text: # -*- coding: utf-8 -*-
  #
  # Licensed 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
  
  install_aliases()
  from builtins import str
  from builtins import object, bytes
  import copy
  from collections import namedtuple
  from datetime import datetime, timedelta
  import dill
  import functools
  import getpass
  import imp
  import importlib
  import zipfile
  import jinja2
  import json
  import logging
  import os
  import pickle
  import re
  import signal
  import socket
  import sys
  import textwrap
  import traceback
  import warnings
  import hashlib
  
  from urllib.parse import urlparse
  
  from sqlalchemy import (
      Column, Integer, String, DateTime, Text, Boolean, ForeignKey, PickleType,
      Index, Float)
  from sqlalchemy import func, or_, and_
  from sqlalchemy.ext.declarative import declarative_base, declared_attr
  from sqlalchemy.dialects.mysql import LONGTEXT
  from sqlalchemy.orm import reconstructor, relationship, synonym
  
  from croniter import croniter
  import six
  
  from airflow import settings, utils
  from airflow.executors import DEFAULT_EXECUTOR, LocalExecutor
  from airflow import configuration
  from airflow.exceptions import AirflowException, AirflowSkipException
  from airflow.dag.base_dag import BaseDag, BaseDagBag
  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.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, is_in, validate_key, pprinttable)
  from airflow.utils.logging import LoggingMixin
  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
  
  Base = declarative_base()
  ID_LEN = 250
  SQL_ALCHEMY_CONN = configuration.get('core', 'SQL_ALCHEMY_CONN')
  DAGS_FOLDER = os.path.expanduser(configuration.get('core', 'DAGS_FOLDER'))
  XCOM_RETURN_KEY = 'return_value'
  
  Stats = settings.Stats
  
  ENCRYPTION_ON = False
  try:
      from cryptography.fernet import Fernet
      FERNET = Fernet(configuration.get('core', 'FERNET_KEY').encode('utf-8'))
      ENCRYPTION_ON = True
  except:
      pass
  
  if 'mysql' in SQL_ALCHEMY_CONN:
      LongText = LONGTEXT
  else:
      LongText = Text
  
  # used by DAG context_managers
  _CONTEXT_MANAGER_DAG = None
  
  
  def clear_task_instances(tis, session, activate_dag_runs=True):
      """
      Clears a set of task instances, but makes sure the running ones
      get killed.
      """
      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)
          # todo: this creates an issue with the webui tests
          # elif ti.state != State.REMOVED:
          #     ti.state = State.NONE
          #     session.merge(ti)
          else:
              session.delete(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:
          execution_dates = {ti.execution_date for ti in tis}
          dag_ids = {ti.dag_id for ti in tis}
          drs = session.query(DagRun).filter(
              DagRun.dag_id.in_(dag_ids),
              DagRun.execution_date.in_(execution_dates),
          ).all()
          for dr in drs:
              dr.state = State.RUNNING
              dr.start_date = datetime.now()
  
  
  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 sync_to_db: whether to sync the properties of the DAGs to
          the metadata DB while finding them, typically should be done
          by the scheduler job only
      :type sync_to_db: bool
      """
      def __init__(
              self,
              dag_folder=None,
              executor=DEFAULT_EXECUTOR,
              include_examples=configuration.getboolean('core', 'LOAD_EXAMPLES')):
  
          dag_folder = dag_folder or DAGS_FOLDER
          self.logger.info("Filling up the DagBag from {}".format(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 = {}
  
          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 [dag.dag_id for 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 = []
  
          # todo: raise exception?
          if 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:
              logging.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')]):
                          return found_dags
  
              self.logger.debug("Importing {}".format(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.getint('core', "DAGBAG_IMPORT_TIMEOUT")):
                  try:
                      m = imp.load_source(mod_name, filepath)
                      mods.append(m)
                  except Exception as e:
                      self.logger.exception("Failed to import: " + 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.logger.warning("Found __init__.{0} at root of {1}".
                                              format(ext, filepath))
  
                      if safe_mode:
                          with zip_file.open(mod.filename) as zf:
                              self.logger.debug("Reading {} from {}".
                                                format(mod.filename, filepath))
                              content = zf.read()
                              if not all([s in content for s in (b'DAG', b'airflow')]):
                                  # todo: create ignore list
                                  return found_dags
  
                      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.logger.exception("Failed to import: " + 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
                      dag.is_subdag = False
                      dag.module_name = m.__name__
                      self.bag_dag(dag, parent_dag=dag, root_dag=dag)
                      found_dags.append(dag)
                      found_dags += dag.subdags
  
