#
# 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 logging
import math
import operator
import os
import signal
import warnings
from collections import defaultdict
from datetime import datetime, timedelta
from functools import partial
from types import TracebackType
from typing import TYPE_CHECKING, Any, Callable, Collection, Generator, Iterable, NamedTuple, Tuple
from urllib.parse import quote
import dill
import jinja2
import lazy_object_proxy
import pendulum
from jinja2 import TemplateAssertionError, UndefinedError
from sqlalchemy import (
Column,
DateTime,
Float,
ForeignKeyConstraint,
Index,
Integer,
PrimaryKeyConstraint,
String,
Text,
and_,
false,
func,
inspect,
or_,
text,
)
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.ext.mutable import MutableDict
from sqlalchemy.orm import reconstructor, relationship
from sqlalchemy.orm.attributes import NO_VALUE, set_committed_value
from sqlalchemy.orm.session import Session
from sqlalchemy.sql.elements import BooleanClauseList
from sqlalchemy.sql.expression import ColumnOperators, case
from airflow import settings
from airflow.compat.functools import cache
from airflow.configuration import conf
from airflow.datasets import Dataset
from airflow.datasets.manager import dataset_manager
from airflow.exceptions import (
AirflowException,
AirflowFailException,
AirflowRescheduleException,
AirflowSensorTimeout,
AirflowSkipException,
AirflowTaskTimeout,
DagRunNotFound,
RemovedInAirflow3Warning,
TaskDeferralError,
TaskDeferred,
UnmappableXComLengthPushed,
UnmappableXComTypePushed,
XComForMappingNotPushed,
)
from airflow.models.base import Base, StringID
from airflow.models.log import Log
from airflow.models.mappedoperator import MappedOperator
from airflow.models.param import process_params
from airflow.models.taskfail import TaskFail
from airflow.models.taskmap import TaskMap
from airflow.models.taskreschedule import TaskReschedule
from airflow.models.xcom import XCOM_RETURN_KEY, LazyXComAccess, XCom
from airflow.plugins_manager import integrate_macros_plugins
from airflow.sentry import Sentry
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.timetables.base import DataInterval
from airflow.typing_compat import Literal, TypeGuard
from airflow.utils import timezone
from airflow.utils.context import ConnectionAccessor, Context, VariableAccessor, context_merge
from airflow.utils.email import send_email
from airflow.utils.helpers import 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 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, State, TaskInstanceState
from airflow.utils.timeout import timeout
_CURRENT_CONTEXT: list[Context] = []
[docs]log = logging.getLogger(__name__)
if TYPE_CHECKING:
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.utils.task_group import MappedTaskGroup, TaskGroup
@contextlib.contextmanager
[docs]def set_current_context(context: Context) -> Generator[Context, None, None]:
"""
Sets 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,
)
[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:
"""
Clears a set of task instances, but makes sure the running ones
get killed.
:param tis: a list of task instances
:param session: current session
:param dag_run_state: state to set DagRun to. If set to False, dagrun 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)))
)
for ti in tis:
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:
task_id = ti.task_id
if dag and dag.has_task(task_id):
task = dag.get_task(task_id)
ti.refresh_from_task(task)
task_retries = task.retries
ti.max_tries = ti.try_number + task_retries - 1
else:
# Ignore errors when updating max_tries if dag is None or
# task not found in dag 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.prev_attempted_tries)
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.base_job import BaseJob
for job in session.query(BaseJob).filter(BaseJob.id.in_(job_ids)).all():
job.state = TaskInstanceState.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:
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
session.flush()
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
[docs]class TaskInstanceKey(NamedTuple):
"""Key used to identify task instance."""
@property
[docs] def primary(self) -> tuple[str, str, str, int]:
"""Return task instance primary key part of the key"""
return self.dag_id, self.task_id, self.run_id, self.map_index
@property
[docs] def reduced(self) -> TaskInstanceKey:
"""Remake the key by subtracting 1 from try number to match in memory information"""
return TaskInstanceKey(
self.dag_id, self.task_id, self.run_id, max(1, self.try_number - 1), self.map_index
)
[docs] def with_try_number(self, try_number: int) -> TaskInstanceKey:
"""Returns TaskInstanceKey with provided ``try_number``"""
return TaskInstanceKey(self.dag_id, self.task_id, self.run_id, try_number, self.map_index)
@property
[docs] def key(self) -> TaskInstanceKey:
"""For API-compatibly with TaskInstance.
