#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
from __future__ import annotations
import collections.abc
import contextlib
import hashlib
import itertools
import logging
import math
import operator
import os
import signal
import warnings
from collections import defaultdict
from contextlib import nullcontext
from datetime import timedelta
from enum import Enum
from typing import TYPE_CHECKING, Any, Callable, Collection, Generator, Iterable, Mapping, Tuple
from urllib.parse import quote
import dill
import jinja2
import lazy_object_proxy
import pendulum
from deprecated import deprecated
from jinja2 import TemplateAssertionError, UndefinedError
from sqlalchemy import (
Column,
DateTime,
Float,
ForeignKey,
ForeignKeyConstraint,
Index,
Integer,
PrimaryKeyConstraint,
String,
Text,
and_,
delete,
false,
func,
inspect,
or_,
text,
update,
)
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.ext.hybrid import hybrid_property
from sqlalchemy.ext.mutable import MutableDict
from sqlalchemy.orm import lazyload, reconstructor, relationship
from sqlalchemy.orm.attributes import NO_VALUE, set_committed_value
from sqlalchemy.sql.expression import case, select
from airflow import settings
from airflow.api_internal.internal_api_call import InternalApiConfig, internal_api_call
from airflow.compat.functools import cache
from airflow.configuration import conf
from airflow.datasets import Dataset, DatasetAlias
from airflow.datasets.manager import dataset_manager
from airflow.exceptions import (
AirflowException,
AirflowFailException,
AirflowRescheduleException,
AirflowSensorTimeout,
AirflowSkipException,
AirflowTaskTerminated,
AirflowTaskTimeout,
DagRunNotFound,
RemovedInAirflow3Warning,
TaskDeferralError,
TaskDeferred,
UnmappableXComLengthPushed,
UnmappableXComTypePushed,
XComForMappingNotPushed,
)
from airflow.listeners.listener import get_listener_manager
from airflow.models.base import Base, StringID, TaskInstanceDependencies, _sentinel
from airflow.models.dagbag import DagBag
from airflow.models.dataset import DatasetAliasModel, DatasetModel
from airflow.models.log import Log
from airflow.models.param import process_params
from airflow.models.renderedtifields import get_serialized_template_fields
from airflow.models.taskfail import TaskFail
from airflow.models.taskinstancekey import TaskInstanceKey
from airflow.models.taskmap import TaskMap
from airflow.models.taskreschedule import TaskReschedule
from airflow.models.xcom import LazyXComSelectSequence, XCom
from airflow.plugins_manager import integrate_macros_plugins
from airflow.sentry import Sentry
from airflow.settings import task_instance_mutation_hook
from airflow.stats import Stats
from airflow.templates import SandboxedEnvironment
from airflow.ti_deps.dep_context import DepContext
from airflow.ti_deps.dependencies_deps import REQUEUEABLE_DEPS, RUNNING_DEPS
from airflow.traces.tracer import Trace
from airflow.utils import timezone
from airflow.utils.context import (
ConnectionAccessor,
Context,
InletEventsAccessors,
OutletEventAccessors,
VariableAccessor,
context_get_outlet_events,
context_merge,
)
from airflow.utils.email import send_email
from airflow.utils.helpers import prune_dict, render_template_to_string
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.net import get_hostname
from airflow.utils.operator_helpers import ExecutionCallableRunner, context_to_airflow_vars
from airflow.utils.platform import getuser
from airflow.utils.retries import run_with_db_retries
from airflow.utils.session import NEW_SESSION, create_session, provide_session
from airflow.utils.sqlalchemy import (
ExecutorConfigType,
ExtendedJSON,
UtcDateTime,
tuple_in_condition,
with_row_locks,
)
from airflow.utils.state import DagRunState, JobState, State, TaskInstanceState
from airflow.utils.task_group import MappedTaskGroup
from airflow.utils.task_instance_session import set_current_task_instance_session
from airflow.utils.timeout import timeout
from airflow.utils.types import AttributeRemoved
from airflow.utils.xcom import XCOM_RETURN_KEY
_CURRENT_CONTEXT: list[Context] = []
[docs]log = logging.getLogger(__name__)
if TYPE_CHECKING:
from datetime import datetime
from pathlib import PurePath
from types import TracebackType
from sqlalchemy.orm.session import Session
from sqlalchemy.sql.elements import BooleanClauseList
from sqlalchemy.sql.expression import ColumnOperators
from airflow.models.abstractoperator import TaskStateChangeCallback
from airflow.models.baseoperator import BaseOperator
from airflow.models.dag import DAG, DagModel
from airflow.models.dagrun import DagRun
from airflow.models.dataset import DatasetEvent
from airflow.models.operator import Operator
from airflow.serialization.pydantic.dag import DagModelPydantic
from airflow.serialization.pydantic.dataset import DatasetEventPydantic
from airflow.serialization.pydantic.taskinstance import TaskInstancePydantic
from airflow.timetables.base import DataInterval
from airflow.typing_compat import Literal, TypeGuard
from airflow.utils.task_group import TaskGroup
[docs]PAST_DEPENDS_MET = "past_depends_met"
class TaskReturnCode(Enum):
"""
Enum to signal manner of exit for task run command.
:meta private:
"""
DEFERRED = 100
"""When task exits with deferral to trigger."""
@internal_api_call
@provide_session
def _merge_ti(ti, session: Session = NEW_SESSION):
session.merge(ti)
session.commit()
@internal_api_call
@provide_session
def _add_log(
event,
task_instance=None,
owner=None,
owner_display_name=None,
extra=None,
session: Session = NEW_SESSION,
**kwargs,
):
session.add(
Log(
event,
task_instance,
owner,
owner_display_name,
extra,
**kwargs,
)
)
def _run_raw_task(
ti: TaskInstance | TaskInstancePydantic,
mark_success: bool = False,
test_mode: bool = False,
job_id: str | None = None,
pool: str | None = None,
raise_on_defer: bool = False,
session: Session | None = None,
) -> TaskReturnCode | None:
"""
Run a task, update the state upon completion, and run any appropriate callbacks.
Immediately runs the task (without checking or changing db state
before execution) and then sets the appropriate final state after
completion and runs any post-execute callbacks. Meant to be called
only after another function changes the state to running.
:param mark_success: Don't run the task, mark its state as success
:param test_mode: Doesn't record success or failure in the DB
:param pool: specifies the pool to use to run the task instance
:param session: SQLAlchemy ORM Session
"""
if TYPE_CHECKING:
assert ti.task
ti.test_mode = test_mode
ti.refresh_from_task(ti.task, pool_override=pool)
ti.refresh_from_db(session=session)
ti.job_id = job_id
ti.hostname = get_hostname()
ti.pid = os.getpid()
if not test_mode:
TaskInstance.save_to_db(ti=ti, session=session)
actual_start_date = timezone.utcnow()
Stats.incr(f"ti.start.{ti.task.dag_id}.{ti.task.task_id}", tags=ti.stats_tags)
# Same metric with tagging
Stats.incr("ti.start", tags=ti.stats_tags)
# Initialize final state counters at zero
for state in State.task_states:
Stats.incr(
f"ti.finish.{ti.task.dag_id}.{ti.task.task_id}.{state}",
count=0,
tags=ti.stats_tags,
)
# Same metric with tagging
Stats.incr(
"ti.finish",
count=0,
tags={**ti.stats_tags, "state": str(state)},
)
with set_current_task_instance_session(session=session):
ti.task = ti.task.prepare_for_execution()
context = ti.get_template_context(ignore_param_exceptions=False, session=session)
try:
if not mark_success:
TaskInstance._execute_task_with_callbacks(
self=ti, # type: ignore[arg-type]
context=context,
test_mode=test_mode,
session=session,
)
if not test_mode:
ti.refresh_from_db(lock_for_update=True, session=session)
ti.state = TaskInstanceState.SUCCESS
except TaskDeferred as defer:
# The task has signalled it wants to defer execution based on
# a trigger.
if raise_on_defer:
raise
ti.defer_task(exception=defer, session=session)
ti.log.info(
"Pausing task as DEFERRED. dag_id=%s, task_id=%s, run_id=%s, execution_date=%s, start_date=%s",
ti.dag_id,
ti.task_id,
ti.run_id,
_date_or_empty(task_instance=ti, attr="execution_date"),
_date_or_empty(task_instance=ti, attr="start_date"),
)
return TaskReturnCode.DEFERRED
except AirflowSkipException as e:
# Recording SKIP
# log only if exception has any arguments to prevent log flooding
if e.args:
ti.log.info(e)
if not test_mode:
ti.refresh_from_db(lock_for_update=True, session=session)
ti.state = TaskInstanceState.SKIPPED
_run_finished_callback(callbacks=ti.task.on_skipped_callback, context=context)
TaskInstance.save_to_db(ti=ti, session=session)
except AirflowRescheduleException as reschedule_exception:
ti._handle_reschedule(actual_start_date, reschedule_exception, test_mode, session=session)
ti.log.info("Rescheduling task, marking task as UP_FOR_RESCHEDULE")
return None
except (AirflowFailException, AirflowSensorTimeout) as e:
# If AirflowFailException is raised, task should not retry.
# If a sensor in reschedule mode reaches timeout, task should not retry.
ti.handle_failure(e, test_mode, context, force_fail=True, session=session) # already saves to db
raise
except (AirflowTaskTimeout, AirflowException, AirflowTaskTerminated) as e:
if not test_mode:
ti.refresh_from_db(lock_for_update=True, session=session)
# for case when task is marked as success/failed externally
# or dagrun timed out and task is marked as skipped
# current behavior doesn't hit the callbacks
if ti.state in State.finished:
ti.clear_next_method_args()
TaskInstance.save_to_db(ti=ti, session=session)
return None
else:
ti.handle_failure(e, test_mode, context, session=session)
raise
except SystemExit as e:
# We have already handled SystemExit with success codes (0 and None) in the `_execute_task`.
# Therefore, here we must handle only error codes.
msg = f"Task failed due to SystemExit({e.code})"
ti.handle_failure(msg, test_mode, context, session=session)
raise AirflowException(msg)
except BaseException as e:
ti.handle_failure(e, test_mode, context, session=session)
raise
finally:
# Print a marker post execution for internals of post task processing
log.info("::group::Post task execution logs")
Stats.incr(
f"ti.finish.{ti.dag_id}.{ti.task_id}.{ti.state}",
tags=ti.stats_tags,
)
# Same metric with tagging
Stats.incr("ti.finish", tags={**ti.stats_tags, "state": str(ti.state)})
# Recording SKIPPED or SUCCESS
ti.clear_next_method_args()
ti.end_date = timezone.utcnow()
_log_state(task_instance=ti)
ti.set_duration()
# run on_success_callback before db committing
# otherwise, the LocalTaskJob sees the state is changed to `success`,
# but the task_runner is still running, LocalTaskJob then treats the state is set externally!
_run_finished_callback(callbacks=ti.task.on_success_callback, context=context)
if not test_mode:
_add_log(event=ti.state, task_instance=ti, session=session)
if ti.state == TaskInstanceState.SUCCESS:
ti._register_dataset_changes(events=context["outlet_events"], session=session)
TaskInstance.save_to_db(ti=ti, session=session)
if ti.state == TaskInstanceState.SUCCESS:
get_listener_manager().hook.on_task_instance_success(
previous_state=TaskInstanceState.RUNNING, task_instance=ti, session=session
)
return None
@contextlib.contextmanager
[docs]def set_current_context(context: Context) -> Generator[Context, None, None]:
"""
Set the current execution context to the provided context object.
This method should be called once per Task execution, before calling operator.execute.
"""
_CURRENT_CONTEXT.append(context)
try:
yield context
finally:
expected_state = _CURRENT_CONTEXT.pop()
if expected_state != context:
log.warning(
"Current context is not equal to the state at context stack. Expected=%s, got=%s",
context,
expected_state,
)
def _stop_remaining_tasks(*, task_instance: TaskInstance | TaskInstancePydantic, session: Session):
"""
Stop non-teardown tasks in dag.
:meta private:
"""
if not task_instance.dag_run:
raise ValueError("``task_instance`` must have ``dag_run`` set")
tis = task_instance.dag_run.get_task_instances(session=session)
if TYPE_CHECKING:
assert task_instance.task
assert isinstance(task_instance.task.dag, DAG)
for ti in tis:
if ti.task_id == task_instance.task_id or ti.state in (
TaskInstanceState.SUCCESS,
TaskInstanceState.FAILED,
):
continue
task = task_instance.task.dag.task_dict[ti.task_id]
if not task.is_teardown:
if ti.state == TaskInstanceState.RUNNING:
log.info("Forcing task %s to fail due to dag's `fail_stop` setting", ti.task_id)
ti.error(session)
else:
log.info("Setting task %s to SKIPPED due to dag's `fail_stop` setting.", ti.task_id)
ti.set_state(state=TaskInstanceState.SKIPPED, session=session)
else:
log.info("Not skipping teardown task '%s'", ti.task_id)
[docs]def clear_task_instances(
tis: list[TaskInstance],
session: Session,
activate_dag_runs: None = None,
dag: DAG | None = None,
dag_run_state: DagRunState | Literal[False] = DagRunState.QUEUED,
) -> None:
"""
Clear a set of task instances, but make sure the running ones get killed.
Also sets Dagrun's `state` to QUEUED and `start_date` to the time of execution.
But only for finished DRs (SUCCESS and FAILED).
Doesn't clear DR's `state` and `start_date`for running
DRs (QUEUED and RUNNING) because clearing the state for already
running DR is redundant and clearing `start_date` affects DR's duration.
:param tis: a list of task instances
:param session: current session
:param dag_run_state: state to set finished DagRuns to.
If set to False, DagRuns state will not be changed.
:param dag: DAG object
:param activate_dag_runs: Deprecated parameter, do not pass
"""
job_ids = []
# Keys: dag_id -> run_id -> map_indexes -> try_numbers -> task_id
task_id_by_key: dict[str, dict[str, dict[int, dict[int, set[str]]]]] = defaultdict(
lambda: defaultdict(lambda: defaultdict(lambda: defaultdict(set)))
)
dag_bag = DagBag(read_dags_from_db=True)
from airflow.models.taskinstancehistory import TaskInstanceHistory
for ti in tis:
TaskInstanceHistory.record_ti(ti, session)
if ti.state == TaskInstanceState.RUNNING:
if ti.job_id:
# If a task is cleared when running, set its state to RESTARTING so that
# the task is terminated and becomes eligible for retry.
ti.state = TaskInstanceState.RESTARTING
job_ids.append(ti.job_id)
else:
ti_dag = dag if dag and dag.dag_id == ti.dag_id else dag_bag.get_dag(ti.dag_id, session=session)
task_id = ti.task_id
if ti_dag and ti_dag.has_task(task_id):
task = ti_dag.get_task(task_id)
ti.refresh_from_task(task)
if TYPE_CHECKING:
assert ti.task
ti.max_tries = ti.try_number + task.retries
else:
# Ignore errors when updating max_tries if the DAG or
# task are not found since database records could be
# outdated. We make max_tries the maximum value of its
# original max_tries or the last attempted try number.
ti.max_tries = max(ti.max_tries, ti.try_number)
ti.state = None
ti.external_executor_id = None
ti.clear_next_method_args()
session.merge(ti)
task_id_by_key[ti.dag_id][ti.run_id][ti.map_index][ti.try_number].add(ti.task_id)
if task_id_by_key:
# Clear all reschedules related to the ti to clear
# This is an optimization for the common case where all tis are for a small number
# of dag_id, run_id, try_number, and map_index. Use a nested dict of dag_id,
# run_id, try_number, map_index, and task_id to construct the where clause in a
# hierarchical manner. This speeds up the delete statement by more than 40x for
# large number of tis (50k+).
conditions = or_(
and_(
TR.dag_id == dag_id,
or_(
and_(
TR.run_id == run_id,
or_(
and_(
TR.map_index == map_index,
or_(
and_(TR.try_number == try_number, TR.task_id.in_(task_ids))
for try_number, task_ids in task_tries.items()
),
)
for map_index, task_tries in map_indexes.items()
),
)
for run_id, map_indexes in run_ids.items()
),
)
for dag_id, run_ids in task_id_by_key.items()
)
delete_qry = TR.__table__.delete().where(conditions)
session.execute(delete_qry)
if job_ids:
from airflow.jobs.job import Job
session.execute(update(Job).where(Job.id.in_(job_ids)).values(state=JobState.RESTARTING))
if activate_dag_runs is not None:
warnings.warn(
"`activate_dag_runs` parameter to clear_task_instances function is deprecated. "
"Please use `dag_run_state`",
RemovedInAirflow3Warning,
stacklevel=2,
)
if not activate_dag_runs:
dag_run_state = False
if dag_run_state is not False and tis:
from airflow.models.dagrun import DagRun # Avoid circular import
run_ids_by_dag_id = defaultdict(set)
for instance in tis:
run_ids_by_dag_id[instance.dag_id].add(instance.run_id)
drs = (
session.query(DagRun)
.filter(
or_(
and_(DagRun.dag_id == dag_id, DagRun.run_id.in_(run_ids))
for dag_id, run_ids in run_ids_by_dag_id.items()
)
)
.all()
)
dag_run_state = DagRunState(dag_run_state) # Validate the state value.
for dr in drs:
if dr.state in State.finished_dr_states:
dr.state = dag_run_state
dr.start_date = timezone.utcnow()
if dag_run_state == DagRunState.QUEUED:
dr.last_scheduling_decision = None
dr.start_date = None
dr.clear_number += 1
session.flush()
@internal_api_call
@provide_session
def _xcom_pull(
*,
ti,
task_ids: str | Iterable[str] | None = None,
dag_id: str | None = None,
key: str = XCOM_RETURN_KEY,
include_prior_dates: bool = False,
session: Session = NEW_SESSION,
map_indexes: int | Iterable[int] | None = None,
default: Any = None,
) -> Any:
"""
Pull XComs that optionally meet certain criteria.