          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.logger.info("Finding 'running' jobs without a recent heartbeat")
          TI = TaskInstance
          secs = (
              configuration.getint('scheduler', 'job_heartbeat_sec') * 3) + 120
          limit_dttm = datetime.now() - timedelta(seconds=secs)
          self.logger.info(
              "Failing jobs without heartbeat after {}".format(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)
                      ti.task = task
                      ti.handle_failure("{} killed as zombie".format(ti))
                      self.logger.info(
                          'Marked zombie job {} as failed'.format(ti))
                      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.
          """
          self.dags[dag.dag_id] = dag
          dag.resolve_template_files()
          dag.last_loaded = datetime.now()
  
          for task in dag.tasks:
              settings.policy(task)
  
          for subdag in dag.subdags:
              subdag.full_filepath = dag.full_filepath
              subdag.parent_dag = dag
              subdag.fileloc = root_dag.full_filepath
              subdag.is_subdag = True
              self.bag_dag(subdag, parent_dag=dag, root_dag=root_dag)
          self.logger.debug('Loaded DAG {dag}'.format(**locals()))
  
      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.
          """
          start_dttm = datetime.now()
          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")
          if os.path.isfile(dag_folder):
              self.process_file(dag_folder, only_if_updated=only_if_updated)
          elif os.path.isdir(dag_folder):
              patterns = []
              for root, dirs, files in os.walk(dag_folder, followlinks=True):
                  ignore_file = [f for f in files if f == '.airflowignore']
                  if ignore_file:
                      f = open(os.path.join(root, ignore_file[0]), 'r')
                      patterns += [p for p in f.read().split('\n') if p]
                      f.close()
                  for f in files:
                      try:
                          filepath = os.path.join(root, f)
                          if not os.path.isfile(filepath):
                              continue
                          mod_name, file_ext = os.path.splitext(
                              os.path.split(filepath)[-1])
                          if file_ext != '.py' and not zipfile.is_zipfile(filepath):
                              continue
                          if not any(
                                  [re.findall(p, filepath) for p in patterns]):
                              ts = datetime.now()
                              found_dags = self.process_file(
                                  filepath, only_if_updated=only_if_updated)
  
                              td = datetime.now() - 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:
                          logging.warning(e)
          Stats.gauge(
              'collect_dags', (datetime.now() - 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.dag_num for o in stats]),
              table=pprinttable(stats),
          )
  
      def deactivate_inactive_dags(self):
          active_dag_ids = [dag.dag_id for dag in list(self.dags.values())]
          session = settings.Session()
          for dag in session.query(
                  DagModel).filter(~DagModel.dag_id.in_(active_dag_ids)).all():
              dag.is_active = False
              session.merge(dag)
          session.commit()
          session.close()
  
      def paused_dags(self):
          session = settings.Session()
          dag_ids = [dp.dag_id for dp in session.query(DagModel).filter(
              DagModel.is_paused.is_(True))]
          session.commit()
          session.close()
          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):
      """
      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 = [
          ('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',),
          ('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'),
      ]
  
      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
  
      def get_password(self):
          if self._password and self.is_encrypted:
              if not ENCRYPTION_ON:
                  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:
              try:
                  self._password = FERNET.encrypt(bytes(value, 'utf-8')).decode()
                  self.is_encrypted = True
              except NameError:
                  self._password = value
                  self.is_encrypted = False
  
      @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:
              if not ENCRYPTION_ON:
                  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:
              try:
                  self._extra = FERNET.encrypt(bytes(value, 'utf-8')).decode()
                  self.is_extra_encrypted = True
              except NameError:
                  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)
          except:
              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:
                  logging.exception(e)
                  logging.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(DateTime, default=func.now())
      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):
      """
      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 SqlAchemy 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(DateTime, primary_key=True)
      start_date = Column(DateTime)
      end_date = Column(DateTime)
      duration = Column(Float)
      state = Column(String(20))
      try_number = Column(Integer, default=0)
      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(DateTime)
  
      __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),
      )
  
      def __init__(self, task, execution_date, state=None):
          self.dag_id = task.dag_id
          self.task_id = task.task_id
          self.execution_date = execution_date
          self.task = task
          self.queue = task.queue
          self.pool = task.pool
          self.priority_weight = task.priority_weight_total
          self.try_number = 0
          self.unixname = getpass.getuser()
          if state:
              self.state = state
          self.hostname = ''
          self.init_on_load()
  