Returns self
"""
return self
def _creator_note(val):
"""Custom creator for the ``note`` association proxy."""
if isinstance(val, str):
return TaskInstanceNote(content=val)
elif isinstance(val, dict):
return TaskInstanceNote(**val)
else:
return TaskInstanceNote(*val)
[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))
_try_number = Column("try_number", 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] queued_dttm = Column(UtcDateTime)
[docs] queued_by_job_id = Column(Integer)
[docs] executor_config = Column(ExecutorConfigType(pickler=dill))
[docs] updated_at = Column(UtcDateTime, default=timezone.utcnow, onupdate=timezone.utcnow)
[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))
# 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", mssql_clustered=True
),
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 = 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)
[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)
[docs] note = association_proxy("task_instance_note", "content", creator=_creator_note)
[docs] task: Operator # Not always set...
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)
# 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
@staticmethod
[docs] def insert_mapping(run_id: str, task: Operator, map_index: int) -> dict[str, Any]:
""":meta private:"""
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": task.priority_weight_total,
"run_as_user": task.run_as_user,
"max_tries": task.retries,
"executor_config": task.executor_config,
"operator": task.task_type,
"map_index": map_index,
}
@reconstructor
[docs] def init_on_load(self) -> None:
"""Initialize the attributes that aren't stored in the DB"""
# correctly config the ti log
self._log = logging.getLogger("airflow.task")
self.test_mode = False # can be changed when calling 'run'
@property
[docs] def try_number(self):
"""
Return the try number that this task number will be when it is actually
run.
If the TaskInstance is currently running, this will match the column in the
database, in all other cases this will be incremented.
"""
# This is designed so that task logs end up in the right file.
if self.state in State.running:
return self._try_number
return self._try_number + 1
@try_number.setter
def try_number(self, value: int) -> None:
self._try_number = value
@property
[docs] def prev_attempted_tries(self) -> int:
"""
Based on this instance's try_number, this will calculate
the number of previously attempted tries, defaulting to 0.
"""
# Expose this for the Task Tries and Gantt graph views.
# Using `try_number` throws off the counts for non-running tasks.
# Also useful in error logging contexts to get
# the try number for the last try that was attempted.
# https://issues.apache.org/jira/browse/AIRFLOW-2143
return self._try_number
@property
[docs] def next_try_number(self) -> int:
return self._try_number + 1
[docs] def command_as_list(
self,
mark_success=False,
ignore_all_deps=False,
ignore_task_deps=False,
ignore_depends_on_past=False,
ignore_ti_state=False,
local=False,
pickle_id=None,
raw=False,
job_id=None,
pool=None,
cfg_path=None,
):
"""
Returns a command that can be executed anywhere where airflow is
installed. This command is part of the message sent to executors by
the orchestrator.
"""
dag: DAG | DagModel
# Use the dag if we have it, else fallback to the ORM dag_model, which might not be loaded
if hasattr(self, "task") and hasattr(self.task, "dag"):
dag = self.task.dag
else:
dag = self.dag_model
should_pass_filepath = not pickle_id and dag
path = None
if should_pass_filepath:
if dag.is_subdag:
path = dag.parent_dag.relative_fileloc
else:
path = dag.relative_fileloc
if path:
if not path.is_absolute():
path = "DAGS_FOLDER" / path
path = str(path)
return TaskInstance.generate_command(
self.dag_id,
self.task_id,
run_id=self.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,
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=self.map_index,
)
@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,
ignore_task_deps: bool = False,
ignore_ti_state: bool = False,
local: bool = False,
pickle_id: int | None = None,
file_path: 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]:
"""
Generates 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 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(["--ignore-depends-on-past"])
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", 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"""
iso = quote(self.execution_date.isoformat())
base_url = conf.get_mandatory_value("webserver", "BASE_URL")
return (
f"{base_url}/log"
f"?execution_date={iso}"
f"&task_id={self.task_id}"
f"&dag_id={self.dag_id}"
f"&map_index={self.map_index}"
)
@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.
:param session: SQLAlchemy ORM Session
"""
return (
session.query(TaskInstance.state)
.filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.run_id == self.run_id,
)
.scalar()
)
@provide_session
[docs] def error(self, session: Session = NEW_SESSION) -> None:
"""
Forces 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 = State.FAILED
session.merge(self)
session.commit()
@provide_session
[docs] def refresh_from_db(self, session: Session = NEW_SESSION, lock_for_update: bool = False) -> None:
"""
Refreshes 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.
