airflow celery_executor 源码
airflow celery_executor 代码
文件路径：/airflow/executors/celery_executor.py
#
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# 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.
"""CeleryExecutor
.. seealso::
For more information on how the CeleryExecutor works, take a look at the guide:
:ref:`executor:CeleryExecutor`
"""
from __future__ import annotations
import datetime
import logging
import math
import operator
import os
import subprocess
import time
import traceback
from collections import Counter
from concurrent.futures import ProcessPoolExecutor
from enum import Enum
from multiprocessing import cpu_count
from typing import Any, Mapping, MutableMapping, Optional, Sequence, Tuple
from celery import Celery, Task, states as celery_states
from celery.backends.base import BaseKeyValueStoreBackend
from celery.backends.database import DatabaseBackend, Task as TaskDb, session_cleanup
from celery.result import AsyncResult
from celery.signals import import_modules as celery_import_modules
from setproctitle import setproctitle
from sqlalchemy.orm.session import Session
import airflow.settings as settings
from airflow.config_templates.default_celery import DEFAULT_CELERY_CONFIG
from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowTaskTimeout
from airflow.executors.base_executor import BaseExecutor, CommandType, EventBufferValueType, TaskTuple
from airflow.models.taskinstance import TaskInstance, TaskInstanceKey
from airflow.stats import Stats
from airflow.utils.dag_parsing_context import _airflow_parsing_context_manager
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.net import get_hostname
from airflow.utils.session import NEW_SESSION, provide_session
from airflow.utils.state import State
from airflow.utils.timeout import timeout
from airflow.utils.timezone import utcnow
log = logging.getLogger(__name__)
# Make it constant for unit test.
CELERY_FETCH_ERR_MSG_HEADER = 'Error fetching Celery task state'
CELERY_SEND_ERR_MSG_HEADER = 'Error sending Celery task'
OPERATION_TIMEOUT = conf.getfloat('celery', 'operation_timeout', fallback=1.0)
'''
To start the celery worker, run the command:
airflow celery worker
'''
if conf.has_option('celery', 'celery_config_options'):
celery_configuration = conf.getimport('celery', 'celery_config_options')
else:
celery_configuration = DEFAULT_CELERY_CONFIG
app = Celery(conf.get('celery', 'CELERY_APP_NAME'), config_source=celery_configuration)
@app.task
def execute_command(command_to_exec: CommandType) -> None:
"""Executes command."""
dag_id, task_id = BaseExecutor.validate_airflow_tasks_run_command(command_to_exec)
celery_task_id = app.current_task.request.id
log.info("[%s] Executing command in Celery: %s", celery_task_id, command_to_exec)
with _airflow_parsing_context_manager(dag_id=dag_id, task_id=task_id):
try:
if settings.EXECUTE_TASKS_NEW_PYTHON_INTERPRETER:
_execute_in_subprocess(command_to_exec, celery_task_id)
else:
_execute_in_fork(command_to_exec, celery_task_id)
except Exception:
Stats.incr("celery.execute_command.failure")
raise
def _execute_in_fork(command_to_exec: CommandType, celery_task_id: str | None = None) -> None:
pid = os.fork()
if pid:
# In parent, wait for the child
pid, ret = os.waitpid(pid, 0)
if ret == 0:
return
msg = f'Celery command failed on host: {get_hostname()} with celery_task_id {celery_task_id}'
raise AirflowException(msg)
from airflow.sentry import Sentry
ret = 1
try:
from airflow.cli.cli_parser import get_parser
settings.engine.pool.dispose()
settings.engine.dispose()
parser = get_parser()
# [1:] - remove "airflow" from the start of the command
args = parser.parse_args(command_to_exec[1:])
args.shut_down_logging = False
if celery_task_id:
args.external_executor_id = celery_task_id
setproctitle(f"airflow task supervisor: {command_to_exec}")
args.func(args)
ret = 0
except Exception as e:
log.exception("[%s] Failed to execute task %s.", celery_task_id, str(e))
ret = 1
finally:
Sentry.flush()
logging.shutdown()
os._exit(ret)
def _execute_in_subprocess(command_to_exec: CommandType, celery_task_id: str | None = None) -> None:
env = os.environ.copy()
if celery_task_id:
env["external_executor_id"] = celery_task_id
try:
subprocess.check_output(command_to_exec, stderr=subprocess.STDOUT, close_fds=True, env=env)
except subprocess.CalledProcessError as e:
log.exception('[%s] execute_command encountered a CalledProcessError', celery_task_id)
log.error(e.output)
msg = f'Celery command failed on host: {get_hostname()} with celery_task_id {celery_task_id}'
raise AirflowException(msg)
class ExceptionWithTraceback:
"""
Wrapper class used to propagate exceptions to parent processes from subprocesses.
