Source code for airflow.models.dagrun

# -*- coding: utf-8 -*-
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from typing import Optional, cast

import six
from sqlalchemy import (
    Column, Integer, String, Boolean, PickleType, Index, UniqueConstraint, func, DateTime, or_,
from sqlalchemy.ext.declarative import declared_attr
from sqlalchemy.orm import synonym
from sqlalchemy.orm.session import Session
from airflow.exceptions import AirflowException
from airflow.models.base import Base, ID_LEN
from airflow.settings import Stats
from airflow.ti_deps.dep_context import DepContext
from airflow.utils import timezone
from airflow.utils.db import provide_session
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.sqlalchemy import UtcDateTime
from airflow.utils.state import State

[docs]class DagRun(Base, LoggingMixin): """ DagRun describes an instance of a Dag. It can be created by the scheduler (for regular runs) or by an external trigger """
[docs] __tablename__ = "dag_run"
[docs] ID_PREFIX = 'scheduled__'
[docs] id = Column(Integer, primary_key=True)
[docs] dag_id = Column(String(ID_LEN))
[docs] execution_date = Column(UtcDateTime, default=timezone.utcnow)
[docs] start_date = Column(UtcDateTime, default=timezone.utcnow)
[docs] end_date = Column(UtcDateTime)
[docs] _state = Column('state', String(50), default=State.RUNNING)
[docs] run_id = Column(String(ID_LEN))
[docs] external_trigger = Column(Boolean, default=True)
[docs] conf = Column(PickleType)
[docs] dag = None
[docs] __table_args__ = ( Index('dag_id_state', dag_id, _state), UniqueConstraint('dag_id', 'execution_date'), UniqueConstraint('dag_id', 'run_id'),
[docs] 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)
[docs] def get_state(self): return self._state
[docs] def set_state(self, state): if self._state != state: self._state = state self.end_date = timezone.utcnow() if self._state in State.finished() else None
[docs] def state(self): return synonym('_state', descriptor=property(self.get_state, self.set_state))
[docs] def id_for_date(cls, date, prefix=ID_FORMAT_PREFIX): return prefix.format(date.isoformat()[:19])
[docs] 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.state = dr.state
@staticmethod @provide_session
[docs] def find(dag_id=None, run_id=None, execution_date=None, state=None, external_trigger=None, no_backfills=False, session=None): """ Returns a set of dag runs for the given search criteria. :param dag_id: the dag_id to find dag runs for :type dag_id: int, list :param run_id: defines the the run id for this dag run :type run_id: str :param execution_date: the execution date :type execution_date: datetime.datetime :param state: the state of the dag run :type state: str :param external_trigger: whether this dag run is externally triggered :type external_trigger: bool :param no_backfills: return no backfills (True), return all (False). Defaults to False :type no_backfills: bool :param session: database session :type session: sqlalchemy.orm.session.Session """ DR = DagRun qry = session.query(DR) if dag_id: qry = qry.filter(DR.dag_id == dag_id) if run_id: qry = qry.filter(DR.run_id == run_id) if execution_date: if isinstance(execution_date, list): qry = qry.filter(DR.execution_date.in_(execution_date)) else: qry = qry.filter(DR.execution_date == execution_date) if state: qry = qry.filter(DR.state == state) if external_trigger is not None: qry = qry.filter(DR.external_trigger == external_trigger) if no_backfills: # in order to prevent a circular dependency from import BackfillJob qry = qry.filter(DR.run_id.notlike(BackfillJob.ID_PREFIX + '%')) dr = qry.order_by(DR.execution_date).all() return dr
[docs] def get_task_instances(self, state=None, session=None): """ Returns the task instances for this dag run """ from airflow.models.taskinstance import TaskInstance # Avoid circular import tis = session.query(TaskInstance).filter( TaskInstance.dag_id == self.dag_id, TaskInstance.execution_date == self.execution_date, ) if state: if isinstance(state, six.string_types): tis = tis.filter(TaskInstance.state == state) else: # this is required to deal with NULL values if None in state: tis = tis.filter( or_(TaskInstance.state.in_(state), TaskInstance.state.is_(None)) ) else: tis = tis.filter(TaskInstance.state.in_(state)) if self.dag and self.dag.partial: tis = tis.filter(TaskInstance.task_id.in_(self.dag.task_ids)) return tis.all()
[docs] 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 """ from airflow.models.taskinstance import TaskInstance # Avoid circular import TI = TaskInstance ti = session.query(TI).filter( TI.dag_id == self.dag_id, TI.execution_date == self.execution_date, TI.task_id == task_id ).first() return ti
[docs] 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
[docs] def get_previous_dagrun(self, state=None, session=None): # type: (Optional[str], Optional[Session]) -> Optional['DagRun'] """The previous DagRun, if there is one""" session = cast(Session, session) # mypy filters = [ DagRun.dag_id == self.dag_id, DagRun.execution_date < self.execution_date, ] if state is not None: filters.append(DagRun.state == state) return session.query(DagRun).filter( *filters ).order_by( DagRun.execution_date.desc()
).first() @provide_session
[docs] def get_previous_scheduled_dagrun(self, session=None): """The previous, SCHEDULED DagRun, if there is one""" dag = self.get_dag() return session.query(DagRun).filter( DagRun.dag_id == self.dag_id, DagRun.execution_date == dag.previous_schedule(self.execution_date)