      @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'
  
      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):
          """
          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_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,
              file_path=path,
              raw=raw,
              job_id=job_id,
              pool=pool)
  
      @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
                           ):
          """
          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 ignoreable 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
          :return: shell command that can be used to run the task instance
          """
          iso = execution_date.isoformat()
          cmd = "airflow run {dag_id} {task_id} {iso} "
          cmd += "--mark_success " if mark_success else ""
          cmd += "--pickle {pickle_id} " if pickle_id else ""
          cmd += "--job_id {job_id} " if job_id else ""
          cmd += "-A " if ignore_all_deps else ""
          cmd += "-i " if ignore_task_deps else ""
          cmd += "-I " if ignore_depends_on_past else ""
          cmd += "--force " if ignore_ti_state else ""
          cmd += "--local " if local else ""
          cmd += "--pool {pool} " if pool else ""
          cmd += "--raw " if raw else ""
          cmd += "-sd {file_path}" if file_path else ""
          return cmd.format(**locals())
  
      @property
      def log_filepath(self):
          iso = self.execution_date.isoformat()
          log = os.path.expanduser(configuration.get('core', 'BASE_LOG_FOLDER'))
          return (
              "{log}/{self.dag_id}/{self.task_id}/{iso}.log".format(**locals()))
  
      @property
      def log_url(self):
          iso = self.execution_date.isoformat()
          BASE_URL = configuration.get('webserver', 'BASE_URL')
          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 = self.execution_date.isoformat()
          BASE_URL = configuration.get('webserver', 'BASE_URL')
          return BASE_URL + (
              "/admin/airflow/action"
              "?action=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.
          """
          logging.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
              self.try_number = ti.try_number
              self.hostname = ti.hostname
          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
  
      def set_state(self, state, session):
          self.state = state
          self.start_date = datetime.now()
          self.end_date = datetime.now()
          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 """
          return session.query(TaskInstance).filter(
              TaskInstance.dag_id == self.dag_id,
              TaskInstance.task_id == self.task.task_id,
              TaskInstance.execution_date ==
              self.task.dag.previous_schedule(self.execution_date),
          ).first()
  
      @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 or not to print details on failed dependencies
          :type verbose: boolean
          """
          dep_context = dep_context or DepContext()
          for dep_status in self.get_failed_dep_statuses(
                  dep_context=dep_context,
                  session=session):
              if verbose:
                  logging.warning(
                      "Dependencies not met for %s, dependency '%s' FAILED: %s",
                      self, dep_status.dep_name, dep_status.reason)
              return False
          if verbose:
              logging.info("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):
                  if dep_status.passed:
                      logging.debug("%s dependency '%s' PASSED: %s",
                                    self,
                                    dep_status.dep_name,
                                    dep_status.reason)
                  else:
                      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:
              delay_backoff_in_seconds = delay.total_seconds() ** self.try_number
              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() < datetime.now())
  
      @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 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):
          """
          Runs the task instance.
  
          :param verbose: whether to turn on more verbose loggin
          :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
          """
          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 = socket.getfqdn()
          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
  
          self.clear_xcom_data()
          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 % (task.retries + 1) + 1,
              total=task.retries + 1)
          self.start_date = datetime.now()
  
          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:
              if self.state != State.QUEUED:
                  # If a task's dependencies are met but it can't be run yet then queue it
                  # instead
                  self.state = State.QUEUED
                  msg = "Queuing attempt {attempt} of {total}".format(
                      attempt=self.try_number % (task.retries + 1) + 1,
                      total=task.retries + 1)
                  logging.info(hr + msg + hr)
  
                  self.queued_dttm = datetime.now()
                  msg = "Queuing into pool {}".format(self.pool)
                  logging.info(msg)
                  session.merge(self)
              session.commit()
              return
  
          # 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)
              logging.warn(msg)
              session.commit()
              return
  
          # print status message
          logging.info(hr + msg + hr)
          self.try_number += 1
  
          if not test_mode:
              session.add(Log(State.RUNNING, self))
          self.state = State.RUNNING
          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 "
              else:
                  msg = "Executing "
              msg += "{self.task} on {self.execution_date}"
  
          context = {}
          try:
              logging.info(msg.format(self=self))
              if not mark_success:
                  context = self.get_template_context()
  
                  task_copy = copy.copy(task)
                  self.task = task_copy
  
                  def signal_handler(signum, frame):
                      '''Setting kill signal handler'''
                      logging.error("Killing subprocess")
                      task_copy.on_kill()
                      raise AirflowException("Task received SIGTERM signal")
                  signal.signal(signal.SIGTERM, signal_handler)
  