:param exception: The exception to wrap
:param exception_traceback: The stacktrace to wrap
"""
def __init__(self, exception: Exception, exception_traceback: str):
self.exception = exception
self.traceback = exception_traceback
# Task instance that is sent over Celery queues
# TaskInstanceKey, Command, queue_name, CallableTask
TaskInstanceInCelery = Tuple[TaskInstanceKey, CommandType, Optional[str], Task]
def send_task_to_executor(
task_tuple: TaskInstanceInCelery,
) -> tuple[TaskInstanceKey, CommandType, AsyncResult | ExceptionWithTraceback]:
"""Sends task to executor."""
key, command, queue, task_to_run = task_tuple
try:
with timeout(seconds=OPERATION_TIMEOUT):
result = task_to_run.apply_async(args=[command], queue=queue)
except Exception as e:
exception_traceback = f"Celery Task ID: {key}\n{traceback.format_exc()}"
result = ExceptionWithTraceback(e, exception_traceback)
return key, command, result
@celery_import_modules.connect
def on_celery_import_modules(*args, **kwargs):
"""
Preload some "expensive" airflow modules so that every task process doesn't have to import it again and
again.
Loading these for each task adds 0.3-0.5s *per task* before the task can run. For long running tasks this
doesn't matter, but for short tasks this starts to be a noticeable impact.
"""
import jinja2.ext # noqa: F401
import airflow.jobs.local_task_job
import airflow.macros
import airflow.operators.bash
import airflow.operators.python
import airflow.operators.subdag # noqa: F401
try:
import numpy # noqa: F401
except ImportError:
pass
try:
import kubernetes.client # noqa: F401
except ImportError:
pass
class _CeleryPendingTaskTimeoutType(Enum):
ADOPTED = 1
STALLED = 2
class CeleryExecutor(BaseExecutor):
"""
CeleryExecutor is recommended for production use of Airflow. It allows
distributing the execution of task instances to multiple worker nodes.
Celery is a simple, flexible and reliable distributed system to process
vast amounts of messages, while providing operations with the tools
required to maintain such a system.
"""
supports_ad_hoc_ti_run: bool = True
def __init__(self):
super().__init__()
# Celery doesn't support bulk sending the tasks (which can become a bottleneck on bigger clusters)
# so we use a multiprocessing pool to speed this up.
# How many worker processes are created for checking celery task state.
self._sync_parallelism = conf.getint('celery', 'SYNC_PARALLELISM')
if self._sync_parallelism == 0:
self._sync_parallelism = max(1, cpu_count() - 1)
self.bulk_state_fetcher = BulkStateFetcher(self._sync_parallelism)
self.tasks = {}
self.stalled_task_timeouts: dict[TaskInstanceKey, datetime.datetime] = {}
self.stalled_task_timeout = datetime.timedelta(
seconds=conf.getint('celery', 'stalled_task_timeout', fallback=0)
)
self.adopted_task_timeouts: dict[TaskInstanceKey, datetime.datetime] = {}
self.task_adoption_timeout = (
datetime.timedelta(seconds=conf.getint('celery', 'task_adoption_timeout', fallback=600))
or self.stalled_task_timeout
)
self.task_publish_retries: Counter[TaskInstanceKey] = Counter()
self.task_publish_max_retries = conf.getint('celery', 'task_publish_max_retries', fallback=3)
def start(self) -> None:
self.log.debug('Starting Celery Executor using %s processes for syncing', self._sync_parallelism)
def _num_tasks_per_send_process(self, to_send_count: int) -> int:
"""
How many Celery tasks should each worker process send.
