#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import warnings
from datetime import datetime
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union
from sqlalchemy import (
Boolean,
Column,
Index,
Integer,
PickleType,
String,
UniqueConstraint,
and_,
func,
or_,
text,
)
from sqlalchemy.exc import IntegrityError
from sqlalchemy.ext.declarative import declared_attr
from sqlalchemy.orm import joinedload, relationship, synonym
from sqlalchemy.orm.session import Session
from sqlalchemy.sql import expression
from airflow import settings
from airflow.configuration import conf as airflow_conf
from airflow.exceptions import AirflowException, TaskNotFound
from airflow.models.base import COLLATION_ARGS, ID_LEN, Base
from airflow.models.taskinstance import TaskInstance as TI
from airflow.stats import Stats
from airflow.ti_deps.dep_context import DepContext
from airflow.ti_deps.dependencies_states import SCHEDULEABLE_STATES
from airflow.utils import callback_requests, timezone
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.session import provide_session
from airflow.utils.sqlalchemy import UtcDateTime, nulls_first, skip_locked, with_row_locks
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.types import DagRunType
if TYPE_CHECKING:
from airflow.models.dag import DAG
[docs]class TISchedulingDecision(NamedTuple):
"""Type of return for DagRun.task_instance_scheduling_decisions"""
[docs] schedulable_tis: List[TI]
[docs] unfinished_tasks: List[TI]
[docs] finished_tasks: List[TI]
[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"
__NO_VALUE = object()
[docs] id = Column(Integer, primary_key=True)
[docs] dag_id = Column(String(ID_LEN, **COLLATION_ARGS), nullable=False)
[docs] queued_at = Column(UtcDateTime)
[docs] execution_date = Column(UtcDateTime, default=timezone.utcnow, nullable=False)
[docs] start_date = Column(UtcDateTime)
[docs] end_date = Column(UtcDateTime)
_state = Column('state', String(50), default=State.QUEUED)
[docs] run_id = Column(String(ID_LEN, **COLLATION_ARGS), nullable=False)
[docs] creating_job_id = Column(Integer)
[docs] external_trigger = Column(Boolean, default=True)
[docs] run_type = Column(String(50), nullable=False)
[docs] conf = Column(PickleType)
# These two must be either both NULL or both datetime.
[docs] data_interval_start = Column(UtcDateTime)
[docs] data_interval_end = Column(UtcDateTime)
# When a scheduler last attempted to schedule TIs for this DagRun
[docs] last_scheduling_decision = Column(UtcDateTime)
[docs] dag_hash = Column(String(32))
[docs] __table_args__ = (
Index('dag_id_state', dag_id, _state),
UniqueConstraint('dag_id', 'execution_date', name='dag_run_dag_id_execution_date_key'),
UniqueConstraint('dag_id', 'run_id', name='dag_run_dag_id_run_id_key'),
Index('idx_last_scheduling_decision', last_scheduling_decision),
Index('idx_dag_run_dag_id', dag_id),
Index(
'idx_dag_run_running_dags',
'state',
'dag_id',
postgresql_where=text("state='running'"),
mssql_where=text("state='running'"),
sqlite_where=text("state='running'"),
),
# since mysql lacks filtered/partial indices, this creates a
# duplicate index on mysql. Not the end of the world
Index(
'idx_dag_run_queued_dags',
'state',
'dag_id',
postgresql_where=text("state='queued'"),
mssql_where=text("state='queued'"),
sqlite_where=text("state='queued'"),
),
)
[docs] task_instances = relationship(
TI, back_populates="dag_run", cascade='save-update, merge, delete, delete-orphan'
)
[docs] DEFAULT_DAGRUNS_TO_EXAMINE = airflow_conf.getint(
'scheduler',
'max_dagruns_per_loop_to_schedule',
fallback=20,
)
def __init__(
self,
dag_id: Optional[str] = None,
run_id: Optional[str] = None,
queued_at: Optional[datetime] = __NO_VALUE,
execution_date: Optional[datetime] = None,
start_date: Optional[datetime] = None,
external_trigger: Optional[bool] = None,
conf: Optional[Any] = None,
state: Optional[DagRunState] = None,
run_type: Optional[str] = None,
dag_hash: Optional[str] = None,
creating_job_id: Optional[int] = None,
data_interval: Optional[Tuple[datetime, datetime]] = None,
):
if data_interval is None:
# Legacy: Only happen for runs created prior to Airflow 2.2.
self.data_interval_start = self.data_interval_end = None
else:
self.data_interval_start, self.data_interval_end = data_interval
self.dag_id = dag_id
self.run_id = run_id
self.execution_date = execution_date
self.start_date = start_date
self.external_trigger = external_trigger
self.conf = conf or {}
self.state = state
if queued_at is self.__NO_VALUE:
self.queued_at = timezone.utcnow() if state == State.QUEUED else None
else:
self.queued_at = queued_at
self.run_type = run_type
self.dag_hash = dag_hash
self.creating_job_id = creating_job_id
super().__init__()
[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,
)
@property
[docs] def logical_date(self) -> datetime:
return self.execution_date
[docs] def get_state(self):
return self._state
[docs] def set_state(self, state: DagRunState):
if self._state != state:
self._state = state
self.end_date = timezone.utcnow() if self._state in State.finished else None
if state == State.QUEUED:
self.queued_at = timezone.utcnow()
@declared_attr
[docs] def state(self):
return synonym('_state', descriptor=property(self.get_state, self.set_state))
@provide_session
[docs] def refresh_from_db(self, session: Session = None):
"""
Reloads the current dagrun from the database
:param session: database session
:type session: Session
"""
dr = session.query(DagRun).filter(DagRun.dag_id == self.dag_id, DagRun.run_id == self.run_id).one()
self.id = dr.id
self.state = dr.state
@classmethod
@provide_session
[docs] def active_runs_of_dags(cls, dag_ids=None, only_running=False, session=None) -> Dict[str, int]:
"""Get the number of active dag runs for each dag."""
query = session.query(cls.dag_id, func.count('*'))
if dag_ids is not None:
# 'set' called to avoid duplicate dag_ids, but converted back to 'list'
# because SQLAlchemy doesn't accept a set here.
query = query.filter(cls.dag_id.in_(list(set(dag_ids))))
if only_running:
query = query.filter(cls.state == State.RUNNING)
else:
query = query.filter(cls.state.in_([State.RUNNING, State.QUEUED]))
query = query.group_by(cls.dag_id)
return {dag_id: count for dag_id, count in query.all()}
@classmethod
[docs] def next_dagruns_to_examine(
cls,
state: DagRunState,
session: Session,
max_number: Optional[int] = None,
):
"""
Return the next DagRuns that the scheduler should attempt to schedule.
This will return zero or more DagRun rows that are row-level-locked with a "SELECT ... FOR UPDATE"
query, you should ensure that any scheduling decisions are made in a single transaction -- as soon as
the transaction is committed it will be unlocked.
:rtype: list[airflow.models.DagRun]
"""
from airflow.models.dag import DagModel
if max_number is None:
max_number = cls.DEFAULT_DAGRUNS_TO_EXAMINE
# TODO: Bake this query, it is run _A lot_
query = (
session.query(cls)
.filter(cls.state == state, cls.run_type != DagRunType.BACKFILL_JOB)
.join(
DagModel,
DagModel.dag_id == cls.dag_id,
)
.filter(
DagModel.is_paused == expression.false(),
DagModel.is_active == expression.true(),
)
)
if state == State.QUEUED:
# For dag runs in the queued state, we check if they have reached the max_active_runs limit
# and if so we drop them
running_drs = (
session.query(DagRun.dag_id, func.count(DagRun.state).label('num_running'))
.filter(DagRun.state == DagRunState.RUNNING)
.group_by(DagRun.dag_id)
.subquery()
)
query = query.outerjoin(running_drs, running_drs.c.dag_id == DagRun.dag_id).filter(
func.coalesce(running_drs.c.num_running, 0) < DagModel.max_active_runs
)
query = query.order_by(
nulls_first(cls.last_scheduling_decision, session=session),
cls.execution_date,
)
if not settings.ALLOW_FUTURE_EXEC_DATES:
query = query.filter(DagRun.execution_date <= func.now())
return with_row_locks(
query.limit(max_number), of=cls, session=session, **skip_locked(session=session)
)
@staticmethod
@provide_session
[docs] def find(
dag_id: Optional[Union[str, List[str]]] = None,
run_id: Optional[str] = None,
execution_date: Optional[datetime] = None,
state: Optional[DagRunState] = None,
external_trigger: Optional[bool] = None,
no_backfills: bool = False,
run_type: Optional[DagRunType] = None,
session: Session = None,
execution_start_date: Optional[datetime] = None,
execution_end_date: Optional[datetime] = None,
) -> List["DagRun"]:
"""
Returns a set of dag runs for the given search criteria.