                  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:
                      with timeout(int(
                              task_copy.execution_timeout.total_seconds())):
                          result = task_copy.execute(context=context)
  
                  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)
  
                  task_copy.post_execute(context=context)
                  Stats.incr('operator_successes_{}'.format(
                      self.task.__class__.__name__), 1, 1)
              self.state = State.SUCCESS
          except AirflowSkipException:
              self.state = State.SKIPPED
          except (Exception, KeyboardInterrupt) as e:
              self.handle_failure(e, test_mode, context)
              raise
  
          # Recording SUCCESS
          self.end_date = datetime.now()
          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:
              logging.error("Failed when executing success callback")
              logging.exception(e3)
  
          session.commit()
  
      def dry_run(self):
          task = self.task
          task_copy = copy.copy(task)
          self.task = task_copy
  
          self.render_templates()
          task_copy.dry_run()
  
      def handle_failure(self, error, test_mode=False, context=None):
          logging.exception(error)
          task = self.task
          session = settings.Session()
          self.end_date = datetime.now()
          self.set_duration()
          Stats.incr('operator_failures_{}'.format(task.__class__.__name__), 1, 1)
          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:
              if task.retries and self.try_number % (task.retries + 1) != 0:
                  self.state = State.UP_FOR_RETRY
                  logging.info('Marking task as UP_FOR_RETRY')
                  if task.email_on_retry and task.email:
                      self.email_alert(error, is_retry=True)
              else:
                  self.state = State.FAILED
                  if task.retries:
                      logging.info('All retries failed; marking task as FAILED')
                  else:
                      logging.info('Marking task as FAILED.')
                  if task.email_on_failure and task.email:
                      self.email_alert(error, is_retry=False)
          except Exception as e2:
              logging.error(
                  'Failed to send email to: ' + str(task.email))
              logging.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:
              logging.error("Failed at executing callback")
              logging.exception(e3)
  
          if not test_mode:
              session.merge(self)
          session.commit()
          logging.error(str(error))
  
      @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.isoformat()[:10]
          ts = self.execution_date.isoformat()
          yesterday_ds = (self.execution_date - timedelta(1)).isoformat()[:10]
          tomorrow_ds = (self.execution_date + timedelta(1)).isoformat()[:10]
  
          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)
  
          class VariableAccessor:
              """
              Wrapper around Variable. This way you can get variables in templates by using
              {var.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:
              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,
              '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,
              '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()
              }
          }
  
      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, is_retry=False):
          task = self.task
          title = "Airflow alert: {self}".format(**locals())
          exception = str(exception).replace('\n', '<br>')
          try_ = task.retries + 1
          body = (
              "Try {self.try_number} out of {try_}<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(**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,
              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)
  
  
  class TaskFail(Base):
      """
      TaskFail tracks the failed run durations of each task instance.
      """
  
      __tablename__ = "task_fail"
  
      task_id = Column(String(ID_LEN), primary_key=True)
      dag_id = Column(String(ID_LEN), primary_key=True)
      execution_date = Column(DateTime, primary_key=True)
      start_date = Column(DateTime)
      end_date = Column(DateTime)
      duration = Column(Float)
  
      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
          self.duration = (self.end_date - self.start_date).total_seconds()
  
  
  class Log(Base):
      """
      Used to actively log events to the database
      """
  
      __tablename__ = "log"
  
      id = Column(Integer, primary_key=True)
      dttm = Column(DateTime)
      dag_id = Column(String(ID_LEN))
      task_id = Column(String(ID_LEN))
      event = Column(String(30))
      execution_date = Column(DateTime)
      owner = Column(String(500))
      extra = Column(Text)
  
      def __init__(self, event, task_instance, owner=None, extra=None, **kwargs):
          self.dttm = datetime.now()
          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
  
  
  @functools.total_ordering
  class BaseOperator(object):
      """
      Abstract base class for all operators. Since operators create objects that
      become node 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 task should perform or trigger certain tasks
      synchronously (wait for completion). Example of operators could be an
      operator the 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.
  
      Note that this class is derived from SQLAlchemy's Base class, which
      allows us to push metadata regarding tasks to the database. Deriving this
      classes needs to implement the polymorphic specificities documented in
      SQLAlchemy. This should become clear while reading the code for other
      operators.
  
