#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import contextlib
import hashlib
import logging
import math
import os
import pickle
import signal
import threading
import warnings
from collections import defaultdict
from datetime import datetime, timedelta
from functools import partial
from inspect import currentframe
from tempfile import NamedTemporaryFile
from types import TracebackType
from typing import IO, TYPE_CHECKING, Any, Iterable, List, NamedTuple, Optional, Tuple, Union
from urllib.parse import quote
import dill
import jinja2
import pendulum
from jinja2 import TemplateAssertionError, UndefinedError
from sqlalchemy import (
Column,
Float,
ForeignKeyConstraint,
Index,
Integer,
PickleType,
String,
and_,
func,
inspect,
or_,
tuple_,
)
from sqlalchemy.ext.associationproxy import association_proxy
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.sqltypes import BigInteger
from airflow import settings
from airflow.compat.functools import cache
from airflow.configuration import conf
from airflow.exceptions import (
AirflowException,
AirflowFailException,
AirflowRescheduleException,
AirflowSensorTimeout,
AirflowSkipException,
AirflowSmartSensorException,
AirflowTaskTimeout,
DagRunNotFound,
TaskDeferralError,
TaskDeferred,
)
from airflow.models.base import COLLATION_ARGS, ID_LEN, Base
from airflow.models.log import Log
from airflow.models.param import ParamsDict
from airflow.models.taskfail import TaskFail
from airflow.models.taskreschedule import TaskReschedule
from airflow.models.xcom import XCOM_RETURN_KEY, XCom
from airflow.plugins_manager import integrate_macros_plugins
from airflow.sentry import Sentry
from airflow.stats import Stats
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
from airflow.utils import timezone
from airflow.utils.context import ConnectionAccessor, Context, VariableAccessor
from airflow.utils.email import send_email
from airflow.utils.helpers import is_container, 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 create_session, provide_session
from airflow.utils.sqlalchemy import ExtendedJSON, UtcDateTime, with_row_locks
from airflow.utils.state import DagRunState, State, TaskInstanceState
from airflow.utils.timeout import timeout
try:
from kubernetes.client.api_client import ApiClient
from airflow.kubernetes.kube_config import KubeConfig
from airflow.kubernetes.pod_generator import PodGenerator
except ImportError:
_CURRENT_CONTEXT: List[Context] = []
[docs]log = logging.getLogger(__name__)
if TYPE_CHECKING:
from airflow.models.baseoperator import BaseOperator
from airflow.models.dag import DAG, DagModel, DagRun
_TASK_EXECUTION_FRAME_LOCAL_STORAGE = threading.local()
@contextlib.contextmanager
[docs]def set_current_context(context: Context) -> 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 load_error_file(fd: IO[bytes]) -> Optional[Union[str, Exception]]:
"""Load and return error from error file"""
if fd.closed:
return None
fd.seek(0, os.SEEK_SET)
data = fd.read()
if not data:
return None
try:
return pickle.loads(data)
except Exception:
return "Failed to load task run error"
[docs]def set_error_file(error_file: str, error: Union[str, Exception]) -> None:
"""Write error into error file by path"""
with open(error_file, "wb") as fd:
try:
pickle.dump(error, fd)
except Exception:
# local class objects cannot be pickled, so we fallback
# to store the string representation instead
pickle.dump(str(error), fd)
[docs]def clear_task_instances(
tis,
session,
activate_dag_runs=None,
dag=None,
dag_run_state: Union[DagRunState, Literal[False]] = DagRunState.QUEUED,
):
"""
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 = []
task_id_by_key = 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
session.merge(ti)
task_id_by_key[ti.dag_id][ti.run_id][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 and try_number. Use a nested dict of dag_id,
# run_id, try_number 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.try_number == try_number, TR.task_id.in_(task_ids))
for try_number, task_ids in task_tries.items()
),
)
for run_id, task_tries 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`",
DeprecationWarning,
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
[docs]class TaskInstanceKey(NamedTuple):
"""Key used to identify task instance."""
