def __repr__(self):
          return "<Task({self.__class__.__name__}): {self.task_id}>".format(
              self=self)
  
      @property
      def task_type(self):
          return self.__class__.__name__
  
      def add_only_new(self, item_set, item):
          if item in item_set:
              raise AirflowException(
                  'Dependency {self}, {item} already registered'
                  ''.format(**locals()))
          else:
              item_set.add(item)
  
      def _set_relatives(self, task_or_task_list, upstream=False):
          try:
              task_list = list(task_or_task_list)
          except TypeError:
              task_list = [task_or_task_list]
  
          for t in task_list:
              if not isinstance(t, BaseOperator):
                  raise AirflowException(
                      "Relationships can only be set between "
                      "Operators; received {}".format(t.__class__.__name__))
  
          # relationships can only be set if the tasks share a single DAG. Tasks
          # without a DAG are assigned to that DAG.
          dags = {t._dag.dag_id: t._dag for t in [self] + task_list if t.has_dag()}
  
          if len(dags) > 1:
              raise AirflowException(
                  'Tried to set relationships between tasks in '
                  'more than one DAG: {}'.format(dags.values()))
          elif len(dags) == 1:
              dag = dags.popitem()[1]
          else:
              raise AirflowException(
                  "Tried to create relationships between tasks that don't have "
                  "DAGs yet. Set the DAG for at least one "
                  "task  and try again: {}".format([self] + task_list))
  
          if dag and not self.has_dag():
              self.dag = dag
  
          for task in task_list:
              if dag and not task.has_dag():
                  task.dag = dag
              if upstream:
                  task.add_only_new(task._downstream_task_ids, self.task_id)
                  self.add_only_new(self._upstream_task_ids, task.task_id)
              else:
                  self.add_only_new(self._downstream_task_ids, task.task_id)
                  task.add_only_new(task._upstream_task_ids, self.task_id)
  
      def set_downstream(self, task_or_task_list):
          """
          Set a task or a task list to be directly downstream from the current
          task.
          """
          self._set_relatives(task_or_task_list, upstream=False)
  
      def set_upstream(self, task_or_task_list):
          """
          Set a task or a task list to be directly upstream from the current
          task.
          """
          self._set_relatives(task_or_task_list, upstream=True)
  
      def xcom_push(
              self,
              context,
              key,
              value,
              execution_date=None):
          """
          See TaskInstance.xcom_push()
          """
          context['ti'].xcom_push(
              key=key,
              value=value,
              execution_date=execution_date)
  
      def xcom_pull(
              self,
              context,
              task_ids=None,
              dag_id=None,
              key=XCOM_RETURN_KEY,
              include_prior_dates=None):
          """
          See TaskInstance.xcom_pull()
          """
          return context['ti'].xcom_pull(
              key=key,
              task_ids=task_ids,
              dag_id=dag_id,
              include_prior_dates=include_prior_dates)
  
  
  class DagModel(Base):
  
      __tablename__ = "dag"
      """
      These items are stored in the database for state related information
      """
      dag_id = Column(String(ID_LEN), primary_key=True)
      # A DAG can be paused from the UI / DB
      # Set this default value of is_paused based on a configuration value!
      is_paused_at_creation = configuration.conf\
          .getboolean('core',
                      'dags_are_paused_at_creation')
      is_paused = Column(Boolean, default=is_paused_at_creation)
      # Whether the DAG is a subdag
      is_subdag = Column(Boolean, default=False)
      # Whether that DAG was seen on the last DagBag load
      is_active = Column(Boolean, default=False)
      # Last time the scheduler started
      last_scheduler_run = Column(UtcDateTime)
      # Last time this DAG was pickled
      last_pickled = Column(UtcDateTime)
      # Time when the DAG last received a refresh signal
      # (e.g. the DAG's "refresh" button was clicked in the web UI)
      last_expired = Column(UtcDateTime)
      # Whether (one  of) the scheduler is scheduling this DAG at the moment
      scheduler_lock = Column(Boolean)
      # Foreign key to the latest pickle_id
      pickle_id = Column(Integer)
      # The location of the file containing the DAG object
      fileloc = Column(String(2000))
      # String representing the owners
      owners = Column(String(2000))
  
      def __repr__(self):
          return "<DAG: {self.dag_id}>".format(self=self)
  
      @classmethod
      @provide_session
      def get_current(cls, dag_id, session=None):
          return session.query(cls).filter(cls.dag_id == dag_id).first()
  
  
  @functools.total_ordering
  class DAG(BaseDag, LoggingMixin):
      """
      A dag (directed acyclic graph) is a collection of tasks with directional
      dependencies. A dag also has a schedule, a start end an end date
      (optional). For each schedule, (say daily or hourly), the DAG needs to run
      each individual tasks as their dependencies are met. Certain tasks have
      the property of depending on their own past, meaning that they can't run
      until their previous schedule (and upstream tasks) are completed.
  
      DAGs essentially act as namespaces for tasks. A task_id can only be
      added once to a DAG.
  
      :param dag_id: The id of the DAG
      :type dag_id: string
      :param description: The description for the DAG to e.g. be shown on the webserver
      :type description: string
      :param schedule_interval: Defines how often that DAG runs, this
          timedelta object gets added to your latest task instance's
          execution_date to figure out the next schedule
      :type schedule_interval: datetime.timedelta or
          dateutil.relativedelta.relativedelta or str that acts as a cron
          expression
      :param start_date: The timestamp from which the scheduler will
          attempt to backfill
      :type start_date: datetime.datetime
      :param end_date: A date beyond which your DAG won't run, leave to None
          for open ended scheduling
      :type end_date: datetime.datetime
      :param template_searchpath: This list of folders (non relative)
          defines where jinja will look for your templates. Order matters.
          Note that jinja/airflow includes the path of your DAG file by
          default
      :type template_searchpath: string or list of stings
      :param user_defined_macros: a dictionary of macros that will be exposed
          in your jinja templates. For example, passing ``dict(foo='bar')``
          to this argument allows you to ``{{ foo }}`` in all jinja
          templates related to this DAG. Note that you can pass any
          type of object here.
      :type user_defined_macros: dict
      :param user_defined_filters: a dictionary of filters that will be exposed
          in your jinja templates. For example, passing
          ``dict(hello=lambda name: 'Hello %s' % name)`` to this argument allows
          you to ``{{ 'world' | hello }}`` in all jinja templates related to
          this DAG.
      :type user_defined_filters: dict
      :param default_args: A dictionary of default parameters to be used
          as constructor keyword parameters when initialising operators.
          Note that operators have the same hook, and precede those defined
          here, meaning that if your dict contains `'depends_on_past': True`
          here and `'depends_on_past': False` in the operator's call
          `default_args`, the actual value will be `False`.
      :type default_args: dict
      :param params: a dictionary of DAG level parameters that are made
          accessible in templates, namespaced under `params`. These
          params can be overridden at the task level.
      :type params: dict
      :param concurrency: the number of task instances allowed to run
          concurrently
      :type concurrency: int
      :param max_active_runs: maximum number of active DAG runs, beyond this
          number of DAG runs in a running state, the scheduler won't create
          new active DAG runs
      :type max_active_runs: int
      :param dagrun_timeout: specify how long a DagRun should be up before
          timing out / failing, so that new DagRuns can be created
      :type dagrun_timeout: datetime.timedelta
      :param sla_miss_callback: specify a function to call when reporting SLA
          timeouts.
      :type sla_miss_callback: types.FunctionType
      :param default_view: Specify DAG default view (tree, graph, duration,
                                                     gantt, landing_times)
      :type default_view: string
      :param orientation: Specify DAG orientation in graph view (LR, TB, RL, BT)
      :type orientation: string
      :param catchup: Perform scheduler catchup (or only run latest)? Defaults to True
      :type catchup: bool
      :param on_failure_callback: A function to be called when a DagRun of this dag fails.
          A context dictionary is passed as a single parameter to this function.
      :type on_failure_callback: callable
      :param on_success_callback: Much like the ``on_failure_callback`` except
          that it is executed when the dag succeeds.
      :type on_success_callback: callable
      """
  
      def __init__(
              self, dag_id,
              description='',
              schedule_interval=timedelta(days=1),
              start_date=None, end_date=None,
              full_filepath=None,
              template_searchpath=None,
              user_defined_macros=None,
              user_defined_filters=None,
              default_args=None,
              concurrency=configuration.conf.getint('core', 'dag_concurrency'),
              max_active_runs=configuration.conf.getint(
                  'core', 'max_active_runs_per_dag'),
              dagrun_timeout=None,
              sla_miss_callback=None,
              default_view=configuration.conf.get('webserver', 'dag_default_view').lower(),
              orientation=configuration.conf.get('webserver', 'dag_orientation'),
              catchup=configuration.conf.getboolean('scheduler', 'catchup_by_default'),
              on_success_callback=None, on_failure_callback=None,
              params=None):
  
          self.user_defined_macros = user_defined_macros
          self.user_defined_filters = user_defined_filters
          self.default_args = default_args or {}
          self.params = params or {}
  