:param dag_id: the dag_id or list of dag_id to find dag runs for
:type dag_id: str or list[str]
:param run_id: defines the run id for this dag run
:type run_id: str
:param run_type: type of DagRun
:type run_type: airflow.utils.types.DagRunType
:param execution_date: the execution date
:type execution_date: datetime.datetime or list[datetime.datetime]
:param state: the state of the dag run
:type state: DagRunState
: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
:param execution_start_date: dag run that was executed from this date
:type execution_start_date: datetime.datetime
:param execution_end_date: dag run that was executed until this date
:type execution_end_date: datetime.datetime
"""
DR = DagRun
qry = session.query(DR)
dag_ids = [dag_id] if isinstance(dag_id, str) else dag_id
if dag_ids:
qry = qry.filter(DR.dag_id.in_(dag_ids))
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 execution_start_date and execution_end_date:
qry = qry.filter(DR.execution_date.between(execution_start_date, execution_end_date))
elif execution_start_date:
qry = qry.filter(DR.execution_date >= execution_start_date)
elif execution_end_date:
qry = qry.filter(DR.execution_date <= execution_end_date)
if state:
qry = qry.filter(DR.state == state)
if external_trigger is not None:
qry = qry.filter(DR.external_trigger == external_trigger)
if run_type:
qry = qry.filter(DR.run_type == run_type)
if no_backfills:
qry = qry.filter(DR.run_type != DagRunType.BACKFILL_JOB)
return qry.order_by(DR.execution_date).all()
@staticmethod
[docs] def generate_run_id(run_type: DagRunType, execution_date: datetime) -> str:
"""Generate Run ID based on Run Type and Execution Date"""
return f"{run_type}__{execution_date.isoformat()}"
@provide_session
[docs] def get_task_instances(
self, state: Optional[Iterable[TaskInstanceState]] = None, session=None
) -> Iterable[TI]:
"""Returns the task instances for this dag run"""
tis = (
session.query(TI)
.options(joinedload(TI.dag_run))
.filter(
TI.dag_id == self.dag_id,
TI.run_id == self.run_id,
)
)
if state:
if isinstance(state, str):
tis = tis.filter(TI.state == state)
else:
# this is required to deal with NULL values
if None in state:
if all(x is None for x in state):
tis = tis.filter(TI.state.is_(None))
else:
not_none_state = [s for s in state if s]
tis = tis.filter(or_(TI.state.in_(not_none_state), TI.state.is_(None)))
else:
tis = tis.filter(TI.state.in_(state))
if self.dag and self.dag.partial:
tis = tis.filter(TI.task_id.in_(self.dag.task_ids))
return tis.all()
@provide_session
[docs] def get_task_instance(self, task_id: str, session: Session = None) -> Optional[TI]:
"""
Returns the task instance specified by task_id for this dag run
:param task_id: the task id
:type task_id: str
:param session: Sqlalchemy ORM Session
:type session: Session
"""
return (
session.query(TI)
.filter(TI.dag_id == self.dag_id, TI.run_id == self.run_id, TI.task_id == task_id)
.one_or_none()
)
[docs] def get_dag(self) -> "DAG":
"""
Returns the Dag associated with this DagRun.