@property
[docs] def primary(self) -> Tuple[str, str, str]:
"""Return task instance primary key part of the key"""
return self.dag_id, self.task_id, self.run_id
@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))
[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)
@property
[docs] def key(self) -> "TaskInstanceKey":
"""For API-compatibly with TaskInstance.
Returns self
"""
return self
[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.
"""
[docs] __tablename__ = "task_instance"
[docs] task_id = Column(String(ID_LEN, **COLLATION_ARGS), primary_key=True, nullable=False)
[docs] dag_id = Column(String(ID_LEN, **COLLATION_ARGS), primary_key=True, nullable=False)
[docs] run_id = Column(String(ID_LEN, **COLLATION_ARGS), primary_key=True, nullable=False)
[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)
[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(PickleType(pickler=dill))
[docs] external_executor_id = Column(String(ID_LEN, **COLLATION_ARGS))
# The trigger to resume on if we are in state DEFERRED
[docs] trigger_id = Column(BigInteger)
# Optional timeout datetime for the trigger (past this, we'll fail)
[docs] trigger_timeout = Column(UtcDateTime)
# 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(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),
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",
primaryjoin="TaskInstance.trigger_id == Trigger.id",
foreign_keys=trigger_id,
uselist=False,
innerjoin=True,
)
[docs] dag_run = relationship("DagRun", back_populates="task_instances", lazy='joined', innerjoin=True)
[docs] execution_date = association_proxy("dag_run", "execution_date")
def __init__(
self, task, execution_date: Optional[datetime] = None, run_id: str = None, state: Optional[str] = None
):
super().__init__()
self.dag_id = task.dag_id
self.task_id = task.task_id
self.refresh_from_task(task)
self._log = logging.getLogger("airflow.task")
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",
DeprecationWarning,
# 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():
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 = ''
self.init_on_load()
# 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
@reconstructor
[docs] def init_on_load(self):
"""Initialize the attributes that aren't stored in the DB"""
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.
# TODO: whether we need sensing here or not (in sensor and task_instance state machine)
if self.state in State.running:
return self._try_number
return self._try_number + 1
@try_number.setter
def try_number(self, value):
self._try_number = value
@property
[docs] def prev_attempted_tries(self):
"""
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):
"""Setting Next Try Number"""
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: Union["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,
)
@staticmethod
[docs] def generate_command(
dag_id: str,
task_id: str,
run_id: str = None,
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: Optional[int] = None,
file_path: Optional[str] = None,
raw: bool = False,
job_id: Optional[str] = None,
pool: Optional[str] = None,
cfg_path: Optional[str] = None,
) -> List[str]:
"""
Generates the shell command required to execute this task instance.
:param dag_id: DAG ID
:type dag_id: str
:param task_id: Task ID
:type task_id: str
:param run_id: The run_id of this task's DagRun
:type run_id: datetime
:param mark_success: Whether to mark the task as successful
:type mark_success: bool
:param ignore_all_deps: Ignore all ignorable dependencies.
Overrides the other ignore_* parameters.
:type ignore_all_deps: bool
:param ignore_depends_on_past: Ignore depends_on_past parameter of DAGs
(e.g. for Backfills)
:type ignore_depends_on_past: bool
:param ignore_task_deps: Ignore task-specific dependencies such as depends_on_past
and trigger rule
:type ignore_task_deps: bool
:param ignore_ti_state: Ignore the task instance's previous failure/success
:type ignore_ti_state: bool
:param local: Whether to run the task locally
:type local: bool
:param pickle_id: If the DAG was serialized to the DB, the ID
associated with the pickled DAG
:type pickle_id: Optional[int]
:param file_path: path to the file containing the DAG definition
:type file_path: Optional[str]
:param raw: raw mode (needs more details)
:type raw: Optional[bool]
:param job_id: job ID (needs more details)
:type job_id: Optional[int]
:param pool: the Airflow pool that the task should run in
:type pool: Optional[str]
:param cfg_path: the Path to the configuration file
:type cfg_path: Optional[str]
:return: shell command that can be used to run the task instance
:rtype: list[str]
"""
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])
return cmd
@property
[docs] def log_url(self):
"""Log URL for TaskInstance"""
iso = quote(self.execution_date.isoformat())
base_url = conf.get('webserver', 'BASE_URL')
return base_url + f"/log?execution_date={iso}&task_id={self.task_id}&dag_id={self.dag_id}"
@property
[docs] def mark_success_url(self):
"""URL to mark TI success"""
iso = quote(self.execution_date.isoformat())
base_url = conf.get('webserver', 'BASE_URL')
return base_url + (
"/confirm"
f"?task_id={self.task_id}"
f"&dag_id={self.dag_id}"
f"&execution_date={iso}"
"&upstream=false"
"&downstream=false"
"&state=success"
)
@provide_session
[docs] def current_state(self, session=None) -> 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
:type session: Session
"""
ti = (
session.query(TaskInstance)
.filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.run_id == self.run_id,
)
.all()
)
if ti:
state = ti[0].state
else:
state = None
return state
@provide_session
[docs] def error(self, session=None):
"""
Forces the task instance's state to FAILED in the database.