          # merging potentially conflicting default_args['params'] into params
          if 'params' in self.default_args:
              self.params.update(self.default_args['params'])
              del self.default_args['params']
  
          validate_key(dag_id)
  
          # Properties from BaseDag
          self._dag_id = dag_id
          self._full_filepath = full_filepath if full_filepath else ''
          self._concurrency = concurrency
          self._pickle_id = None
  
          self._description = description
          # set file location to caller source path
          self.fileloc = sys._getframe().f_back.f_code.co_filename
          self.task_dict = dict()
  
          # set timezone
          if start_date and start_date.tzinfo:
              self.timezone = start_date.tzinfo
          elif 'start_date' in self.default_args and self.default_args['start_date']:
              if isinstance(self.default_args['start_date'], six.string_types):
                  self.default_args['start_date'] = (
                      timezone.parse(self.default_args['start_date'])
                  )
              self.timezone = self.default_args['start_date'].tzinfo
          else:
              self.timezone = settings.TIMEZONE
  
          self.start_date = timezone.convert_to_utc(start_date)
          self.end_date = timezone.convert_to_utc(end_date)
  
          # also convert tasks
          if 'start_date' in self.default_args:
              self.default_args['start_date'] = (
                  timezone.convert_to_utc(self.default_args['start_date'])
              )
          if 'end_date' in self.default_args:
              self.default_args['end_date'] = (
                  timezone.convert_to_utc(self.default_args['end_date'])
              )
  
          self.schedule_interval = schedule_interval
          if isinstance(schedule_interval, Hashable) and schedule_interval in cron_presets:
              self._schedule_interval = cron_presets.get(schedule_interval)
          elif schedule_interval == '@once':
              self._schedule_interval = None
          else:
              self._schedule_interval = schedule_interval
          if isinstance(template_searchpath, six.string_types):
              template_searchpath = [template_searchpath]
          self.template_searchpath = template_searchpath
          self.parent_dag = None  # Gets set when DAGs are loaded
          self.last_loaded = timezone.utcnow()
          self.safe_dag_id = dag_id.replace('.', '__dot__')
          self.max_active_runs = max_active_runs
          self.dagrun_timeout = dagrun_timeout
          self.sla_miss_callback = sla_miss_callback
          self.default_view = default_view
          self.orientation = orientation
          self.catchup = catchup
          self.is_subdag = False  # DagBag.bag_dag() will set this to True if appropriate
  
          self.partial = False
          self.on_success_callback = on_success_callback
          self.on_failure_callback = on_failure_callback
  
          self._comps = {
              'dag_id',
              'task_ids',
              'parent_dag',
              'start_date',
              'schedule_interval',
              'full_filepath',
              'template_searchpath',
              'last_loaded',
          }
  
      def __repr__(self):
          return "<DAG: {self.dag_id}>".format(self=self)
  
      def __eq__(self, other):
          return (
              type(self) == type(other) and
              # Use getattr() instead of __dict__ as __dict__ doesn't return
              # correct values for properties.
              all(getattr(self, c, None) == getattr(other, c, None)
                  for c in self._comps))
  
      def __ne__(self, other):
          return not self == other
  
      def __lt__(self, other):
          return self.dag_id < other.dag_id
  
      def __hash__(self):
          hash_components = [type(self)]
          for c in self._comps:
              # task_ids returns a list and lists can't be hashed
              if c == 'task_ids':
                  val = tuple(self.task_dict.keys())
              else:
                  val = getattr(self, c, None)
              try:
                  hash(val)
                  hash_components.append(val)
              except TypeError:
                  hash_components.append(repr(val))
          return hash(tuple(hash_components))
  
      # Context Manager -----------------------------------------------
  
      def __enter__(self):
          global _CONTEXT_MANAGER_DAG
          self._old_context_manager_dag = _CONTEXT_MANAGER_DAG
          _CONTEXT_MANAGER_DAG = self
          return self
  
      def __exit__(self, _type, _value, _tb):
          global _CONTEXT_MANAGER_DAG
          _CONTEXT_MANAGER_DAG = self._old_context_manager_dag
  
      # /Context Manager ----------------------------------------------
  
      def date_range(self, start_date, num=None, end_date=timezone.utcnow()):
          if num:
              end_date = None
          return utils_date_range(
              start_date=start_date, end_date=end_date,
              num=num, delta=self._schedule_interval)
  
      def following_schedule(self, dttm):
          """
          Calculates the following schedule for this dag in local time
  
          :param dttm: utc datetime
          :return: utc datetime
          """
          if isinstance(self._schedule_interval, six.string_types):
              dttm = timezone.make_naive(dttm, self.timezone)
              cron = croniter(self._schedule_interval, dttm)
              following = timezone.make_aware(cron.get_next(datetime), self.timezone)
              return timezone.convert_to_utc(following)
          elif self._schedule_interval is not None:
              return dttm + self._schedule_interval
  
      def previous_schedule(self, dttm):
          """
          Calculates the previous schedule for this dag in local time
  
          :param dttm: utc datetime
          :return: utc datetime
          """
          if isinstance(self._schedule_interval, six.string_types):
              dttm = timezone.make_naive(dttm, self.timezone)
              cron = croniter(self._schedule_interval, dttm)
              prev = timezone.make_aware(cron.get_prev(datetime), self.timezone)
              return timezone.convert_to_utc(prev)
          elif self._schedule_interval is not None:
              return dttm - self._schedule_interval
  
      def get_run_dates(self, start_date, end_date=None):
          """
          Returns a list of dates between the interval received as parameter using this
          dag's schedule interval. Returned dates can be used for execution dates.
  
          :param start_date: the start date of the interval
          :type start_date: datetime
          :param end_date: the end date of the interval, defaults to timezone.utcnow()
          :type end_date: datetime
          :return: a list of dates within the interval following the dag's schedule
          :rtype: list
          """
          run_dates = []
  
          using_start_date = start_date
          using_end_date = end_date
  
          # dates for dag runs
          using_start_date = using_start_date or min([t.start_date for t in self.tasks])
          using_end_date = using_end_date or timezone.utcnow()
  
          # next run date for a subdag isn't relevant (schedule_interval for subdags
          # is ignored) so we use the dag run's start date in the case of a subdag
          next_run_date = (self.normalize_schedule(using_start_date)
                           if not self.is_subdag else using_start_date)
  
          while next_run_date and next_run_date <= using_end_date:
              run_dates.append(next_run_date)
              next_run_date = self.following_schedule(next_run_date)
  
          return run_dates
  
      def normalize_schedule(self, dttm):
          """
          Returns dttm + interval unless dttm is first interval then it returns dttm
          """
          following = self.following_schedule(dttm)
  
          # in case of @once
          if not following:
              return dttm
          if self.previous_schedule(following) != dttm:
              return following
  
          return dttm
  
      @provide_session
      def get_last_dagrun(self, session=None, include_externally_triggered=False):
          """
          Returns the last dag run for this dag, None if there was none.
          Last dag run can be any type of run eg. scheduled or backfilled.
          Overridden DagRuns are ignored
          """
          DR = DagRun
          qry = session.query(DR).filter(
              DR.dag_id == self.dag_id,
          )
          if not include_externally_triggered:
              qry = qry.filter(DR.external_trigger.__eq__(False))
  
          qry = qry.order_by(DR.execution_date.desc())
  
          last = qry.first()
  
          return last
  
      @property
      def dag_id(self):
          return self._dag_id
  
      @dag_id.setter
      def dag_id(self, value):
          self._dag_id = value
  
      @property
      def full_filepath(self):
          return self._full_filepath
  
      @full_filepath.setter
      def full_filepath(self, value):
          self._full_filepath = value
  
      @property
      def concurrency(self):
          return self._concurrency
  
      @concurrency.setter
      def concurrency(self, value):
          self._concurrency = value
  
      @property
      def description(self):
          return self._description
  
      @property
      def pickle_id(self):
          return self._pickle_id
  
      @pickle_id.setter
      def pickle_id(self, value):
          self._pickle_id = value
  
      @property
      def tasks(self):
          return list(self.task_dict.values())
  
      @tasks.setter
      def tasks(self, val):
          raise AttributeError(
              'DAG.tasks can not be modified. Use dag.add_task() instead.')
  