"""
self.log.debug("Refreshing TaskInstance %s from DB", self)
if self in session:
session.refresh(self, TaskInstance.__mapper__.column_attrs.keys())
qry = (
# To avoid joining any relationships, by default select all
# columns, not the object. This also means we get (effectively) a
# namedtuple back, not a TI object
session.query(*TaskInstance.__table__.columns).filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.run_id == self.run_id,
TaskInstance.map_index == self.map_index,
)
)
if lock_for_update:
for attempt in run_with_db_retries(logger=self.log):
with attempt:
ti: TaskInstance | None = qry.with_for_update().one_or_none()
else:
ti = qry.one_or_none()
if ti:
# Fields ordered per model definition
self.start_date = ti.start_date
self.end_date = ti.end_date
self.duration = ti.duration
self.state = ti.state
# Since we selected columns, not the object, this is the raw value
self.try_number = ti.try_number
self.max_tries = ti.max_tries
self.hostname = ti.hostname
self.unixname = ti.unixname
self.job_id = ti.job_id
self.pool = ti.pool
self.pool_slots = ti.pool_slots or 1
self.queue = ti.queue
self.priority_weight = ti.priority_weight
self.operator = ti.operator
self.queued_dttm = ti.queued_dttm
self.queued_by_job_id = ti.queued_by_job_id
self.pid = ti.pid
self.executor_config = ti.executor_config
self.external_executor_id = ti.external_executor_id
self.trigger_id = ti.trigger_id
self.next_method = ti.next_method
self.next_kwargs = ti.next_kwargs
else:
self.state = None
[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
"""
self.task = task
self.queue = task.queue
self.pool = pool_override or task.pool
self.pool_slots = task.pool_slots
self.priority_weight = task.priority_weight_total
self.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.
self.executor_config = task.executor_config
self.operator = task.task_type
@provide_session
[docs] def clear_xcom_data(self, 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 session: SQLAlchemy ORM Session
"""
self.log.debug("Clearing XCom data")
if self.map_index < 0:
map_index: int | None = None
else:
map_index = self.map_index
XCom.clear(
dag_id=self.dag_id,
task_id=self.task_id,
run_id=self.run_id,
map_index=map_index,
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)
@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
"""
if self.state == state:
return False
current_time = timezone.utcnow()
self.log.debug("Setting task state for %s to %s", self, state)
self.state = state
self.start_date = self.start_date or current_time
if self.state in State.finished or self.state == State.UP_FOR_RETRY:
self.end_date = self.end_date or current_time
self.duration = (self.end_date - self.start_date).total_seconds()
session.merge(self)
return True
@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 == State.UP_FOR_RETRY and not self.ready_for_retry()
@provide_session
[docs] def are_dependents_done(self, session: Session = NEW_SESSION) -> bool:
"""
Checks 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 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_([State.SKIPPED, State.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:
"""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.
"""
dag = self.task.dag
if dag is None:
return None
dr = self.get_dagrun(session=session)
dr.dag = dag
# 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_run
if dag.catchup is True and not ignore_schedule:
last_dagrun = dr.get_previous_scheduled_dagrun(session=session)
else:
last_dagrun = dr.get_previous_dagrun(session=session, state=state)
if last_dagrun:
return last_dagrun
return None
@provide_session
[docs] def get_previous_ti(
self,
state: DagRunState | None = None,
session: Session = NEW_SESSION,
) -> TaskInstance | None:
"""
The task instance for the task that ran before this task instance.
:param state: If passed, it only take into account instances of a specific state.
:param session: SQLAlchemy ORM Session
"""
dagrun = self.get_previous_dagrun(state, session=session)
if dagrun is None:
return None
return dagrun.get_task_instance(self.task_id, session=session)
@property
[docs] def previous_ti(self) -> TaskInstance | None:
"""
This attribute is deprecated.
Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method.
"""
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 | None:
"""
This attribute is deprecated.
Please use `airflow.models.taskinstance.TaskInstance.get_previous_ti` method.
"""
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:
"""
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
"""
self.log.debug("previous_execution_date was called")
prev_ti = self.get_previous_ti(state=state, session=session)
return prev_ti and pendulum.instance(prev_ti.execution_date)
@provide_session
[docs] def get_previous_start_date(
self, state: DagRunState | None = None, session: Session = NEW_SESSION
) -> pendulum.DateTime | None:
"""
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
"""
self.log.debug("previous_start_date was called")
prev_ti = self.get_previous_ti(state=state, session=session)
# prev_ti may not exist and prev_ti.start_date may be None.
return prev_ti and prev_ti.start_date and pendulum.instance(prev_ti.start_date)
@property
[docs] def previous_start_date_success(self) -> pendulum.DateTime | None:
"""
This attribute is deprecated.
Please use `airflow.models.taskinstance.TaskInstance.get_previous_start_date` method.
"""
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:
"""
Returns whether or not all the conditions are met for this task instance to be run
given the context for the dependencies (e.g. a task instance being force run from
the UI will ignore some dependencies).
:param dep_context: The execution context that determines the dependencies that
should be evaluated.
: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 %s", self)
return True
@provide_session
[docs] def get_failed_dep_statuses(self, dep_context: DepContext | None = None, session: Session = NEW_SESSION):
"""Get failed Dependencies"""
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.