:param session: SQLAlchemy ORM Session
:type session: 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=None, lock_for_update=False) -> None:
"""
Refreshes the task instance from the database based on the primary key
:param session: SQLAlchemy ORM Session
:type session: 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.
:type lock_for_update: bool
"""
self.log.debug("Refreshing TaskInstance %s from DB", self)
qry = session.query(TaskInstance).filter(
TaskInstance.dag_id == self.dag_id,
TaskInstance.task_id == self.task_id,
TaskInstance.run_id == self.run_id,
)
if lock_for_update:
for attempt in run_with_db_retries(logger=self.log):
with attempt:
ti: Optional[TaskInstance] = qry.with_for_update().first()
else:
ti = qry.first()
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
# Get the raw value of try_number column, don't read through the
# accessor here otherwise it will be incremented by one already.
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
self.log.debug("Refreshed TaskInstance %s", self)
[docs] def refresh_from_task(self, task, pool_override=None):
"""
Copy common attributes from the given task.
:param task: The task object to copy from
:type task: airflow.models.BaseOperator
:param pool_override: Use the pool_override instead of task's pool
:type pool_override: str
"""
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=None):
"""
Clears all XCom data from the database for the task instance
:param session: SQLAlchemy ORM Session
:type session: Session
"""
self.log.debug("Clearing XCom data")
XCom.clear(
dag_id=self.dag_id,
task_id=self.task_id,
execution_date=self.execution_date,
session=session,
)
self.log.debug("XCom data cleared")
@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)
@provide_session
[docs] def set_state(self, state: str, session=None):
"""
Set TaskInstance state.
:param state: State to set for the TI
:type state: str
:param session: SQLAlchemy ORM Session
:type session: Session
"""
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)
@property
[docs] def is_premature(self):
"""
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=None):
"""
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
:type session: 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: Optional[str] = None,
session: Optional[Session] = None,
) -> Optional["DagRun"]:
"""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)
# LEGACY: most likely running from unit tests
if not dr:
# Means that this TaskInstance is NOT being run from a DR, but from a catchup
try:
# XXX: This uses DAG internals, but as the outer comment
# said, the block is only reached for legacy reasons for
# development code, so that's OK-ish.
schedule = dag.timetable._schedule
except AttributeError:
return None
dt = pendulum.instance(self.execution_date)
return TaskInstance(
task=self.task,
execution_date=schedule.get_prev(dt),
)
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: Optional[str] = None, session: Session = None
) -> Optional['TaskInstance']:
"""
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):
"""
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.
""",
DeprecationWarning,
stacklevel=2,
)
return self.get_previous_ti()
@property
[docs] def previous_ti_success(self) -> Optional['TaskInstance']:
"""
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.
""",
DeprecationWarning,
stacklevel=2,
)
return self.get_previous_ti(state=State.SUCCESS)
@provide_session
[docs] def get_previous_execution_date(
self,
state: Optional[str] = None,
session: Session = None,
) -> Optional[pendulum.DateTime]:
"""
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: Optional[str] = None, session: Session = None
) -> Optional[pendulum.DateTime]:
"""
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) -> Optional[pendulum.DateTime]:
"""
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.