      @property
      def task_ids(self):
          return list(self.task_dict.keys())
  
      @property
      def active_task_ids(self):
          return list(k for k, v in self.task_dict.items() if not v.adhoc)
  
      @property
      def active_tasks(self):
          return [t for t in self.tasks if not t.adhoc]
  
      @property
      def filepath(self):
          """
          File location of where the dag object is instantiated
          """
          fn = self.full_filepath.replace(settings.DAGS_FOLDER + '/', '')
          fn = fn.replace(os.path.dirname(__file__) + '/', '')
          return fn
  
      @property
      def folder(self):
          """
          Folder location of where the dag object is instantiated
          """
          return os.path.dirname(self.full_filepath)
  
      @property
      def owner(self):
          return ", ".join(list(set([t.owner for t in self.tasks])))
  
      @property
      @provide_session
      def concurrency_reached(self, session=None):
          """
          Returns a boolean indicating whether the concurrency limit for this DAG
          has been reached
          """
          TI = TaskInstance
          qry = session.query(func.count(TI.task_id)).filter(
              TI.dag_id == self.dag_id,
              TI.state == State.RUNNING,
          )
          return qry.scalar() >= self.concurrency
  
      @property
      @provide_session
      def is_paused(self, session=None):
          """
          Returns a boolean indicating whether this DAG is paused
          """
          qry = session.query(DagModel).filter(
              DagModel.dag_id == self.dag_id)
          return qry.value('is_paused')
  
      @provide_session
      def handle_callback(self, dagrun, success=True, reason=None, session=None):
          """
          Triggers the appropriate callback depending on the value of success, namely the
          on_failure_callback or on_success_callback. This method gets the context of a
          single TaskInstance part of this DagRun and passes that to the callable along
          with a 'reason', primarily to differentiate DagRun failures.
          .. note::
              The logs end up in $AIRFLOW_HOME/logs/scheduler/latest/PROJECT/DAG_FILE.py.log
          :param dagrun: DagRun object
          :param success: Flag to specify if failure or success callback should be called
          :param reason: Completion reason
          :param session: Database session
          """
          callback = self.on_success_callback if success else self.on_failure_callback
          if callback:
              self.log.info('Executing dag callback function: {}'.format(callback))
              tis = dagrun.get_task_instances(session=session)
              ti = tis[-1]  # get first TaskInstance of DagRun
              ti.task = self.get_task(ti.task_id)
              context = ti.get_template_context(session=session)
              context.update({'reason': reason})
              callback(context)
  
      @provide_session
      def get_active_runs(self, session=None):
          """
          Returns a list of dag run execution dates currently running
  
          :param session:
          :return: List of execution dates
          """
          runs = DagRun.find(dag_id=self.dag_id, state=State.RUNNING)
  
          active_dates = []
          for run in runs:
              active_dates.append(run.execution_date)
  
          return active_dates
  
      @provide_session
      def get_num_active_runs(self, external_trigger=None, session=None):
          """
          Returns the number of active "running" dag runs
  
          :param external_trigger: True for externally triggered active dag runs
          :type external_trigger: bool
          :param session:
          :return: number greater than 0 for active dag runs
          """
          query = (session
                   .query(DagRun)
                   .filter(DagRun.dag_id == self.dag_id)
                   .filter(DagRun.state == State.RUNNING))
  
          if external_trigger is not None:
              query = query.filter(DagRun.external_trigger == external_trigger)
  
          return query.count()
  
      @provide_session
      def get_dagrun(self, execution_date, session=None):
          """
          Returns the dag run for a given execution date if it exists, otherwise
          none.
  
          :param execution_date: The execution date of the DagRun to find.
          :param session:
          :return: The DagRun if found, otherwise None.
          """
          dagrun = (
              session.query(DagRun)
              .filter(
                  DagRun.dag_id == self.dag_id,
                  DagRun.execution_date == execution_date)
              .first())
  
          return dagrun
  
      @property
      @provide_session
      def latest_execution_date(self, session=None):
          """
          Returns the latest date for which at least one dag run exists
          """
          execution_date = session.query(func.max(DagRun.execution_date)).filter(
              DagRun.dag_id == self.dag_id
          ).scalar()
          return execution_date
  
      @property
      def subdags(self):
          """
          Returns a list of the subdag objects associated to this DAG
          """
          # Check SubDag for class but don't check class directly, see
          # https://github.com/airbnb/airflow/issues/1168
          from airflow.operators.subdag_operator import SubDagOperator
          subdag_lst = []
          for task in self.tasks:
              if (isinstance(task, SubDagOperator) or
                      # TODO remove in Airflow 2.0
                      type(task).__name__ == 'SubDagOperator'):
                  subdag_lst.append(task.subdag)
                  subdag_lst += task.subdag.subdags
          return subdag_lst
  
      def resolve_template_files(self):
          for t in self.tasks:
              t.resolve_template_files()
  
      def get_template_env(self):
          """
          Returns a jinja2 Environment while taking into account the DAGs
          template_searchpath, user_defined_macros and user_defined_filters
          """
          searchpath = [self.folder]
          if self.template_searchpath:
              searchpath += self.template_searchpath
  
          env = jinja2.Environment(
              loader=jinja2.FileSystemLoader(searchpath),
              extensions=["jinja2.ext.do"],
              cache_size=0)
          if self.user_defined_macros:
              env.globals.update(self.user_defined_macros)
          if self.user_defined_filters:
              env.filters.update(self.user_defined_filters)
  
          return env
  
      def set_dependency(self, upstream_task_id, downstream_task_id):
          """
          Simple utility method to set dependency between two tasks that
          already have been added to the DAG using add_task()
          """
          self.get_task(upstream_task_id).set_downstream(
              self.get_task(downstream_task_id))
  
      def get_task_instances(
              self, session, start_date=None, end_date=None, state=None):
          TI = TaskInstance
          if not start_date:
              start_date = (timezone.utcnow() - timedelta(30)).date()
              start_date = timezone.make_aware(
                  datetime.combine(start_date, datetime.min.time()))
          end_date = end_date or timezone.utcnow()
          tis = session.query(TI).filter(
              TI.dag_id == self.dag_id,
              TI.execution_date >= start_date,
              TI.execution_date <= end_date,
              TI.task_id.in_([t.task_id for t in self.tasks]),
          )
          if state:
              tis = tis.filter(TI.state == state)
          tis = tis.order_by(TI.execution_date).all()
          return tis
  
      @property
      def roots(self):
          return [t for t in self.tasks if not t.downstream_list]
  
      def topological_sort(self):
          """
          Sorts tasks in topographical order, such that a task comes after any of its
          upstream dependencies.
  
          Heavily inspired by:
          http://blog.jupo.org/2012/04/06/topological-sorting-acyclic-directed-graphs/
  
          :return: list of tasks in topological order
          """
  
          # copy the the tasks so we leave it unmodified
          graph_unsorted = self.tasks[:]
  
          graph_sorted = []
  
          # special case
          if len(self.tasks) == 0:
              return tuple(graph_sorted)
  
          # Run until the unsorted graph is empty.
          while graph_unsorted:
              # Go through each of the node/edges pairs in the unsorted
              # graph. If a set of edges doesn't contain any nodes that
              # haven't been resolved, that is, that are still in the
              # unsorted graph, remove the pair from the unsorted graph,
              # and append it to the sorted graph. Note here that by using
              # using the items() method for iterating, a copy of the
              # unsorted graph is used, allowing us to modify the unsorted
              # graph as we move through it. We also keep a flag for
              # checking that that graph is acyclic, which is true if any
              # nodes are resolved during each pass through the graph. If
              # not, we need to bail out as the graph therefore can't be
              # sorted.
              acyclic = False
              for node in list(graph_unsorted):
                  for edge in node.upstream_list:
                      if edge in graph_unsorted:
                          break
                  # no edges in upstream tasks
                  else:
                      acyclic = True
                      graph_unsorted.remove(node)
                      graph_sorted.append(node)
  
              if not acyclic:
                  raise AirflowException("A cyclic dependency occurred in dag: {}"
                                         .format(self.dag_id))
  
          return tuple(graph_sorted)
  
      @provide_session
      def set_dag_runs_state(
              self,
              state=State.RUNNING,
              session=None,
              start_date=None,
              end_date=None,
      ):
          query = session.query(DagRun).filter_by(dag_id=self.dag_id)
          if start_date:
              query = query.filter(DagRun.execution_date >= start_date)
          if end_date:
              query = query.filter(DagRun.execution_date <= end_date)
          drs = query.all()
  
          dirty_ids = []
          for dr in drs:
              dr.state = state
              dirty_ids.append(dr.dag_id)
          DagStat.update(dirty_ids, session=session)
  
      @provide_session
      def clear(
              self, start_date=None, end_date=None,
              only_failed=False,
              only_running=False,
              confirm_prompt=False,
              include_subdags=True,
              reset_dag_runs=True,
              dry_run=False,
              session=None,
      ):
          """
          Clears a set of task instances associated with the current dag for
          a specified date range.
          """
          TI = TaskInstance
          tis = session.query(TI)
          if include_subdags:
              # Crafting the right filter for dag_id and task_ids combo
              conditions = []
              for dag in self.subdags + [self]:
                  conditions.append(
                      TI.dag_id.like(dag.dag_id) &
                      TI.task_id.in_(dag.task_ids)
                  )
              tis = tis.filter(or_(*conditions))
          else:
              tis = session.query(TI).filter(TI.dag_id == self.dag_id)
              tis = tis.filter(TI.task_id.in_(self.task_ids))
  
          if start_date:
              tis = tis.filter(TI.execution_date >= start_date)
          if end_date:
              tis = tis.filter(TI.execution_date <= end_date)
          if only_failed:
              tis = tis.filter(TI.state == State.FAILED)
          if only_running:
              tis = tis.filter(TI.state == State.RUNNING)
  
          if dry_run:
              tis = tis.all()
              session.expunge_all()
              return tis
  
          count = tis.count()
          do_it = True
          if count == 0:
              return 0
          if confirm_prompt:
              ti_list = "\n".join([str(t) for t in tis])
              question = (
                  "You are about to delete these {count} tasks:\n"
                  "{ti_list}\n\n"
                  "Are you sure? (yes/no): ").format(**locals())
              do_it = utils.helpers.ask_yesno(question)
  
          if do_it:
              clear_task_instances(tis.all(),
                                   session,
                                   dag=self,
                                   )
              if reset_dag_runs:
                  self.set_dag_runs_state(session=session,
                                          start_date=start_date,
                                          end_date=end_date,
                                          )
          else:
              count = 0
              print("Bail. Nothing was cleared.")
  