:return: DAG
"""
if not self.dag:
raise AirflowException(f"The DAG (.dag) for {self} needs to be set")
return self.dag
@provide_session
[docs] def get_previous_dagrun(
self, state: Optional[DagRunState] = None, session: Session = None
) -> Optional['DagRun']:
"""The previous DagRun, if there is one"""
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: Session = None) -> Optional['DagRun']:
"""The previous, SCHEDULED DagRun, if there is one"""
return (
session.query(DagRun)
.filter(
DagRun.dag_id == self.dag_id,
DagRun.execution_date < self.execution_date,
DagRun.run_type != DagRunType.MANUAL,
)
.order_by(DagRun.execution_date.desc())
.first()
)
@provide_session
[docs] def update_state(
self, session: Session = None, execute_callbacks: bool = True
) -> Tuple[List[TI], Optional[callback_requests.DagCallbackRequest]]:
"""
Determines the overall state of the DagRun based on the state
of its TaskInstances.
:param session: Sqlalchemy ORM Session
:type session: Session
:param execute_callbacks: Should dag callbacks (success/failure, SLA etc) be invoked
directly (default: true) or recorded as a pending request in the ``callback`` property
:type execute_callbacks: bool
:return: Tuple containing tis that can be scheduled in the current loop & `callback` that
needs to be executed
"""
# Callback to execute in case of Task Failures
callback: Optional[callback_requests.DagCallbackRequest] = None
start_dttm = timezone.utcnow()
self.last_scheduling_decision = start_dttm
with Stats.timer(f"dagrun.dependency-check.{self.dag_id}"):
dag = self.get_dag()
info = self.task_instance_scheduling_decisions(session)
tis = info.tis
schedulable_tis = info.schedulable_tis
changed_tis = info.changed_tis
finished_tasks = info.finished_tasks
unfinished_tasks = info.unfinished_tasks
none_depends_on_past = all(not t.task.depends_on_past for t in unfinished_tasks)
none_task_concurrency = all(t.task.max_active_tis_per_dag is None for t in unfinished_tasks)
none_deferred = all(t.state != State.DEFERRED for t in unfinished_tasks)
if unfinished_tasks and none_depends_on_past and none_task_concurrency and none_deferred:
# small speed up
are_runnable_tasks = (
schedulable_tis
or self._are_premature_tis(unfinished_tasks, finished_tasks, session)
or changed_tis
)
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_states for leaf_ti in leaf_tis):
self.log.error('Marking run %s failed', self)
self.set_state(State.FAILED)
if execute_callbacks:
dag.handle_callback(self, success=False, reason='task_failure', session=session)
elif dag.has_on_failure_callback:
callback = callback_requests.DagCallbackRequest(
full_filepath=dag.fileloc,
dag_id=self.dag_id,
run_id=self.run_id,
is_failure_callback=True,
msg='task_failure',
)
# if all leaves succeeded and no unfinished tasks, the run succeeded
elif not unfinished_tasks and all(leaf_ti.state in State.success_states for leaf_ti in leaf_tis):
self.log.info('Marking run %s successful', self)
self.set_state(State.SUCCESS)
if execute_callbacks:
dag.handle_callback(self, success=True, reason='success', session=session)
elif dag.has_on_success_callback:
callback = callback_requests.DagCallbackRequest(
full_filepath=dag.fileloc,
dag_id=self.dag_id,
run_id=self.run_id,
is_failure_callback=False,
msg='success',
)
# if *all tasks* are deadlocked, the run failed
elif (
unfinished_tasks
and none_depends_on_past
and none_task_concurrency
and none_deferred
and not are_runnable_tasks
):
self.log.error('Deadlock; marking run %s failed', self)
self.set_state(State.FAILED)
if execute_callbacks:
dag.handle_callback(self, success=False, reason='all_tasks_deadlocked', session=session)
elif dag.has_on_failure_callback:
callback = callback_requests.DagCallbackRequest(
full_filepath=dag.fileloc,
dag_id=self.dag_id,
run_id=self.run_id,
is_failure_callback=True,
msg='all_tasks_deadlocked',
)
# finally, if the roots aren't done, the dag is still running
else:
self.set_state(State.RUNNING)
if self._state == State.FAILED or self._state == State.SUCCESS:
msg = (
"DagRun Finished: dag_id=%s, execution_date=%s, run_id=%s, "