""",
DeprecationWarning,
stacklevel=2,
)
return self.get_previous_start_date(state=State.SUCCESS)
@provide_session
[docs] def are_dependencies_met(self, dep_context=None, session=None, verbose=False):
"""
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.
:type dep_context: DepContext
:param session: database session
:type session: sqlalchemy.orm.session.Session
:param verbose: whether log details on failed dependencies on
info or debug log level
:type verbose: bool
"""
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=None, session=None):
"""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):
return f"<TaskInstance: {self.dag_id}.{self.task_id} {self.run_id} [{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(
"{}#{}#{}#{}".format(
self.dag_id, self.task_id, self.execution_date, self.try_number
).encode('utf-8')
).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):
"""
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 = None):
"""
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: Optional[str] = None,
pool: Optional[str] = None,
external_executor_id: Optional[str] = None,
session=None,
) -> 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
:type verbose: bool
:param ignore_all_deps: Ignore all of the non-critical dependencies, just runs
:type ignore_all_deps: bool
:param ignore_depends_on_past: Ignore depends_on_past DAG attribute
:type ignore_depends_on_past: bool
:param ignore_task_deps: Don't check the dependencies of this TaskInstance's task
:type ignore_task_deps: bool
:param ignore_ti_state: Disregards previous task instance state
:type ignore_ti_state: bool
:param mark_success: Don't run the task, mark its state as success
:type mark_success: bool
:param test_mode: Doesn't record success or failure in the DB
:type test_mode: bool
:param job_id: Job (BackfillJob / LocalTaskJob / SchedulerJob) ID
:type job_id: str
:param pool: specifies the pool to use to run the task instance
:type pool: str
:param external_executor_id: The identifier of the celery executor
:type external_executor_id: str
:param session: SQLAlchemy ORM Session
:type session: Session
:return: whether the state was changed to running or not
:rtype: bool
"""
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
self.start_date = 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)
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):
result = getattr(self, attr, None) # type: datetime
return result.strftime('%Y%m%dT%H%M%S') if result else ''
def _log_state(self, lead_msg: str = ''):
self.log.info(
'%sMarking task as %s.'
+ ' dag_id=%s, task_id=%s,'
+ ' execution_date=%s, start_date=%s, end_date=%s',
lead_msg,
self.state.upper(),
self.dag_id,
self.task_id,
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):
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: Optional[str] = None,
pool: Optional[str] = None,
error_file: Optional[str] = None,
session=None,
) -> 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
:type mark_success: bool
:param test_mode: Doesn't record success or failure in the DB
:type test_mode: bool
:param pool: specifies the pool to use to run the task instance
:type pool: str
:param session: SQLAlchemy ORM Session
:type session: 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}')
try:
if not mark_success:
self.task = self.task.prepare_for_execution()
context = self.get_template_context(ignore_param_exceptions=False)
self._execute_task_with_callbacks(context)
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 AirflowSmartSensorException as e:
self.log.info(e)
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, force_fail=True, error_file=error_file, 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, error_file=error_file, session=session)
session.commit()
raise
except (Exception, KeyboardInterrupt) as e:
self.handle_failure(e, test_mode, error_file=error_file, session=session)
session.commit()
raise
finally:
Stats.incr(f'ti.finish.{self.task.dag_id}.{self.task.task_id}.{self.state}')
# Recording SKIPPED or SUCCESS
self.clear_next_method_args()
self.end_date = timezone.utcnow()
self._log_state()
self.set_duration()
if not test_mode:
session.add(Log(self.state, self))
session.merge(self)
session.commit()
def _execute_task_with_callbacks(self, context):
"""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
self.clear_xcom_data()
with Stats.timer(f'dag.{self.task.dag_id}.{self.task.task_id}.duration'):
self.render_templates(context=context)
RenderedTaskInstanceFields.write(RenderedTaskInstanceFields(ti=self, render_templates=False))
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)
self.log.info(
"Exporting the following env vars:\n%s",
'\n'.join(f"{k}={v}" for k, v in airflow_context_vars.items()),
)
os.environ.update(airflow_context_vars)
# Run pre_execute callback
self.task.pre_execute(context=context)
# Run on_execute callback
self._run_execute_callback(context, self.task)
if self.task.is_smart_sensor_compatible():
# Try to register it in the smart sensor service.
registered = False
try:
registered = self.task.register_in_sensor_service(self, context)
except Exception:
self.log.warning(
"Failed to register in sensor service."