          session.commit()
          return count
  
      @classmethod
      def clear_dags(
              cls, dags,
              start_date=None,
              end_date=None,
              only_failed=False,
              only_running=False,
              confirm_prompt=False,
              include_subdags=True,
              reset_dag_runs=True,
              dry_run=False,
      ):
          all_tis = []
          for dag in dags:
              tis = dag.clear(
                  start_date=start_date,
                  end_date=end_date,
                  only_failed=only_failed,
                  only_running=only_running,
                  confirm_prompt=False,
                  include_subdags=include_subdags,
                  reset_dag_runs=reset_dag_runs,
                  dry_run=True)
              all_tis.extend(tis)
  
          if dry_run:
              return all_tis
  
          count = len(all_tis)
          do_it = True
          if count == 0:
              print("Nothing to clear.")
              return 0
          if confirm_prompt:
              ti_list = "\n".join([str(t) for t in all_tis])
              question = (
                  "You are about to delete these {} tasks:\n"
                  "{}\n\n"
                  "Are you sure? (yes/no): ").format(count, ti_list)
              do_it = utils.helpers.ask_yesno(question)
  
          if do_it:
              for dag in dags:
                  dag.clear(start_date=start_date,
                            end_date=end_date,
                            only_failed=only_failed,
                            only_running=only_running,
                            confirm_prompt=False,
                            include_subdags=include_subdags,
                            reset_dag_runs=reset_dag_runs,
                            dry_run=False,
                            )
          else:
              count = 0
              print("Bail. Nothing was cleared.")
          return count
  
      def __deepcopy__(self, memo):
          # Swiwtcharoo to go around deepcopying objects coming through the
          # backdoor
          cls = self.__class__
          result = cls.__new__(cls)
          memo[id(self)] = result
          for k, v in list(self.__dict__.items()):
              if k not in ('user_defined_macros', 'user_defined_filters', 'params'):
                  setattr(result, k, copy.deepcopy(v, memo))
  
          result.user_defined_macros = self.user_defined_macros
          result.user_defined_filters = self.user_defined_filters
          result.params = self.params
          return result
  
      def sub_dag(self, task_regex, include_downstream=False,
                  include_upstream=True):
          """
          Returns a subset of the current dag as a deep copy of the current dag
          based on a regex that should match one or many tasks, and includes
          upstream and downstream neighbours based on the flag passed.
          """
  
          # deep-copying self.task_dict takes a long time, and we don't want all
          # the tasks anyway, so we copy the tasks manually later
          task_dict = self.task_dict
          self.task_dict = {}
          dag = copy.deepcopy(self)
          self.task_dict = task_dict
  
          regex_match = [
              t for t in self.tasks if re.findall(task_regex, t.task_id)]
          also_include = []
          for t in regex_match:
              if include_downstream:
                  also_include += t.get_flat_relatives(upstream=False)
              if include_upstream:
                  also_include += t.get_flat_relatives(upstream=True)
  
          # Compiling the unique list of tasks that made the cut
          # Make sure to not recursively deepcopy the dag while copying the task
          dag.task_dict = {t.task_id: copy.deepcopy(t, {id(t.dag): t.dag})
                           for t in regex_match + also_include}
          for t in dag.tasks:
              # Removing upstream/downstream references to tasks that did not
              # made the cut
              t._upstream_task_ids = t._upstream_task_ids.intersection(dag.task_dict.keys())
              t._downstream_task_ids = t._downstream_task_ids.intersection(
                  dag.task_dict.keys())
  
          if len(dag.tasks) < len(self.tasks):
              dag.partial = True
  
          return dag
  
      def has_task(self, task_id):
          return task_id in (t.task_id for t in self.tasks)
  
      def get_task(self, task_id):
          if task_id in self.task_dict:
              return self.task_dict[task_id]
          raise AirflowException("Task {task_id} not found".format(**locals()))
  
      @provide_session
      def pickle_info(self, session=None):
          d = {}
          d['is_picklable'] = True
          try:
              dttm = timezone.utcnow()
              pickled = pickle.dumps(self)
              d['pickle_len'] = len(pickled)
              d['pickling_duration'] = "{}".format(timezone.utcnow() - dttm)
          except Exception as e:
              self.log.debug(e)
              d['is_picklable'] = False
              d['stacktrace'] = traceback.format_exc()
          return d
  
      @provide_session
      def pickle(self, session=None):
          dag = session.query(
              DagModel).filter(DagModel.dag_id == self.dag_id).first()
          dp = None
          if dag and dag.pickle_id:
              dp = session.query(DagPickle).filter(
                  DagPickle.id == dag.pickle_id).first()
          if not dp or dp.pickle != self:
              dp = DagPickle(dag=self)
              session.add(dp)
              self.last_pickled = timezone.utcnow()
              session.commit()
              self.pickle_id = dp.id
  
          return dp
  
      def tree_view(self):
          """
          Shows an ascii tree representation of the DAG
          """
          def get_downstream(task, level=0):
              print((" " * level * 4) + str(task))
              level += 1
              for t in task.upstream_list:
                  get_downstream(t, level)
  
          for t in self.roots:
              get_downstream(t)
  
      def add_task(self, task):
          """
          Add a task to the DAG
  
          :param task: the task you want to add
          :type task: task
          """
          if not self.start_date and not task.start_date:
              raise AirflowException("Task is missing the start_date parameter")
          # if the task has no start date, assign it the same as the DAG
          elif not task.start_date:
              task.start_date = self.start_date
          # otherwise, the task will start on the later of its own start date and
          # the DAG's start date
          elif self.start_date:
              task.start_date = max(task.start_date, self.start_date)
  
          # if the task has no end date, assign it the same as the dag
          if not task.end_date:
              task.end_date = self.end_date
          # otherwise, the task will end on the earlier of its own end date and
          # the DAG's end date
          elif task.end_date and self.end_date:
              task.end_date = min(task.end_date, self.end_date)
  
          if task.task_id in self.task_dict:
              # TODO: raise an error in Airflow 2.0
              warnings.warn(
                  'The requested task could not be added to the DAG because a '
                  'task with task_id {} is already in the DAG. Starting in '
                  'Airflow 2.0, trying to overwrite a task will raise an '
                  'exception.'.format(task.task_id),
                  category=PendingDeprecationWarning)
          else:
              self.task_dict[task.task_id] = task
              task.dag = self
  
          self.task_count = len(self.task_dict)
  
      def add_tasks(self, tasks):
          """
          Add a list of tasks to the DAG
  
          :param tasks: a lit of tasks you want to add
          :type tasks: list of tasks
          """
          for task in tasks:
              self.add_task(task)
  
      @provide_session
      def db_merge(self, session=None):
          BO = BaseOperator
          tasks = session.query(BO).filter(BO.dag_id == self.dag_id).all()
          for t in tasks:
              session.delete(t)
          session.commit()
          session.merge(self)
          session.commit()
  
      def run(
              self,
              start_date=None,
              end_date=None,
              mark_success=False,
              local=False,
              executor=None,
              donot_pickle=configuration.conf.getboolean('core', 'donot_pickle'),
              ignore_task_deps=False,
              ignore_first_depends_on_past=False,
              pool=None,
              delay_on_limit_secs=1.0,
              verbose=False,
              conf=None,
              rerun_failed_tasks=False,
      ):
          """
          Runs the DAG.
  
          :param start_date: the start date of the range to run
          :type start_date: datetime
          :param end_date: the end date of the range to run
          :type end_date: datetime
          :param mark_success: True to mark jobs as succeeded without running them
          :type mark_success: bool
          :param local: True to run the tasks using the LocalExecutor
          :type local: bool
          :param executor: The executor instance to run the tasks
          :type executor: BaseExecutor
          :param donot_pickle: True to avoid pickling DAG object and send to workers
          :type donot_pickle: bool
          :param ignore_task_deps: True to skip upstream tasks
          :type ignore_task_deps: bool
          :param ignore_first_depends_on_past: True to ignore depends_on_past
              dependencies for the first set of tasks only
          :type ignore_first_depends_on_past: bool
          :param pool: Resource pool to use
          :type pool: string
          :param delay_on_limit_secs: Time in seconds to wait before next attempt to run
              dag run when max_active_runs limit has been reached
          :type delay_on_limit_secs: float
          :param verbose: Make logging output more verbose
          :type verbose: boolean
          :param conf: user defined dictionary passed from CLI
          :type conf: dict
          """
          from airflow.jobs import BackfillJob
          if not executor and local:
              executor = LocalExecutor()
          elif not executor:
              executor = GetDefaultExecutor()
          job = BackfillJob(
              self,
              start_date=start_date,
              end_date=end_date,
              mark_success=mark_success,
              executor=executor,
              donot_pickle=donot_pickle,
              ignore_task_deps=ignore_task_deps,
              ignore_first_depends_on_past=ignore_first_depends_on_past,
              pool=pool,
              delay_on_limit_secs=delay_on_limit_secs,
              verbose=verbose,
              conf=conf,
              rerun_failed_tasks=rerun_failed_tasks,
          )
          job.run()
  
      def cli(self):
          """
          Exposes a CLI specific to this DAG
          """
          from airflow.bin import cli
          parser = cli.CLIFactory.get_parser(dag_parser=True)
          args = parser.parse_args()
          args.func(args, self)
  
      @provide_session
      def create_dagrun(self,
                        run_id,
                        state,
                        execution_date=None,
                        start_date=None,
                        external_trigger=False,
                        conf=None,
                        session=None):
          """
          Creates a dag run from this dag including the tasks associated with this dag.
          Returns the dag run.
  