      :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 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 dan 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`` excepts
          that it is executed when retries occur.
      :param on_success_callback: much like the ``on_failure_callback`` excepts
          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
      """
  
      # For derived classes to define which fields will get jinjaified
      template_fields = []
      # Defines wich files extensions to look for in the templated fields
      template_ext = []
      # Defines the color in the UI
      ui_color = '#fff'
      ui_fgcolor = '#000'
  
      @apply_defaults
      def __init__(
              self,
              task_id,
              owner=configuration.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,
              queue=configuration.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,
              *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):
              logging.warning(
                  "start_date for {} isn't datetime.datetime".format(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, 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:
              logging.warning(
                  "schedule_interval is used for {}, though it has "
                  "been deprecated as a task parameter, you need to "
                  "specify it as a DAG parameter instead".format(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:
              logging.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
          self.resources = Resources(**(resources or {}))
  
          # Private attributes
          self._upstream_task_ids = []
          self._downstream_task_ids = []
  
          if not dag and _CONTEXT_MANAGER_DAG:
              dag = _CONTEXT_MANAGER_DAG
          if dag:
              self.dag = dag
  
          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/overriden 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):
          return sum([
              t.priority_weight
              for t in self.get_flat_relatives(upstream=False)
          ]) + self.priority_weight
  
      def pre_execute(self, context):
          """
          This is triggered right before self.execute, it's mostly a hook
          for people deriving operators.
          """
          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()
  
      def post_execute(self, context):
          """
          This is triggered right after self.execute, it's mostly a hook
          for people deriving operators.
          """
          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
  
          for k, v in list(self.__dict__.items()):
              if k not in ('user_defined_macros', 'params'):
                  setattr(result, k, copy.deepcopy(v, memo))
          result.params = self.params
          if hasattr(self, 'user_defined_macros'):
              result.user_defined_macros = self.user_defined_macros
          return result
  
      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, 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:
                      logging.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
  
      def clear(
              self, start_date=None, end_date=None,
              upstream=False, downstream=False):
          """
          Clears the state of task instances associated with the task, following
          the parameters specified.
          """
          session = settings.Session()
  
          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, session)
  
          session.commit()
          session.close()
          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 datetime.now()
          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_relatives(self, upstream=False, l=None):
          """
          Get a flat list of relatives, either upstream or downstream.
          """
          if not l:
              l = []
          for t in self.get_direct_relatives(upstream):
              if not is_in(t, l):
                  l.append(t)
                  t.get_flat_relatives(upstream, l)
          return l
  
      def detect_downstream_cycle(self, task=None):
          """
          When invoked, this routine will raise an exception if a cycle is
          detected downstream from self. It is invoked when tasks are added to
          the DAG to detect cycles.
          """
          if not task:
              task = self
          for t in self.get_direct_relatives():
              if task is t:
                  msg = "Cycle detected in DAG. Faulty task: {0}".format(task)
                  raise AirflowException(msg)
              else:
                  t.detect_downstream_cycle(task=task)
          return False
  
      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 datetime.now()
  
          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):
          logging.info('Dry run')
          for attr in self.template_fields:
              content = getattr(self, attr)
              if content and isinstance(content, six.string_types):
                  logging.info('Rendering template for {0}'.format(attr))
                  logging.info(content)
  
      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 append_only_new(self, l, item):
          if any([item is t for t in l]):
              raise AirflowException(
                  'Dependency {self}, {item} already registered'
                  ''.format(**locals()))
          else:
              l.append(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 = set(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))
          elif len(dags) == 1:
              dag = list(dags)[0]
          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.append_only_new(task._downstream_task_ids, self.task_id)
                  self.append_only_new(self._upstream_task_ids, task.task_id)
              else:
                  self.append_only_new(self._downstream_task_ids, task.task_id)
                  task.append_only_new(task._upstream_task_ids, self.task_id)
  
          self.detect_downstream_cycle()
  
      def set_downstream(self, task_or_task_list):
          """
          Set a task, or a task task 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 task 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,
              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.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(DateTime)
      # Last time this DAG was pickled
      last_pickled = Column(DateTime)
      # 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(DateTime)
      # 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
      def get_current(cls, dag_id):
          session = settings.Session()
          obj = session.query(cls).filter(cls.dag_id == dag_id).first()
          session.expunge_all()
          session.commit()
          session.close()
          return obj
  