"""
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.
min_backoff = int(math.ceil(delay.total_seconds() * (2 ** (self.try_number - 2))))
# 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.
delay_backoff_in_seconds = min(modded_hash, timedelta.max.total_seconds() - 1)
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:
"""
Checks on whether the task instance is in the right state and timeframe
to be retried.
"""
return self.state == State.UP_FOR_RETRY and self.next_retry_datetime() < timezone.utcnow()
@provide_session
[docs] def get_dagrun(self, session: Session = NEW_SESSION) -> DagRun:
"""
Returns 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:
return self.dag_run
from airflow.models.dagrun import DagRun # Avoid circular import
dr = session.query(DagRun).filter(DagRun.dag_id == self.dag_id, DagRun.run_id == self.run_id).one()
# 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
@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,
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:
"""
Checks 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 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 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
"""
task = self.task
self.refresh_from_task(task, pool_override=pool)
self.test_mode = test_mode
self.refresh_from_db(session=session, lock_for_update=True)
self.job_id = job_id
self.hostname = get_hostname()
self.pid = None
if not ignore_all_deps and not ignore_ti_state and self.state == State.SUCCESS:
Stats.incr("previously_succeeded", 1, 1)
# TODO: Logging needs cleanup, not clear what is being printed
hr_line_break = "\n" + ("-" * 80) # Line break
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,
ignore_task_deps=ignore_task_deps,
)
if not self.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
self.start_date = self.start_date if self.next_method else timezone.utcnow()
if self.state == State.UP_FOR_RESCHEDULE:
task_reschedule: TR = TR.query_for_task_instance(self, session=session).first()
if task_reschedule:
self.start_date = task_reschedule.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,
ignore_task_deps=ignore_task_deps,
ignore_ti_state=ignore_ti_state,
)
if not self.are_dependencies_met(dep_context=dep_context, session=session, verbose=True):
self.state = State.NONE
self.log.warning(hr_line_break)
self.log.warning(
"Rescheduling due to concurrency limits reached "
"at task runtime. Attempt %s of "
"%s. State set to NONE.",
self.try_number,
self.max_tries + 1,
)
self.log.warning(hr_line_break)
self.queued_dttm = timezone.utcnow()
session.merge(self)
session.commit()
return False
# print status message
self.log.info(hr_line_break)
self.log.info("Starting attempt %s of %s", self.try_number, self.max_tries + 1)
self.log.info(hr_line_break)
self._try_number += 1
if not test_mode:
session.add(Log(State.RUNNING, self))
self.state = State.RUNNING
self.external_executor_id = external_executor_id
self.end_date = None
if not test_mode:
session.merge(self).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:
self.log.info("Marking success for %s on %s", self.task, self.execution_date)
else:
self.log.info("Executing %s on %s", self.task, self.execution_date)
return True
def _date_or_empty(self, attr: str) -> str:
result: datetime | None = getattr(self, attr, None)
return result.strftime("%Y%m%dT%H%M%S") if result else ""
def _log_state(self, lead_msg: str = "") -> None:
params = [
lead_msg,
str(self.state).upper(),
self.dag_id,
self.task_id,
]
message = "%sMarking task as %s. dag_id=%s, task_id=%s, "
if self.map_index >= 0:
params.append(self.map_index)
message += "map_index=%d, "
self.log.info(
message + "execution_date=%s, start_date=%s, end_date=%s",
*params,
self._date_or_empty("execution_date"),
self._date_or_empty("start_date"),
self._date_or_empty("end_date"),
)
# Ensure we unset next_method and next_kwargs to ensure that any
# retries don't re-use them.
[docs] def clear_next_method_args(self) -> None:
self.log.debug("Clearing next_method and next_kwargs.")
self.next_method = None
self.next_kwargs = None
@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,
session: Session = NEW_SESSION,
) -> None:
"""
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
"""
self.test_mode = test_mode
self.refresh_from_task(self.task, pool_override=pool)
self.refresh_from_db(session=session)
self.job_id = job_id
self.hostname = get_hostname()
self.pid = os.getpid()
if not test_mode:
session.merge(self)
session.commit()
actual_start_date = timezone.utcnow()
Stats.incr(f"ti.start.{self.task.dag_id}.{self.task.task_id}")
# Initialize final state counters at zero
for state in State.task_states:
Stats.incr(f"ti.finish.{self.task.dag_id}.{self.task.task_id}.{state}", count=0)
self.task = self.task.prepare_for_execution()
context = self.get_template_context(ignore_param_exceptions=False)
try:
if not mark_success:
self._execute_task_with_callbacks(context, test_mode)
if not test_mode:
self.refresh_from_db(lock_for_update=True, session=session)
self.state = State.SUCCESS
except TaskDeferred as defer:
# The task has signalled it wants to defer execution based on
# a trigger.
self._defer_task(defer=defer, session=session)
self.log.info(
"Pausing task as DEFERRED. dag_id=%s, task_id=%s, execution_date=%s, start_date=%s",
self.dag_id,
self.task_id,
self._date_or_empty("execution_date"),
self._date_or_empty("start_date"),
)
if not test_mode:
session.add(Log(self.state, self))
session.merge(self)
session.commit()
return
except AirflowSkipException as e:
# Recording SKIP
# log only if exception has any arguments to prevent log flooding
if e.args:
self.log.info(e)
if not test_mode:
self.refresh_from_db(lock_for_update=True, session=session)
self.state = State.SKIPPED
except AirflowRescheduleException as reschedule_exception:
self._handle_reschedule(actual_start_date, reschedule_exception, test_mode, session=session)
session.commit()
return
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.
self.handle_failure(e, test_mode, context, force_fail=True, session=session)
session.commit()
raise
except AirflowException as e:
if not test_mode:
self.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 self.state in State.finished:
self.clear_next_method_args()
session.merge(self)
session.commit()
return
else:
self.handle_failure(e, test_mode, context, session=session)
session.commit()
raise
except (Exception, KeyboardInterrupt) as e:
self.handle_failure(e, test_mode, context, session=session)
session.commit()
raise
finally:
Stats.incr(f"ti.finish.{self.dag_id}.{self.task_id}.{self.state}")
# Recording SKIPPED or SUCCESS
self.clear_next_method_args()
self.end_date = timezone.utcnow()
self._log_state()
self.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!
self._run_finished_callback(self.task.on_success_callback, context, "on_success")
if not test_mode:
session.add(Log(self.state, self))
session.merge(self).task = self.task
if self.state == TaskInstanceState.SUCCESS:
self._register_dataset_changes(session=session)
session.commit()
def _register_dataset_changes(self, *, session: Session) -> None:
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,
session=session,
)
def _execute_task_with_callbacks(self, context, test_mode=False):
"""Prepare Task for Execution"""
from airflow.models.renderedtifields import RenderedTaskInstanceFields
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 AirflowException("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"):
# Set the validated/merged params on the task object.
self.task.params = context["params"]
task_orig = self.render_templates(context=context)
if not test_mode:
rtif = RenderedTaskInstanceFields(ti=self, render_templates=False)
RenderedTaskInstanceFields.write(rtif)
RenderedTaskInstanceFields.delete_old_records(self.task_id, self.dag_id)
# 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 the following env vars:\n%s",
"\n".join(f"{k}={v}" 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)
# Execute the task
with set_current_context(context):
result = self._execute_task(context, task_orig)
# Run post_execute callback
self.task.post_execute(context=context, result=result)
Stats.incr(f"operator_successes_{self.task.task_type}", 1, 1)
Stats.incr("ti_successes")
def _run_finished_callback(
self, callback: TaskStateChangeCallback | None, context: Context, callback_type: str
) -> None:
"""Run callback after task finishes"""
try:
if callback:
callback(context)
except Exception: # pylint: disable=broad-except
self.log.exception(f"Error when executing {callback_type} callback")
def _execute_task(self, context, task_orig):
"""Executes Task (optionally with a Timeout) and pushes Xcom results"""
task_to_execute = self.task
# 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
if self.next_method:
# __fail__ is a special signal value for next_method that indicates
# this task was scheduled specifically to fail.
if self.next_method == "__fail__":
next_kwargs = self.next_kwargs or {}
traceback = self.next_kwargs.get("traceback")
if traceback is not None:
self.log.error("Trigger failed:\n%s", "\n".join(traceback))
raise TaskDeferralError(next_kwargs.get("error", "Unknown"))
# Grab the callable off the Operator/Task and add in any kwargs
execute_callable = getattr(task_to_execute, self.next_method)
if self.next_kwargs:
execute_callable = partial(execute_callable, **self.next_kwargs)
else:
execute_callable = task_to_execute.execute
# 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 self.next_method:
timeout_seconds = (
task_to_execute.execution_timeout - (timezone.utcnow() - self.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)
except AirflowTaskTimeout:
task_to_execute.on_kill()
raise
else:
result = execute_callable(context=context)
with create_session() as session:
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.
self.xcom_push(key=XCOM_RETURN_KEY, value=xcom_value, session=session)
self._record_task_map_for_downstreams(task_orig, xcom_value, session=session)
return result
@provide_session
def _defer_task(self, session: Session, defer: TaskDeferred) -> None:
"""
Marks the task as deferred and sets up the trigger that is needed
to resume it.