:return: Number of tasks that should be sent per process
:rtype: int
"""
return max(1, int(math.ceil(1.0 * to_send_count / self._sync_parallelism)))
def _process_tasks(self, task_tuples: list[TaskTuple]) -> None:
task_tuples_to_send = [task_tuple[:3] + (execute_command,) for task_tuple in task_tuples]
first_task = next(t[3] for t in task_tuples_to_send)
# Celery state queries will stuck if we do not use one same backend
# for all tasks.
cached_celery_backend = first_task.backend
key_and_async_results = self._send_tasks_to_celery(task_tuples_to_send)
self.log.debug('Sent all tasks.')
for key, _, result in key_and_async_results:
if isinstance(result, ExceptionWithTraceback) and isinstance(
result.exception, AirflowTaskTimeout
):
retries = self.task_publish_retries[key]
if retries < self.task_publish_max_retries:
Stats.incr("celery.task_timeout_error")
self.log.info(
"[Try %s of %s] Task Timeout Error for Task: (%s).",
self.task_publish_retries[key] + 1,
self.task_publish_max_retries,
key,
)
self.task_publish_retries[key] = retries + 1
continue
self.queued_tasks.pop(key)
self.task_publish_retries.pop(key, None)
if isinstance(result, ExceptionWithTraceback):
self.log.error(CELERY_SEND_ERR_MSG_HEADER + ": %s\n%s\n", result.exception, result.traceback)
self.event_buffer[key] = (State.FAILED, None)
elif result is not None:
result.backend = cached_celery_backend
self.running.add(key)
self.tasks[key] = result
self._set_celery_pending_task_timeout(key, _CeleryPendingTaskTimeoutType.STALLED)
# Store the Celery task_id in the event buffer. This will get "overwritten" if the task
# has another event, but that is fine, because the only other events are success/failed at
# which point we don't need the ID anymore anyway
self.event_buffer[key] = (State.QUEUED, result.task_id)
# If the task runs _really quickly_ we may already have a result!
self.update_task_state(key, result.state, getattr(result, 'info', None))
def _send_tasks_to_celery(self, task_tuples_to_send: list[TaskInstanceInCelery]):
if len(task_tuples_to_send) == 1 or self._sync_parallelism == 1:
# One tuple, or max one process -> send it in the main thread.
return list(map(send_task_to_executor, task_tuples_to_send))
# Use chunks instead of a work queue to reduce context switching
# since tasks are roughly uniform in size
chunksize = self._num_tasks_per_send_process(len(task_tuples_to_send))
num_processes = min(len(task_tuples_to_send), self._sync_parallelism)
with ProcessPoolExecutor(max_workers=num_processes) as send_pool:
key_and_async_results = list(
send_pool.map(send_task_to_executor, task_tuples_to_send, chunksize=chunksize)
)
return key_and_async_results
def sync(self) -> None:
if not self.tasks:
self.log.debug("No task to query celery, skipping sync")
return
self.update_all_task_states()
self._check_for_timedout_adopted_tasks()
self._check_for_stalled_tasks()
def _check_for_timedout_adopted_tasks(self) -> None:
timedout_keys = self._get_timedout_ti_keys(self.adopted_task_timeouts)
if timedout_keys:
self.log.error(
"Adopted tasks were still pending after %s, assuming they never made it to celery "
"and sending back to the scheduler:\n\t%s",
self.task_adoption_timeout,
"\n\t".join(repr(x) for x in timedout_keys),
)
self._send_stalled_tis_back_to_scheduler(timedout_keys)
def _check_for_stalled_tasks(self) -> None:
timedout_keys = self._get_timedout_ti_keys(self.stalled_task_timeouts)
if timedout_keys:
self.log.error(
"Tasks were still pending after %s, assuming they never made it to celery "
"and sending back to the scheduler:\n\t%s",
self.stalled_task_timeout,
"\n\t".join(repr(x) for x in timedout_keys),
)
self._send_stalled_tis_back_to_scheduler(timedout_keys)
def _get_timedout_ti_keys(
self, task_timeouts: dict[TaskInstanceKey, datetime.datetime]
) -> list[TaskInstanceKey]:
"""
These timeouts exist to check to see if any of our tasks have not progressed
in the expected time. This can happen for few different reasons, usually related
to race conditions while shutting down schedulers and celery workers.