: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 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 *None*.
:param task_ids: Only XComs from tasks with matching ids will be
pulled. Pass *None* to remove the filter.
:param dag_id: If provided, only pulls XComs from this DAG. If *None*
(default), the DAG of the calling task is used.
:param map_indexes: If provided, only pull XComs with matching indexes.
If *None* (default), this is inferred from the task(s) being pulled
(see below for details).
: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.
When pulling one single task (``task_id`` is *None* or a str) without
specifying ``map_indexes``, the return value is inferred from whether
the specified task is mapped. If not, value from the one single task
instance is returned. If the task to pull is mapped, an iterator (not a
list) yielding XComs from mapped task instances is returned. In either
case, ``default`` (*None* if not specified) is returned if no matching
XComs are found.
When pulling multiple tasks (i.e. either ``task_id`` or ``map_index`` is
a non-str iterable), a list of matching XComs is returned. Elements in
the list is ordered by item ordering in ``task_id`` and ``map_index``.
"""
if dag_id is None:
dag_id = ti.dag_id
query = XCom.get_many(
key=key,
run_id=ti.run_id,
dag_ids=dag_id,
task_ids=task_ids,
map_indexes=map_indexes,
include_prior_dates=include_prior_dates,
session=session,
)
# NOTE: Since we're only fetching the value field and not the whole
# class, the @recreate annotation does not kick in. Therefore we need to
# call XCom.deserialize_value() manually.
# We are only pulling one single task.
if (task_ids is None or isinstance(task_ids, str)) and not isinstance(map_indexes, Iterable):
first = query.with_entities(
XCom.run_id, XCom.task_id, XCom.dag_id, XCom.map_index, XCom.value
).first()
if first is None: # No matching XCom at all.
return default
if map_indexes is not None or first.map_index < 0:
return XCom.deserialize_value(first)
return LazyXComSelectSequence.from_select(
query.with_entities(XCom.value).order_by(None).statement,
order_by=[XCom.map_index],
session=session,
)
# At this point either task_ids or map_indexes is explicitly multi-value.
# Order return values to match task_ids and map_indexes ordering.
ordering = []
if task_ids is None or isinstance(task_ids, str):
ordering.append(XCom.task_id)
elif task_id_whens := {tid: i for i, tid in enumerate(task_ids)}:
ordering.append(case(task_id_whens, value=XCom.task_id))
else:
ordering.append(XCom.task_id)
if map_indexes is None or isinstance(map_indexes, int):
ordering.append(XCom.map_index)
elif isinstance(map_indexes, range):
order = XCom.map_index
if map_indexes.step < 0:
order = order.desc()
ordering.append(order)
elif map_index_whens := {map_index: i for i, map_index in enumerate(map_indexes)}:
ordering.append(case(map_index_whens, value=XCom.map_index))
else:
ordering.append(XCom.map_index)
return LazyXComSelectSequence.from_select(
query.with_entities(XCom.value).order_by(None).statement,
order_by=ordering,
session=session,
)
def _is_mappable_value(value: Any) -> TypeGuard[Collection]:
"""
Whether a value can be used for task mapping.
We only allow collections with guaranteed ordering, but exclude character
sequences since that's usually not what users would expect to be mappable.
"""
if not isinstance(value, (collections.abc.Sequence, dict)):
return False
if isinstance(value, (bytearray, bytes, str)):
return False
return True
def _creator_note(val):
"""Creator the ``note`` association proxy."""
if isinstance(val, str):
return TaskInstanceNote(content=val)
elif isinstance(val, dict):
return TaskInstanceNote(**val)
else:
return TaskInstanceNote(*val)
def _execute_task(task_instance: TaskInstance | TaskInstancePydantic, context: Context, task_orig: Operator):
"""
Execute Task (optionally with a Timeout) and push Xcom results.
:param task_instance: the task instance
:param context: Jinja2 context
:param task_orig: origin task
:meta private:
"""
from airflow.models.mappedoperator import MappedOperator
task_to_execute = task_instance.task
if TYPE_CHECKING:
assert task_to_execute
if isinstance(task_to_execute, MappedOperator):
raise AirflowException("MappedOperator cannot be executed.")
# If the task has been deferred and is being executed due to a trigger,
# then we need to pick the right method to come back to, otherwise
# we go for the default execute
execute_callable_kwargs: dict[str, Any] = {}
execute_callable: Callable
if task_instance.next_method:
if task_instance.next_method == "execute":
if not task_instance.next_kwargs:
task_instance.next_kwargs = {}
task_instance.next_kwargs[f"{task_to_execute.__class__.__name__}__sentinel"] = _sentinel
execute_callable = task_to_execute.resume_execution
execute_callable_kwargs["next_method"] = task_instance.next_method
execute_callable_kwargs["next_kwargs"] = task_instance.next_kwargs
else:
execute_callable = task_to_execute.execute
if execute_callable.__name__ == "execute":
execute_callable_kwargs[f"{task_to_execute.__class__.__name__}__sentinel"] = _sentinel
def _execute_callable(context: Context, **execute_callable_kwargs):
try:
# Print a marker for log grouping of details before task execution
log.info("::endgroup::")
return ExecutionCallableRunner(
execute_callable,
context_get_outlet_events(context),
logger=log,
).run(context=context, **execute_callable_kwargs)
except SystemExit as e:
# Handle only successful cases here. Failure cases will be handled upper
# in the exception chain.
if e.code is not None and e.code != 0:
raise
return None
# If a timeout is specified for the task, make it fail
# if it goes beyond
if task_to_execute.execution_timeout:
# If we are coming in with a next_method (i.e. from a deferral),
# calculate the timeout from our start_date.
if task_instance.next_method and task_instance.start_date:
timeout_seconds = (
task_to_execute.execution_timeout - (timezone.utcnow() - task_instance.start_date)
).total_seconds()
else:
timeout_seconds = task_to_execute.execution_timeout.total_seconds()
try:
# It's possible we're already timed out, so fast-fail if true
if timeout_seconds <= 0:
raise AirflowTaskTimeout()
# Run task in timeout wrapper
with timeout(timeout_seconds):
result = _execute_callable(context=context, **execute_callable_kwargs)
except AirflowTaskTimeout:
task_to_execute.on_kill()
raise
else:
result = _execute_callable(context=context, **execute_callable_kwargs)
cm = nullcontext() if InternalApiConfig.get_use_internal_api() else create_session()
with cm as session_or_null:
if task_to_execute.do_xcom_push:
xcom_value = result
else:
xcom_value = None
if xcom_value is not None: # If the task returns a result, push an XCom containing it.
if task_to_execute.multiple_outputs:
if not isinstance(xcom_value, Mapping):
raise AirflowException(
f"Returned output was type {type(xcom_value)} "
"expected dictionary for multiple_outputs"
)
for key in xcom_value.keys():
if not isinstance(key, str):
raise AirflowException(
"Returned dictionary keys must be strings when using "
f"multiple_outputs, found {key} ({type(key)}) instead"
)
for key, value in xcom_value.items():
task_instance.xcom_push(key=key, value=value, session=session_or_null)
task_instance.xcom_push(key=XCOM_RETURN_KEY, value=xcom_value, session=session_or_null)
if TYPE_CHECKING:
assert task_orig.dag
_record_task_map_for_downstreams(
task_instance=task_instance,
task=task_orig,
dag=task_orig.dag,
value=xcom_value,
session=session_or_null,
)
return result
def _set_ti_attrs(target, source, include_dag_run=False):
# Fields ordered per model definition
target.start_date = source.start_date
target.end_date = source.end_date
target.duration = source.duration
target.state = source.state
target.try_number = source.try_number
target.max_tries = source.max_tries
target.hostname = source.hostname
target.unixname = source.unixname
target.job_id = source.job_id
target.pool = source.pool
target.pool_slots = source.pool_slots or 1
target.queue = source.queue
target.priority_weight = source.priority_weight
target.operator = source.operator
target.custom_operator_name = source.custom_operator_name
target.queued_dttm = source.queued_dttm
target.queued_by_job_id = source.queued_by_job_id
target.pid = source.pid
target.executor = source.executor
target.executor_config = source.executor_config
target.external_executor_id = source.external_executor_id
target.trigger_id = source.trigger_id
target.next_method = source.next_method
target.next_kwargs = source.next_kwargs
if include_dag_run:
target.execution_date = source.execution_date
target.dag_run.id = source.dag_run.id
target.dag_run.dag_id = source.dag_run.dag_id
target.dag_run.queued_at = source.dag_run.queued_at
target.dag_run.execution_date = source.dag_run.execution_date
target.dag_run.start_date = source.dag_run.start_date
target.dag_run.end_date = source.dag_run.end_date
target.dag_run.state = source.dag_run.state
target.dag_run.run_id = source.dag_run.run_id
target.dag_run.creating_job_id = source.dag_run.creating_job_id
target.dag_run.external_trigger = source.dag_run.external_trigger
target.dag_run.run_type = source.dag_run.run_type
target.dag_run.conf = source.dag_run.conf
target.dag_run.data_interval_start = source.dag_run.data_interval_start
target.dag_run.data_interval_end = source.dag_run.data_interval_end
target.dag_run.last_scheduling_decision = source.dag_run.last_scheduling_decision
target.dag_run.dag_hash = source.dag_run.dag_hash
target.dag_run.updated_at = source.dag_run.updated_at
target.dag_run.log_template_id = source.dag_run.log_template_id
def _refresh_from_db(
*,
task_instance: TaskInstance | TaskInstancePydantic,
session: Session | None = None,
lock_for_update: bool = False,
) -> None:
"""
Refresh the task instance from the database based on the primary key.
:param task_instance: the task instance
:param session: SQLAlchemy ORM Session
:param lock_for_update: if True, indicates that the database should
lock the TaskInstance (issuing a FOR UPDATE clause) until the
session is committed.
:meta private:
"""
if not InternalApiConfig.get_use_internal_api():
if session and task_instance in session:
session.refresh(task_instance, TaskInstance.__mapper__.column_attrs.keys())
ti = TaskInstance.get_task_instance(
dag_id=task_instance.dag_id,
task_id=task_instance.task_id,
run_id=task_instance.run_id,
map_index=task_instance.map_index,
lock_for_update=lock_for_update,
session=session,
)
if ti:
from airflow.serialization.pydantic.taskinstance import TaskInstancePydantic
include_dag_run = isinstance(ti, TaskInstancePydantic)
# in case of internal API, what we get is TaskInstancePydantic value, and we are supposed
# to also update dag_run information as it might not be available. We cannot always do it in
# case ti is TaskInstance, because it might be detached/ not loaded yet and dag_run might
# not be available.
_set_ti_attrs(task_instance, ti, include_dag_run=include_dag_run)
else:
task_instance.state = None
def _set_duration(*, task_instance: TaskInstance | TaskInstancePydantic) -> None:
"""
Set task instance duration.
:param task_instance: the task instance
:meta private:
"""
if task_instance.end_date and task_instance.start_date:
task_instance.duration = (task_instance.end_date - task_instance.start_date).total_seconds()
else:
task_instance.duration = None
log.debug("Task Duration set to %s", task_instance.duration)
def _stats_tags(*, task_instance: TaskInstance | TaskInstancePydantic) -> dict[str, str]:
"""
Return task instance tags.
:param task_instance: the task instance
:meta private:
"""
return prune_dict({"dag_id": task_instance.dag_id, "task_id": task_instance.task_id})
def _clear_next_method_args(*, task_instance: TaskInstance | TaskInstancePydantic) -> None:
"""
Ensure we unset next_method and next_kwargs to ensure that any retries don't reuse them.
:param task_instance: the task instance
:meta private:
"""
log.debug("Clearing next_method and next_kwargs.")
task_instance.next_method = None
task_instance.next_kwargs = None
@internal_api_call
def _get_template_context(
*,
task_instance: TaskInstance | TaskInstancePydantic,
dag: DAG,
session: Session | None = None,
ignore_param_exceptions: bool = True,
) -> Context:
"""
Return TI Context.
:param task_instance: the task instance for the task
:param dag: dag for the task
:param session: SQLAlchemy ORM Session
:param ignore_param_exceptions: flag to suppress value exceptions while initializing the ParamsDict
:meta private:
"""
# Do not use provide_session here -- it expunges everything on exit!
if not session:
session = settings.Session()
from airflow import macros
from airflow.models.abstractoperator import NotMapped
integrate_macros_plugins()
task = task_instance.task
if TYPE_CHECKING:
assert task_instance.task
assert task
assert task.dag
if task.dag.__class__ is AttributeRemoved:
task.dag = dag # required after deserialization
dag_run = task_instance.get_dagrun(session)
data_interval = dag.get_run_data_interval(dag_run)
validated_params = process_params(dag, task, dag_run, suppress_exception=ignore_param_exceptions)
logical_date: DateTime = timezone.coerce_datetime(task_instance.execution_date)
ds = logical_date.strftime("%Y-%m-%d")
ds_nodash = ds.replace("-", "")
ts = logical_date.isoformat()
ts_nodash = logical_date.strftime("%Y%m%dT%H%M%S")
ts_nodash_with_tz = ts.replace("-", "").replace(":", "")
@cache # Prevent multiple database access.