"""
from airflow.models.trigger import Trigger
# First, make the trigger entry
trigger_row = Trigger.from_object(defer.trigger)
session.add(trigger_row)
session.flush()
# 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
self.state = State.DEFERRED
self.trigger_id = trigger_row.id
self.next_method = defer.method_name
self.next_kwargs = defer.kwargs or {}
# Decrement try number so the next one is the same try
self._try_number -= 1
# Calculate timeout too if it was passed
if defer.timeout is not None:
self.trigger_timeout = timezone.utcnow() + defer.timeout
else:
self.trigger_timeout = None
# If an execution_timeout is set, set the timeout to the minimum of
# it and the trigger timeout
execution_timeout = self.task.execution_timeout
if execution_timeout:
if self.trigger_timeout:
self.trigger_timeout = min(self.start_date + execution_timeout, self.trigger_timeout)
else:
self.trigger_timeout = self.start_date + execution_timeout
def _run_execute_callback(self, context: Context, task: Operator) -> None:
"""Functions that need to be run before a Task is executed"""
try:
if task.on_execute_callback:
task.on_execute_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,
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,
) -> 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,
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
)
[docs] def dry_run(self) -> None:
"""Only Renders Templates for the TI"""
from airflow.models.baseoperator import BaseOperator
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, reschedule_exception, test_mode=False, session=NEW_SESSION
):
# Don't record reschedule request in test mode
if test_mode:
return
from airflow.models.dagrun import DagRun # Avoid circular import
self.refresh_from_db(session)
self.end_date = timezone.utcnow()
self.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=self.dag_id,
run_id=self.run_id,
),
session=session,
).one()
# Log reschedule request
session.add(
TaskReschedule(
self.task,
self.run_id,
self._try_number,
actual_start_date,
self.end_date,
reschedule_exception.reschedule_date,
self.map_index,
)
)
# set state
self.state = State.UP_FOR_RESCHEDULE
# Decrement try_number so subsequent runs will use the same try number and write
# to same log file.
self._try_number -= 1
self.clear_next_method_args()
session.merge(self)
session.commit()
self.log.info("Rescheduling task, marking task as UP_FOR_RESCHEDULE")
@staticmethod
[docs] def get_truncated_error_traceback(error: BaseException, truncate_to: Callable) -> TracebackType | None:
"""
Truncates 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__
@provide_session
[docs] def handle_failure(
self,
error: None | str | Exception | KeyboardInterrupt,
test_mode: bool | None = None,
context: Context | None = None,
force_fail: bool = False,
session: Session = NEW_SESSION,
) -> None:
"""Handle Failure for the TaskInstance"""
if test_mode is None:
test_mode = self.test_mode
if error:
if isinstance(error, BaseException):
tb = self.get_truncated_error_traceback(error, truncate_to=self._execute_task)
self.log.error("Task failed with exception", exc_info=(type(error), error, tb))
else:
self.log.error("%s", error)
if not test_mode:
self.refresh_from_db(session)
self.end_date = timezone.utcnow()
self.set_duration()
Stats.incr(f"operator_failures_{self.operator}")
Stats.incr("ti_failures")
if not test_mode:
session.add(Log(State.FAILED, self))
# Log failure duration
session.add(TaskFail(ti=self))
self.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(self, "task", None):
context = self.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(self, "task", None) and context:
task = self.task.unmap((context, session))
except Exception:
self.log.error("Unable to unmap task to determine if we need to send an alert email")
if force_fail or not self.is_eligible_to_retry():
self.state = State.FAILED
email_for_state = operator.attrgetter("email_on_failure")
callback = task.on_failure_callback if task else None
callback_type = "on_failure"
else:
if self.state == State.QUEUED:
# We increase the try_number so as to fail the task if it fails to start after sometime
self._try_number += 1
self.state = State.UP_FOR_RETRY
email_for_state = operator.attrgetter("email_on_retry")
callback = task.on_retry_callback if task else None
callback_type = "on_retry"
self._log_state("Immediate failure requested. " if force_fail else "")
if task and email_for_state(task) and task.email:
try:
self.email_alert(error, task)
except Exception:
self.log.exception("Failed to send email to: %s", task.email)
if callback and context:
self._run_finished_callback(callback, context, callback_type)
if not test_mode:
session.merge(self)
session.flush()
[docs] def is_eligible_to_retry(self):
"""Is task instance is eligible for retry"""
if self.state == State.RESTARTING:
# If a task is cleared when running, it goes into RESTARTING state and is always
# eligible for retry
return True
if not getattr(self, "task", None):
# Couldn't load the task, don't know number of retries, guess:
return self.try_number <= self.max_tries
return self.task.retries and self.try_number <= self.max_tries
[docs] def get_template_context(
self, session: Session = NEW_SESSION, ignore_param_exceptions: bool = True
) -> Context:
"""Return TI Context"""
# 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 = self.task
if TYPE_CHECKING:
assert task.dag
dag: DAG = task.dag
dag_run = self.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 = timezone.coerce_datetime(self.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 self.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)
@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(r"%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]]:
nonlocal dag_run
# The dag_run may not be attached to the session anymore (code base is over-zealous with use of
# `session.expunge_all()`) so re-attach it if we get called
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]] = defaultdict(list)
for event in dataset_events:
triggering_events[event.dataset.uri].append(event)
return triggering_events
try:
expanded_ti_count: int | None = task.get_mapped_ti_count(self.run_id, session=session)
except NotMapped:
expanded_ti_count = None
# NOTE: If you add anything to this dict, make sure to also update the
# definition in airflow/utils/context.pyi, and KNOWN_CONTEXT_KEYS in
# airflow/utils/context.py!
context = {
"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),
"ds": ds,
"ds_nodash": ds_nodash,
"execution_date": logical_date,
"expanded_ti_count": expanded_ti_count,
"inlets": task.inlets,
"logical_date": logical_date,
"macros": macros,
"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": self.get_previous_execution_date(
state=DagRunState.SUCCESS,
session=session,
),
"prev_start_date_success": get_prev_start_date_success(),
"run_id": self.run_id,
"task": task,
"task_instance": self,
"task_instance_key_str": f"{task.dag_id}__{task.task_id}__{ds_nodash}",
"test_mode": self.test_mode,
"ti": self,
"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
@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
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
@provide_session
[docs] def get_rendered_k8s_spec(self, session: Session = NEW_SESSION):
"""Fetch rendered template fields from DB"""
from airflow.models.renderedtifields import RenderedTaskInstanceFields
rendered_k8s_spec = RenderedTaskInstanceFields.get_k8s_pod_yaml(self, session=session)
if not rendered_k8s_spec:
try:
rendered_k8s_spec = self.render_k8s_pod_yaml()
except (TemplateAssertionError, UndefinedError) as e:
raise AirflowException(f"Unable to render a k8s spec for this taskinstance: {e}") from e
return rendered_k8s_spec
[docs] def overwrite_params_with_dag_run_conf(self, params, dag_run):
"""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) -> 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.
"""
if not context:
context = self.get_template_context()
original_task = self.task
# 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)
return original_task
[docs] def render_k8s_pod_yaml(self) -> dict | None:
"""Render k8s pod yaml"""
from kubernetes.client.api_client import ApiClient
from airflow.kubernetes.kube_config import KubeConfig
from airflow.kubernetes.kubernetes_helper_functions import create_pod_id # Circular import
from airflow.kubernetes.pod_generator import PodGenerator
kube_config = KubeConfig()
pod = PodGenerator.construct_pod(
dag_id=self.dag_id,
run_id=self.run_id,
task_id=self.task_id,
map_index=self.map_index,
date=None,
pod_id=create_pod_id(self.dag_id, self.task_id),
try_number=self.try_number,
kube_image=kube_config.kube_image,
args=self.command_as_list(),
pod_override_object=PodGenerator.from_obj(self.executor_config),
scheduler_job_id="0",
namespace=kube_config.executor_namespace,
base_worker_pod=PodGenerator.deserialize_model_file(kube_config.pod_template_file),
)
settings.pod_mutation_hook(pod)
sanitized_pod = ApiClient().sanitize_for_serialization(pod)
return sanitized_pod
[docs] def get_email_subject_content(
self, exception: BaseException, task: BaseOperator | None = None
) -> tuple[str, str, str]:
"""Get the email subject content for exceptions."""
# For a ti from DB (without ti.task), return the default value
if task is None:
task = getattr(self, "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>'
)
# This function is called after changing the state from State.RUNNING,
# so we need to subtract 1 from self.try_number here.
current_try_number = self.try_number - 1
additional_context: dict[str, Any] = {
"exception": exception,
"exception_html": exception_html,
"try_number": current_try_number,
"max_tries": self.max_tries,
}
if use_default:
default_context = {"ti": self, **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:
# 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 = self.task.get_dag()
if dag:
jinja_env = dag.get_template_env(force_sandboxed=True)
else:
jinja_env = SandboxedEnvironment(cache_size=0)
jinja_context = self.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:
self.log.warning(f"Could not find email template file '{path!r}'. Using defaults...")
except OSError:
self.log.exception(f"Error while using email template '{path!r}'. Using defaults...")