          :param run_id: defines the the run id for this dag run
          :type run_id: string
          :param execution_date: the execution date of this dag run
          :type execution_date: datetime
          :param state: the state of the dag run
          :type state: State
          :param start_date: the date this dag run should be evaluated
          :type start_date: datetime
          :param external_trigger: whether this dag run is externally triggered
          :type external_trigger: bool
          :param session: database session
          :type session: Session
          """
          run = DagRun(
              dag_id=self.dag_id,
              run_id=run_id,
              execution_date=execution_date,
              start_date=start_date,
              external_trigger=external_trigger,
              conf=conf,
              state=state
          )
          session.add(run)
  
          DagStat.set_dirty(dag_id=self.dag_id, session=session)
  
          session.commit()
  
          run.dag = self
  
          # create the associated task instances
          # state is None at the moment of creation
          run.verify_integrity(session=session)
  
          run.refresh_from_db()
  
          return run
  
      @provide_session
      def sync_to_db(self, owner=None, sync_time=None, session=None):
          """
          Save attributes about this DAG to the DB. Note that this method
          can be called for both DAGs and SubDAGs. A SubDag is actually a
          SubDagOperator.
  
          :param dag: the DAG object to save to the DB
          :type dag: DAG
          :param sync_time: The time that the DAG should be marked as sync'ed
          :type sync_time: datetime
          :return: None
          """
  
          if owner is None:
              owner = self.owner
          if sync_time is None:
              sync_time = timezone.utcnow()
  
          orm_dag = session.query(
              DagModel).filter(DagModel.dag_id == self.dag_id).first()
          if not orm_dag:
              orm_dag = DagModel(dag_id=self.dag_id)
              self.log.info("Creating ORM DAG for %s", self.dag_id)
          orm_dag.fileloc = self.fileloc
          orm_dag.is_subdag = self.is_subdag
          orm_dag.owners = owner
          orm_dag.is_active = True
          orm_dag.last_scheduler_run = sync_time
          session.merge(orm_dag)
          session.commit()
  
          for subdag in self.subdags:
              subdag.sync_to_db(owner=owner, sync_time=sync_time, session=session)
  
      @staticmethod
      @provide_session
      def deactivate_unknown_dags(active_dag_ids, session=None):
          """
          Given a list of known DAGs, deactivate any other DAGs that are
          marked as active in the ORM
  
          :param active_dag_ids: list of DAG IDs that are active
          :type active_dag_ids: list[unicode]
          :return: None
          """
  
          if len(active_dag_ids) == 0:
              return
          for dag in session.query(
                  DagModel).filter(~DagModel.dag_id.in_(active_dag_ids)).all():
              dag.is_active = False
              session.merge(dag)
  
      @staticmethod
      @provide_session
      def deactivate_stale_dags(expiration_date, session=None):
          """
          Deactivate any DAGs that were last touched by the scheduler before
          the expiration date. These DAGs were likely deleted.
  
          :param expiration_date: set inactive DAGs that were touched before this
              time
          :type expiration_date: datetime
          :return: None
          """
          log = LoggingMixin().log
          for dag in session.query(
                  DagModel).filter(DagModel.last_scheduler_run < expiration_date,
                                   DagModel.is_active).all():
              log.info(
                  "Deactivating DAG ID %s since it was last touched by the scheduler at %s",
                  dag.dag_id, dag.last_scheduler_run.isoformat()
              )
              dag.is_active = False
              session.merge(dag)
              session.commit()
  
      @staticmethod
      @provide_session
      def get_num_task_instances(dag_id, task_ids, states=None, session=None):
          """
          Returns the number of task instances in the given DAG.
  
          :param session: ORM session
          :param dag_id: ID of the DAG to get the task concurrency of
          :type dag_id: unicode
          :param task_ids: A list of valid task IDs for the given DAG
          :type task_ids: list[unicode]
          :param states: A list of states to filter by if supplied
          :type states: list[state]
          :return: The number of running tasks
          :rtype: int
          """
          qry = session.query(func.count(TaskInstance.task_id)).filter(
              TaskInstance.dag_id == dag_id,
              TaskInstance.task_id.in_(task_ids))
          if states is not None:
              if None in states:
                  qry = qry.filter(or_(
                      TaskInstance.state.in_(states),
                      TaskInstance.state.is_(None)))
              else:
                  qry = qry.filter(TaskInstance.state.in_(states))
          return qry.scalar()
  
      def test_cycle(self):
          """
          Check to see if there are any cycles in the DAG. Returns False if no cycle found,
          otherwise raises exception.
          """
  
          # default of int is 0 which corresponds to CYCLE_NEW
          visit_map = defaultdict(int)
          for task_id in self.task_dict.keys():
              # print('starting %s' % task_id)
              if visit_map[task_id] == DagBag.CYCLE_NEW:
                  self._test_cycle_helper(visit_map, task_id)
          return False
  
      def _test_cycle_helper(self, visit_map, task_id):
          """
          Checks if a cycle exists from the input task using DFS traversal
          """
  
          # print('Inspecting %s' % task_id)
          if visit_map[task_id] == DagBag.CYCLE_DONE:
              return False
  
          visit_map[task_id] = DagBag.CYCLE_IN_PROGRESS
  
          task = self.task_dict[task_id]
          for descendant_id in task.get_direct_relative_ids():
              if visit_map[descendant_id] == DagBag.CYCLE_IN_PROGRESS:
                  msg = "Cycle detected in DAG. Faulty task: {0} to {1}".format(
                      task_id, descendant_id)
                  raise AirflowDagCycleException(msg)
              else:
                  self._test_cycle_helper(visit_map, descendant_id)
  
          visit_map[task_id] = DagBag.CYCLE_DONE
  
  
  class Chart(Base):
      __tablename__ = "chart"
  
      id = Column(Integer, primary_key=True)
      label = Column(String(200))
      conn_id = Column(String(ID_LEN), nullable=False)
      user_id = Column(Integer(), ForeignKey('users.id'), nullable=True)
      chart_type = Column(String(100), default="line")
      sql_layout = Column(String(50), default="series")
      sql = Column(Text, default="SELECT series, x, y FROM table")
      y_log_scale = Column(Boolean)
      show_datatable = Column(Boolean)
      show_sql = Column(Boolean, default=True)
      height = Column(Integer, default=600)
      default_params = Column(String(5000), default="{}")
      owner = relationship(
          "User", cascade=False, cascade_backrefs=False, backref='charts')
      x_is_date = Column(Boolean, default=True)
      iteration_no = Column(Integer, default=0)
      last_modified = Column(UtcDateTime, default=timezone.utcnow)
  
      def __repr__(self):
          return self.label
  
  
  class KnownEventType(Base):
      __tablename__ = "known_event_type"
  
      id = Column(Integer, primary_key=True)
      know_event_type = Column(String(200))
  
      def __repr__(self):
          return self.know_event_type
  
  
  class KnownEvent(Base):
      __tablename__ = "known_event"
  
      id = Column(Integer, primary_key=True)
      label = Column(String(200))
      start_date = Column(UtcDateTime)
      end_date = Column(UtcDateTime)
      user_id = Column(Integer(), ForeignKey('users.id'),)
      known_event_type_id = Column(Integer(), ForeignKey('known_event_type.id'),)
      reported_by = relationship(
          "User", cascade=False, cascade_backrefs=False, backref='known_events')
      event_type = relationship(
          "KnownEventType",
          cascade=False,
          cascade_backrefs=False, backref='known_events')
      description = Column(Text)
  
      def __repr__(self):
          return self.label
  
  
  class Variable(Base, LoggingMixin):
      __tablename__ = "variable"
  
      id = Column(Integer, primary_key=True)
      key = Column(String(ID_LEN), unique=True)
      _val = Column('val', Text)
      is_encrypted = Column(Boolean, unique=False, default=False)
  
      def __repr__(self):
          # Hiding the value
          return '{} : {}'.format(self.key, self._val)
  
      def get_val(self):
          log = LoggingMixin().log
          if self._val and self.is_encrypted:
              try:
                  fernet = get_fernet()
                  return fernet.decrypt(bytes(self._val, 'utf-8')).decode()
              except InvalidFernetToken:
                  log.error("Can't decrypt _val for key={}, invalid token "
                            "or value".format(self.key))
                  return None
              except Exception:
                  log.error("Can't decrypt _val for key={}, FERNET_KEY "
                            "configuration missing".format(self.key))
                  return None
          else:
              return self._val
  
      def set_val(self, value):
          if value:
              fernet = get_fernet()
              self._val = fernet.encrypt(bytes(value, 'utf-8')).decode()
              self.is_encrypted = fernet.is_encrypted
  
      @declared_attr
      def val(cls):
          return synonym('_val',
                         descriptor=property(cls.get_val, cls.set_val))
  
      @classmethod
      def setdefault(cls, key, default, deserialize_json=False):
          """
          Like a Python builtin dict object, setdefault returns the current value
          for a key, and if it isn't there, stores the default value and returns it.
  