  
  @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 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 orientation: Specify DAG orientation in graph view (LR, TB, RL, BT)
      :type orientation: string
      """
  
      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,
              default_args=None,
              concurrency=configuration.getint('core', 'dag_concurrency'),
              max_active_runs=configuration.getint(
                  'core', 'max_active_runs_per_dag'),
              dagrun_timeout=None,
              sla_miss_callback=None,
              orientation=configuration.get('webserver', 'dag_orientation'),
              params=None):
  
          self.user_defined_macros = user_defined_macros
          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
          self.task_dict = dict()
          self.start_date = start_date
          self.end_date = end_date
          self.schedule_interval = schedule_interval
          if 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 = datetime.now()
          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.orientation = orientation
  
          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=datetime.now()):
          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):
          if isinstance(self._schedule_interval, six.string_types):
              cron = croniter(self._schedule_interval, dttm)
              return cron.get_next(datetime)
          elif isinstance(self._schedule_interval, timedelta):
              return dttm + self._schedule_interval
  
      def previous_schedule(self, dttm):
          if isinstance(self._schedule_interval, six.string_types):
              cron = croniter(self._schedule_interval, dttm)
              return cron.get_prev(datetime)
          elif isinstance(self._schedule_interval, timedelta):
              return dttm - self._schedule_interval
  
      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.
          Overriden 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.is_(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(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.task_id.in_(self.task_ids),
              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 get_active_runs(self, session=None):
          """
          Returns a list of "running" tasks
          :param session:
          :return: List of execution dates
          """
          runs = (
             session.query(DagRun)
             .filter(
             DagRun.dag_id == self.dag_id,
             DagRun.state == State.RUNNING)
             .order_by(DagRun.execution_date)
             .all())
  
          active_dates = []
          for run in runs:
              active_dates.append(run.execution_date)
  
          return active_dates
  
      @property
      def latest_execution_date(self):
          """
          Returns the latest date for which at least one task instance exists
          """
          TI = TaskInstance
          session = settings.Session()
          execution_date = session.query(func.max(TI.execution_date)).filter(
              TI.dag_id == self.dag_id,
              TI.task_id.in_(self.task_ids)
          ).scalar()
          session.commit()
          session.close()
          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
          l = []
          for task in self.tasks:
              if (
                      task.__class__.__name__ == 'SubDagOperator' and
                      hasattr(task, 'subdag')):
                  l.append(task.subdag)
                  l += task.subdag.subdags
          return l
  
      def resolve_template_files(self):
          for t in self.tasks:
              t.resolve_template_files()
  
      def crawl_for_tasks(objects):
          """
          Typically called at the end of a script by passing globals() as a
          parameter. This allows to not explicitly add every single task to the
          dag explicitly.
          """
          raise NotImplementedError("")
  
      def get_template_env(self):
          """
          Returns a jinja2 Environment while taking into account the DAGs
          template_searchpath and user_defined_macros
          """
          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)
  
          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 = (datetime.today()-timedelta(30)).date()
              start_date = datetime.combine(start_date, datetime.min.time())
          end_date = end_date or datetime.now()
          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.all()
          return tis
  
      @property
      def roots(self):
          return [t for t in self.tasks if not t.downstream_list]
  
      @provide_session
      def set_dag_runs_state(
              self, state=State.RUNNING, session=None):
          drs = session.query(DagModel).filter_by(dag_id=self.dag_id).all()
          dirty_ids = []
          for dr in drs:
              dr.state = state
              dirty_ids.append(dr.dag_id)
          DagStat.clean_dirty(dirty_ids, session=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):
          """
          Clears a set of task instances associated with the current dag for
          a specified date range.
          """
          session = settings.Session()
          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:
              print("Nothing to clear.")
              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, session)
              if reset_dag_runs:
                  self.set_dag_runs_state(session=session)
          else:
              count = 0
              print("Bail. Nothing was cleared.")
  
          session.commit()
          session.close()
          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', 'params'):
                  setattr(result, k, copy.deepcopy(v, memo))
  
          result.user_defined_macros = self.user_defined_macros
          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.
          """
  
          dag = copy.deepcopy(self)
  
          regex_match = [
              t for t in dag.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
          dag.task_dict = {t.task_id: t 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 = [
                  tid for tid in t._upstream_task_ids if tid in dag.task_ids]
              t._downstream_task_ids = [
                  tid for tid in t._downstream_task_ids if tid in dag.task_ids]
          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 = datetime.now()
              pickled = pickle.dumps(self)
              d['pickle_len'] = len(pickled)
              d['pickling_duration'] = "{}".format(datetime.now() - dttm)
          except Exception as e:
              logging.exception(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 = datetime.now()
              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 not task.start_date:
              task.start_date = self.start_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.tasks.append(task)
              self.task_dict[task.task_id] = task
              task.dag = self
  
          self.task_count = len(self.tasks)
  
      def add_tasks(self, tasks):
          """
          Add a list of tasks to the DAG
  