def _get_previous_dagrun_success() -> DagRun | None:
return task_instance.get_previous_dagrun(state=DagRunState.SUCCESS, session=session)
def _get_previous_dagrun_data_interval_success() -> DataInterval | None:
dagrun = _get_previous_dagrun_success()
if dagrun is None:
return None
return dag.get_run_data_interval(dagrun)
def get_prev_data_interval_start_success() -> pendulum.DateTime | None:
data_interval = _get_previous_dagrun_data_interval_success()
if data_interval is None:
return None
return data_interval.start
def get_prev_data_interval_end_success() -> pendulum.DateTime | None:
data_interval = _get_previous_dagrun_data_interval_success()
if data_interval is None:
return None
return data_interval.end
def get_prev_start_date_success() -> pendulum.DateTime | None:
dagrun = _get_previous_dagrun_success()
if dagrun is None:
return None
return timezone.coerce_datetime(dagrun.start_date)
def get_prev_end_date_success() -> pendulum.DateTime | None:
dagrun = _get_previous_dagrun_success()
if dagrun is None:
return None
return timezone.coerce_datetime(dagrun.end_date)
@cache
def get_yesterday_ds() -> str:
return (logical_date - timedelta(1)).strftime("%Y-%m-%d")
def get_yesterday_ds_nodash() -> str:
return get_yesterday_ds().replace("-", "")
@cache
def get_tomorrow_ds() -> str:
return (logical_date + timedelta(1)).strftime("%Y-%m-%d")
def get_tomorrow_ds_nodash() -> str:
return get_tomorrow_ds().replace("-", "")
@cache
def get_next_execution_date() -> pendulum.DateTime | None:
# For manually triggered dagruns that aren't run on a schedule,
# the "next" execution date doesn't make sense, and should be set
# to execution date for consistency with how execution_date is set
# for manually triggered tasks, i.e. triggered_date == execution_date.
if dag_run.external_trigger:
return logical_date
if dag is None:
return None
next_info = dag.next_dagrun_info(data_interval, restricted=False)
if next_info is None:
return None
return timezone.coerce_datetime(next_info.logical_date)
def get_next_ds() -> str | None:
execution_date = get_next_execution_date()
if execution_date is None:
return None
return execution_date.strftime("%Y-%m-%d")
def get_next_ds_nodash() -> str | None:
ds = get_next_ds()
if ds is None:
return ds
return ds.replace("-", "")
@cache
def get_prev_execution_date():
# For manually triggered dagruns that aren't run on a schedule,
# the "previous" execution date doesn't make sense, and should be set
# to execution date for consistency with how execution_date is set
# for manually triggered tasks, i.e. triggered_date == execution_date.
if dag_run.external_trigger:
return logical_date
with warnings.catch_warnings():
warnings.simplefilter("ignore", RemovedInAirflow3Warning)
return dag.previous_schedule(logical_date)
@cache
def get_prev_ds() -> str | None:
execution_date = get_prev_execution_date()
if execution_date is None:
return None
return execution_date.strftime("%Y-%m-%d")
def get_prev_ds_nodash() -> str | None:
prev_ds = get_prev_ds()
if prev_ds is None:
return None
return prev_ds.replace("-", "")
def get_triggering_events() -> dict[str, list[DatasetEvent | DatasetEventPydantic]]:
if TYPE_CHECKING:
assert session is not None
# The dag_run may not be attached to the session anymore since the
# code base is over-zealous with use of session.expunge_all().
# Re-attach it if we get called.
nonlocal dag_run
if dag_run not in session:
dag_run = session.merge(dag_run, load=False)
dataset_events = dag_run.consumed_dataset_events
triggering_events: dict[str, list[DatasetEvent | DatasetEventPydantic]] = defaultdict(list)
for event in dataset_events:
if event.dataset:
triggering_events[event.dataset.uri].append(event)
return triggering_events
try:
expanded_ti_count: int | None = task.get_mapped_ti_count(task_instance.run_id, session=session)
except NotMapped:
expanded_ti_count = None
# NOTE: If you add to this dict, make sure to also update the following:
# * Context in airflow/utils/context.pyi
# * KNOWN_CONTEXT_KEYS in airflow/utils/context.py
# * Table in docs/apache-airflow/templates-ref.rst
context: dict[str, Any] = {
"conf": conf,
"dag": dag,
"dag_run": dag_run,
"data_interval_end": timezone.coerce_datetime(data_interval.end),
"data_interval_start": timezone.coerce_datetime(data_interval.start),
"outlet_events": OutletEventAccessors(),
"ds": ds,
"ds_nodash": ds_nodash,
"execution_date": logical_date,
"expanded_ti_count": expanded_ti_count,
"inlets": task.inlets,
"inlet_events": InletEventsAccessors(task.inlets, session=session),
"logical_date": logical_date,
"macros": macros,
"map_index_template": task.map_index_template,
"next_ds": get_next_ds(),
"next_ds_nodash": get_next_ds_nodash(),
"next_execution_date": get_next_execution_date(),
"outlets": task.outlets,
"params": validated_params,
"prev_data_interval_start_success": get_prev_data_interval_start_success(),
"prev_data_interval_end_success": get_prev_data_interval_end_success(),
"prev_ds": get_prev_ds(),
"prev_ds_nodash": get_prev_ds_nodash(),
"prev_execution_date": get_prev_execution_date(),
"prev_execution_date_success": task_instance.get_previous_execution_date(
state=DagRunState.SUCCESS,
session=session,
),
"prev_start_date_success": get_prev_start_date_success(),
"prev_end_date_success": get_prev_end_date_success(),
"run_id": task_instance.run_id,
"task": task,
"task_instance": task_instance,
"task_instance_key_str": f"{task.dag_id}__{task.task_id}__{ds_nodash}",
"test_mode": task_instance.test_mode,
"ti": task_instance,
"tomorrow_ds": get_tomorrow_ds(),
"tomorrow_ds_nodash": get_tomorrow_ds_nodash(),
"triggering_dataset_events": lazy_object_proxy.Proxy(get_triggering_events),
"ts": ts,
"ts_nodash": ts_nodash,
"ts_nodash_with_tz": ts_nodash_with_tz,
"var": {
"json": VariableAccessor(deserialize_json=True),
"value": VariableAccessor(deserialize_json=False),
},
"conn": ConnectionAccessor(),
"yesterday_ds": get_yesterday_ds(),
"yesterday_ds_nodash": get_yesterday_ds_nodash(),
}
# Mypy doesn't like turning existing dicts in to a TypeDict -- and we "lie" in the type stub to say it
# is one, but in practice it isn't. See https://github.com/python/mypy/issues/8890
return Context(context) # type: ignore
def _is_eligible_to_retry(*, task_instance: TaskInstance | TaskInstancePydantic):
"""
Is task instance is eligible for retry.
:param task_instance: the task instance
:meta private:
"""
if task_instance.state == TaskInstanceState.RESTARTING:
# If a task is cleared when running, it goes into RESTARTING state and is always
# eligible for retry
return True
if not getattr(task_instance, "task", None):
# Couldn't load the task, don't know number of retries, guess:
return task_instance.try_number <= task_instance.max_tries
if TYPE_CHECKING:
assert task_instance.task
return task_instance.task.retries and task_instance.try_number <= task_instance.max_tries
@provide_session
@internal_api_call
def _handle_failure(
*,
task_instance: TaskInstance | TaskInstancePydantic,
error: None | str | BaseException,
session: Session,
test_mode: bool | None = None,
context: Context | None = None,
force_fail: bool = False,
fail_stop: bool = False,
) -> None:
"""
Handle Failure for a task instance.
:param task_instance: the task instance
:param error: if specified, log the specific exception if thrown
:param session: SQLAlchemy ORM Session
:param test_mode: doesn't record success or failure in the DB if True
:param context: Jinja2 context
:param force_fail: if True, task does not retry
:meta private:
"""
if test_mode is None:
test_mode = task_instance.test_mode
task_instance = _coalesce_to_orm_ti(ti=task_instance, session=session)
failure_context = TaskInstance.fetch_handle_failure_context(
ti=task_instance, # type: ignore[arg-type]
error=error,
test_mode=test_mode,
context=context,
force_fail=force_fail,
session=session,
fail_stop=fail_stop,
)
_log_state(task_instance=task_instance, lead_msg="Immediate failure requested. " if force_fail else "")
if (
failure_context["task"]
and failure_context["email_for_state"](failure_context["task"])
and failure_context["task"].email
):
try:
task_instance.email_alert(error, failure_context["task"])
except Exception:
log.exception("Failed to send email to: %s", failure_context["task"].email)
if failure_context["callbacks"] and failure_context["context"]:
_run_finished_callback(
callbacks=failure_context["callbacks"],
context=failure_context["context"],
)
if not test_mode:
TaskInstance.save_to_db(failure_context["ti"], session)
with Trace.start_span_from_taskinstance(ti=task_instance) as span:
# ---- error info ----
span.set_attribute("error", "true")
span.set_attribute("error_msg", str(error))
span.set_attribute("context", context)
span.set_attribute("force_fail", force_fail)
# ---- common info ----
span.set_attribute("category", "DAG runs")
span.set_attribute("task_id", task_instance.task_id)
span.set_attribute("dag_id", task_instance.dag_id)
span.set_attribute("state", task_instance.state)
span.set_attribute("start_date", str(task_instance.start_date))
span.set_attribute("end_date", str(task_instance.end_date))
span.set_attribute("duration", task_instance.duration)
span.set_attribute("executor_config", str(task_instance.executor_config))
span.set_attribute("execution_date", str(task_instance.execution_date))
span.set_attribute("hostname", task_instance.hostname)
if isinstance(task_instance, TaskInstance):
span.set_attribute("log_url", task_instance.log_url)
span.set_attribute("operator", str(task_instance.operator))
def _refresh_from_task(
*, task_instance: TaskInstance | TaskInstancePydantic, task: Operator, pool_override: str | None = None
) -> None:
"""
Copy common attributes from the given task.
:param task_instance: the task instance
:param task: The task object to copy from
:param pool_override: Use the pool_override instead of task's pool
:meta private:
"""
task_instance.task = task
task_instance.queue = task.queue
task_instance.pool = pool_override or task.pool
task_instance.pool_slots = task.pool_slots
with contextlib.suppress(Exception):
# This method is called from the different places, and sometimes the TI is not fully initialized
task_instance.priority_weight = task_instance.task.weight_rule.get_weight(
task_instance # type: ignore[arg-type]
)
task_instance.run_as_user = task.run_as_user
# Do not set max_tries to task.retries here because max_tries is a cumulative
# value that needs to be stored in the db.
task_instance.executor = task.executor
task_instance.executor_config = task.executor_config
task_instance.operator = task.task_type
task_instance.custom_operator_name = getattr(task, "custom_operator_name", None)
# Re-apply cluster policy here so that task default do not overload previous data
task_instance_mutation_hook(task_instance)
@internal_api_call
@provide_session
def _record_task_map_for_downstreams(
*,
task_instance: TaskInstance | TaskInstancePydantic,
task: Operator,
dag: DAG,
value: Any,
session: Session,
) -> None:
"""
Record the task map for downstream tasks.
:param task_instance: the task instance
:param task: The task object
:param dag: the dag associated with the task
:param value: The value
:param session: SQLAlchemy ORM Session
:meta private:
"""
from airflow.models.mappedoperator import MappedOperator
if task.dag.__class__ is AttributeRemoved:
task.dag = dag # required after deserialization
if next(task.iter_mapped_dependants(), None) is None: # No mapped dependants, no need to validate.
return
# TODO: We don't push TaskMap for mapped task instances because it's not
# currently possible for a downstream to depend on one individual mapped
# task instance. This will change when we implement task mapping inside
# a mapped task group, and we'll need to further analyze the case.
if isinstance(task, MappedOperator):
return
if value is None:
raise XComForMappingNotPushed()
if not _is_mappable_value(value):
raise UnmappableXComTypePushed(value)
task_map = TaskMap.from_task_instance_xcom(task_instance, value)
max_map_length = conf.getint("core", "max_map_length", fallback=1024)
if task_map.length > max_map_length:
raise UnmappableXComLengthPushed(value, max_map_length)
session.merge(task_map)
def _get_previous_dagrun(
*,
task_instance: TaskInstance | TaskInstancePydantic,
state: DagRunState | None = None,
session: Session | None = None,
) -> DagRun | None:
"""
Return the DagRun that ran prior to this task instance's DagRun.
:param task_instance: the task instance
:param state: If passed, it only take into account instances of a specific state.
:param session: SQLAlchemy ORM Session.
:meta private:
"""
if TYPE_CHECKING:
assert task_instance.task
dag = task_instance.task.dag
if dag is None:
return None
dr = task_instance.get_dagrun(session=session)
dr.dag = dag
from airflow.models.dagrun import DagRun # Avoid circular import
# We always ignore schedule in dagrun lookup when `state` is given
# or the DAG is never scheduled. For legacy reasons, when
# `catchup=True`, we use `get_previous_scheduled_dagrun` unless
# `ignore_schedule` is `True`.
ignore_schedule = state is not None or not dag.timetable.can_be_scheduled
if dag.catchup is True and not ignore_schedule:
last_dagrun = DagRun.get_previous_scheduled_dagrun(dr.id, session=session)
else:
last_dagrun = DagRun.get_previous_dagrun(dag_run=dr, session=session, state=state)
if last_dagrun:
return last_dagrun
return None
def _get_previous_execution_date(
*,
task_instance: TaskInstance | TaskInstancePydantic,
state: DagRunState | None,
session: Session,
) -> pendulum.DateTime | None:
"""
Get execution date from property previous_ti_success.
:param task_instance: the task instance
:param session: SQLAlchemy ORM Session
:param state: If passed, it only take into account instances of a specific state.
:meta private:
"""
log.debug("previous_execution_date was called")
prev_ti = task_instance.get_previous_ti(state=state, session=session)
return pendulum.instance(prev_ti.execution_date) if prev_ti and prev_ti.execution_date else None
def _get_previous_start_date(
*,
task_instance: TaskInstance | TaskInstancePydantic,
state: DagRunState | None,
session: Session,
) -> pendulum.DateTime | None:
"""
Return the start date from property previous_ti_success.
:param task_instance: the task instance
:param state: If passed, it only take into account instances of a specific state.
:param session: SQLAlchemy ORM Session
"""
log.debug("previous_start_date was called")
prev_ti = task_instance.get_previous_ti(state=state, session=session)
# prev_ti may not exist and prev_ti.start_date may be None.
return pendulum.instance(prev_ti.start_date) if prev_ti and prev_ti.start_date else None
def _email_alert(
*, task_instance: TaskInstance | TaskInstancePydantic, exception, task: BaseOperator
) -> None:
"""
Send alert email with exception information.
:param task_instance: the task instance
:param exception: the exception
:param task: task related to the exception
:meta private:
"""
subject, html_content, html_content_err = task_instance.get_email_subject_content(exception, task=task)
if TYPE_CHECKING:
assert task.email
try:
send_email(task.email, subject, html_content)
except Exception:
send_email(task.email, subject, html_content_err)
def _get_email_subject_content(
*,
task_instance: TaskInstance | TaskInstancePydantic,
exception: BaseException,
task: BaseOperator | None = None,
) -> tuple[str, str, str]:
"""
Get the email subject content for exceptions.
:param task_instance: the task instance
:param exception: the exception sent in the email
:param task:
:meta private:
"""
# For a ti from DB (without ti.task), return the default value
if task is None:
task = getattr(task_instance, "task")
use_default = task is None
exception_html = str(exception).replace("\n", "<br>")
default_subject = "Airflow alert: {{ti}}"
# For reporting purposes, we report based on 1-indexed,
# not 0-indexed lists (i.e. Try 1 instead of
# Try 0 for the first attempt).
default_html_content = (
"Try {{try_number}} out of {{max_tries + 1}}<br>"
"Exception:<br>{{exception_html}}<br>"
'Log: <a href="{{ti.log_url}}">Link</a><br>'
"Host: {{ti.hostname}}<br>"
'Mark success: <a href="{{ti.mark_success_url}}">Link</a><br>'
)
default_html_content_err = (
"Try {{try_number}} out of {{max_tries + 1}}<br>"
"Exception:<br>Failed attempt to attach error logs<br>"
'Log: <a href="{{ti.log_url}}">Link</a><br>'
"Host: {{ti.hostname}}<br>"
'Mark success: <a href="{{ti.mark_success_url}}">Link</a><br>'
)
additional_context: dict[str, Any] = {
"exception": exception,
"exception_html": exception_html,
"try_number": task_instance.try_number,
"max_tries": task_instance.max_tries,
}
if use_default:
default_context = {"ti": task_instance, **additional_context}
jinja_env = jinja2.Environment(
loader=jinja2.FileSystemLoader(os.path.dirname(__file__)), autoescape=True
)
subject = jinja_env.from_string(default_subject).render(**default_context)
html_content = jinja_env.from_string(default_html_content).render(**default_context)
html_content_err = jinja_env.from_string(default_html_content_err).render(**default_context)
else:
if TYPE_CHECKING:
assert task_instance.task
# Use the DAG's get_template_env() to set force_sandboxed. Don't add
# the flag to the function on task object -- that function can be
# overridden, and adding a flag breaks backward compatibility.
dag = task_instance.task.get_dag()
if dag:
jinja_env = dag.get_template_env(force_sandboxed=True)
else:
jinja_env = SandboxedEnvironment(cache_size=0)
jinja_context = task_instance.get_template_context()
context_merge(jinja_context, additional_context)
def render(key: str, content: str) -> str:
if conf.has_option("email", key):
path = conf.get_mandatory_value("email", key)
try:
with open(path) as f:
content = f.read()
except FileNotFoundError:
log.warning("Could not find email template file '%s'. Using defaults...", path)
except OSError:
log.exception("Error while using email template %s. Using defaults...", path)
return render_template_to_string(jinja_env.from_string(content), jinja_context)
subject = render("subject_template", default_subject)
html_content = render("html_content_template", default_html_content)
html_content_err = render("html_content_template", default_html_content_err)
return subject, html_content, html_content_err
def _run_finished_callback(
*,
callbacks: None | TaskStateChangeCallback | list[TaskStateChangeCallback],
context: Context,
) -> None:
"""
Run callback after task finishes.
:param callbacks: callbacks to run
:param context: callbacks context
:meta private:
"""
if callbacks:
callbacks = callbacks if isinstance(callbacks, list) else [callbacks]
def get_callback_representation(callback: TaskStateChangeCallback) -> Any:
with contextlib.suppress(AttributeError):
return callback.__name__
with contextlib.suppress(AttributeError):
return callback.__class__.__name__
return callback
for idx, callback in enumerate(callbacks):
callback_repr = get_callback_representation(callback)
log.info("Executing callback at index %d: %s", idx, callback_repr)
try:
callback(context)
except Exception:
log.exception("Error in callback at index %d: %s", idx, callback_repr)
def _log_state(*, task_instance: TaskInstance | TaskInstancePydantic, lead_msg: str = "") -> None:
"""
Log task state.