          :param key: Dict key for this Variable
          :type key: String
          :param default: Default value to set and return if the variable
          isn't already in the DB
          :type default: Mixed
          :param deserialize_json: Store this as a JSON encoded value in the DB
           and un-encode it when retrieving a value
          :return: Mixed
          """
          default_sentinel = object()
          obj = Variable.get(key, default_var=default_sentinel,
                             deserialize_json=deserialize_json)
          if obj is default_sentinel:
              if default is not None:
                  Variable.set(key, default, serialize_json=deserialize_json)
                  return default
              else:
                  raise ValueError('Default Value must be set')
          else:
              return obj
  
      @classmethod
      @provide_session
      def get(cls, key, default_var=None, deserialize_json=False, session=None):
          obj = session.query(cls).filter(cls.key == key).first()
          if obj is None:
              if default_var is not None:
                  return default_var
              else:
                  raise KeyError('Variable {} does not exist'.format(key))
          else:
              if deserialize_json:
                  return json.loads(obj.val)
              else:
                  return obj.val
  
      @classmethod
      @provide_session
      def set(cls, key, value, serialize_json=False, session=None):
  
          if serialize_json:
              stored_value = json.dumps(value)
          else:
              stored_value = str(value)
  
          session.query(cls).filter(cls.key == key).delete()
          session.add(Variable(key=key, val=stored_value))
          session.flush()
  
  
  class XCom(Base, LoggingMixin):
      """
      Base class for XCom objects.
      """
      __tablename__ = "xcom"
  
      id = Column(Integer, primary_key=True)
      key = Column(String(512))
      value = Column(LargeBinary)
      timestamp = Column(
          UtcDateTime, default=timezone.utcnow, nullable=False)
      execution_date = Column(UtcDateTime, nullable=False)
  
      # source information
      task_id = Column(String(ID_LEN), nullable=False)
      dag_id = Column(String(ID_LEN), nullable=False)
  
      __table_args__ = (
          Index('idx_xcom_dag_task_date', dag_id, task_id, execution_date, unique=False),
      )
  
      """
      TODO: "pickling" has been deprecated and JSON is preferred.
            "pickling" will be removed in Airflow 2.0.
      """
      @reconstructor
      def init_on_load(self):
          enable_pickling = configuration.getboolean('core', 'enable_xcom_pickling')
          if enable_pickling:
              self.value = pickle.loads(self.value)
          else:
              try:
                  self.value = json.loads(self.value.decode('UTF-8'))
              except (UnicodeEncodeError, ValueError):
                  # For backward-compatibility.
                  # Preventing errors in webserver
                  # due to XComs mixed with pickled and unpickled.
                  self.value = pickle.loads(self.value)
  
      def __repr__(self):
          return '<XCom "{key}" ({task_id} @ {execution_date})>'.format(
              key=self.key,
              task_id=self.task_id,
              execution_date=self.execution_date)
  
      @classmethod
      @provide_session
      def set(
              cls,
              key,
              value,
              execution_date,
              task_id,
              dag_id,
              session=None):
          """
          Store an XCom value.
          TODO: "pickling" has been deprecated and JSON is preferred.
                "pickling" will be removed in Airflow 2.0.
          :return: None
          """
          session.expunge_all()
  
          enable_pickling = configuration.getboolean('core', 'enable_xcom_pickling')
          if enable_pickling:
              value = pickle.dumps(value)
          else:
              try:
                  value = json.dumps(value).encode('UTF-8')
              except ValueError:
                  log = LoggingMixin().log
                  log.error("Could not serialize the XCOM value into JSON. "
                            "If you are using pickles instead of JSON "
                            "for XCOM, then you need to enable pickle "
                            "support for XCOM in your airflow config.")
                  raise
  
          # remove any duplicate XComs
          session.query(cls).filter(
              cls.key == key,
              cls.execution_date == execution_date,
              cls.task_id == task_id,
              cls.dag_id == dag_id).delete()
  
          session.commit()
  
          # insert new XCom
          session.add(XCom(
              key=key,
              value=value,
              execution_date=execution_date,
              task_id=task_id,
              dag_id=dag_id))
  
          session.commit()
  
      @classmethod
      @provide_session
      def get_one(cls,
                  execution_date,
                  key=None,
                  task_id=None,
                  dag_id=None,
                  include_prior_dates=False,
                  session=None):
          """
          Retrieve an XCom value, optionally meeting certain criteria.
          TODO: "pickling" has been deprecated and JSON is preferred.
                "pickling" will be removed in Airflow 2.0.
          :return: XCom value
          """
          filters = []
          if key:
              filters.append(cls.key == key)
          if task_id:
              filters.append(cls.task_id == task_id)
          if dag_id:
              filters.append(cls.dag_id == dag_id)
          if include_prior_dates:
              filters.append(cls.execution_date <= execution_date)
          else:
              filters.append(cls.execution_date == execution_date)
  
          query = (
              session.query(cls.value).filter(and_(*filters))
                     .order_by(cls.execution_date.desc(), cls.timestamp.desc()))
  
          result = query.first()
          if result:
              enable_pickling = configuration.getboolean('core', 'enable_xcom_pickling')
              if enable_pickling:
                  return pickle.loads(result.value)
              else:
                  try:
                      return json.loads(result.value.decode('UTF-8'))
                  except ValueError:
                      log = LoggingMixin().log
                      log.error("Could not deserialize the XCOM value from JSON. "
                                "If you are using pickles instead of JSON "
                                "for XCOM, then you need to enable pickle "
                                "support for XCOM in your airflow config.")
                      raise
  
      @classmethod
      @provide_session
      def get_many(cls,
                   execution_date,
                   key=None,
                   task_ids=None,
                   dag_ids=None,
                   include_prior_dates=False,
                   limit=100,
                   session=None):
          """
          Retrieve an XCom value, optionally meeting certain criteria
          TODO: "pickling" has been deprecated and JSON is preferred.
                "pickling" will be removed in Airflow 2.0.
          """
          filters = []
          if key:
              filters.append(cls.key == key)
          if task_ids:
              filters.append(cls.task_id.in_(as_tuple(task_ids)))
          if dag_ids:
              filters.append(cls.dag_id.in_(as_tuple(dag_ids)))
          if include_prior_dates:
              filters.append(cls.execution_date <= execution_date)
          else:
              filters.append(cls.execution_date == execution_date)
  
          query = (
              session.query(cls).filter(and_(*filters))
                                .order_by(cls.execution_date.desc(), cls.timestamp.desc())
                                .limit(limit))
          results = query.all()
          return results
  
      @classmethod
      @provide_session
      def delete(cls, xcoms, session=None):
          if isinstance(xcoms, XCom):
              xcoms = [xcoms]
          for xcom in xcoms:
              if not isinstance(xcom, XCom):
                  raise TypeError(
                      'Expected XCom; received {}'.format(xcom.__class__.__name__)
                  )
              session.delete(xcom)
          session.commit()
  
  
  class DagStat(Base):
      __tablename__ = "dag_stats"
  
      dag_id = Column(String(ID_LEN), primary_key=True)
      state = Column(String(50), primary_key=True)
      count = Column(Integer, default=0, nullable=False)
      dirty = Column(Boolean, default=False, nullable=False)
  
      def __init__(self, dag_id, state, count=0, dirty=False):
          self.dag_id = dag_id
          self.state = state
          self.count = count
          self.dirty = dirty
  
      @staticmethod
      @provide_session
      def set_dirty(dag_id, session=None):
          """
          :param dag_id: the dag_id to mark dirty
          :param session: database session
          :return:
          """
          DagStat.create(dag_id=dag_id, session=session)
  
          try:
              stats = session.query(DagStat).filter(
                  DagStat.dag_id == dag_id
              ).with_for_update().all()
  
              for stat in stats:
                  stat.dirty = True
              session.commit()
          except Exception as e:
              session.rollback()
              log = LoggingMixin().log
              log.warning("Could not update dag stats for %s", dag_id)
              log.exception(e)
  
      @staticmethod
      @provide_session
      def update(dag_ids=None, dirty_only=True, session=None):
          """
          Updates the stats for dirty/out-of-sync dags
  