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
[docs] def email_alert(self, exception, task: BaseOperator) -> None:
"""Send alert email with exception information."""
subject, html_content, html_content_err = self.get_email_subject_content(exception, task=task)
assert task.email
try:
send_email(task.email, subject, html_content)
except Exception:
send_email(task.email, subject, html_content_err)
[docs] def set_duration(self) -> None:
"""Set TI duration"""
if self.end_date and self.start_date:
self.duration = (self.end_date - self.start_date).total_seconds()
else:
self.duration = None
self.log.debug("Task Duration set to %s", self.duration)
def _record_task_map_for_downstreams(self, task: Operator, value: Any, *, session: Session) -> None:
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(self, 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)
@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``.
"""
if dag_id is None:
dag_id = self.dag_id
query = XCom.get_many(
key=key,
run_id=self.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)
query = query.order_by(None).order_by(XCom.map_index.asc())
return LazyXComAccess.build_from_xcom_query(query)
# At this point either task_ids or map_indexes is explicitly multi-value.
# Order return values to match task_ids and map_indexes ordering.
query = query.order_by(None)
if task_ids is None or isinstance(task_ids, str):
query = query.order_by(XCom.task_id)
else:
task_id_whens = {tid: i for i, tid in enumerate(task_ids)}
if task_id_whens:
query = query.order_by(case(task_id_whens, value=XCom.task_id))
else:
query = query.order_by(XCom.task_id)
if map_indexes is None or isinstance(map_indexes, int):
query = query.order_by(XCom.map_index)
elif isinstance(map_indexes, range):
order = XCom.map_index
if map_indexes.step < 0:
order = order.desc()
query = query.order_by(order)
else:
map_index_whens = {map_index: i for i, map_index in enumerate(map_indexes)}
if map_index_whens:
query = query.order_by(case(map_index_whens, value=XCom.map_index))
else:
query = query.order_by(XCom.map_index)
return LazyXComAccess.build_from_xcom_query(query)
@provide_session
[docs] def get_num_running_task_instances(self, session: Session) -> int:
"""Return Number of running TIs from the DB"""
# .count() is inefficient
return (
session.query(func.count())
.filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.state == State.RUNNING,
)
.scalar()
)
[docs] def init_run_context(self, raw: bool = False) -> None:
"""Sets the log context."""
self.raw = raw
self._set_context(self)
@staticmethod
[docs] def filter_for_tis(tis: Iterable[TaskInstance | TaskInstanceKey]) -> BooleanClauseList | None:
"""Returns 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 in dag_ids:
for cur_run_id in 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
[docs] def ti_selector_condition(cls, vals: Collection[str | tuple[str, int]]) -> ColumnOperators:
"""
Build an SQLAlchemy filter for a list where each element can contain
whether a task_id, or a tuple 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)
@Sentry.enrich_errors
@provide_session
[docs] def schedule_downstream_tasks(self, session=None):
"""
The mini-scheduler for scheduling downstream tasks of this task instance
:meta: private
"""
from sqlalchemy.exc import OperationalError
from airflow.models import DagRun
try:
# Re-select the row with a lock
dag_run = with_row_locks(
session.query(DagRun).filter_by(
dag_id=self.dag_id,
run_id=self.run_id,
),
session=session,
).one()
task = self.task
if TYPE_CHECKING:
assert task.dag
# Get a partial DAG with just the specific tasks we want to examine.
# In order for dep checks to work correctly, we include ourself (so
# TriggerRuleDep can check the state of the task we just executed).
partial_dag = task.dag.partial_subset(
task.downstream_task_ids,
include_downstream=True,
include_upstream=False,
include_direct_upstream=True,
)
dag_run.dag = partial_dag
info = dag_run.task_instance_scheduling_decisions(session)
skippable_task_ids = {
task_id for task_id in partial_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]
for schedulable_ti in schedulable_tis:
if not hasattr(schedulable_ti, "task"):
schedulable_ti.task = task.dag.get_task(schedulable_ti.task_id)
num = dag_run.schedule_tis(schedulable_tis, session=session)
self.log.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.
self.log.info(
"Skipping mini scheduling run due to exception: %s",
e.statement,
exc_info=True,
)
session.rollback()
[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).
"""
# 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
# This value should never be None since we already know the current task
# is in a mapped task group, and should have been expanded. The check
# exists mainly to satisfy Mypy.
if ti_count 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 _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."""
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, str]
[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_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_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 __eq__(self, other):
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_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(Base):
"""For storage of arbitrary notes concerning the task instance."""
[docs] __tablename__ = "task_instance_note"
[docs] user_id = Column(Integer, 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", mssql_clustered=True
),
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",
),
ForeignKeyConstraint(
(user_id,),
["ab_user.id"],
name="task_instance_note_user_fkey",
),
)
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.base_job import BaseJob
[docs] TaskInstance.queued_by_job = relationship(BaseJob)