It is, of course, always possible that these tasks are not actually
stalled - they could just be waiting in a long celery queue.
Unfortunately there's no way for us to know for sure, so we'll just
reschedule them and let the normal scheduler loop requeue them.
"""
now = utcnow()
timedout_keys = []
for key, stalled_after in task_timeouts.items():
if stalled_after > now:
# Since items are stored sorted, if we get to a stalled_after
# in the future then we can stop
break
# If the task gets updated to STARTED (which Celery does) or has
# already finished, then it will be removed from this list -- so
# the only time it's still in this list is when it a) never made it
# to celery in the first place (i.e. race condition somewhere in
# the dying executor), b) celery lost the task before execution
# started, or c) a really long celery queue and it just
# hasn't started yet -- better cancel it and let the scheduler
# re-queue rather than have this task risk stalling for ever
timedout_keys.append(key)
return timedout_keys
@provide_session
def _send_stalled_tis_back_to_scheduler(
self, keys: list[TaskInstanceKey], session: Session = NEW_SESSION
) -> None:
try:
session.query(TaskInstance).filter(
TaskInstance.filter_for_tis(keys),
TaskInstance.state == State.QUEUED,
TaskInstance.queued_by_job_id == self.job_id,
).update(
{
TaskInstance.state: State.SCHEDULED,
TaskInstance.queued_dttm: None,
TaskInstance.queued_by_job_id: None,
TaskInstance.external_executor_id: None,
},
synchronize_session=False,
)
session.commit()
except Exception:
self.log.exception("Error sending tasks back to scheduler")
session.rollback()
return
for key in keys:
self._set_celery_pending_task_timeout(key, None)
self.running.discard(key)
celery_async_result = self.tasks.pop(key, None)
if celery_async_result:
try:
app.control.revoke(celery_async_result.task_id)
except Exception as ex:
self.log.error("Error revoking task instance %s from celery: %s", key, ex)
def debug_dump(self) -> None:
"""Called in response to SIGUSR2 by the scheduler"""
super().debug_dump()
self.log.info(
"executor.tasks (%d)\n\t%s", len(self.tasks), "\n\t".join(map(repr, self.tasks.items()))
)
self.log.info(
"executor.adopted_task_timeouts (%d)\n\t%s",
len(self.adopted_task_timeouts),
"\n\t".join(map(repr, self.adopted_task_timeouts.items())),
)
self.log.info(
"executor.stalled_task_timeouts (%d)\n\t%s",
len(self.stalled_task_timeouts),
"\n\t".join(map(repr, self.stalled_task_timeouts.items())),
)
def update_all_task_states(self) -> None:
"""Updates states of the tasks."""
self.log.debug("Inquiring about %s celery task(s)", len(self.tasks))
state_and_info_by_celery_task_id = self.bulk_state_fetcher.get_many(self.tasks.values())
self.log.debug("Inquiries completed.")
for key, async_result in list(self.tasks.items()):
state, info = state_and_info_by_celery_task_id.get(async_result.task_id)
if state:
self.update_task_state(key, state, info)
def change_state(self, key: TaskInstanceKey, state: str, info=None) -> None:
super().change_state(key, state, info)
self.tasks.pop(key, None)
self._set_celery_pending_task_timeout(key, None)
def update_task_state(self, key: TaskInstanceKey, state: str, info: Any) -> None:
"""Updates state of a single task."""