:param task_instance: the task instance
:param lead_msg: lead message
:meta private:
"""
params = [
lead_msg,
str(task_instance.state).upper(),
task_instance.dag_id,
task_instance.task_id,
task_instance.run_id,
]
message = "%sMarking task as %s. dag_id=%s, task_id=%s, run_id=%s, "
if task_instance.map_index >= 0:
params.append(task_instance.map_index)
message += "map_index=%d, "
message += "execution_date=%s, start_date=%s, end_date=%s"
log.info(
message,
*params,
_date_or_empty(task_instance=task_instance, attr="execution_date"),
_date_or_empty(task_instance=task_instance, attr="start_date"),
_date_or_empty(task_instance=task_instance, attr="end_date"),
stacklevel=2,
)
def _date_or_empty(*, task_instance: TaskInstance | TaskInstancePydantic, attr: str) -> str:
"""
Fetch a date attribute or None of it does not exist.
:param task_instance: the task instance
:param attr: the attribute name
:meta private:
"""
result: datetime | None = getattr(task_instance, attr, None)
return result.strftime("%Y%m%dT%H%M%S") if result else ""
def _get_previous_ti(
*,
task_instance: TaskInstance | TaskInstancePydantic,
session: Session,
state: DagRunState | None = None,
) -> TaskInstance | TaskInstancePydantic | None:
"""
Get task instance for the task that ran before this task instance.
:param task_instance: the task instance
:param state: If passed, it only take into account instances of a specific state.
:param session: SQLAlchemy ORM Session
:meta private:
"""
dagrun = task_instance.get_previous_dagrun(state, session=session)
if dagrun is None:
return None
return dagrun.get_task_instance(task_instance.task_id, session=session)
@internal_api_call
@provide_session
def _update_rtif(ti, rendered_fields, session: Session = NEW_SESSION):
from airflow.models.renderedtifields import RenderedTaskInstanceFields
rtif = RenderedTaskInstanceFields(ti=ti, render_templates=False, rendered_fields=rendered_fields)
RenderedTaskInstanceFields.write(rtif, session=session)
session.flush()
RenderedTaskInstanceFields.delete_old_records(ti.task_id, ti.dag_id, session=session)
def _coalesce_to_orm_ti(*, ti: TaskInstancePydantic | TaskInstance, session: Session):
from airflow.models.dagrun import DagRun
from airflow.serialization.pydantic.taskinstance import TaskInstancePydantic
if isinstance(ti, TaskInstancePydantic):
orm_ti = DagRun.fetch_task_instance(
dag_id=ti.dag_id,
dag_run_id=ti.run_id,
task_id=ti.task_id,
map_index=ti.map_index,
session=session,
)
if TYPE_CHECKING:
assert orm_ti
ti, pydantic_ti = orm_ti, ti
_set_ti_attrs(ti, pydantic_ti)
ti.task = pydantic_ti.task
return ti
@internal_api_call
@provide_session
def _defer_task(
ti: TaskInstance | TaskInstancePydantic,
exception: TaskDeferred | None = None,
session: Session = NEW_SESSION,
) -> TaskInstancePydantic | TaskInstance:
from airflow.models.trigger import Trigger
if exception is not None:
trigger_row = Trigger.from_object(exception.trigger)
next_method = exception.method_name
next_kwargs = exception.kwargs
timeout = exception.timeout
elif ti.task is not None and ti.task.start_trigger_args is not None:
context = ti.get_template_context()
start_trigger_args = ti.task.expand_start_trigger_args(context=context, session=session)
if start_trigger_args is None:
raise TaskDeferralError(
"A none 'None' start_trigger_args has been change to 'None' during expandion"
)
trigger_kwargs = start_trigger_args.trigger_kwargs or {}
next_kwargs = start_trigger_args.next_kwargs
next_method = start_trigger_args.next_method
timeout = start_trigger_args.timeout
trigger_row = Trigger(
classpath=ti.task.start_trigger_args.trigger_cls,
kwargs=trigger_kwargs,
)
else:
raise TaskDeferralError("exception and ti.task.start_trigger_args cannot both be None")
# First, make the trigger entry
session.add(trigger_row)
session.flush()
ti = _coalesce_to_orm_ti(ti=ti, session=session) # ensure orm obj in case it's pydantic
if TYPE_CHECKING:
assert ti.task
# Then, update ourselves so it matches the deferral request
# Keep an eye on the logic in `check_and_change_state_before_execution()`
# depending on self.next_method semantics
ti.state = TaskInstanceState.DEFERRED
ti.trigger_id = trigger_row.id
ti.next_method = next_method
ti.next_kwargs = next_kwargs or {}
# Calculate timeout too if it was passed
if timeout is not None:
ti.trigger_timeout = timezone.utcnow() + timeout
else:
ti.trigger_timeout = None
# If an execution_timeout is set, set the timeout to the minimum of
# it and the trigger timeout
execution_timeout = ti.task.execution_timeout
if execution_timeout:
if TYPE_CHECKING:
assert ti.start_date
if ti.trigger_timeout:
ti.trigger_timeout = min(ti.start_date + execution_timeout, ti.trigger_timeout)
else:
ti.trigger_timeout = ti.start_date + execution_timeout
if ti.test_mode:
_add_log(event=ti.state, task_instance=ti, session=session)
if exception is not None:
session.merge(ti)
session.commit()
return ti
@internal_api_call
@provide_session
def _handle_reschedule(
ti,
actual_start_date: datetime,
reschedule_exception: AirflowRescheduleException,
test_mode: bool = False,
session: Session = NEW_SESSION,
):
# Don't record reschedule request in test mode
if test_mode:
return
ti = _coalesce_to_orm_ti(ti=ti, session=session)
from airflow.models.dagrun import DagRun # Avoid circular import
ti.refresh_from_db(session)
if TYPE_CHECKING:
assert ti.task
ti.end_date = timezone.utcnow()
ti.set_duration()
# Lock DAG run to be sure not to get into a deadlock situation when trying to insert
# TaskReschedule which apparently also creates lock on corresponding DagRun entity
with_row_locks(
session.query(DagRun).filter_by(
dag_id=ti.dag_id,
run_id=ti.run_id,
),
session=session,
).one()
# Log reschedule request
session.add(
TaskReschedule(
ti.task_id,
ti.dag_id,
ti.run_id,
ti.try_number,
actual_start_date,
ti.end_date,
reschedule_exception.reschedule_date,
ti.map_index,
)
)
# set state
ti.state = TaskInstanceState.UP_FOR_RESCHEDULE
ti.clear_next_method_args()
session.merge(ti)
session.commit()
return ti
[docs]class TaskInstance(Base, LoggingMixin):
"""
Task instances store the state of a task instance.
This table is the authority and single source of truth around what tasks
have run and the state they are in.
The SqlAlchemy model doesn't have a SqlAlchemy foreign key to the task or
dag model deliberately to have more control over transactions.
Database transactions on this table should insure double triggers and
any confusion around what task instances are or aren't ready to run
even while multiple schedulers may be firing task instances.
A value of -1 in map_index represents any of: a TI without mapped tasks;
a TI with mapped tasks that has yet to be expanded (state=pending);
a TI with mapped tasks that expanded to an empty list (state=skipped).
"""
[docs] __tablename__ = "task_instance"
[docs] task_id = Column(StringID(), primary_key=True, nullable=False)
[docs] dag_id = Column(StringID(), primary_key=True, nullable=False)
[docs] run_id = Column(StringID(), primary_key=True, nullable=False)
[docs] map_index = Column(Integer, primary_key=True, nullable=False, server_default=text("-1"))
[docs] start_date = Column(UtcDateTime)
[docs] end_date = Column(UtcDateTime)
[docs] duration = Column(Float)
[docs] state = Column(String(20))
[docs] try_number = Column(Integer, default=0)
[docs] max_tries = Column(Integer, server_default=text("-1"))
[docs] hostname = Column(String(1000))
[docs] unixname = Column(String(1000))
[docs] job_id = Column(Integer)
[docs] pool = Column(String(256), nullable=False)
[docs] pool_slots = Column(Integer, default=1, nullable=False)
[docs] queue = Column(String(256))
[docs] priority_weight = Column(Integer)
[docs] operator = Column(String(1000))
[docs] custom_operator_name = Column(String(1000))
[docs] queued_dttm = Column(UtcDateTime)
[docs] queued_by_job_id = Column(Integer)
[docs] executor = Column(String(1000))
[docs] executor_config = Column(ExecutorConfigType(pickler=dill))
[docs] updated_at = Column(UtcDateTime, default=timezone.utcnow, onupdate=timezone.utcnow)
[docs] rendered_map_index = Column(String(250))
[docs] external_executor_id = Column(StringID())
# The trigger to resume on if we are in state DEFERRED
[docs] trigger_id = Column(Integer)
# Optional timeout datetime for the trigger (past this, we'll fail)
[docs] trigger_timeout = Column(DateTime)
# The trigger_timeout should be TIMESTAMP(using UtcDateTime) but for ease of
# migration, we are keeping it as DateTime pending a change where expensive
# migration is inevitable.
# The method to call next, and any extra arguments to pass to it.
# Usually used when resuming from DEFERRED.
[docs] next_method = Column(String(1000))
[docs] next_kwargs = Column(MutableDict.as_mutable(ExtendedJSON))
_task_display_property_value = Column("task_display_name", String(2000), nullable=True)
# If adding new fields here then remember to add them to
# refresh_from_db() or they won't display in the UI correctly
[docs] __table_args__ = (
Index("ti_dag_state", dag_id, state),
Index("ti_dag_run", dag_id, run_id),
Index("ti_state", state),
Index("ti_state_lkp", dag_id, task_id, run_id, state),
Index("ti_pool", pool, state, priority_weight),
Index("ti_job_id", job_id),
Index("ti_trigger_id", trigger_id),
PrimaryKeyConstraint("dag_id", "task_id", "run_id", "map_index", name="task_instance_pkey"),
ForeignKeyConstraint(
[trigger_id],
["trigger.id"],
name="task_instance_trigger_id_fkey",
ondelete="CASCADE",
),
ForeignKeyConstraint(
[dag_id, run_id],
["dag_run.dag_id", "dag_run.run_id"],
name="task_instance_dag_run_fkey",
ondelete="CASCADE",
),
)
[docs] dag_model: DagModel = relationship(
"DagModel",
primaryjoin="TaskInstance.dag_id == DagModel.dag_id",
foreign_keys=dag_id,
uselist=False,
innerjoin=True,
viewonly=True,
)
[docs] trigger = relationship("Trigger", uselist=False, back_populates="task_instance")
[docs] triggerer_job = association_proxy("trigger", "triggerer_job")
[docs] dag_run = relationship("DagRun", back_populates="task_instances", lazy="joined", innerjoin=True)
[docs] rendered_task_instance_fields = relationship("RenderedTaskInstanceFields", lazy="noload", uselist=False)
[docs] execution_date = association_proxy("dag_run", "execution_date")
[docs] task_instance_note = relationship(
"TaskInstanceNote",
back_populates="task_instance",
uselist=False,
cascade="all, delete, delete-orphan",
)
[docs] note = association_proxy("task_instance_note", "content", creator=_creator_note)
[docs] task: Operator | None = None
[docs] test_mode: bool = False
[docs] is_trigger_log_context: bool = False
[docs] run_as_user: str | None = None
raw: bool | None = None
"""Indicate to FileTaskHandler that logging context should be set up for trigger logging.
:meta private:
"""
_logger_name = "airflow.task"
def __init__(
self,
task: Operator,
execution_date: datetime | None = None,
run_id: str | None = None,
state: str | None = None,
map_index: int = -1,
):
super().__init__()
self.dag_id = task.dag_id
self.task_id = task.task_id
self.map_index = map_index
self.refresh_from_task(task)
if TYPE_CHECKING:
assert self.task
# init_on_load will config the log
self.init_on_load()
if run_id is None and execution_date is not None:
from airflow.models.dagrun import DagRun # Avoid circular import
warnings.warn(
"Passing an execution_date to `TaskInstance()` is deprecated in favour of passing a run_id",
RemovedInAirflow3Warning,
# Stack level is 4 because SQLA adds some wrappers around the constructor
stacklevel=4,
)
# make sure we have a localized execution_date stored in UTC
if execution_date and not timezone.is_localized(execution_date):
self.log.warning(
"execution date %s has no timezone information. Using default from dag or system",
execution_date,
)
if self.task.has_dag():
if TYPE_CHECKING:
assert self.task.dag
execution_date = timezone.make_aware(execution_date, self.task.dag.timezone)
else:
execution_date = timezone.make_aware(execution_date)
execution_date = timezone.convert_to_utc(execution_date)
with create_session() as session:
run_id = (
session.query(DagRun.run_id)
.filter_by(dag_id=self.dag_id, execution_date=execution_date)
.scalar()
)
if run_id is None:
raise DagRunNotFound(
f"DagRun for {self.dag_id!r} with date {execution_date} not found"
) from None
self.run_id = run_id
self.try_number = 0
self.max_tries = self.task.retries
self.unixname = getuser()
if state:
self.state = state
self.hostname = ""
# Is this TaskInstance being currently running within `airflow tasks run --raw`.
# Not persisted to the database so only valid for the current process
self.raw = False
# can be changed when calling 'run'
self.test_mode = False
[docs] def __hash__(self):
return hash((self.task_id, self.dag_id, self.run_id, self.map_index))
@property
@deprecated(reason="Use try_number instead.", version="2.10.0", category=RemovedInAirflow3Warning)
def _try_number(self):
"""
Do not use. For semblance of backcompat.
:meta private:
"""
return self.try_number
@_try_number.setter
@deprecated(reason="Use try_number instead.", version="2.10.0", category=RemovedInAirflow3Warning)
def _try_number(self, val):
"""
Do not use. For semblance of backcompat.
:meta private:
"""
self.try_number = val
@property
@staticmethod
def insert_mapping(run_id: str, task: Operator, map_index: int) -> dict[str, Any]:
"""
Insert mapping.
:meta private:
"""
priority_weight = task.weight_rule.get_weight(
TaskInstance(task=task, run_id=run_id, map_index=map_index)
)
return {
"dag_id": task.dag_id,
"task_id": task.task_id,
"run_id": run_id,
"try_number": 0,
"hostname": "",
"unixname": getuser(),
"queue": task.queue,
"pool": task.pool,
"pool_slots": task.pool_slots,
"priority_weight": priority_weight,
"run_as_user": task.run_as_user,
"max_tries": task.retries,
"executor": task.executor,
"executor_config": task.executor_config,
"operator": task.task_type,
"custom_operator_name": getattr(task, "custom_operator_name", None),
"map_index": map_index,
"_task_display_property_value": task.task_display_name,
}
@reconstructor
[docs] def init_on_load(self) -> None:
"""Initialize the attributes that aren't stored in the DB."""
self.test_mode = False # can be changed when calling 'run'
@property
@deprecated(reason="Use try_number instead.", version="2.10.0", category=RemovedInAirflow3Warning)
def prev_attempted_tries(self) -> int:
"""
Calculate the total number of attempted tries, defaulting to 0.
This used to be necessary because try_number did not always tell the truth.
:meta private:
"""
return self.try_number
@property
[docs] def next_try_number(self) -> int:
# todo (dstandish): deprecate this property; we don't need a property that is just + 1
return self.try_number + 1
@property
[docs] def operator_name(self) -> str | None:
"""@property: use a more friendly display name for the operator, if set."""
return self.custom_operator_name or self.operator
@hybrid_property
[docs] def task_display_name(self) -> str:
return self._task_display_property_value or self.task_id
@staticmethod
def _command_as_list(
ti: TaskInstance | TaskInstancePydantic,
mark_success: bool = False,
ignore_all_deps: bool = False,
ignore_task_deps: bool = False,
ignore_depends_on_past: bool = False,
wait_for_past_depends_before_skipping: bool = False,
ignore_ti_state: bool = False,
local: bool = False,
pickle_id: int | None = None,
raw: bool = False,
job_id: str | None = None,
pool: str | None = None,
cfg_path: str | None = None,
) -> list[str]:
dag: DAG | DagModel | DagModelPydantic | None
# Use the dag if we have it, else fallback to the ORM dag_model, which might not be loaded
if hasattr(ti, "task") and getattr(ti.task, "dag", None) is not None:
if TYPE_CHECKING:
assert ti.task
dag = ti.task.dag
else:
dag = ti.dag_model
if dag is None:
raise ValueError("DagModel is empty")
should_pass_filepath = not pickle_id and dag
path: PurePath | None = None
if should_pass_filepath:
if dag.is_subdag:
if TYPE_CHECKING:
assert dag.parent_dag is not None
path = dag.parent_dag.relative_fileloc
else:
path = dag.relative_fileloc
if path:
if not path.is_absolute():
path = "DAGS_FOLDER" / path
return TaskInstance.generate_command(
ti.dag_id,
ti.task_id,
run_id=ti.run_id,
mark_success=mark_success,
ignore_all_deps=ignore_all_deps,
ignore_task_deps=ignore_task_deps,
ignore_depends_on_past=ignore_depends_on_past,
wait_for_past_depends_before_skipping=wait_for_past_depends_before_skipping,
ignore_ti_state=ignore_ti_state,
local=local,
pickle_id=pickle_id,
file_path=path,
raw=raw,
job_id=job_id,
pool=pool,
cfg_path=cfg_path,
map_index=ti.map_index,
)
[docs] def command_as_list(
self,
mark_success: bool = False,
ignore_all_deps: bool = False,
ignore_task_deps: bool = False,
ignore_depends_on_past: bool = False,
wait_for_past_depends_before_skipping: bool = False,
ignore_ti_state: bool = False,
local: bool = False,
pickle_id: int | None = None,
raw: bool = False,
job_id: str | None = None,
pool: str | None = None,
cfg_path: str | None = None,
) -> list[str]:
"""
Return a command that can be executed anywhere where airflow is installed.