" Continue to run task in non smart sensor mode.",
exc_info=True,
)
if registered:
# Will raise AirflowSmartSensorException to avoid long running execution.
self._update_ti_state_for_sensing()
# Execute the task
with set_current_context(context):
result = self._execute_task(context, self.task)
# 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')
@provide_session
def _update_ti_state_for_sensing(self, session=None):
self.log.info('Submitting %s to sensor service', self)
self.state = State.SENSING
self.start_date = timezone.utcnow()
session.merge(self)
session.commit()
# Raise exception for sensing state
raise AirflowSmartSensorException("Task successfully registered in smart sensor.")
def _execute_task(self, context, task_copy):
"""Executes Task (optionally with a Timeout) and pushes Xcom results"""
# 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_copy, self.next_method)
if self.next_kwargs:
execute_callable = partial(execute_callable, **self.next_kwargs)
else:
execute_callable = task_copy.execute
# If a timeout is specified for the task, make it fail
# if it goes beyond
try:
if task_copy.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_copy.execution_timeout - (timezone.utcnow() - self.start_date)
).total_seconds()
else:
timeout_seconds = task_copy.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_copy.on_kill()
raise
else:
result = execute_callable(context=context)
except: # noqa: E722
_TASK_EXECUTION_FRAME_LOCAL_STORAGE.frame = currentframe()
raise
# If the task returns a result, push an XCom containing it
if task_copy.do_xcom_push and result is not None:
self.xcom_push(key=XCOM_RETURN_KEY, value=result)
return result
@provide_session
def _defer_task(self, session, defer: TaskDeferred):
"""
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
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):
"""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")
def _run_finished_callback(self, error: Optional[Union[str, Exception]] = None) -> None:
"""
Call callback defined for finished state change.
NOTE: Only invoke this function from caller of self._run_raw_task or
self.run
"""
if self.state == State.FAILED:
task = self.task
if task.on_failure_callback is not None:
context = self.get_template_context()
context["exception"] = error
try:
task.on_failure_callback(context)
except Exception:
self.log.exception("Error when executing on_failure_callback")
elif self.state == State.SUCCESS:
task = self.task
if task.on_success_callback is not None:
context = self.get_template_context()
try:
task.on_success_callback(context)
except Exception:
self.log.exception("Error when executing on_success_callback")
elif self.state == State.UP_FOR_RETRY:
task = self.task
if task.on_retry_callback is not None:
context = self.get_template_context()
context["exception"] = error
try:
task.on_retry_callback(context)
except Exception:
self.log.exception("Error when executing on_retry_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: Optional[str] = None,
pool: Optional[str] = None,
session=None,
) -> 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
try:
error_fd = NamedTemporaryFile(delete=True)
self._run_raw_task(
mark_success=mark_success,
test_mode=test_mode,
job_id=job_id,
pool=pool,
error_file=error_fd.name,
session=session,
)
finally:
error = None if self.state == State.SUCCESS else load_error_file(error_fd)
error_fd.close()
self._run_finished_callback(error=error)
[docs] def dry_run(self):
"""Only Renders Templates for the TI"""
task = self.task
task_copy = task.prepare_for_execution()
self.task = task_copy
self.render_templates()
task_copy.dry_run()
@provide_session
def _handle_reschedule(self, actual_start_date, reschedule_exception, test_mode=False, session=None):
# 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,
)
)
# 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')
[docs] def get_truncated_error_traceback(self, error: BaseException) -> Optional[TracebackType]:
"""
Returns truncated error traceback.
This method returns traceback of the error truncated to the
frame saved by earlier try/except along the way. If the frame
is found, the traceback will be truncated to below the frame.
:param error: exception to get traceback from
:return: traceback to print
"""
tb = error.__traceback__
try:
execution_frame = _TASK_EXECUTION_FRAME_LOCAL_STORAGE.frame
except AttributeError:
self.log.warning(
"We expected to get frame set in local storage but it was not."