          :param dag_ids: dag_ids to be updated
          :type dag_ids: list
          :param dirty_only: only updated for marked dirty, defaults to True
          :type dirty_only: bool
          :param session: db session to use
          :type session: Session
          """
          try:
              qry = session.query(DagStat)
              if dag_ids:
                  qry = qry.filter(DagStat.dag_id.in_(set(dag_ids)))
              if dirty_only:
                  qry = qry.filter(DagStat.dirty == True) # noqa
  
              qry = qry.with_for_update().all()
  
              ids = set([dag_stat.dag_id for dag_stat in qry])
  
              # avoid querying with an empty IN clause
              if len(ids) == 0:
                  session.commit()
                  return
  
              dagstat_states = set(itertools.product(ids, State.dag_states))
              qry = (
                  session.query(DagRun.dag_id, DagRun.state, func.count('*'))
                  .filter(DagRun.dag_id.in_(ids))
                  .group_by(DagRun.dag_id, DagRun.state)
              )
  
              counts = {(dag_id, state): count for dag_id, state, count in qry}
              for dag_id, state in dagstat_states:
                  count = 0
                  if (dag_id, state) in counts:
                      count = counts[(dag_id, state)]
  
                  session.merge(
                      DagStat(dag_id=dag_id, state=state, count=count, dirty=False)
                  )
  
              session.commit()
          except Exception as e:
              session.rollback()
              log = LoggingMixin().log
              log.warning("Could not update dag stat table")
              log.exception(e)
  
      @staticmethod
      @provide_session
      def create(dag_id, session=None):
          """
          Creates the missing states the stats table for the dag specified
  
          :param dag_id: dag id of the dag to create stats for
          :param session: database session
          :return:
          """
          # unfortunately sqlalchemy does not know upsert
          qry = session.query(DagStat).filter(DagStat.dag_id == dag_id).all()
          states = [dag_stat.state for dag_stat in qry]
          for state in State.dag_states:
              if state not in states:
                  try:
                      session.merge(DagStat(dag_id=dag_id, state=state))
                      session.commit()
                  except Exception as e:
                      session.rollback()
                      log = LoggingMixin().log
                      log.warning("Could not create stat record")
                      log.exception(e)
  
  
  class DagRun(Base, LoggingMixin):
      """
      DagRun describes an instance of a Dag. It can be created
      by the scheduler (for regular runs) or by an external trigger
      """
      __tablename__ = "dag_run"
  
      ID_PREFIX = 'scheduled__'
      ID_FORMAT_PREFIX = ID_PREFIX + '{0}'
  
      id = Column(Integer, primary_key=True)
      dag_id = Column(String(ID_LEN))
      execution_date = Column(UtcDateTime, default=timezone.utcnow)
      start_date = Column(UtcDateTime, default=timezone.utcnow)
      end_date = Column(UtcDateTime)
      _state = Column('state', String(50), default=State.RUNNING)
      run_id = Column(String(ID_LEN))
      external_trigger = Column(Boolean, default=True)
      conf = Column(PickleType)
  
      dag = None
  
      __table_args__ = (
          Index('dag_id_state', dag_id, _state),
          UniqueConstraint('dag_id', 'execution_date'),
          UniqueConstraint('dag_id', 'run_id'),
      )
  
      def __repr__(self):
          return (
              '<DagRun {dag_id} @ {execution_date}: {run_id}, '
              'externally triggered: {external_trigger}>'
          ).format(
              dag_id=self.dag_id,
              execution_date=self.execution_date,
              run_id=self.run_id,
              external_trigger=self.external_trigger)
  
      def get_state(self):
          return self._state
  
      def set_state(self, state):
          if self._state != state:
              self._state = state
              if self.dag_id is not None:
                  # FIXME: Due to the scoped_session factor we we don't get a clean
                  # session here, so something really weird goes on:
                  # if you try to close the session dag runs will end up detached
                  session = settings.Session()
                  DagStat.set_dirty(self.dag_id, session=session)
  
      @declared_attr
      def state(self):
          return synonym('_state',
                         descriptor=property(self.get_state, self.set_state))
  
      @classmethod
      def id_for_date(cls, date, prefix=ID_FORMAT_PREFIX):
          return prefix.format(date.isoformat()[:19])
  
      @provide_session
      def refresh_from_db(self, session=None):
          """
          Reloads the current dagrun from the database
          :param session: database session
          """
          DR = DagRun
  
          exec_date = func.cast(self.execution_date, DateTime)
  
          dr = session.query(DR).filter(
              DR.dag_id == self.dag_id,
              func.cast(DR.execution_date, DateTime) == exec_date,
              DR.run_id == self.run_id
          ).one()
  
          self.id = dr.id
          self.state = dr.state
  
      @staticmethod
      @provide_session
      def find(dag_id=None, run_id=None, execution_date=None,
               state=None, external_trigger=None, no_backfills=False,
               session=None):
          """
          Returns a set of dag runs for the given search criteria.
  
          :param dag_id: the dag_id to find dag runs for
          :type dag_id: integer, list
          :param run_id: defines the the run id for this dag run
          :type run_id: string
          :param execution_date: the execution date
          :type execution_date: datetime
          :param state: the state of the dag run
          :type state: State
          :param external_trigger: whether this dag run is externally triggered
          :type external_trigger: bool
          :param no_backfills: return no backfills (True), return all (False).
          Defaults to False
          :type no_backfills: bool
          :param session: database session
          :type session: Session
          """
          DR = DagRun
  
          qry = session.query(DR)
          if dag_id:
              qry = qry.filter(DR.dag_id == dag_id)
          if run_id:
              qry = qry.filter(DR.run_id == run_id)
          if execution_date:
              if isinstance(execution_date, list):
                  qry = qry.filter(DR.execution_date.in_(execution_date))
              else:
                  qry = qry.filter(DR.execution_date == execution_date)
          if state:
              qry = qry.filter(DR.state == state)
          if external_trigger is not None:
              qry = qry.filter(DR.external_trigger == external_trigger)
          if no_backfills:
              # in order to prevent a circular dependency
              from airflow.jobs import BackfillJob
              qry = qry.filter(DR.run_id.notlike(BackfillJob.ID_PREFIX + '%'))
  
          dr = qry.order_by(DR.execution_date).all()
  
          return dr
  
      @provide_session
      def get_task_instances(self, state=None, session=None):
          """
          Returns the task instances for this dag run
          """
          TI = TaskInstance
          tis = session.query(TI).filter(
              TI.dag_id == self.dag_id,
              TI.execution_date == self.execution_date,
          )
          if state:
              if isinstance(state, six.string_types):
                  tis = tis.filter(TI.state == state)
              else:
                  # this is required to deal with NULL values
                  if None in state:
                      tis = tis.filter(
                          or_(TI.state.in_(state),
                              TI.state.is_(None))
                      )
                  else:
                      tis = tis.filter(TI.state.in_(state))
  
          if self.dag and self.dag.partial:
              tis = tis.filter(TI.task_id.in_(self.dag.task_ids))
  
          return tis.all()
  
      @provide_session
      def get_task_instance(self, task_id, session=None):
          """
          Returns the task instance specified by task_id for this dag run
  
          :param task_id: the task id
          """
  
          TI = TaskInstance
          ti = session.query(TI).filter(
              TI.dag_id == self.dag_id,
              TI.execution_date == self.execution_date,
              TI.task_id == task_id
          ).first()
  
          return ti
  
      def get_dag(self):
          """
          Returns the Dag associated with this DagRun.
  
          :return: DAG
          """
          if not self.dag:
              raise AirflowException("The DAG (.dag) for {} needs to be set"
                                     .format(self))
  
          return self.dag
  
      @provide_session
      def get_previous_dagrun(self, session=None):
          """The previous DagRun, if there is one"""
  
          return session.query(DagRun).filter(
              DagRun.dag_id == self.dag_id,
              DagRun.execution_date < self.execution_date
          ).order_by(
              DagRun.execution_date.desc()
          ).first()
  
      @provide_session
      def get_previous_scheduled_dagrun(self, session=None):
          """The previous, SCHEDULED DagRun, if there is one"""
          dag = self.get_dag()
  
          return session.query(DagRun).filter(
              DagRun.dag_id == self.dag_id,
              DagRun.execution_date == dag.previous_schedule(self.execution_date)
          ).first()
  
      @provide_session
      def update_state(self, session=None):
          """
          Determines the overall state of the DagRun based on the state
          of its TaskInstances.
  