This command is part of the message sent to executors by the orchestrator.
"""
return TaskInstance._command_as_list(
ti=self,
mark_success=mark_success,
ignore_all_deps=ignore_all_deps,
ignore_task_deps=ignore_task_deps,
ignore_depends_on_past=ignore_depends_on_past,
wait_for_past_depends_before_skipping=wait_for_past_depends_before_skipping,
ignore_ti_state=ignore_ti_state,
local=local,
pickle_id=pickle_id,
raw=raw,
job_id=job_id,
pool=pool,
cfg_path=cfg_path,
)
@staticmethod
[docs] def generate_command(
dag_id: str,
task_id: str,
run_id: str,
mark_success: bool = False,
ignore_all_deps: bool = False,
ignore_depends_on_past: bool = False,
wait_for_past_depends_before_skipping: bool = False,
ignore_task_deps: bool = False,
ignore_ti_state: bool = False,
local: bool = False,
pickle_id: int | None = None,
file_path: PurePath | str | None = None,
raw: bool = False,
job_id: str | None = None,
pool: str | None = None,
cfg_path: str | None = None,
map_index: int = -1,
) -> list[str]:
"""
Generate the shell command required to execute this task instance.
:param dag_id: DAG ID
:param task_id: Task ID
:param run_id: The run_id of this task's DagRun
:param mark_success: Whether to mark the task as successful
:param ignore_all_deps: Ignore all ignorable dependencies.
Overrides the other ignore_* parameters.
:param ignore_depends_on_past: Ignore depends_on_past parameter of DAGs
(e.g. for Backfills)
:param wait_for_past_depends_before_skipping: Wait for past depends before marking the ti as skipped
:param ignore_task_deps: Ignore task-specific dependencies such as depends_on_past
and trigger rule
:param ignore_ti_state: Ignore the task instance's previous failure/success
:param local: Whether to run the task locally
:param pickle_id: If the DAG was serialized to the DB, the ID
associated with the pickled DAG
: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
:param cfg_path: the Path to the configuration file
:return: shell command that can be used to run the task instance
"""
cmd = ["airflow", "tasks", "run", dag_id, task_id, run_id]
if mark_success:
cmd.extend(["--mark-success"])
if pickle_id:
cmd.extend(["--pickle", str(pickle_id)])
if job_id:
cmd.extend(["--job-id", str(job_id)])
if ignore_all_deps:
cmd.extend(["--ignore-all-dependencies"])
if ignore_task_deps:
cmd.extend(["--ignore-dependencies"])
if ignore_depends_on_past:
cmd.extend(["--depends-on-past", "ignore"])
elif wait_for_past_depends_before_skipping:
cmd.extend(["--depends-on-past", "wait"])
if ignore_ti_state:
cmd.extend(["--force"])
if local:
cmd.extend(["--local"])
if pool:
cmd.extend(["--pool", pool])
if raw:
cmd.extend(["--raw"])
if file_path:
cmd.extend(["--subdir", os.fspath(file_path)])
if cfg_path:
cmd.extend(["--cfg-path", cfg_path])
if map_index != -1:
cmd.extend(["--map-index", str(map_index)])
return cmd
@property
[docs] def log_url(self) -> str:
"""Log URL for TaskInstance."""
run_id = quote(self.run_id)
base_date = quote(self.execution_date.strftime("%Y-%m-%dT%H:%M:%S%z"))
base_url = conf.get_mandatory_value("webserver", "BASE_URL")
map_index = f"&map_index={self.map_index}" if self.map_index >= 0 else ""
return (
f"{base_url}"
f"/dags"
f"/{self.dag_id}"
f"/grid"
f"?dag_run_id={run_id}"
f"&task_id={self.task_id}"
f"{map_index}"
f"&base_date={base_date}"
"&tab=logs"
)
@property
[docs] def mark_success_url(self) -> str:
"""URL to mark TI success."""
base_url = conf.get_mandatory_value("webserver", "BASE_URL")
return (
f"{base_url}"
"/confirm"
f"?task_id={self.task_id}"
f"&dag_id={self.dag_id}"
f"&dag_run_id={quote(self.run_id)}"
"&upstream=false"
"&downstream=false"
"&state=success"
)
@provide_session
[docs] def current_state(self, session: Session = NEW_SESSION) -> str:
"""
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.
sqlalchemy.inspect is used here to get the primary keys ensuring that if they change
it will not regress
:param session: SQLAlchemy ORM Session
"""
filters = (col == getattr(self, col.name) for col in inspect(TaskInstance).primary_key)
return session.query(TaskInstance.state).filter(*filters).scalar()
@provide_session
[docs] def error(self, session: Session = NEW_SESSION) -> None:
"""
Force the task instance's state to FAILED in the database.
:param session: SQLAlchemy ORM Session
"""
self.log.error("Recording the task instance as FAILED")
self.state = TaskInstanceState.FAILED
session.merge(self)
session.commit()
@classmethod
@internal_api_call
@provide_session
[docs] def get_task_instance(
cls,
dag_id: str,
run_id: str,
task_id: str,
map_index: int,
lock_for_update: bool = False,
session: Session = NEW_SESSION,
) -> TaskInstance | TaskInstancePydantic | None:
query = (
session.query(TaskInstance)
.options(lazyload(TaskInstance.dag_run)) # lazy load dag run to avoid locking it
.filter_by(
dag_id=dag_id,
run_id=run_id,
task_id=task_id,
map_index=map_index,
)
)
if lock_for_update:
for attempt in run_with_db_retries(logger=cls.logger()):
with attempt:
return query.with_for_update().one_or_none()
else:
return query.one_or_none()
return None
@provide_session
[docs] def refresh_from_db(self, session: Session = NEW_SESSION, lock_for_update: bool = False) -> None:
"""
Refresh the task instance from the database based on the primary key.
:param session: SQLAlchemy ORM Session
:param lock_for_update: if True, indicates that the database should
lock the TaskInstance (issuing a FOR UPDATE clause) until the
session is committed.
"""
_refresh_from_db(task_instance=self, session=session, lock_for_update=lock_for_update)
[docs] def refresh_from_task(self, task: Operator, pool_override: str | None = None) -> None:
"""
Copy common attributes from the given task.
:param task: The task object to copy from
:param pool_override: Use the pool_override instead of task's pool
"""
_refresh_from_task(task_instance=self, task=task, pool_override=pool_override)
@staticmethod
@internal_api_call
@provide_session
def _clear_xcom_data(ti: TaskInstance | TaskInstancePydantic, session: Session = NEW_SESSION) -> None:
"""
Clear all XCom data from the database for the task instance.
If the task is unmapped, all XComs matching this task ID in the same DAG
run are removed. If the task is mapped, only the one with matching map
index is removed.
:param ti: The TI for which we need to clear xcoms.
:param session: SQLAlchemy ORM Session
"""
ti.log.debug("Clearing XCom data")
if ti.map_index < 0:
map_index: int | None = None
else:
map_index = ti.map_index
XCom.clear(
dag_id=ti.dag_id,
task_id=ti.task_id,
run_id=ti.run_id,
map_index=map_index,
session=session,
)
@provide_session
[docs] def clear_xcom_data(self, session: Session = NEW_SESSION):
self._clear_xcom_data(ti=self, session=session)
@property
[docs] def key(self) -> TaskInstanceKey:
"""Returns a tuple that identifies the task instance uniquely."""
return TaskInstanceKey(self.dag_id, self.task_id, self.run_id, self.try_number, self.map_index)
@staticmethod
@internal_api_call
def _set_state(ti: TaskInstance | TaskInstancePydantic, state, session: Session) -> bool:
if not isinstance(ti, TaskInstance):
ti = session.scalars(
select(TaskInstance).where(
TaskInstance.task_id == ti.task_id,
TaskInstance.dag_id == ti.dag_id,
TaskInstance.run_id == ti.run_id,
TaskInstance.map_index == ti.map_index,
)
).one()
if ti.state == state:
return False
current_time = timezone.utcnow()
ti.log.debug("Setting task state for %s to %s", ti, state)
ti.state = state
ti.start_date = ti.start_date or current_time
if ti.state in State.finished or ti.state == TaskInstanceState.UP_FOR_RETRY:
ti.end_date = ti.end_date or current_time
ti.duration = (ti.end_date - ti.start_date).total_seconds()
session.merge(ti)
return True
@provide_session
[docs] def set_state(self, state: str | None, session: Session = NEW_SESSION) -> bool:
"""
Set TaskInstance state.
:param state: State to set for the TI
:param session: SQLAlchemy ORM Session
:return: Was the state changed
"""
return self._set_state(ti=self, state=state, session=session)
@property
[docs] def is_premature(self) -> bool:
"""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 == TaskInstanceState.UP_FOR_RETRY and not self.ready_for_retry()
@provide_session
[docs] def are_dependents_done(self, session: Session = NEW_SESSION) -> bool:
"""
Check whether the immediate dependents of this task instance have succeeded or have been skipped.
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.
:param session: SQLAlchemy ORM Session
"""
task = self.task
if TYPE_CHECKING:
assert 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.run_id == self.run_id,
TaskInstance.state.in_((TaskInstanceState.SKIPPED, TaskInstanceState.SUCCESS)),
)
count = ti[0][0]
return count == len(task.downstream_task_ids)
@provide_session
[docs] def get_previous_dagrun(
self,
state: DagRunState | None = None,
session: Session | None = None,
) -> DagRun | None:
"""
Return the DagRun that ran before this task instance's DagRun.
:param state: If passed, it only take into account instances of a specific state.
:param session: SQLAlchemy ORM Session.
"""
return _get_previous_dagrun(task_instance=self, state=state, session=session)
@provide_session
[docs] def get_previous_ti(
self,
state: DagRunState | None = None,
session: Session = NEW_SESSION,
) -> TaskInstance | TaskInstancePydantic | None:
"""
Return the task instance for the task that ran before this task instance.
:param session: SQLAlchemy ORM Session
:param state: If passed, it only take into account instances of a specific state.
"""
return _get_previous_ti(task_instance=self, state=state, session=session)
@property
[docs] def previous_ti(self) -> TaskInstance | TaskInstancePydantic | None:
"""
This attribute is deprecated.
Please use :class:`airflow.models.taskinstance.TaskInstance.get_previous_ti`.
"""
warnings.warn(
"""
This attribute is deprecated.
Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method.
""",
RemovedInAirflow3Warning,
stacklevel=2,
)
return self.get_previous_ti()
@property
[docs] def previous_ti_success(self) -> TaskInstance | TaskInstancePydantic | None:
"""
This attribute is deprecated.
Please use :class:`airflow.models.taskinstance.TaskInstance.get_previous_ti`.
"""
warnings.warn(
"""
This attribute is deprecated.
Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method.
""",
RemovedInAirflow3Warning,
stacklevel=2,
)
return self.get_previous_ti(state=DagRunState.SUCCESS)
@provide_session
[docs] def get_previous_execution_date(
self,
state: DagRunState | None = None,
session: Session = NEW_SESSION,
) -> pendulum.DateTime | None:
"""
Return the execution date from property previous_ti_success.
:param state: If passed, it only take into account instances of a specific state.
:param session: SQLAlchemy ORM Session
"""
return _get_previous_execution_date(task_instance=self, state=state, session=session)
@provide_session
[docs] def get_previous_start_date(
self, state: DagRunState | None = None, session: Session = NEW_SESSION
) -> pendulum.DateTime | None:
"""
Return the start date from property previous_ti_success.
:param state: If passed, it only take into account instances of a specific state.
:param session: SQLAlchemy ORM Session
"""
return _get_previous_start_date(task_instance=self, state=state, session=session)
@property
[docs] def previous_start_date_success(self) -> pendulum.DateTime | None:
"""
This attribute is deprecated.
Please use :class:`airflow.models.taskinstance.TaskInstance.get_previous_start_date`.
"""
warnings.warn(
"""
This attribute is deprecated.
Please use `airflow.models.taskinstance.TaskInstance.get_previous_start_date` method.
""",
RemovedInAirflow3Warning,
stacklevel=2,
)
return self.get_previous_start_date(state=DagRunState.SUCCESS)
@provide_session
[docs] def are_dependencies_met(
self, dep_context: DepContext | None = None, session: Session = NEW_SESSION, verbose: bool = False
) -> bool:
"""
Are all conditions 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.
:param session: database session
:param verbose: whether log details on failed dependencies on info or debug log level
"""
dep_context = dep_context or DepContext()
failed = False
verbose_aware_logger = self.log.info if verbose else self.log.debug
for dep_status in self.get_failed_dep_statuses(dep_context=dep_context, session=session):
failed = True
verbose_aware_logger(
"Dependencies not met for %s, dependency '%s' FAILED: %s",
self,
dep_status.dep_name,
dep_status.reason,
)
if failed:
return False
verbose_aware_logger("Dependencies all met for dep_context=%s ti=%s", dep_context.description, self)
return True
@provide_session
[docs] def get_failed_dep_statuses(self, dep_context: DepContext | None = None, session: Session = NEW_SESSION):
"""Get failed Dependencies."""
if TYPE_CHECKING:
assert self.task
dep_context = dep_context or DepContext()
for dep in dep_context.deps | self.task.deps:
for dep_status in dep.get_dep_statuses(self, session, dep_context):
self.log.debug(
"%s dependency '%s' PASSED: %s, %s",
self,
dep_status.dep_name,
dep_status.passed,
dep_status.reason,
)
if not dep_status.passed:
yield dep_status
[docs] def __repr__(self) -> str:
prefix = f"<TaskInstance: {self.dag_id}.{self.task_id} {self.run_id} "
if self.map_index != -1:
prefix += f"map_index={self.map_index} "
return prefix + f"[{self.state}]>"
[docs] 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.
"""
from airflow.models.abstractoperator import MAX_RETRY_DELAY
delay = self.task.retry_delay
if self.task.retry_exponential_backoff:
# If the min_backoff calculation is below 1, it will be converted to 0 via int. Thus,
# we must round up prior to converting to an int, otherwise a divide by zero error
# will occur in the modded_hash calculation.
# this probably gives unexpected results if a task instance has previously been cleared,
# because try_number can increase without bound
min_backoff = math.ceil(delay.total_seconds() * (2 ** (self.try_number - 1)))
# In the case when delay.total_seconds() is 0, min_backoff will not be rounded up to 1.
# To address this, we impose a lower bound of 1 on min_backoff. This effectively makes
# the ceiling function unnecessary, but the ceiling function was retained to avoid
# introducing a breaking change.
if min_backoff < 1:
min_backoff = 1
# deterministic per task instance
ti_hash = int(
hashlib.sha1(
f"{self.dag_id}#{self.task_id}#{self.execution_date}#{self.try_number}".encode()
).hexdigest(),
16,
)
# between 1 and 1.0 * delay * (2^retry_number)
modded_hash = min_backoff + ti_hash % min_backoff
# timedelta has a maximum representable value. The exponentiation
# here means this value can be exceeded after a certain number
# of tries (around 50 if the initial delay is 1s, even fewer if
# the delay is larger). Cap the value here before creating a
# timedelta object so the operation doesn't fail with "OverflowError".
delay_backoff_in_seconds = min(modded_hash, MAX_RETRY_DELAY)
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
[docs] def ready_for_retry(self) -> bool:
"""Check on whether the task instance is in the right state and timeframe to be retried."""
return self.state == TaskInstanceState.UP_FOR_RETRY and self.next_retry_datetime() < timezone.utcnow()
@staticmethod
@internal_api_call
def _get_dagrun(dag_id, run_id, session) -> DagRun:
from airflow.models.dagrun import DagRun # Avoid circular import
dr = session.query(DagRun).filter(DagRun.dag_id == dag_id, DagRun.run_id == run_id).one()
return dr
@provide_session
[docs] def get_dagrun(self, session: Session = NEW_SESSION) -> DagRun:
"""
Return the DagRun for this TaskInstance.
