# 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.
"""
Utilities and classes used by the Celery Executor.
Much of this code is expensive to import/load, be careful where this module is imported.
"""
from __future__ import annotations
import contextlib
import logging
import math
import os
import subprocess
import traceback
import warnings
from concurrent.futures import ProcessPoolExecutor
from typing import TYPE_CHECKING, Any, Mapping, MutableMapping, Optional, 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, retry, session_cleanup
from celery.signals import import_modules as celery_import_modules
from setproctitle import setproctitle
from sqlalchemy import select
import airflow.settings as settings
from airflow.configuration import conf
from airflow.exceptions import AirflowException, RemovedInAirflow3Warning
from airflow.executors.base_executor import BaseExecutor
from airflow.providers.celery.executors.default_celery import DEFAULT_CELERY_CONFIG
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.timeout import timeout
[docs]log = logging.getLogger(__name__)
if TYPE_CHECKING:
from celery.result import AsyncResult
from airflow.executors.base_executor import CommandType, EventBufferValueType
from airflow.models.taskinstance import TaskInstanceKey
[docs] TaskInstanceInCelery = Tuple[TaskInstanceKey, CommandType, Optional[str], Task]
[docs]OPERATION_TIMEOUT = conf.getfloat("celery", "operation_timeout")
# Make it constant for unit test.
if conf.has_option("celery", "celery_config_options"):
[docs] celery_configuration = conf.getimport("celery", "celery_config_options")
else:
celery_configuration = DEFAULT_CELERY_CONFIG
[docs]celery_app_name = conf.get("celery", "CELERY_APP_NAME")
if celery_app_name == "airflow.executors.celery_executor":
warnings.warn(
"The celery.CELERY_APP_NAME configuration uses deprecated package name: "
"'airflow.executors.celery_executor'. "
"Change it to `airflow.providers.celery.executors.celery_executor`, and "
"update the `-app` flag in your Celery Health Checks "
"to use `airflow.providers.celery.executors.celery_executor.app`.",
RemovedInAirflow3Warning,
)
[docs]app = Celery(celery_app_name, config_source=celery_configuration)
@celery_import_modules.connect
[docs]def on_celery_import_modules(*args, **kwargs):
"""
Preload some "expensive" airflow modules once, so other task processes won't have to import it 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_runner
import airflow.macros
import airflow.operators.bash
import airflow.operators.python
import airflow.operators.subdag # noqa: F401
with contextlib.suppress(ImportError):
import numpy # noqa: F401
with contextlib.suppress(ImportError):
import kubernetes.client # noqa: F401
@app.task
[docs]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)
[docs]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
[docs]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
[docs]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
"""
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
[docs]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}
[docs] 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)
@retry
def _query_task_cls_from_db_backend(self, task_ids, **kwargs):
session = app.backend.ResultSession()
task_cls = getattr(app.backend, "task_cls", TaskDb)
with session_cleanup(session):
return session.scalars(select(task_cls).where(task_cls.task_id.in_(task_ids))).all()
def _get_many_from_db_backend(self, async_tasks) -> Mapping[str, EventBufferValueType]:
task_ids = self._tasks_list_to_task_ids(async_tasks)
tasks = self._query_task_cls_from_db_backend(task_ids)
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.ceil(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