"run_start_date=%s, run_end_date=%s, run_duration=%s, "
"state=%s, external_trigger=%s, run_type=%s, "
"data_interval_start=%s, data_interval_end=%s, dag_hash=%s"
)
self.log.info(
msg,
self.dag_id,
self.execution_date,
self.run_id,
self.start_date,
self.end_date,
(self.end_date - self.start_date).total_seconds()
if self.start_date and self.end_date
else None,
self._state,
self.external_trigger,
self.run_type,
self.data_interval_start,
self.data_interval_end,
self.dag_hash,
)
self._emit_true_scheduling_delay_stats_for_finished_state(finished_tasks)
self._emit_duration_stats_for_finished_state()
session.merge(self)
return schedulable_tis, callback
@provide_session
[docs] def task_instance_scheduling_decisions(self, session: Session = None) -> TISchedulingDecision:
schedulable_tis: List[TI] = []
changed_tis = False
tis = list(self.get_task_instances(session=session, state=State.task_states))
self.log.debug("number of tis tasks for %s: %s task(s)", self, len(tis))
for ti in tis:
try:
ti.task = self.get_dag().get_task(ti.task_id)
except TaskNotFound:
self.log.warning(
"Failed to get task '%s' for dag '%s'. Marking it as removed.", ti, ti.dag_id
)
ti.state = State.REMOVED
session.flush()
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]
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))
schedulable_tis, changed_tis = self._get_ready_tis(scheduleable_tasks, finished_tasks, session)
return TISchedulingDecision(
tis=tis,
schedulable_tis=schedulable_tis,
changed_tis=changed_tis,
unfinished_tasks=unfinished_tasks,
finished_tasks=finished_tasks,
)
def _get_ready_tis(
self,
scheduleable_tasks: List[TI],
finished_tasks: List[TI],
session: Session,
) -> Tuple[List[TI], bool]:
old_states = {}
ready_tis: List[TI] = []
changed_tis = False
if not scheduleable_tasks:
return ready_tis, changed_tis
# Check dependencies
for st in scheduleable_tasks:
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)
else:
old_states[st.key] = old_state
# Check if any ti changed state
tis_filter = TI.filter_for_tis(old_states.keys())
if tis_filter is not None:
fresh_tis = session.query(TI).filter(tis_filter).all()
changed_tis = any(ti.state != old_states[ti.key] for ti in fresh_tis)
return ready_tis, changed_tis
def _are_premature_tis(
self,
unfinished_tasks: List[TI],
finished_tasks: List[TI],
session: Session,
) -> bool:
# there might be runnable tasks that are up for retry and for 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
return False
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.timetable), 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 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.timetable.periodic:
# We can't emit this metric if there is no following schedule to calculate 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:
# TODO: Logically, this should be DagRunInfo.run_after, but the
# information is not stored on a DagRun, only before the actual
# execution on DagModel.next_dagrun_create_after. We should add
# a field on DagRun for this instead of relying on the run
# always happening immediately after the data interval.
data_interval_end = dag.get_run_data_interval(self).end
true_delay = first_start_date - data_interval_end
if true_delay.total_seconds() > 0:
Stats.timing(f'dagrun.{dag.dag_id}.first_task_scheduling_delay', true_delay)
except Exception as e:
self.log.warning(f'Failed to record first_task_scheduling_delay metric:\n{e}')
def _emit_duration_stats_for_finished_state(self):
if self.state == State.RUNNING:
return
if self.start_date is None:
self.log.warning('Failed to record duration of %s: start_date is not set.', self)
return
if self.end_date is None:
self.log.warning('Failed to record duration of %s: end_date is not set.', self)
return
duration = self.end_date - self.start_date
if self.state == State.SUCCESS:
Stats.timing(f'dagrun.duration.success.{self.dag_id}', duration)
elif self.state == State.FAILED:
Stats.timing(f'dagrun.duration.failed.{self.dag_id}', duration)
@provide_session
[docs] def verify_integrity(self, session: 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.