" Please report this as an issue with full logs"
" at https://github.com/apache/airflow/issues/new",
exc_info=True,
)
return tb
_TASK_EXECUTION_FRAME_LOCAL_STORAGE.frame = None
while tb is not None:
if tb.tb_frame is execution_frame:
tb = tb.tb_next
break
tb = tb.tb_next
return tb or error.__traceback__
@provide_session
[docs] def handle_failure(
self,
error: Union[str, Exception],
test_mode: Optional[bool] = None,
force_fail: bool = False,
error_file: Optional[str] = None,
session=None,
) -> None:
"""Handle Failure for the TaskInstance"""
if test_mode is None:
test_mode = self.test_mode
if error:
if isinstance(error, Exception):
tb = self.get_truncated_error_traceback(error)
self.log.error("Task failed with exception", exc_info=(type(error), error, tb))
else:
self.log.error("%s", error)
# external monitoring process provides pickle file so _run_raw_task
# can send its runtime errors for access by failure callback
if error_file:
set_error_file(error_file, error)
if not test_mode:
self.refresh_from_db(session)
task = self.task
self.end_date = timezone.utcnow()
self.set_duration()
Stats.incr(f'operator_failures_{task.task_type}', 1, 1)
Stats.incr('ti_failures')
if not test_mode:
session.add(Log(State.FAILED, self))
# Log failure duration
dag_run = self.get_dagrun(session=session) # self.dag_run not populated by refresh_from_db
session.add(TaskFail(task, dag_run.execution_date, self.start_date, self.end_date))
self.clear_next_method_args()
# 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
if force_fail or not self.is_eligible_to_retry():
self.state = State.FAILED
email_for_state = task.email_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 = task.email_on_retry
self._log_state('Immediate failure requested. ' if force_fail else '')
if email_for_state and task.email:
try:
self.email_alert(error)
except Exception:
self.log.exception('Failed to send email to: %s', task.email)
if not test_mode:
session.merge(self)
session.flush()
@provide_session
[docs] def handle_failure_with_callback(
self,
error: Union[str, Exception],
test_mode: Optional[bool] = None,
force_fail: bool = False,
session=None,
) -> None:
self.handle_failure(error=error, test_mode=test_mode, force_fail=force_fail, session=session)
self._run_finished_callback(error=error)
[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
return self.task.retries and self.try_number <= self.max_tries
[docs] def get_template_context(self, session: Session = None, 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
integrate_macros_plugins()
task: "BaseOperator" = self.task
dag: DAG = task.dag
dag_run = self.get_dagrun(session)
data_interval = dag.get_run_data_interval(dag_run)
# Validates Params and convert them into a simple dict.
params = ParamsDict(suppress_exception=ignore_param_exceptions)
with contextlib.suppress(AttributeError):
params.update(dag.params)
if task.params:
params.update(task.params)
if conf.getboolean('core', 'dag_run_conf_overrides_params'):
self.overwrite_params_with_dag_run_conf(params=params, dag_run=dag_run)
task.params = params.validate()
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() -> Optional["DagRun"]:
return self.get_previous_dagrun(state=State.SUCCESS, session=session)
def _get_previous_dagrun_data_interval_success() -> Optional["DataInterval"]:
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() -> Optional[pendulum.DateTime]:
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() -> Optional[pendulum.DateTime]:
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() -> Optional[pendulum.DateTime]:
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() -> Optional[pendulum.DateTime]:
# 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
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() -> Optional[str]:
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() -> Optional[str]:
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", DeprecationWarning)
return dag.previous_schedule(logical_date)
@cache
def get_prev_ds() -> Optional[str]:
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() -> Optional[str]:
prev_ds = get_prev_ds()
if prev_ds is None:
return None
return prev_ds.replace('-', '')
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,
'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': task.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=State.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(),
'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(),
}
return Context(context)
@provide_session
[docs] def get_rendered_template_fields(self, session=None):
"""Fetch rendered template fields from DB"""
from airflow.models.renderedtifields import RenderedTaskInstanceFields
rendered_task_instance_fields = RenderedTaskInstanceFields.get_templated_fields(self, session=session)
if rendered_task_instance_fields:
for field_name, rendered_value in rendered_task_instance_fields.items():
setattr(self.task, field_name, rendered_value)
else:
try:
self.render_templates()
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=None):
"""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: Optional[Context] = None) -> None:
"""Render templates in the operator fields."""