try:
if state == celery_states.SUCCESS:
self.success(key, info)
elif state in (celery_states.FAILURE, celery_states.REVOKED):
self.fail(key, info)
elif state == celery_states.STARTED:
# It's now actually running, so we know it made it to celery okay!
self._set_celery_pending_task_timeout(key, None)
elif state == celery_states.PENDING:
pass
else:
self.log.info("Unexpected state for %s: %s", key, state)
except Exception:
self.log.exception("Error syncing the Celery executor, ignoring it.")
def end(self, synchronous: bool = False) -> None:
if synchronous:
while any(task.state not in celery_states.READY_STATES for task in self.tasks.values()):
time.sleep(5)
self.sync()
def terminate(self):
pass
def try_adopt_task_instances(self, tis: Sequence[TaskInstance]) -> Sequence[TaskInstance]:
# See which of the TIs are still alive (or have finished even!)
#
# Since Celery doesn't store "SENT" state for queued commands (if we create an AsyncResult with a made
# up id it just returns PENDING state for it), we have to store Celery's task_id against the TI row to
# look at in future.
#
# This process is not perfect -- we could have sent the task to celery, and crashed before we were
# able to record the AsyncResult.task_id in the TaskInstance table, in which case we won't adopt the
# task (it'll either run and update the TI state, or the scheduler will clear and re-queue it. Either
# way it won't get executed more than once)
#
# (If we swapped it around, and generated a task_id for Celery, stored that in TI and enqueued that
# there is also still a race condition where we could generate and store the task_id, but die before
# we managed to enqueue the command. Since neither way is perfect we always have to deal with this
# process not being perfect.)
celery_tasks = {}
not_adopted_tis = []
for ti in tis:
if ti.external_executor_id is not None:
celery_tasks[ti.external_executor_id] = (AsyncResult(ti.external_executor_id), ti)
else:
not_adopted_tis.append(ti)
if not celery_tasks:
# Nothing to adopt
return tis
states_by_celery_task_id = self.bulk_state_fetcher.get_many(
list(map(operator.itemgetter(0), celery_tasks.values()))
)
adopted = []
cached_celery_backend = next(iter(celery_tasks.values()))[0].backend
for celery_task_id, (state, info) in states_by_celery_task_id.items():
result, ti = celery_tasks[celery_task_id]
result.backend = cached_celery_backend
# Set the correct elements of the state dicts, then update this
# like we just queried it.
self._set_celery_pending_task_timeout(ti.key, _CeleryPendingTaskTimeoutType.ADOPTED)
self.tasks[ti.key] = result
self.running.add(ti.key)
self.update_task_state(ti.key, state, info)
adopted.append(f"{ti} in state {state}")
if adopted:
task_instance_str = '\n\t'.join(adopted)
self.log.info(
"Adopted the following %d tasks from a dead executor\n\t%s", len(adopted), task_instance_str
)
return not_adopted_tis
def _set_celery_pending_task_timeout(
self, key: TaskInstanceKey, timeout_type: _CeleryPendingTaskTimeoutType | None
) -> None:
"""
We use the fact that dicts maintain insertion order, and the the timeout for a
task is always "now + delta" to maintain the property that oldest item = first to
time out.
"""
self.adopted_task_timeouts.pop(key, None)
self.stalled_task_timeouts.pop(key, None)
if timeout_type == _CeleryPendingTaskTimeoutType.ADOPTED and self.task_adoption_timeout:
self.adopted_task_timeouts[key] = utcnow() + self.task_adoption_timeout
elif timeout_type == _CeleryPendingTaskTimeoutType.STALLED and self.stalled_task_timeout:
self.stalled_task_timeouts[key] = utcnow() + self.stalled_task_timeout
def fetch_celery_task_state(async_result: AsyncResult) -> tuple[str, str | ExceptionWithTraceback, Any]:
"""
Fetch and return the state of the given celery task. The scope of this function is
global so that it can be called by subprocesses in the pool.