:param session: SQLAlchemy ORM Session
:return: DagRun
"""
info = inspect(self)
if info.attrs.dag_run.loaded_value is not NO_VALUE:
if getattr(self, "task", None) is not None:
if TYPE_CHECKING:
assert self.task
self.dag_run.dag = self.task.dag
return self.dag_run
dr = self._get_dagrun(self.dag_id, self.run_id, session)
if getattr(self, "task", None) is not None:
if TYPE_CHECKING:
assert self.task
dr.dag = self.task.dag
# Record it in the instance for next time. This means that `self.execution_date` will work correctly
set_committed_value(self, "dag_run", dr)
return dr
@classmethod
@provide_session
[docs] def ensure_dag(
cls, task_instance: TaskInstance | TaskInstancePydantic, session: Session = NEW_SESSION
) -> DAG:
"""Ensure that task has a dag object associated, might have been removed by serialization."""
if TYPE_CHECKING:
assert task_instance.task
if task_instance.task.dag is None or task_instance.task.dag.__class__ is AttributeRemoved:
task_instance.task.dag = DagBag(read_dags_from_db=True).get_dag(
dag_id=task_instance.dag_id, session=session
)
if TYPE_CHECKING:
assert task_instance.task.dag
return task_instance.task.dag
@classmethod
@internal_api_call
@provide_session
def _check_and_change_state_before_execution(
cls,
task_instance: TaskInstance | TaskInstancePydantic,
verbose: bool = True,
ignore_all_deps: bool = False,
ignore_depends_on_past: bool = False,
wait_for_past_depends_before_skipping: bool = False,
ignore_task_deps: bool = False,
ignore_ti_state: bool = False,
mark_success: bool = False,
test_mode: bool = False,
hostname: str = "",
job_id: str | None = None,
pool: str | None = None,
external_executor_id: str | None = None,
session: Session = NEW_SESSION,
) -> bool:
"""
Check dependencies and then sets state to RUNNING if they are met.
Returns True if and only if state is set to RUNNING, which implies that task should be
executed, in preparation for _run_raw_task.
:param verbose: whether to turn on more verbose logging
:param ignore_all_deps: Ignore all of the non-critical dependencies, just runs
:param ignore_depends_on_past: Ignore depends_on_past DAG attribute
:param wait_for_past_depends_before_skipping: Wait for past depends before mark the ti as skipped
:param ignore_task_deps: Don't check the dependencies of this TaskInstance's task
:param ignore_ti_state: Disregards previous task instance state
:param mark_success: Don't run the task, mark its state as success
:param test_mode: Doesn't record success or failure in the DB
:param hostname: The hostname of the worker running the task instance.
:param job_id: Job (BackfillJob / LocalTaskJob / SchedulerJob) ID
:param pool: specifies the pool to use to run the task instance
:param external_executor_id: The identifier of the celery executor
:param session: SQLAlchemy ORM Session
:return: whether the state was changed to running or not
"""
if TYPE_CHECKING:
assert task_instance.task
if isinstance(task_instance, TaskInstance):
ti: TaskInstance = task_instance
else: # isinstance(task_instance, TaskInstancePydantic)
filters = (col == getattr(task_instance, col.name) for col in inspect(TaskInstance).primary_key)
ti = session.query(TaskInstance).filter(*filters).scalar()
dag = DagBag(read_dags_from_db=True).get_dag(task_instance.dag_id, session=session)
task_instance.task = dag.task_dict[ti.task_id]
ti.task = task_instance.task
task = task_instance.task
if TYPE_CHECKING:
assert task
ti.refresh_from_task(task, pool_override=pool)
ti.test_mode = test_mode
ti.refresh_from_db(session=session, lock_for_update=True)
ti.job_id = job_id
ti.hostname = hostname
ti.pid = None
if not ignore_all_deps and not ignore_ti_state and ti.state == TaskInstanceState.SUCCESS:
Stats.incr("previously_succeeded", tags=ti.stats_tags)
if not mark_success:
# Firstly find non-runnable and non-requeueable tis.
# Since mark_success is not set, we do nothing.
non_requeueable_dep_context = DepContext(
deps=RUNNING_DEPS - REQUEUEABLE_DEPS,
ignore_all_deps=ignore_all_deps,
ignore_ti_state=ignore_ti_state,
ignore_depends_on_past=ignore_depends_on_past,
wait_for_past_depends_before_skipping=wait_for_past_depends_before_skipping,
ignore_task_deps=ignore_task_deps,
description="non-requeueable deps",
)
if not ti.are_dependencies_met(
dep_context=non_requeueable_dep_context, session=session, verbose=True
):
session.commit()
return False
# 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).
# Set the task start date. In case it was re-scheduled use the initial
# start date that is recorded in task_reschedule table
# If the task continues after being deferred (next_method is set), use the original start_date
ti.start_date = ti.start_date if ti.next_method else timezone.utcnow()
if ti.state == TaskInstanceState.UP_FOR_RESCHEDULE:
tr_start_date = session.scalar(
TR.stmt_for_task_instance(ti, descending=False).with_only_columns(TR.start_date).limit(1)
)
if tr_start_date:
ti.start_date = tr_start_date
# Secondly we find non-runnable but requeueable tis. We reset its state.
# This is because we might have hit concurrency limits,
# e.g. because of backfilling.
dep_context = DepContext(
deps=REQUEUEABLE_DEPS,
ignore_all_deps=ignore_all_deps,
ignore_depends_on_past=ignore_depends_on_past,
wait_for_past_depends_before_skipping=wait_for_past_depends_before_skipping,
ignore_task_deps=ignore_task_deps,
ignore_ti_state=ignore_ti_state,
description="requeueable deps",
)
if not ti.are_dependencies_met(dep_context=dep_context, session=session, verbose=True):
ti.state = None
cls.logger().warning(
"Rescheduling due to concurrency limits reached "
"at task runtime. Attempt %s of "
"%s. State set to NONE.",
ti.try_number,
ti.max_tries + 1,
)
ti.queued_dttm = timezone.utcnow()
session.merge(ti)
session.commit()
return False
if ti.next_kwargs is not None:
cls.logger().info("Resuming after deferral")
else:
cls.logger().info("Starting attempt %s of %s", ti.try_number, ti.max_tries + 1)
if not test_mode:
session.add(Log(TaskInstanceState.RUNNING.value, ti))
ti.state = TaskInstanceState.RUNNING
ti.emit_state_change_metric(TaskInstanceState.RUNNING)
if external_executor_id:
ti.external_executor_id = external_executor_id
ti.end_date = None
if not test_mode:
session.merge(ti).task = task
session.commit()
# Closing all pooled connections to prevent
# "max number of connections reached"
settings.engine.dispose() # type: ignore
if verbose:
if mark_success:
cls.logger().info("Marking success for %s on %s", ti.task, ti.execution_date)
else:
cls.logger().info("Executing %s on %s", ti.task, ti.execution_date)
return True
@provide_session
[docs] def check_and_change_state_before_execution(
self,
verbose: bool = True,
ignore_all_deps: bool = False,
ignore_depends_on_past: bool = False,
wait_for_past_depends_before_skipping: bool = False,
ignore_task_deps: bool = False,
ignore_ti_state: bool = False,
mark_success: bool = False,
test_mode: bool = False,
job_id: str | None = None,
pool: str | None = None,
external_executor_id: str | None = None,
session: Session = NEW_SESSION,
) -> bool:
return TaskInstance._check_and_change_state_before_execution(
task_instance=self,
verbose=verbose,
ignore_all_deps=ignore_all_deps,
ignore_depends_on_past=ignore_depends_on_past,
wait_for_past_depends_before_skipping=wait_for_past_depends_before_skipping,
ignore_task_deps=ignore_task_deps,
ignore_ti_state=ignore_ti_state,
mark_success=mark_success,
test_mode=test_mode,
hostname=get_hostname(),
job_id=job_id,
pool=pool,
external_executor_id=external_executor_id,
session=session,
)
[docs] def emit_state_change_metric(self, new_state: TaskInstanceState) -> None:
"""
Send a time metric representing how much time a given state transition took.
The previous state and metric name is deduced from the state the task was put in.
:param new_state: The state that has just been set for this task.
We do not use `self.state`, because sometimes the state is updated directly in the DB and not in
the local TaskInstance object.
Supported states: QUEUED and RUNNING
"""
if self.end_date:
# if the task has an end date, it means that this is not its first round.
# we send the state transition time metric only on the first try, otherwise it gets more complex.
return
# switch on state and deduce which metric to send
if new_state == TaskInstanceState.RUNNING:
metric_name = "queued_duration"
if self.queued_dttm is None:
# this should not really happen except in tests or rare cases,
# but we don't want to create errors just for a metric, so we just skip it
self.log.warning(
"cannot record %s for task %s because previous state change time has not been saved",
metric_name,
self.task_id,
)
return
timing = timezone.utcnow() - self.queued_dttm
elif new_state == TaskInstanceState.QUEUED:
metric_name = "scheduled_duration"
if self.start_date is None:
# This check does not work correctly before fields like `scheduled_dttm` are implemented.
# TODO: Change the level to WARNING once it's viable.
# see #30612 #34493 and #34771 for more details
self.log.debug(
"cannot record %s for task %s because previous state change time has not been saved",
metric_name,
self.task_id,
)
return
timing = timezone.utcnow() - self.start_date
else:
raise NotImplementedError("no metric emission setup for state %s", new_state)
# send metric twice, once (legacy) with tags in the name and once with tags as tags
Stats.timing(f"dag.{self.dag_id}.{self.task_id}.{metric_name}", timing)
Stats.timing(f"task.{metric_name}", timing, tags={"task_id": self.task_id, "dag_id": self.dag_id})
[docs] def clear_next_method_args(self) -> None:
"""Ensure we unset next_method and next_kwargs to ensure that any retries don't reuse them."""
_clear_next_method_args(task_instance=self)
@provide_session
@Sentry.enrich_errors
def _run_raw_task(
self,
mark_success: bool = False,
test_mode: bool = False,
job_id: str | None = None,
pool: str | None = None,
raise_on_defer: bool = False,
session: Session = NEW_SESSION,
) -> TaskReturnCode | None:
"""
Run a task, update the state upon completion, and run any appropriate callbacks.
Immediately runs the task (without checking or changing db state
before execution) and then sets the appropriate final state after
completion and runs any post-execute callbacks. Meant to be called
only after another function changes the state to running.
:param mark_success: Don't run the task, mark its state as success
:param test_mode: Doesn't record success or failure in the DB
:param pool: specifies the pool to use to run the task instance
:param session: SQLAlchemy ORM Session
"""
if TYPE_CHECKING:
assert self.task
return _run_raw_task(
ti=self,
mark_success=mark_success,
test_mode=test_mode,
job_id=job_id,
pool=pool,
raise_on_defer=raise_on_defer,
session=session,
)
def _register_dataset_changes(self, *, events: OutletEventAccessors, session: Session) -> None:
if TYPE_CHECKING:
assert self.task
# One task only triggers one dataset event for each dataset with the same extra.
# This tuple[dataset uri, extra] to sets alias names mapping is used to find whether
# there're datasets with same uri but different extra that we need to emit more than one dataset events.
dataset_tuple_to_alias_names_mapping: dict[tuple[str, frozenset], set[str]] = defaultdict(set)
for obj in self.task.outlets or []:
self.log.debug("outlet obj %s", obj)
# Lineage can have other types of objects besides datasets
if isinstance(obj, Dataset):
dataset_manager.register_dataset_change(
task_instance=self,
dataset=obj,
extra=events[obj].extra,
session=session,
)
elif isinstance(obj, DatasetAlias):
for dataset_alias_event in events[obj].dataset_alias_events:
dataset_alias_name = dataset_alias_event["source_alias_name"]
dataset_uri = dataset_alias_event["dest_dataset_uri"]
extra = dataset_alias_event["extra"]
frozen_extra = frozenset(extra.items())
dataset_tuple_to_alias_names_mapping[(dataset_uri, frozen_extra)].add(dataset_alias_name)
dataset_objs_cache: dict[str, DatasetModel] = {}
for (uri, extra_items), alias_names in dataset_tuple_to_alias_names_mapping.items():
if uri not in dataset_objs_cache:
dataset_obj = session.scalar(select(DatasetModel).where(DatasetModel.uri == uri).limit(1))
dataset_objs_cache[uri] = dataset_obj
else:
dataset_obj = dataset_objs_cache[uri]
if not dataset_obj:
dataset_obj = DatasetModel(uri=uri)
dataset_manager.create_datasets(dataset_models=[dataset_obj], session=session)
self.log.warning("Created a new %r as it did not exist.", dataset_obj)
dataset_objs_cache[uri] = dataset_obj
for alias in alias_names:
alias_obj = session.scalar(
select(DatasetAliasModel).where(DatasetAliasModel.name == alias).limit(1)
)
dataset_obj.aliases.append(alias_obj)
extra = {k: v for k, v in extra_items}
self.log.info(
'Creating event for %r through aliases "%s"',
dataset_obj,
", ".join(alias_names),
)
dataset_manager.register_dataset_change(
task_instance=self,
dataset=dataset_obj,
extra=extra,
session=session,
source_alias_names=alias_names,
)
def _execute_task_with_callbacks(self, context: Context, test_mode: bool = False, *, session: Session):
"""Prepare Task for Execution."""
if TYPE_CHECKING:
assert self.task
parent_pid = os.getpid()
def signal_handler(signum, frame):
pid = os.getpid()
# If a task forks during execution (from DAG code) for whatever
# reason, we want to make sure that we react to the signal only in
# the process that we've spawned ourselves (referred to here as the
# parent process).
if pid != parent_pid:
os._exit(1)
return
self.log.error("Received SIGTERM. Terminating subprocesses.")
self.task.on_kill()
raise AirflowTaskTerminated("Task received SIGTERM signal")
signal.signal(signal.SIGTERM, signal_handler)
# Don't clear Xcom until the task is certain to execute, and check if we are resuming from deferral.
if not self.next_method:
self.clear_xcom_data()
with Stats.timer(f"dag.{self.task.dag_id}.{self.task.task_id}.duration"), Stats.timer(
"task.duration", tags=self.stats_tags
):
# Set the validated/merged params on the task object.
self.task.params = context["params"]
with set_current_context(context):
dag = self.task.get_dag()
if dag is not None:
jinja_env = dag.get_template_env()
else:
jinja_env = None
task_orig = self.render_templates(context=context, jinja_env=jinja_env)
# The task is never MappedOperator at this point.
if TYPE_CHECKING:
assert isinstance(self.task, BaseOperator)
if not test_mode:
rendered_fields = get_serialized_template_fields(task=self.task)
_update_rtif(ti=self, rendered_fields=rendered_fields)
# Export context to make it available for operators to use.
airflow_context_vars = context_to_airflow_vars(context, in_env_var_format=True)
os.environ.update(airflow_context_vars)
# Log context only for the default execution method, the assumption
# being that otherwise we're resuming a deferred task (in which
# case there's no need to log these again).
if not self.next_method:
self.log.info(
"Exporting env vars: %s",
" ".join(f"{k}={v!r}" for k, v in airflow_context_vars.items()),
)
# Run pre_execute callback
self.task.pre_execute(context=context)
# Run on_execute callback
self._run_execute_callback(context, self.task)
# Run on_task_instance_running event
get_listener_manager().hook.on_task_instance_running(
previous_state=TaskInstanceState.QUEUED, task_instance=self, session=session
)
def _render_map_index(context: Context, *, jinja_env: jinja2.Environment | None) -> str | None:
"""Render named map index if the DAG author defined map_index_template at the task level."""
if jinja_env is None or (template := context.get("map_index_template")) is None:
return None
rendered_map_index = jinja_env.from_string(template).render(context)
log.debug("Map index rendered as %s", rendered_map_index)
return rendered_map_index
# Execute the task.
with set_current_context(context):
try:
result = self._execute_task(context, task_orig)
except Exception:
# If the task failed, swallow rendering error so it doesn't mask the main error.
with contextlib.suppress(jinja2.TemplateSyntaxError, jinja2.UndefinedError):
self.rendered_map_index = _render_map_index(context, jinja_env=jinja_env)
raise
else: # If the task succeeded, render normally to let rendering error bubble up.
self.rendered_map_index = _render_map_index(context, jinja_env=jinja_env)
# Run post_execute callback
self.task.post_execute(context=context, result=result)
Stats.incr(f"operator_successes_{self.task.task_type}", tags=self.stats_tags)
# Same metric with tagging
Stats.incr("operator_successes", tags={**self.stats_tags, "task_type": self.task.task_type})
Stats.incr("ti_successes", tags=self.stats_tags)
def _execute_task(self, context: Context, task_orig: Operator):
"""
Execute Task (optionally with a Timeout) and push Xcom results.