          :return: State
          """
  
          dag = self.get_dag()
  
          tis = self.get_task_instances(session=session)
  
          self.log.debug("Updating state for %s considering %s task(s)", self, len(tis))
  
          for ti in list(tis):
              # skip in db?
              if ti.state == State.REMOVED:
                  tis.remove(ti)
              else:
                  ti.task = dag.get_task(ti.task_id)
  
          # pre-calculate
          # db is faster
          start_dttm = timezone.utcnow()
          unfinished_tasks = self.get_task_instances(
              state=State.unfinished(),
              session=session
          )
          none_depends_on_past = all(not t.task.depends_on_past for t in unfinished_tasks)
          none_task_concurrency = all(t.task.task_concurrency is None
                                      for t in unfinished_tasks)
          # small speed up
          if unfinished_tasks and none_depends_on_past and none_task_concurrency:
              # todo: this can actually get pretty slow: one task costs between 0.01-015s
              no_dependencies_met = True
              for ut in unfinished_tasks:
                  # We need to flag upstream and check for changes because upstream
                  # failures can result in deadlock false positives
                  old_state = ut.state
                  deps_met = ut.are_dependencies_met(
                      dep_context=DepContext(
                          flag_upstream_failed=True,
                          ignore_in_retry_period=True),
                      session=session)
                  if deps_met or old_state != ut.current_state(session=session):
                      no_dependencies_met = False
                      break
  
          duration = (timezone.utcnow() - start_dttm).total_seconds() * 1000
          Stats.timing("dagrun.dependency-check.{}".format(self.dag_id), duration)
  
          # future: remove the check on adhoc tasks (=active_tasks)
          if len(tis) == len(dag.active_tasks):
              root_ids = [t.task_id for t in dag.roots]
              roots = [t for t in tis if t.task_id in root_ids]
  
              # if all roots finished and at least one failed, the run failed
              if (not unfinished_tasks and
                      any(r.state in (State.FAILED, State.UPSTREAM_FAILED) for r in roots)):
                  self.log.info('Marking run %s failed', self)
                  self.state = State.FAILED
                  dag.handle_callback(self, success=False, reason='task_failure',
                                      session=session)
  
              # if all roots succeeded and no unfinished tasks, the run succeeded
              elif not unfinished_tasks and all(r.state in (State.SUCCESS, State.SKIPPED)
                                                for r in roots):
                  self.log.info('Marking run %s successful', self)
                  self.state = State.SUCCESS
                  dag.handle_callback(self, success=True, reason='success', session=session)
  
              # if *all tasks* are deadlocked, the run failed
              elif (unfinished_tasks and none_depends_on_past and
                    none_task_concurrency and no_dependencies_met):
                  self.log.info('Deadlock; marking run %s failed', self)
                  self.state = State.FAILED
                  dag.handle_callback(self, success=False, reason='all_tasks_deadlocked',
                                      session=session)
  
              # finally, if the roots aren't done, the dag is still running
              else:
                  self.state = State.RUNNING
  
          # todo: determine we want to use with_for_update to make sure to lock the run
          session.merge(self)
          session.commit()
  
          return self.state
  
      @provide_session
      def verify_integrity(self, session=None):
          """
          Verifies the DagRun by checking for removed tasks or tasks that are not in the
          database yet. It will set state to removed or add the task if required.
          """
          dag = self.get_dag()
          tis = self.get_task_instances(session=session)
  
          # check for removed or restored tasks
          task_ids = []
          for ti in tis:
              task_ids.append(ti.task_id)
              task = None
              try:
                  task = dag.get_task(ti.task_id)
              except AirflowException:
                  if ti.state == State.REMOVED:
                      pass  # ti has already been removed, just ignore it
                  elif self.state is not State.RUNNING and not dag.partial:
                      self.log.warning("Failed to get task '{}' for dag '{}'. "
                                       "Marking it as removed.".format(ti, dag))
                      Stats.incr(
                          "task_removed_from_dag.{}".format(dag.dag_id), 1, 1)
                      ti.state = State.REMOVED
  
              is_task_in_dag = task is not None
              should_restore_task = is_task_in_dag and ti.state == State.REMOVED
              if should_restore_task:
                  self.log.info("Restoring task '{}' which was previously "
                                "removed from DAG '{}'".format(ti, dag))
                  Stats.incr("task_restored_to_dag.{}".format(dag.dag_id), 1, 1)
                  ti.state = State.NONE
  
          # check for missing tasks
          for task in six.itervalues(dag.task_dict):
              if task.adhoc:
                  continue
  
              if task.task_id not in task_ids:
                  ti = TaskInstance(task, self.execution_date)
                  session.add(ti)
  
          session.commit()
  
      @staticmethod
      def get_run(session, dag_id, execution_date):
          """
          :param dag_id: DAG ID
          :type dag_id: unicode
          :param execution_date: execution date
          :type execution_date: datetime
          :return: DagRun corresponding to the given dag_id and execution date
          if one exists. None otherwise.
          :rtype: DagRun
          """
          qry = session.query(DagRun).filter(
              DagRun.dag_id == dag_id,
              DagRun.external_trigger == False, # noqa
              DagRun.execution_date == execution_date,
          )
          return qry.first()
  
      @property
      def is_backfill(self):
          from airflow.jobs import BackfillJob
          return (
              self.run_id is not None and
              self.run_id.startswith(BackfillJob.ID_PREFIX)
          )
  
      @classmethod
      @provide_session
      def get_latest_runs(cls, session):
          """Returns the latest DagRun for each DAG. """
          subquery = (
              session
              .query(
                  cls.dag_id,
                  func.max(cls.execution_date).label('execution_date'))
              .group_by(cls.dag_id)
              .subquery()
          )
          dagruns = (
              session
              .query(cls)
              .join(subquery,
                    and_(cls.dag_id == subquery.c.dag_id,
                         cls.execution_date == subquery.c.execution_date))
              .all()
          )
          return dagruns
  
  
  class Pool(Base):
      __tablename__ = "slot_pool"
  
      id = Column(Integer, primary_key=True)
      pool = Column(String(50), unique=True)
      slots = Column(Integer, default=0)
      description = Column(Text)
  
      def __repr__(self):
          return self.pool
  
      def to_json(self):
          return {
              'id': self.id,
              'pool': self.pool,
              'slots': self.slots,
              'description': self.description,
          }
  
      @provide_session
      def used_slots(self, session):
          """
          Returns the number of slots used at the moment
          """
          running = (
              session
              .query(TaskInstance)
              .filter(TaskInstance.pool == self.pool)
              .filter(TaskInstance.state == State.RUNNING)
              .count()
          )
          return running
  
      @provide_session
      def queued_slots(self, session):
          """
          Returns the number of slots used at the moment
          """
          return (
              session
              .query(TaskInstance)
              .filter(TaskInstance.pool == self.pool)
              .filter(TaskInstance.state == State.QUEUED)
              .count()
          )
  
      @provide_session
      def open_slots(self, session):
          """
          Returns the number of slots open at the moment
          """
          used_slots = self.used_slots(session=session)
          queued_slots = self.queued_slots(session=session)
          return self.slots - used_slots - queued_slots
  
  
  class SlaMiss(Base):
      """
      Model that stores a history of the SLA that have been missed.
      It is used to keep track of SLA failures over time and to avoid double
      triggering alert emails.
      """
      __tablename__ = "sla_miss"
  
      task_id = Column(String(ID_LEN), primary_key=True)
      dag_id = Column(String(ID_LEN), primary_key=True)
      execution_date = Column(UtcDateTime, primary_key=True)
      email_sent = Column(Boolean, default=False)
      timestamp = Column(UtcDateTime)
      description = Column(Text)
      notification_sent = Column(Boolean, default=False)
  
      def __repr__(self):
          return str((
              self.dag_id, self.task_id, self.execution_date.isoformat()))
  
  
  class ImportError(Base):
      __tablename__ = "import_error"
      id = Column(Integer, primary_key=True)
      timestamp = Column(UtcDateTime)
      filename = Column(String(1024))
      stacktrace = Column(Text)
  
  
  class KubeResourceVersion(Base):
      __tablename__ = "kube_resource_version"
      one_row_id = Column(Boolean, server_default=sqltrue(), primary_key=True)
      resource_version = Column(String(255))
  
      @staticmethod
      @provide_session
      def get_current_resource_version(session=None):
          (resource_version,) = session.query(KubeResourceVersion.resource_version).one()
          return resource_version
  
      @staticmethod
      @provide_session
      def checkpoint_resource_version(resource_version, session=None):
          if resource_version:
              session.query(KubeResourceVersion).update({
                  KubeResourceVersion.resource_version: resource_version
              })
              session.commit()
  
      @staticmethod
      @provide_session
      def reset_resource_version(session=None):
          session.query(KubeResourceVersion).update({
              KubeResourceVersion.resource_version: '0'
          })
          session.commit()
          return '0'
  
  
  class KubeWorkerIdentifier(Base):
      __tablename__ = "kube_worker_uuid"
      one_row_id = Column(Boolean, server_default=sqltrue(), primary_key=True)
      worker_uuid = Column(String(255))
  
      @staticmethod
      @provide_session
      def get_or_create_current_kube_worker_uuid(session=None):
          (worker_uuid,) = session.query(KubeWorkerIdentifier.worker_uuid).one()
          if worker_uuid == '':
              worker_uuid = str(uuid.uuid4())
              KubeWorkerIdentifier.checkpoint_kube_worker_uuid(worker_uuid, session)
          return worker_uuid
  
      @staticmethod
      @provide_session
      def checkpoint_kube_worker_uuid(worker_uuid, session=None):
          if worker_uuid:
              session.query(KubeWorkerIdentifier).update({
                  KubeWorkerIdentifier.worker_uuid: worker_uuid
              })
              session.commit()
  