:param async_result: a tuple of the Celery task key and the async Celery object used
to fetch the task's state
:return: a tuple of the Celery task key and the Celery state and the celery info
of the task
:rtype: tuple[str, str, str]
"""
try:
with timeout(seconds=OPERATION_TIMEOUT):
# Accessing state property of celery task will make actual network request
# to get the current state of the task
info = async_result.info if hasattr(async_result, 'info') else None
return async_result.task_id, async_result.state, info
except Exception as e:
exception_traceback = f"Celery Task ID: {async_result}\n{traceback.format_exc()}"
return async_result.task_id, ExceptionWithTraceback(e, exception_traceback), None
class BulkStateFetcher(LoggingMixin):
"""
Gets status for many Celery tasks using the best method available
If BaseKeyValueStoreBackend is used as result backend, the mget method is used.
If DatabaseBackend is used as result backend, the SELECT ...WHERE task_id IN (...) query is used
Otherwise, multiprocessing.Pool will be used. Each task status will be downloaded individually.
"""
def __init__(self, sync_parallelism=None):
super().__init__()
self._sync_parallelism = sync_parallelism
def _tasks_list_to_task_ids(self, async_tasks) -> set[str]:
return {a.task_id for a in async_tasks}
def get_many(self, async_results) -> Mapping[str, EventBufferValueType]:
"""Gets status for many Celery tasks using the best method available."""
if isinstance(app.backend, BaseKeyValueStoreBackend):
result = self._get_many_from_kv_backend(async_results)
elif isinstance(app.backend, DatabaseBackend):
result = self._get_many_from_db_backend(async_results)
else:
result = self._get_many_using_multiprocessing(async_results)
self.log.debug("Fetched %d state(s) for %d task(s)", len(result), len(async_results))
return result
def _get_many_from_kv_backend(self, async_tasks) -> Mapping[str, EventBufferValueType]:
task_ids = self._tasks_list_to_task_ids(async_tasks)
keys = [app.backend.get_key_for_task(k) for k in task_ids]
values = app.backend.mget(keys)
task_results = [app.backend.decode_result(v) for v in values if v]
task_results_by_task_id = {task_result["task_id"]: task_result for task_result in task_results}
return self._prepare_state_and_info_by_task_dict(task_ids, task_results_by_task_id)
def _get_many_from_db_backend(self, async_tasks) -> Mapping[str, EventBufferValueType]:
task_ids = self._tasks_list_to_task_ids(async_tasks)
session = app.backend.ResultSession()
task_cls = getattr(app.backend, "task_cls", TaskDb)
with session_cleanup(session):
tasks = session.query(task_cls).filter(task_cls.task_id.in_(task_ids)).all()
task_results = [app.backend.meta_from_decoded(task.to_dict()) for task in tasks]
task_results_by_task_id = {task_result["task_id"]: task_result for task_result in task_results}
return self._prepare_state_and_info_by_task_dict(task_ids, task_results_by_task_id)
@staticmethod
def _prepare_state_and_info_by_task_dict(
task_ids, task_results_by_task_id
) -> Mapping[str, EventBufferValueType]:
state_info: MutableMapping[str, EventBufferValueType] = {}
for task_id in task_ids:
task_result = task_results_by_task_id.get(task_id)
if task_result:
state = task_result["status"]
info = None if not hasattr(task_result, "info") else task_result["info"]
else:
state = celery_states.PENDING
info = None
state_info[task_id] = state, info
return state_info
def _get_many_using_multiprocessing(self, async_results) -> Mapping[str, EventBufferValueType]:
num_process = min(len(async_results), self._sync_parallelism)
with ProcessPoolExecutor(max_workers=num_process) as sync_pool:
chunksize = max(1, math.floor(math.ceil(1.0 * len(async_results) / self._sync_parallelism)))
task_id_to_states_and_info = list(
sync_pool.map(fetch_celery_task_state, async_results, chunksize=chunksize)
)
states_and_info_by_task_id: MutableMapping[str, EventBufferValueType] = {}
for task_id, state_or_exception, info in task_id_to_states_and_info:
if isinstance(state_or_exception, ExceptionWithTraceback):
self.log.error(
CELERY_FETCH_ERR_MSG_HEADER + ":%s\n%s\n",
state_or_exception.exception,
state_or_exception.traceback,
)
else:
states_and_info_by_task_id[task_id] = state_or_exception, info
return states_and_info_by_task_id
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