:param context: Jinja2 context
:param task_orig: origin task
"""
return _execute_task(self, context, task_orig)
@provide_session
[docs] def defer_task(self, exception: TaskDeferred | None, session: Session = NEW_SESSION) -> None:
"""
Mark the task as deferred and sets up the trigger that is needed to resume it when TaskDeferred is raised.
:meta: private
"""
_defer_task(ti=self, exception=exception, session=session)
def _run_execute_callback(self, context: Context, task: BaseOperator) -> None:
"""Functions that need to be run before a Task is executed."""
if not (callbacks := task.on_execute_callback):
return
for callback in callbacks if isinstance(callbacks, list) else [callbacks]:
try:
callback(context)
except Exception:
self.log.exception("Failed when executing execute callback")
@provide_session
[docs] def run(
self,
verbose: bool = True,
ignore_all_deps: bool = False,
ignore_depends_on_past: bool = False,
wait_for_past_depends_before_skipping: bool = False,
ignore_task_deps: bool = False,
ignore_ti_state: bool = False,
mark_success: bool = False,
test_mode: bool = False,
job_id: str | None = None,
pool: str | None = None,
session: Session = NEW_SESSION,
raise_on_defer: bool = False,
) -> None:
"""Run TaskInstance."""
res = self.check_and_change_state_before_execution(
verbose=verbose,
ignore_all_deps=ignore_all_deps,
ignore_depends_on_past=ignore_depends_on_past,
wait_for_past_depends_before_skipping=wait_for_past_depends_before_skipping,
ignore_task_deps=ignore_task_deps,
ignore_ti_state=ignore_ti_state,
mark_success=mark_success,
test_mode=test_mode,
job_id=job_id,
pool=pool,
session=session,
)
if not res:
return
self._run_raw_task(
mark_success=mark_success,
test_mode=test_mode,
job_id=job_id,
pool=pool,
session=session,
raise_on_defer=raise_on_defer,
)
[docs] def dry_run(self) -> None:
"""Only Renders Templates for the TI."""
if TYPE_CHECKING:
assert self.task
self.task = self.task.prepare_for_execution()
self.render_templates()
if TYPE_CHECKING:
assert isinstance(self.task, BaseOperator)
self.task.dry_run()
@provide_session
def _handle_reschedule(
self,
actual_start_date: datetime,
reschedule_exception: AirflowRescheduleException,
test_mode: bool = False,
session: Session = NEW_SESSION,
):
_handle_reschedule(
ti=self,
actual_start_date=actual_start_date,
reschedule_exception=reschedule_exception,
test_mode=test_mode,
session=session,
)
@staticmethod
def get_truncated_error_traceback(error: BaseException, truncate_to: Callable) -> TracebackType | None:
"""
Truncate the traceback of an exception to the first frame called from within a given function.
:param error: exception to get traceback from
:param truncate_to: Function to truncate TB to. Must have a ``__code__`` attribute
:meta private:
"""
tb = error.__traceback__
code = truncate_to.__func__.__code__ # type: ignore[attr-defined]
while tb is not None:
if tb.tb_frame.f_code is code:
return tb.tb_next
tb = tb.tb_next
return tb or error.__traceback__
@classmethod
[docs] def fetch_handle_failure_context(
cls,
ti: TaskInstance,
error: None | str | BaseException,
test_mode: bool | None = None,
context: Context | None = None,
force_fail: bool = False,
*,
session: Session,
fail_stop: bool = False,
):
"""
Handle Failure for the TaskInstance.
:param fail_stop: if true, stop remaining tasks in dag
"""
if error:
if isinstance(error, BaseException):
tb = TaskInstance.get_truncated_error_traceback(error, truncate_to=ti._execute_task)
cls.logger().error("Task failed with exception", exc_info=(type(error), error, tb))
else:
cls.logger().error("%s", error)
if not test_mode:
ti.refresh_from_db(session)
ti.end_date = timezone.utcnow()
ti.set_duration()
Stats.incr(f"operator_failures_{ti.operator}", tags=ti.stats_tags)
# Same metric with tagging
Stats.incr("operator_failures", tags={**ti.stats_tags, "operator": ti.operator})
Stats.incr("ti_failures", tags=ti.stats_tags)
if not test_mode:
session.add(Log(TaskInstanceState.FAILED.value, ti))
# Log failure duration
session.add(TaskFail(ti=ti))
ti.clear_next_method_args()
# In extreme cases (zombie in case of dag with parse error) we might _not_ have a Task.
if context is None and getattr(ti, "task", None):
context = ti.get_template_context(session)
if context is not None:
context["exception"] = error
# Set state correctly and figure out how to log it and decide whether
# to email
# Note, callback invocation needs to be handled by caller of
# _run_raw_task to avoid race conditions which could lead to duplicate
# invocations or miss invocation.
# Since this function is called only when the TaskInstance state is running,
# try_number contains the current try_number (not the next). We
# only mark task instance as FAILED if the next task instance
# try_number exceeds the max_tries ... or if force_fail is truthy
task: BaseOperator | None = None
try:
if getattr(ti, "task", None) and context:
if TYPE_CHECKING:
assert ti.task
task = ti.task.unmap((context, session))
except Exception:
cls.logger().error("Unable to unmap task to determine if we need to send an alert email")
if force_fail or not ti.is_eligible_to_retry():
ti.state = TaskInstanceState.FAILED
email_for_state = operator.attrgetter("email_on_failure")
callbacks = task.on_failure_callback if task else None
if task and fail_stop:
_stop_remaining_tasks(task_instance=ti, session=session)
else:
if ti.state == TaskInstanceState.RUNNING:
# If the task instance is in the running state, it means it raised an exception and
# about to retry so we record the task instance history. For other states, the task
# instance was cleared and already recorded in the task instance history.
from airflow.models.taskinstancehistory import TaskInstanceHistory
TaskInstanceHistory.record_ti(ti, session=session)
ti.state = State.UP_FOR_RETRY
email_for_state = operator.attrgetter("email_on_retry")
callbacks = task.on_retry_callback if task else None
get_listener_manager().hook.on_task_instance_failed(
previous_state=TaskInstanceState.RUNNING, task_instance=ti, error=error, session=session
)
return {
"ti": ti,
"email_for_state": email_for_state,
"task": task,
"callbacks": callbacks,
"context": context,
}
@staticmethod
@internal_api_call
@provide_session
[docs] def save_to_db(ti: TaskInstance | TaskInstancePydantic, session: Session = NEW_SESSION):
ti = _coalesce_to_orm_ti(ti=ti, session=session)
ti.updated_at = timezone.utcnow()
session.merge(ti)
session.flush()
session.commit()
@provide_session
[docs] def handle_failure(
self,
error: None | str | BaseException,
test_mode: bool | None = None,
context: Context | None = None,
force_fail: bool = False,
session: Session = NEW_SESSION,
) -> None:
"""
Handle Failure for a task instance.
:param error: if specified, log the specific exception if thrown
:param session: SQLAlchemy ORM Session
:param test_mode: doesn't record success or failure in the DB if True
:param context: Jinja2 context
:param force_fail: if True, task does not retry
"""
if TYPE_CHECKING:
assert self.task
assert self.task.dag
try:
fail_stop = self.task.dag.fail_stop
except Exception:
fail_stop = False
_handle_failure(
task_instance=self,
error=error,
session=session,
test_mode=test_mode,
context=context,
force_fail=force_fail,
fail_stop=fail_stop,
)
[docs] def is_eligible_to_retry(self):
"""Is task instance is eligible for retry."""
return _is_eligible_to_retry(task_instance=self)
[docs] def get_template_context(
self,
session: Session | None = None,
ignore_param_exceptions: bool = True,
) -> Context:
"""
Return TI Context.
:param session: SQLAlchemy ORM Session
:param ignore_param_exceptions: flag to suppress value exceptions while initializing the ParamsDict
"""
if TYPE_CHECKING:
assert self.task
assert self.task.dag
return _get_template_context(
task_instance=self,
dag=self.task.dag,
session=session,
ignore_param_exceptions=ignore_param_exceptions,
)
@provide_session
[docs] def get_rendered_template_fields(self, session: Session = NEW_SESSION) -> None:
"""
Update task with rendered template fields for presentation in UI.
If task has already run, will fetch from DB; otherwise will render.
"""
from airflow.models.renderedtifields import RenderedTaskInstanceFields
if TYPE_CHECKING:
assert self.task
rendered_task_instance_fields = RenderedTaskInstanceFields.get_templated_fields(self, session=session)
if rendered_task_instance_fields:
self.task = self.task.unmap(None)
for field_name, rendered_value in rendered_task_instance_fields.items():
setattr(self.task, field_name, rendered_value)
return
try:
# If we get here, either the task hasn't run or the RTIF record was purged.
from airflow.utils.log.secrets_masker import redact
self.render_templates()
for field_name in self.task.template_fields:
rendered_value = getattr(self.task, field_name)
setattr(self.task, field_name, redact(rendered_value, field_name))
except (TemplateAssertionError, UndefinedError) as e:
raise AirflowException(
"Webserver does not have access to User-defined Macros or Filters "
"when Dag Serialization is enabled. Hence for the task that have not yet "
"started running, please use 'airflow tasks render' for debugging the "
"rendering of template_fields."
) from e
[docs] def overwrite_params_with_dag_run_conf(self, params: dict, dag_run: DagRun):
"""Overwrite Task Params with DagRun.conf."""
if dag_run and dag_run.conf:
self.log.debug("Updating task params (%s) with DagRun.conf (%s)", params, dag_run.conf)
params.update(dag_run.conf)
[docs] def render_templates(
self, context: Context | None = None, jinja_env: jinja2.Environment | None = None
) -> Operator:
"""
Render templates in the operator fields.
If the task was originally mapped, this may replace ``self.task`` with
the unmapped, fully rendered BaseOperator. The original ``self.task``
before replacement is returned.
"""
from airflow.models.mappedoperator import MappedOperator
if not context:
context = self.get_template_context()
original_task = self.task
ti = context["ti"]
if TYPE_CHECKING:
assert original_task
assert self.task
assert ti.task
if ti.task.dag.__class__ is AttributeRemoved:
ti.task.dag = self.task.dag
# If self.task is mapped, this call replaces self.task to point to the
# unmapped BaseOperator created by this function! This is because the
# MappedOperator is useless for template rendering, and we need to be
# able to access the unmapped task instead.
original_task.render_template_fields(context, jinja_env)
if isinstance(self.task, MappedOperator):
self.task = context["ti"].task
return original_task
[docs] def render_k8s_pod_yaml(self) -> dict | None:
"""Render the k8s pod yaml."""
try:
from airflow.providers.cncf.kubernetes.template_rendering import (
render_k8s_pod_yaml as render_k8s_pod_yaml_from_provider,
)
except ImportError:
raise RuntimeError(
"You need to have the `cncf.kubernetes` provider installed to use this feature. "
"Also rather than calling it directly you should import "
"render_k8s_pod_yaml from airflow.providers.cncf.kubernetes.template_rendering "
"and call it with TaskInstance as the first argument."
)
warnings.warn(
"You should not call `task_instance.render_k8s_pod_yaml` directly. This method will be removed"
"in Airflow 3. Rather than calling it directly you should import "
"`render_k8s_pod_yaml` from `airflow.providers.cncf.kubernetes.template_rendering` "
"and call it with `TaskInstance` as the first argument.",
DeprecationWarning,
stacklevel=2,
)
return render_k8s_pod_yaml_from_provider(self)
@provide_session
[docs] def get_rendered_k8s_spec(self, session: Session = NEW_SESSION):
"""Render the k8s pod yaml."""
try:
from airflow.providers.cncf.kubernetes.template_rendering import (
get_rendered_k8s_spec as get_rendered_k8s_spec_from_provider,
)
except ImportError:
raise RuntimeError(
"You need to have the `cncf.kubernetes` provider installed to use this feature. "
"Also rather than calling it directly you should import "
"`get_rendered_k8s_spec` from `airflow.providers.cncf.kubernetes.template_rendering` "
"and call it with `TaskInstance` as the first argument."
)
warnings.warn(
"You should not call `task_instance.render_k8s_pod_yaml` directly. This method will be removed"
"in Airflow 3. Rather than calling it directly you should import "
"`get_rendered_k8s_spec` from `airflow.providers.cncf.kubernetes.template_rendering` "
"and call it with `TaskInstance` as the first argument.",
DeprecationWarning,
stacklevel=2,
)
return get_rendered_k8s_spec_from_provider(self, session=session)
[docs] def get_email_subject_content(
self, exception: BaseException, task: BaseOperator | None = None
) -> tuple[str, str, str]:
"""
Get the email subject content for exceptions.
:param exception: the exception sent in the email
:param task:
"""
return _get_email_subject_content(task_instance=self, exception=exception, task=task)
[docs] def email_alert(self, exception, task: BaseOperator) -> None:
"""
Send alert email with exception information.
:param exception: the exception
:param task: task related to the exception
"""
_email_alert(task_instance=self, exception=exception, task=task)
[docs] def set_duration(self) -> None:
"""Set task instance duration."""
_set_duration(task_instance=self)
@provide_session
[docs] def xcom_push(
self,
key: str,
value: Any,
execution_date: datetime | None = None,
session: Session = NEW_SESSION,
) -> None:
"""
Make an XCom available for tasks to pull.
:param key: Key to store the value under.
:param value: Value to store. What types are possible depends on whether
``enable_xcom_pickling`` is true or not. If so, this can be any
picklable object; only be JSON-serializable may be used otherwise.
:param execution_date: Deprecated parameter that has no effect.
"""
if execution_date is not None:
self_execution_date = self.get_dagrun(session).execution_date
if execution_date < self_execution_date:
raise ValueError(
f"execution_date can not be in the past (current execution_date is "
f"{self_execution_date}; received {execution_date})"
)
elif execution_date is not None:
message = "Passing 'execution_date' to 'TaskInstance.xcom_push()' is deprecated."
warnings.warn(message, RemovedInAirflow3Warning, stacklevel=3)
XCom.set(
key=key,
value=value,
task_id=self.task_id,
dag_id=self.dag_id,
run_id=self.run_id,
map_index=self.map_index,
session=session,
)
@provide_session
[docs] def xcom_pull(
self,
task_ids: str | Iterable[str] | None = None,
dag_id: str | None = None,
key: str = XCOM_RETURN_KEY,
include_prior_dates: bool = False,
session: Session = NEW_SESSION,
*,
map_indexes: int | Iterable[int] | None = None,
default: Any = None,
) -> Any:
"""
Pull XComs that optionally meet certain criteria.
: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 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 *None*.
:param task_ids: Only XComs from tasks with matching ids will be
pulled. Pass *None* to remove the filter.
:param dag_id: If provided, only pulls XComs from this DAG. If *None*
(default), the DAG of the calling task is used.
:param map_indexes: If provided, only pull XComs with matching indexes.
If *None* (default), this is inferred from the task(s) being pulled
(see below for details).
: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.
When pulling one single task (``task_id`` is *None* or a str) without
specifying ``map_indexes``, the return value is inferred from whether
the specified task is mapped. If not, value from the one single task
instance is returned. If the task to pull is mapped, an iterator (not a
list) yielding XComs from mapped task instances is returned. In either
case, ``default`` (*None* if not specified) is returned if no matching
XComs are found.
When pulling multiple tasks (i.e. either ``task_id`` or ``map_index`` is
a non-str iterable), a list of matching XComs is returned. Elements in
the list is ordered by item ordering in ``task_id`` and ``map_index``.
"""
return _xcom_pull(
ti=self,
task_ids=task_ids,
dag_id=dag_id,
key=key,
include_prior_dates=include_prior_dates,
session=session,
map_indexes=map_indexes,
default=default,
)
@provide_session
[docs] def get_num_running_task_instances(self, session: Session, same_dagrun: bool = False) -> int:
"""Return Number of running TIs from the DB."""