          :param task: a lit of tasks you want to add
          :type task: list of tasks
          """
          for task in tasks:
              self.add_task(task)
  
      def db_merge(self):
          BO = BaseOperator
          session = settings.Session()
          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,
              include_adhoc=False,
              local=False,
              executor=None,
              donot_pickle=configuration.getboolean('core', 'donot_pickle'),
              ignore_task_deps=False,
              ignore_first_depends_on_past=False,
              pool=None):
          """
          Runs the DAG.
          """
          from airflow.jobs import BackfillJob
          if not executor and local:
              executor = LocalExecutor()
          elif not executor:
              executor = DEFAULT_EXECUTOR
          job = BackfillJob(
              self,
              start_date=start_date,
              end_date=end_date,
              mark_success=mark_success,
              include_adhoc=include_adhoc,
              executor=executor,
              donot_pickle=donot_pickle,
              ignore_task_deps=ignore_task_deps,
              ignore_first_depends_on_past=ignore_first_depends_on_past,
              pool=pool)
          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)
          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()
          DagStat.set_dirty(self.dag_id, session=session)
  
          # add a placeholder row into DagStat table
          if not session.query(DagStat).filter(DagStat.dag_id == self.dag_id).first():
              session.add(DagStat(dag_id=self.dag_id, state=State.RUNNING, count=0, dirty=True))
          session.commit()
          return run
  
      @staticmethod
      @provide_session
      def sync_to_db(dag, owner, sync_time, 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
          :own
          :param sync_time: The time that the DAG should be marked as sync'ed
          :type sync_time: datetime
          :return: None
          """
          orm_dag = session.query(
              DagModel).filter(DagModel.dag_id == dag.dag_id).first()
          if not orm_dag:
              orm_dag = DagModel(dag_id=dag.dag_id)
              logging.info("Creating ORM DAG for %s",
                           dag.dag_id)
          orm_dag.fileloc = dag.full_filepath
          orm_dag.is_subdag = dag.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 dag.subdags:
              DAG.sync_to_db(subdag, owner, 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
          """
          for dag in session.query(
                  DagModel).filter(DagModel.last_scheduler_run < expiration_date,
                                   DagModel.is_active).all():
              logging.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()
  
  
  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(DateTime, default=func.now())
  
      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(DateTime)
      end_date = Column(DateTime)
      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):
      __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):
          if self._val and self.is_encrypted:
              if not ENCRYPTION_ON:
                  raise AirflowException(
                      "Can't decrypt _val for key={}, FERNET_KEY configuration \
                      missing".format(self.key))
              return FERNET.decrypt(bytes(self._val, 'utf-8')).decode()
          else:
              return self._val
  
      def set_val(self, value):
          if value:
              try:
                  self._val = FERNET.encrypt(bytes(value, 'utf-8')).decode()
                  self.is_encrypted = True
              except NameError:
                  self._val = value
                  self.is_encrypted = False
  
      @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=False)
          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:
              if deserialize_json:
                  return json.loads(obj.val)
              else:
                  return obj.val
  
  
      @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 = value
  
          session.query(cls).filter(cls.key == key).delete()
          session.add(Variable(key=key, val=stored_value))
          session.flush()
  
  
  class XCom(Base):
      """
      Base class for XCom objects.
      """
      __tablename__ = "xcom"
  
      id = Column(Integer, primary_key=True)
      key = Column(String(512))
      value = Column(PickleType(pickler=dill))
      timestamp = Column(
          DateTime, default=func.now(), nullable=False)
      execution_date = Column(DateTime, 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),
      )
  
      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.
          """
          session.expunge_all()
  
          # 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()
  
          # 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
          """
          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())
              .limit(1))
  
          result = query.first()
          if result:
              return result.value
  
      @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
          """
          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))
  
          return query.all()
  
      @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)
      dirty = Column(Boolean, default=False)
  
      def __init__(self, dag_id, state, count, 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):
          for dag in session.query(DagStat).filter(DagStat.dag_id == dag_id):
              dag.dirty = True
          session.commit()
  