if not context:
context = self.get_template_context()
self.task.render_template_fields(context)
[docs] def render_k8s_pod_yaml(self) -> Optional[dict]:
"""Render k8s pod yaml"""
from airflow.kubernetes.kubernetes_helper_functions import create_pod_id # Circular import
kube_config = KubeConfig()
pod = PodGenerator.construct_pod(
dag_id=self.dag_id,
task_id=self.task_id,
pod_id=create_pod_id(self.dag_id, self.task_id),
try_number=self.try_number,
kube_image=kube_config.kube_image,
date=self.execution_date,
args=self.command_as_list(),
pod_override_object=PodGenerator.from_obj(self.executor_config),
scheduler_job_id="worker-config",
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) -> Tuple[str, str, str]:
"""Get the email subject content for exceptions."""
# For a ti from DB (without ti.task), return the default value
# Reuse it for smart sensor to send default email alert
use_default = not hasattr(self, 'task')
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 = {
"exception": exception,
"exception_html": exception_html,
"try_number": current_try_number,
"max_tries": self.max_tries,
}
if use_default:
jinja_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(**jinja_context)
html_content = jinja_env.from_string(default_html_content).render(**jinja_context)
html_content_err = jinja_env.from_string(default_html_content_err).render(**jinja_context)
else:
jinja_context = self.get_template_context()
jinja_context.update(additional_context)
jinja_env = self.task.get_template_env()
def render(key: str, content: str) -> str:
if conf.has_option('email', key):
path = conf.get('email', key)
with open(path) as f:
content = f.read()
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):
"""Send alert email with exception information."""
subject, html_content, html_content_err = self.get_email_subject_content(exception)
try:
send_email(self.task.email, subject, html_content)
except Exception:
send_email(self.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)
@provide_session
[docs] def xcom_push(
self,
key: str,
value: Any,
execution_date: Optional[datetime] = None,
session: Session = None,
) -> None:
"""
Make an XCom available for tasks to pull.
:param key: A key for the XCom
:type key: str
:param value: A value for the XCom. The value is pickled and stored
in the database.
:type value: any picklable object
:param execution_date: if provided, the XCom will not be visible until
this date. This can be used, for example, to send a message to a
task on a future date without it being immediately visible.
:type execution_date: datetime
:param session: Sqlalchemy ORM Session
:type session: Session
"""
self_execution_date = self.get_dagrun(session).execution_date
if execution_date and execution_date < self_execution_date:
raise ValueError(
'execution_date can not be in the past (current '
'execution_date is {}; received {})'.format(self_execution_date, execution_date)
)
XCom.set(
key=key,
value=value,
task_id=self.task_id,
dag_id=self.dag_id,
execution_date=execution_date or self_execution_date,
session=session,
)
@provide_session
[docs] def xcom_pull(
self,
task_ids: Optional[Union[str, Iterable[str]]] = None,
dag_id: Optional[str] = None,
key: str = XCOM_RETURN_KEY,
include_prior_dates: bool = False,
session: Session = None,
) -> Any:
"""
Pull XComs that optionally meet certain criteria.
The default value for `key` limits the search to XComs
that were returned by other tasks (as opposed to those that were pushed
manually). To remove this filter, pass key=None (or any desired value).
If a single task_id string is provided, the result is the value of the
most recent matching XCom from that task_id. If multiple task_ids are
provided, a tuple of matching values is returned. None is returned
whenever no matches are found.
: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 a 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 key=None.
:type key: str
:param task_ids: Only XComs from tasks with matching ids will be
pulled. Can pass None to remove the filter.
:type task_ids: str or iterable of strings (representing task_ids)
:param dag_id: If provided, only pulls XComs from this DAG.