# .count() is inefficient
num_running_task_instances_query = session.query(func.count()).filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.state == TaskInstanceState.RUNNING,
)
if same_dagrun:
num_running_task_instances_query = num_running_task_instances_query.filter(
TaskInstance.run_id == self.run_id
)
return num_running_task_instances_query.scalar()
[docs] def init_run_context(self, raw: bool = False) -> None:
"""Set the log context."""
self.raw = raw
self._set_context(self)
@staticmethod
[docs] def filter_for_tis(tis: Iterable[TaskInstance | TaskInstanceKey]) -> BooleanClauseList | None:
"""Return SQLAlchemy filter to query selected task instances."""
# DictKeys type, (what we often pass here from the scheduler) is not directly indexable :(
# Or it might be a generator, but we need to be able to iterate over it more than once
tis = list(tis)
if not tis:
return None
first = tis[0]
dag_id = first.dag_id
run_id = first.run_id
map_index = first.map_index
first_task_id = first.task_id
# pre-compute the set of dag_id, run_id, map_indices and task_ids
dag_ids, run_ids, map_indices, task_ids = set(), set(), set(), set()
for t in tis:
dag_ids.add(t.dag_id)
run_ids.add(t.run_id)
map_indices.add(t.map_index)
task_ids.add(t.task_id)
# Common path optimisations: when all TIs are for the same dag_id and run_id, or same dag_id
# and task_id -- this can be over 150x faster for huge numbers of TIs (20k+)
if dag_ids == {dag_id} and run_ids == {run_id} and map_indices == {map_index}:
return and_(
TaskInstance.dag_id == dag_id,
TaskInstance.run_id == run_id,
TaskInstance.map_index == map_index,
TaskInstance.task_id.in_(task_ids),
)
if dag_ids == {dag_id} and task_ids == {first_task_id} and map_indices == {map_index}:
return and_(
TaskInstance.dag_id == dag_id,
TaskInstance.run_id.in_(run_ids),
TaskInstance.map_index == map_index,
TaskInstance.task_id == first_task_id,
)
if dag_ids == {dag_id} and run_ids == {run_id} and task_ids == {first_task_id}:
return and_(
TaskInstance.dag_id == dag_id,
TaskInstance.run_id == run_id,
TaskInstance.map_index.in_(map_indices),
TaskInstance.task_id == first_task_id,
)
filter_condition = []
# create 2 nested groups, both primarily grouped by dag_id and run_id,
# and in the nested group 1 grouped by task_id the other by map_index.
task_id_groups: dict[tuple, dict[Any, list[Any]]] = defaultdict(lambda: defaultdict(list))
map_index_groups: dict[tuple, dict[Any, list[Any]]] = defaultdict(lambda: defaultdict(list))
for t in tis:
task_id_groups[(t.dag_id, t.run_id)][t.task_id].append(t.map_index)
map_index_groups[(t.dag_id, t.run_id)][t.map_index].append(t.task_id)
# this assumes that most dags have dag_id as the largest grouping, followed by run_id. even
# if its not, this is still a significant optimization over querying for every single tuple key
for cur_dag_id, cur_run_id in itertools.product(dag_ids, run_ids):
# we compare the group size between task_id and map_index and use the smaller group
dag_task_id_groups = task_id_groups[(cur_dag_id, cur_run_id)]
dag_map_index_groups = map_index_groups[(cur_dag_id, cur_run_id)]
if len(dag_task_id_groups) <= len(dag_map_index_groups):
for cur_task_id, cur_map_indices in dag_task_id_groups.items():
filter_condition.append(
and_(
TaskInstance.dag_id == cur_dag_id,
TaskInstance.run_id == cur_run_id,
TaskInstance.task_id == cur_task_id,
TaskInstance.map_index.in_(cur_map_indices),
)
)
else:
for cur_map_index, cur_task_ids in dag_map_index_groups.items():
filter_condition.append(
and_(
TaskInstance.dag_id == cur_dag_id,
TaskInstance.run_id == cur_run_id,
TaskInstance.task_id.in_(cur_task_ids),
TaskInstance.map_index == cur_map_index,
)
)
return or_(*filter_condition)
@classmethod
def ti_selector_condition(cls, vals: Collection[str | tuple[str, int]]) -> ColumnOperators:
"""
Build an SQLAlchemy filter for a list of task_ids or tuples of (task_id,map_index).
:meta private:
"""
# Compute a filter for TI.task_id and TI.map_index based on input values
# For each item, it will either be a task_id, or (task_id, map_index)
task_id_only = [v for v in vals if isinstance(v, str)]
with_map_index = [v for v in vals if not isinstance(v, str)]
filters: list[ColumnOperators] = []
if task_id_only:
filters.append(cls.task_id.in_(task_id_only))
if with_map_index:
filters.append(tuple_in_condition((cls.task_id, cls.map_index), with_map_index))
if not filters:
return false()
if len(filters) == 1:
return filters[0]
return or_(*filters)
@classmethod
@provide_session
def _schedule_downstream_tasks(
cls,
ti: TaskInstance | TaskInstancePydantic,
session: Session = NEW_SESSION,
max_tis_per_query: int | None = None,
):
from sqlalchemy.exc import OperationalError
from airflow.models.dagrun import DagRun
try:
# Re-select the row with a lock
dag_run = with_row_locks(
session.query(DagRun).filter_by(
dag_id=ti.dag_id,
run_id=ti.run_id,
),
session=session,
skip_locked=True,
).one_or_none()
if not dag_run:
cls.logger().debug("Skip locked rows, rollback")
session.rollback()
return
task = ti.task
if TYPE_CHECKING:
assert task
assert task.dag
# Previously, this section used task.dag.partial_subset to retrieve a partial DAG.
# However, this approach is unsafe as it can result in incomplete or incorrect task execution,
# leading to potential bad cases. As a result, the operation has been removed.
# For more details, refer to the discussion in PR #[https://github.com/apache/airflow/pull/42582].
dag_run.dag = task.dag
info = dag_run.task_instance_scheduling_decisions(session)
skippable_task_ids = {
task_id for task_id in task.dag.task_ids if task_id not in task.downstream_task_ids
}
schedulable_tis = [
ti
for ti in info.schedulable_tis
if ti.task_id not in skippable_task_ids
and not (
ti.task.inherits_from_empty_operator
and not ti.task.on_execute_callback
and not ti.task.on_success_callback
and not ti.task.outlets
)
]
for schedulable_ti in schedulable_tis:
if getattr(schedulable_ti, "task", None) is None:
schedulable_ti.task = task.dag.get_task(schedulable_ti.task_id)
num = dag_run.schedule_tis(schedulable_tis, session=session, max_tis_per_query=max_tis_per_query)
cls.logger().info("%d downstream tasks scheduled from follow-on schedule check", num)
session.flush()
except OperationalError as e:
# Any kind of DB error here is _non fatal_ as this block is just an optimisation.
cls.logger().warning(
"Skipping mini scheduling run due to exception: %s",
e.statement,
exc_info=True,
)
session.rollback()
@provide_session
[docs] def schedule_downstream_tasks(self, session: Session = NEW_SESSION, max_tis_per_query: int | None = None):
"""
Schedule downstream tasks of this task instance.
:meta: private
"""
try:
return TaskInstance._schedule_downstream_tasks(
ti=self, session=session, max_tis_per_query=max_tis_per_query
)
except Exception:
self.log.exception(
"Error scheduling downstream tasks. Skipping it as this is entirely optional optimisation. "
"There might be various reasons for it, please take a look at the stack trace to figure "
"out if the root cause can be diagnosed and fixed. See the issue "
"https://github.com/apache/airflow/issues/39717 for details and an example problem. If you "
"would like to get help in solving root cause, open discussion with all details with your "
"managed service support or in Airflow repository."
)
[docs] def get_relevant_upstream_map_indexes(
self,
upstream: Operator,
ti_count: int | None,
*,
session: Session,
) -> int | range | None:
"""
Infer the map indexes of an upstream "relevant" to this ti.
The bulk of the logic mainly exists to solve the problem described by
the following example, where 'val' must resolve to different values,
depending on where the reference is being used::
@task
def this_task(v): # This is self.task.
return v * 2
@task_group
def tg1(inp):
val = upstream(inp) # This is the upstream task.
this_task(val) # When inp is 1, val here should resolve to 2.
return val
# This val is the same object returned by tg1.
val = tg1.expand(inp=[1, 2, 3])
@task_group
def tg2(inp):
another_task(inp, val) # val here should resolve to [2, 4, 6].
tg2.expand(inp=["a", "b"])
The surrounding mapped task groups of ``upstream`` and ``self.task`` are
inspected to find a common "ancestor". If such an ancestor is found,
we need to return specific map indexes to pull a partial value from
upstream XCom.
:param upstream: The referenced upstream task.
:param ti_count: The total count of task instance this task was expanded
by the scheduler, i.e. ``expanded_ti_count`` in the template context.
:return: Specific map index or map indexes to pull, or ``None`` if we
want to "whole" return value (i.e. no mapped task groups involved).
"""
if TYPE_CHECKING:
assert self.task
# This value should never be None since we already know the current task
# is in a mapped task group, and should have been expanded, despite that,
# we need to check that it is not None to satisfy Mypy.
# But this value can be 0 when we expand an empty list, for that it is
# necessary to check that ti_count is not 0 to avoid dividing by 0.
if not ti_count:
return None
# Find the innermost common mapped task group between the current task
# If the current task and the referenced task does not have a common
# mapped task group, the two are in different task mapping contexts
# (like another_task above), and we should use the "whole" value.
common_ancestor = _find_common_ancestor_mapped_group(self.task, upstream)
if common_ancestor is None:
return None
# At this point we know the two tasks share a mapped task group, and we
# should use a "partial" value. Let's break down the mapped ti count
# between the ancestor and further expansion happened inside it.
ancestor_ti_count = common_ancestor.get_mapped_ti_count(self.run_id, session=session)
ancestor_map_index = self.map_index * ancestor_ti_count // ti_count
# If the task is NOT further expanded inside the common ancestor, we
# only want to reference one single ti. We must walk the actual DAG,
# and "ti_count == ancestor_ti_count" does not work, since the further
# expansion may be of length 1.
if not _is_further_mapped_inside(upstream, common_ancestor):
return ancestor_map_index
# Otherwise we need a partial aggregation for values from selected task
# instances in the ancestor's expansion context.
further_count = ti_count // ancestor_ti_count
map_index_start = ancestor_map_index * further_count
return range(map_index_start, map_index_start + further_count)
def clear_db_references(self, session: Session):
"""
Clear db tables that have a reference to this instance.
:param session: ORM Session
:meta private:
"""
from airflow.models.renderedtifields import RenderedTaskInstanceFields
tables: list[type[TaskInstanceDependencies]] = [
TaskFail,
TaskInstanceNote,
TaskReschedule,
XCom,
RenderedTaskInstanceFields,
TaskMap,
]
for table in tables:
session.execute(
delete(table).where(
table.dag_id == self.dag_id,
table.task_id == self.task_id,
table.run_id == self.run_id,
table.map_index == self.map_index,
)
)
def _find_common_ancestor_mapped_group(node1: Operator, node2: Operator) -> MappedTaskGroup | None:
"""Given two operators, find their innermost common mapped task group."""
if node1.dag is None or node2.dag is None or node1.dag_id != node2.dag_id:
return None
parent_group_ids = {g.group_id for g in node1.iter_mapped_task_groups()}
common_groups = (g for g in node2.iter_mapped_task_groups() if g.group_id in parent_group_ids)
return next(common_groups, None)
def _is_further_mapped_inside(operator: Operator, container: TaskGroup) -> bool:
"""Whether given operator is *further* mapped inside a task group."""
from airflow.models.mappedoperator import MappedOperator
if isinstance(operator, MappedOperator):
return True
task_group = operator.task_group
while task_group is not None and task_group.group_id != container.group_id:
if isinstance(task_group, MappedTaskGroup):
return True
task_group = task_group.parent_group
return False
# State of the task instance.
# Stores string version of the task state.
[docs]TaskInstanceStateType = Tuple[TaskInstanceKey, TaskInstanceState]
[docs]class SimpleTaskInstance:
"""
Simplified Task Instance.
Used to send data between processes via Queues.
"""
def __init__(
self,
dag_id: str,
task_id: str,
run_id: str,
start_date: datetime | None,
end_date: datetime | None,
try_number: int,
map_index: int,
state: str,
executor: str | None,
executor_config: Any,
pool: str,
queue: str,
key: TaskInstanceKey,
run_as_user: str | None = None,
priority_weight: int | None = None,
):
self.dag_id = dag_id
self.task_id = task_id
self.run_id = run_id
self.map_index = map_index
self.start_date = start_date
self.end_date = end_date
self.try_number = try_number
self.state = state
self.executor = executor
self.executor_config = executor_config
self.run_as_user = run_as_user
self.pool = pool
self.priority_weight = priority_weight
self.queue = queue
self.key = key
[docs] def __repr__(self) -> str:
attrs = ", ".join(f"{k}={v!r}" for k, v in self.__dict__.items())
return f"SimpleTaskInstance({attrs})"
[docs] def __eq__(self, other) -> bool:
if isinstance(other, self.__class__):
return self.__dict__ == other.__dict__
return NotImplemented
[docs] def as_dict(self):
warnings.warn(
"This method is deprecated. Use BaseSerialization.serialize.",
RemovedInAirflow3Warning,
stacklevel=2,
)
new_dict = dict(self.__dict__)
for key in new_dict:
if key in ["start_date", "end_date"]:
val = new_dict[key]
if not val or isinstance(val, str):
continue
new_dict.update({key: val.isoformat()})
return new_dict
@classmethod
[docs] def from_ti(cls, ti: TaskInstance) -> SimpleTaskInstance:
return cls(
dag_id=ti.dag_id,
task_id=ti.task_id,
run_id=ti.run_id,
map_index=ti.map_index,
start_date=ti.start_date,
end_date=ti.end_date,
try_number=ti.try_number,
state=ti.state,
executor=ti.executor,
executor_config=ti.executor_config,
pool=ti.pool,
queue=ti.queue,
key=ti.key,
run_as_user=ti.run_as_user if hasattr(ti, "run_as_user") else None,
priority_weight=ti.priority_weight if hasattr(ti, "priority_weight") else None,
)
@classmethod
[docs] def from_dict(cls, obj_dict: dict) -> SimpleTaskInstance:
warnings.warn(
"This method is deprecated. Use BaseSerialization.deserialize.",
RemovedInAirflow3Warning,
stacklevel=2,
)
ti_key = TaskInstanceKey(*obj_dict.pop("key"))
start_date = None
end_date = None
start_date_str: str | None = obj_dict.pop("start_date")
end_date_str: str | None = obj_dict.pop("end_date")
if start_date_str:
start_date = timezone.parse(start_date_str)
if end_date_str:
end_date = timezone.parse(end_date_str)
return cls(**obj_dict, start_date=start_date, end_date=end_date, key=ti_key)
[docs]class TaskInstanceNote(TaskInstanceDependencies):
"""For storage of arbitrary notes concerning the task instance."""
[docs] __tablename__ = "task_instance_note"
[docs] user_id = Column(Integer, ForeignKey("ab_user.id", name="task_instance_note_user_fkey"), nullable=True)
[docs] task_id = Column(StringID(), primary_key=True, nullable=False)
[docs] dag_id = Column(StringID(), primary_key=True, nullable=False)
[docs] run_id = Column(StringID(), primary_key=True, nullable=False)
[docs] map_index = Column(Integer, primary_key=True, nullable=False)
[docs] content = Column(String(1000).with_variant(Text(1000), "mysql"))
[docs] created_at = Column(UtcDateTime, default=timezone.utcnow, nullable=False)
[docs] updated_at = Column(UtcDateTime, default=timezone.utcnow, onupdate=timezone.utcnow, nullable=False)
[docs] task_instance = relationship("TaskInstance", back_populates="task_instance_note")
[docs] __table_args__ = (
PrimaryKeyConstraint("task_id", "dag_id", "run_id", "map_index", name="task_instance_note_pkey"),
ForeignKeyConstraint(
(dag_id, task_id, run_id, map_index),
[
"task_instance.dag_id",
"task_instance.task_id",
"task_instance.run_id",
"task_instance.map_index",
],
name="task_instance_note_ti_fkey",
ondelete="CASCADE",
),
)
def __init__(self, content, user_id=None):
self.content = content
self.user_id = user_id
[docs] def __repr__(self):
prefix = f"<{self.__class__.__name__}: {self.dag_id}.{self.task_id} {self.run_id}"
if self.map_index != -1:
prefix += f" map_index={self.map_index}"
return prefix + ">"
STATICA_HACK = True
globals()["kcah_acitats"[::-1].upper()] = False
if STATICA_HACK: # pragma: no cover
from airflow.jobs.job import Job
TaskInstance.queued_by_job = relationship(Job)