      @staticmethod
      @provide_session
      def clean_dirty(dag_ids, session=None):
          """
          Cleans out the dirty/out-of-sync rows from dag_stats table
  
          :param dag_ids: dag_ids that may be dirty
          :type dag_ids: list
          :param full_query: whether to check dag_runs for new drs not in dag_stats
          :type full_query: bool
          """
          dag_ids = set(dag_ids)
          ds_ids = set(session.query(DagStat.dag_id).all())
  
          qry = (
              session.query(DagStat)
              .filter(and_(DagStat.dag_id.in_(dag_ids), DagStat.dirty == True))
          )
  
          dirty_ids = {dag.dag_id for dag in qry.all()}
          qry.delete(synchronize_session='fetch')
          session.commit()
  
          qry = (
              session.query(DagRun.dag_id, DagRun.state, func.count('*'))
              .filter(DagRun.dag_id.in_(dirty_ids))
              .group_by(DagRun.dag_id, DagRun.state)
          )
  
          for dag_id, state, count in qry:
              session.add(DagStat(dag_id=dag_id, state=state, count=count))
  
          session.commit()
  
  
  class DagRun(Base):
      """
      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(DateTime, default=func.now())
      start_date = Column(DateTime, default=func.now())
      end_date = Column(DateTime)
      _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('dr_run_id', dag_id, run_id, unique=True),
      )
  
      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
              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, 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 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:
              qry = qry.filter(DR.execution_date == execution_date)
          if state:
              qry = qry.filter(DR.state == state)
          if external_trigger:
              qry = qry.filter(DR.external_trigger == external_trigger)
  
          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))
  
          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
          ).one()
  
          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 update_state(self, session=None):
          """
          Determines the overall state of the DagRun based on the state
          of its TaskInstances.
          :returns State:
          """
  
          dag = self.get_dag()
          tis = self.get_task_instances(session=session)
  
          logging.info("Updating state for {} considering {} task(s)"
                       .format(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 = datetime.now()
          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)
          # small speed up
          if unfinished_tasks and none_depends_on_past:
              # todo: this can actually get pretty slow: one task costs between 0.01-015s
              no_dependencies_met = all(not t.are_dependencies_met(session=session)
                                        for t in unfinished_tasks)
  
          duration = (datetime.now() - start_dttm).total_seconds() * 1000
          Stats.timing("dagrun.dependency-check.{}.{}".
                       format(self.dag_id, self.execution_date), duration)
  
          # future: remove the check on adhoc tasks (=active_tasks)
          if len(tis) == len(dag.active_tasks):
              # if any roots failed, the run failed
              root_ids = [t.task_id for t in dag.roots]
              roots = [t for t in tis if t.task_id in root_ids]
  
              if any(r.state in (State.FAILED, State.UPSTREAM_FAILED)
                     for r in roots):
                  logging.info('Marking run {} failed'.format(self))
                  self.state = State.FAILED
  
              # if all roots succeeded, the run succeeded
              elif all(r.state in (State.SUCCESS, State.SKIPPED)
                       for r in roots):
                  logging.info('Marking run {} successful'.format(self))
                  self.state = State.SUCCESS
  
              # if *all tasks* are deadlocked, the run failed
              elif unfinished_tasks and none_depends_on_past and no_dependencies_met:
                  logging.info(
                      'Deadlock; marking run {} failed'.format(self))
                  self.state = State.FAILED
  
              # 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 tasks
          task_ids = []
          for ti in tis:
              task_ids.append(ti.task_id)
              try:
                  dag.get_task(ti.task_id)
              except AirflowException:
                  if self.state is not State.RUNNING:
                      ti.state = State.REMOVED
  
          # check for missing tasks
          for task in dag.tasks:
              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_running_tasks(session, dag_id, task_ids):
          """
          Returns the number of tasks running 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]
          :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),
              TaskInstance.state == State.RUNNING,
          )
          return qry.scalar()
  
      @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,
              DagRun.execution_date == execution_date,
          )
          return qry.first()
  
      @property
      def is_backfill(self):
          if "backfill" in self.run_id:
              return True
  
          return False
  
  
  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
  
      @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(DateTime, primary_key=True)
      email_sent = Column(Boolean, default=False)
      timestamp = Column(DateTime)
      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(DateTime)
      filename = Column(String(1024))
      stacktrace = Column(Text)
  