If None (default), the DAG of the calling task is used.
:type dag_id: str
: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.
:type include_prior_dates: bool
:param session: Sqlalchemy ORM Session
:type session: Session
"""
if dag_id is None:
dag_id = self.dag_id
execution_date = self.get_dagrun(session).execution_date
query = XCom.get_many(
execution_date=execution_date,
key=key,
dag_ids=dag_id,
task_ids=task_ids,
include_prior_dates=include_prior_dates,
session=session,
)
# Since we're only fetching the values field, and not the
# whole class, the @recreate annotation does not kick in.
# Therefore we need to deserialize the fields by ourselves.
if is_container(task_ids):
vals_kv = {
result.task_id: XCom.deserialize_value(result)
for result in query.with_entities(XCom.task_id, XCom.value)
}
values_ordered_by_id = [vals_kv.get(task_id) for task_id in task_ids]
return values_ordered_by_id
else:
xcom = query.with_entities(XCom.value).first()
if xcom:
return XCom.deserialize_value(xcom)
@provide_session
[docs] def get_num_running_task_instances(self, session):
"""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=False):
"""Sets the log context."""
self.raw = raw
self._set_context(self)
@staticmethod
[docs] def filter_for_tis(tis: Iterable[Union["TaskInstance", TaskInstanceKey]]) -> Optional[BooleanClauseList]:
"""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
first_task_id = first.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 for huge numbers of TIs (20k+)
if all(t.dag_id == dag_id and t.run_id == run_id for t in tis):
return and_(
TaskInstance.dag_id == dag_id,
TaskInstance.run_id == run_id,
TaskInstance.task_id.in_(t.task_id for t in tis),
)
if all(t.dag_id == dag_id and t.task_id == first_task_id for t in tis):
return and_(
TaskInstance.dag_id == dag_id,
TaskInstance.run_id.in_(t.run_id for t in tis),
TaskInstance.task_id == first_task_id,
)
if settings.Session.bind.dialect.name == 'mssql':
return or_(
and_(
TaskInstance.dag_id == ti.dag_id,
TaskInstance.task_id == ti.task_id,
TaskInstance.run_id == ti.run_id,
)
for ti in tis
)
else:
return tuple_(TaskInstance.dag_id, TaskInstance.task_id, TaskInstance.run_id).in_(
[ti.key.primary for ti in tis]
)
# 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, ti: TaskInstance):
self._dag_id: str = ti.dag_id
self._task_id: str = ti.task_id
self._run_id: datetime = ti.run_id
self._start_date: datetime = ti.start_date
self._end_date: datetime = ti.end_date
self._try_number: int = ti.try_number
self._state: str = ti.state
self._executor_config: Any = ti.executor_config
self._run_as_user: Optional[str] = None
if hasattr(ti, 'run_as_user'):
self._run_as_user = ti.run_as_user
self._pool: str = ti.pool
self._priority_weight: Optional[int] = None
if hasattr(ti, 'priority_weight'):
self._priority_weight = ti.priority_weight
self._queue: str = ti.queue
self._key = ti.key
@property
[docs] def dag_id(self) -> str:
return self._dag_id
@property
[docs] def task_id(self) -> str:
return self._task_id
@property
[docs] def run_id(self) -> str:
return self._run_id
@property
[docs] def start_date(self) -> datetime:
return self._start_date
@property
[docs] def end_date(self) -> datetime:
return self._end_date
@property
[docs] def try_number(self) -> int:
return self._try_number
@property
[docs] def state(self) -> str:
return self._state
@property
[docs] def pool(self) -> str:
return self._pool
@property
[docs] def priority_weight(self) -> Optional[int]:
return self._priority_weight
@property
[docs] def queue(self) -> str:
return self._queue
@property
[docs] def key(self) -> TaskInstanceKey:
return self._key
@property
[docs] def executor_config(self):
return self._executor_config
STATICA_HACK = True
globals()['kcah_acitats'[::-1].upper()] = False
if STATICA_HACK: # pragma: no cover
from airflow.job.base_job import BaseJob
[docs] TaskInstance.queued_by_job = relationship(BaseJob)