:param session: Sqlalchemy ORM Session
:type session: Session
"""
from airflow.settings import task_instance_mutation_hook
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 != State.RUNNING and not dag.partial:
self.log.warning("Failed to get task '%s' for dag '%s'. Marking it as removed.", ti, dag)
Stats.incr(f"task_removed_from_dag.{dag.dag_id}", 1, 1)
ti.state = State.REMOVED
should_restore_task = (task is not None) and ti.state == State.REMOVED
if should_restore_task:
self.log.info("Restoring task '%s' which was previously removed from DAG '%s'", ti, dag)
Stats.incr(f"task_restored_to_dag.{dag.dag_id}", 1, 1)
ti.state = State.NONE
session.merge(ti)
# check for missing tasks
for task in dag.task_dict.values():
if task.start_date > self.execution_date and not self.is_backfill:
continue
if task.task_id not in task_ids:
Stats.incr(f"task_instance_created-{task.task_type}", 1, 1)
ti = TI(task, execution_date=None, run_id=self.run_id)
task_instance_mutation_hook(ti)
session.add(ti)
try:
session.flush()
except IntegrityError as err:
self.log.info(str(err))
self.log.info('Hit IntegrityError while creating the TIs for %s- %s', dag.dag_id, self.run_id)
self.log.info('Doing session rollback.')
# TODO[HA]: We probably need to savepoint this so we can keep the transaction alive.
session.rollback()
@staticmethod
[docs] def get_run(session: Session, dag_id: str, execution_date: datetime) -> Optional['DagRun']:
"""
Get a single DAG Run
:meta private:
:param session: Sqlalchemy ORM Session
:type session: Session
: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
"""
warnings.warn(
"This method is deprecated. Please use SQLAlchemy directly",
DeprecationWarning,
stacklevel=2,
)
return (
session.query(DagRun)
.filter(
DagRun.dag_id == dag_id,
DagRun.external_trigger == False, # noqa
DagRun.execution_date == execution_date,
)
.first()
)
@property
[docs] def is_backfill(self) -> bool:
return self.run_type == DagRunType.BACKFILL_JOB
@classmethod
@provide_session
[docs] def get_latest_runs(cls, session=None) -> List['DagRun']:
"""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()
)
return (
session.query(cls)
.join(
subquery,
and_(cls.dag_id == subquery.c.dag_id, cls.execution_date == subquery.c.execution_date),
)
.all()
)
@provide_session
[docs] def schedule_tis(self, schedulable_tis: Iterable[TI], session: Session = None) -> int:
"""
Set the given task instances in to the scheduled state.
Each element of ``schedulable_tis`` should have it's ``task`` attribute already set.
Any DummyOperator without callbacks is instead set straight to the success state.
All the TIs should belong to this DagRun, but this code is in the hot-path, this is not checked -- it
is the caller's responsibility to call this function only with TIs from a single dag run.
"""
# Get list of TI IDs that do not need to executed, these are
# tasks using DummyOperator and without on_execute_callback / on_success_callback
dummy_ti_ids = []
schedulable_ti_ids = []
for ti in schedulable_tis:
if (
ti.task.inherits_from_dummy_operator
and not ti.task.on_execute_callback
and not ti.task.on_success_callback
):
dummy_ti_ids.append(ti.task_id)
else:
schedulable_ti_ids.append(ti.task_id)
count = 0
if schedulable_ti_ids:
count += (
session.query(TI)
.filter(
TI.dag_id == self.dag_id,
TI.run_id == self.run_id,
TI.task_id.in_(schedulable_ti_ids),
)
.update({TI.state: State.SCHEDULED}, synchronize_session=False)
)
# Tasks using DummyOperator should not be executed, mark them as success
if dummy_ti_ids:
count += (
session.query(TI)
.filter(
TI.dag_id == self.dag_id,
TI.run_id == self.run_id,
TI.task_id.in_(dummy_ti_ids),
)
.update(
{
TI.state: State.SUCCESS,
TI.start_date: timezone.utcnow(),
TI.end_date: timezone.utcnow(),
TI.duration: 0,
},
synchronize_session=False,
)
)
return count