).first() @provide_session
[docs] def update_state(self, session=None): """ Determines the overall state of the DagRun based on the state of its TaskInstances. :return: State """ dag = self.get_dag() tis = self.get_task_instances(session=session) self.log.debug("Updating state for %s considering %s task(s)", self, len(tis)) for ti in list(tis): # skip in db? if ti.state == State.REMOVED: tis.remove(ti) else: ti.task = dag.get_task(ti.task_id) # pre-calculate # db is faster start_dttm = timezone.utcnow() unfinished_tasks = self.get_task_instances( state=State.unfinished(), session=session ) none_depends_on_past = all(not t.task.depends_on_past for t in unfinished_tasks) none_task_concurrency = all(t.task.task_concurrency is None for t in unfinished_tasks) # small speed up if unfinished_tasks and none_depends_on_past and none_task_concurrency: # todo: this can actually get pretty slow: one task costs between 0.01-015s no_dependencies_met = True for ut in unfinished_tasks: # We need to flag upstream and check for changes because upstream # failures/re-schedules can result in deadlock false positives old_state = ut.state deps_met = ut.are_dependencies_met( dep_context=DepContext( flag_upstream_failed=True, ignore_in_retry_period=True, ignore_in_reschedule_period=True), session=session) if deps_met or old_state != ut.current_state(session=session): no_dependencies_met = False break duration = (timezone.utcnow() - start_dttm).total_seconds() * 1000 Stats.timing("dagrun.dependency-check.{}".format(self.dag_id), duration) leaf_tis = [ti for ti in tis if ti.task_id in {t.task_id for t in dag.leaves}] # if all roots finished and at least one failed, the run failed if not unfinished_tasks and any( leaf_ti.state in {State.FAILED, State.UPSTREAM_FAILED} for leaf_ti in leaf_tis ):'Marking run %s failed', self) self.set_state(State.FAILED) dag.handle_callback(self, success=False, reason='task_failure', session=session) # if all leafs succeeded and no unfinished tasks, the run succeeded elif not unfinished_tasks and all( leaf_ti.state in {State.SUCCESS, State.SKIPPED} for leaf_ti in leaf_tis ):'Marking run %s successful', self) self.set_state(State.SUCCESS) dag.handle_callback(self, success=True, reason='success', session=session) # if *all tasks* are deadlocked, the run failed elif (unfinished_tasks and none_depends_on_past and none_task_concurrency and no_dependencies_met):'Deadlock; marking run %s failed', self) self.set_state(State.FAILED) dag.handle_callback(self, success=False, reason='all_tasks_deadlocked', session=session) # finally, if the roots aren't done, the dag is still running else: self.set_state(State.RUNNING) self._emit_duration_stats_for_finished_state() # todo: determine we want to use with_for_update to make sure to lock the run session.merge(self) session.commit() return self.state
[docs] def _emit_duration_stats_for_finished_state(self): if self.state == State.RUNNING: return duration = (self.end_date - self.start_date) if self.state is State.SUCCESS: Stats.timing('dagrun.duration.success.{}'.format(self.dag_id), duration) elif self.state == State.FAILED: Stats.timing('dagrun.duration.failed.{}'.format(self.dag_id), duration)
[docs] 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. """ from airflow.models.taskinstance import TaskInstance # Avoid circular import dag = self.get_dag() tis = self.get_task_instances(session=session) # check for removed or restored tasks task_ids = [] for ti in tis: task_ids.append(ti.task_id) task = None try: task = dag.get_task(ti.task_id) except AirflowException: if ti.state == State.REMOVED: pass # ti has already been removed, just ignore it elif self.state is not State.RUNNING and not dag.partial: self.log.warning("Failed to get task '{}' for dag '{}'. " "Marking it as removed.".format(ti, dag)) Stats.incr( "task_removed_from_dag.{}".format(dag.dag_id), 1, 1) ti.state = State.REMOVED is_task_in_dag = task is not None should_restore_task = is_task_in_dag and ti.state == State.REMOVED if should_restore_task:"Restoring task '{}' which was previously " "removed from DAG '{}'".format(ti, dag)) Stats.incr("task_restored_to_dag.{}".format(dag.dag_id), 1, 1) ti.state = State.NONE # check for missing tasks for task in six.itervalues(dag.task_dict): if task.start_date > self.execution_date and not self.is_backfill: continue if task.task_id not in task_ids: Stats.incr( "task_instance_created-{}".format(task.__class__.__name__), 1, 1) ti = TaskInstance(task, self.execution_date) session.add(ti) session.commit()
[docs] 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: airflow.models.DagRun """ qry = session.query(DagRun).filter( DagRun.dag_id == dag_id, DagRun.external_trigger == False, # noqa DagRun.execution_date == execution_date, ) return qry.first()
[docs] def is_backfill(self): from import BackfillJob return ( self.run_id is not None and self.run_id.startswith(BackfillJob.ID_PREFIX)
) @classmethod @provide_session
[docs] def get_latest_runs(cls, session): """Returns the latest DagRun for each DAG. """ subquery = ( session .query( cls.dag_id, func.max(cls.execution_date).label('execution_date')) .group_by(cls.dag_id) .subquery() ) dagruns = ( session .query(cls) .join(subquery, and_(cls.dag_id == subquery.c.dag_id, cls.execution_date == subquery.c.execution_date)) .all() ) return dagruns

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