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.exc import IntegrityError
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 ID_LEN, Base
from airflow.settings import Stats, task_instance_mutation_hook
from airflow.ti_deps.dep_context import SCHEDULEABLE_STATES, 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: ready_tis: the tis that can be scheduled in the current loop :rtype ready_tis: list[airflow.models.TaskInstance] """ dag = self.get_dag() ready_tis = [] tis = [ti for ti in self.get_task_instances(session=session, state=State.task_states + (State.SHUTDOWN,))] self.log.debug("number of tis tasks for %s: %s task(s)", self, len(tis)) for ti in list(tis): ti.task = dag.get_task(ti.task_id) start_dttm = timezone.utcnow() unfinished_tasks = [t for t in tis if t.state in State.unfinished()] finished_tasks = [t for t in tis if t.state in State.finished() + [State.UPSTREAM_FAILED]] 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: scheduleable_tasks = [ut for ut in unfinished_tasks if ut.state in SCHEDULEABLE_STATES] self.log.debug( "number of scheduleable tasks for %s: %s task(s)", self, len(scheduleable_tasks)) ready_tis, changed_tis = self._get_ready_tis(scheduleable_tasks, finished_tasks, session) self.log.debug("ready tis length for %s: %s task(s)", self, len(ready_tis)) if none_depends_on_past and none_task_concurrency: # small speed up are_runnable_tasks = ready_tis or self._are_premature_tis( unfinished_tasks, finished_tasks, session) or changed_tis duration = (timezone.utcnow() - start_dttm).total_seconds() * 1000 Stats.timing("dagrun.dependency-check.{}".format(self.dag_id), duration) leaf_task_ids = {t.task_id for t in dag.leaves} leaf_tis = [ti for ti in tis if ti.task_id in leaf_task_ids] # 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 not are_runnable_tasks):'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_true_scheduling_delay_stats_for_finished_state(finished_tasks) 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 ready_tis
[docs] def _get_ready_tis(self, scheduleable_tasks, finished_tasks, session): ready_tis = [] changed_tis = False for st in scheduleable_tasks: st_old_state = st.state if st.are_dependencies_met( dep_context=DepContext( flag_upstream_failed=True, finished_tasks=finished_tasks), session=session): ready_tis.append(st) elif st_old_state != st.current_state(session=session): changed_tis = True return ready_tis, changed_tis
[docs] def _are_premature_tis(self, unfinished_tasks, finished_tasks, session): # there might be runnable tasks that are up for retry and from some reason(retry delay, etc) are # not ready yet so we set the flags to count them in for ut in unfinished_tasks: if ut.are_dependencies_met( dep_context=DepContext( flag_upstream_failed=True, ignore_in_retry_period=True, ignore_in_reschedule_period=True, finished_tasks=finished_tasks), session=session): return True
[docs] def _emit_true_scheduling_delay_stats_for_finished_state(self, finished_tis): """ This is a helper method to emit the true scheduling delay stats, which is defined as the time when the first task in DAG starts minus the expected DAG run datetime. This method will be used in the update_state method when the state of the DagRun is updated to a completed status (either success or failure). The method will find the first started task within the DAG and calculate the expected DagRun start time (based on dag.execution_date & dag.schedule_interval), and minus these two values to get the delay. The emitted data may contains outlier (e.g. when the first task was cleared, so the second task's start_date will be used), but we can get rid of the the outliers on the stats side through the dashboards tooling built. Note, the stat will only be emitted if the DagRun is a scheduler triggered one (i.e. external_trigger is False). """ if self.state == State.RUNNING: return if self.external_trigger: return if not finished_tis: return try: dag = self.get_dag() if not self.dag.schedule_interval or self.dag.schedule_interval == "@once": # We can't emit this metric if there is no following schedule to cacluate from! return ordered_tis_by_start_date = [ti for ti in finished_tis if ti.start_date] ordered_tis_by_start_date.sort(key=lambda ti: ti.start_date, reverse=False) first_start_date = ordered_tis_by_start_date[0].start_date if first_start_date: # dag.following_schedule calculates the expected start datetime for a scheduled dagrun # i.e. a daily flow for execution date 1/1/20 actually runs on 1/2/20 hh:mm:ss, # and ti.start_date will be 1/2/20 hh:mm:ss so the following schedule is comparison true_delay = first_start_date - dag.following_schedule(self.execution_date) if true_delay.total_seconds() > 0: Stats.timing('dagrun.{}.first_task_scheduling_delay'.format(dag.dag_id), true_delay) except Exception as e: self.log.warning('Failed to record first_task_scheduling_delay metric:\n', e)
[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 = set() for ti in tis: task_instance_mutation_hook(ti) task_ids.add(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 session.merge(ti) # 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) task_instance_mutation_hook(ti) session.add(ti) try: session.commit() except IntegrityError as err: 'Hit IntegrityError while creating the TIs for %s - %s', dag.dag_id, self.execution_date )'Doing session rollback.') session.rollback()
[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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