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Source code for airflow.providers.celery.executors.celery_executor

#
# 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.
"""
CeleryExecutor.

.. seealso::
    For more information on how the CeleryExecutor works, take a look at the guide:
    :doc:`/celery_executor`
"""

from __future__ import annotations

import logging
import math
import operator
import time
from collections import Counter
from collections.abc import Sequence
from concurrent.futures import ProcessPoolExecutor
from multiprocessing import cpu_count
from typing import TYPE_CHECKING, Any

from deprecated import deprecated

from airflow.cli.cli_config import (
    ARG_DAEMON,
    ARG_LOG_FILE,
    ARG_PID,
    ARG_SKIP_SERVE_LOGS,
    ARG_STDERR,
    ARG_STDOUT,
    ARG_VERBOSE,
    ActionCommand,
    Arg,
    GroupCommand,
    lazy_load_command,
)
from airflow.configuration import conf
from airflow.exceptions import AirflowProviderDeprecationWarning, AirflowTaskTimeout
from airflow.executors.base_executor import BaseExecutor
from airflow.providers.celery.version_compat import AIRFLOW_V_3_0_PLUS
from airflow.stats import Stats
from airflow.utils.state import TaskInstanceState
from celery import states as celery_states



log = logging.getLogger(__name__)





CELERY_SEND_ERR_MSG_HEADER = "Error sending Celery task"



if TYPE_CHECKING:
    import argparse

    from sqlalchemy.orm import Session

    from airflow.executors import workloads
    from airflow.executors.base_executor import TaskTuple
    from airflow.models.taskinstance import TaskInstance
    from airflow.models.taskinstancekey import TaskInstanceKey
    from airflow.providers.celery.executors.celery_executor_utils import TaskInstanceInCelery


# PEP562


def __getattr__(name):
    # This allows us to make the Celery app accessible through the
    # celery_executor module without the time cost of its import and
    # construction
    if name == "app":
        from airflow.providers.celery.executors.celery_executor_utils import app

        return app
    raise AttributeError(f"module '{__name__}' has no attribute '{name}'")



"""
To start the celery worker, run the command:
airflow celery worker
"""


# flower cli args


ARG_BROKER_API = Arg(("-a", "--broker-api"), help="Broker API")



ARG_FLOWER_HOSTNAME = Arg(
    ("-H", "--hostname"),
    default=conf.get("celery", "FLOWER_HOST"),
    help="Set the hostname on which to run the server",
)



ARG_FLOWER_PORT = Arg(
    ("-p", "--port"),
    default=conf.getint("celery", "FLOWER_PORT"),
    type=int,
    help="The port on which to run the server",
)



ARG_FLOWER_CONF = Arg(("-c", "--flower-conf"), help="Configuration file for flower")



ARG_FLOWER_URL_PREFIX = Arg(
    ("-u", "--url-prefix"),
    default=conf.get("celery", "FLOWER_URL_PREFIX"),
    help="URL prefix for Flower",
)



ARG_FLOWER_BASIC_AUTH = Arg(
    ("-A", "--basic-auth"),
    default=conf.get("celery", "FLOWER_BASIC_AUTH"),
    help=(
        "Securing Flower with Basic Authentication. "
        "Accepts user:password pairs separated by a comma. "
        "Example: flower_basic_auth = user1:password1,user2:password2"
    ),
)


# worker cli args


ARG_AUTOSCALE = Arg(("-a", "--autoscale"), help="Minimum and Maximum number of worker to autoscale")



ARG_QUEUES = Arg(
    ("-q", "--queues"),
    help="Comma delimited list of queues to serve",
    default=conf.get("operators", "DEFAULT_QUEUE"),
)



ARG_CONCURRENCY = Arg(
    ("-c", "--concurrency"),
    type=int,
    help="The number of worker processes",
    default=conf.getint("celery", "worker_concurrency"),
)



ARG_CELERY_HOSTNAME = Arg(
    ("-H", "--celery-hostname"),
    help="Set the hostname of celery worker if you have multiple workers on a single machine",
)



ARG_UMASK = Arg(
    ("-u", "--umask"),
    help="Set the umask of celery worker in daemon mode",
)




ARG_WITHOUT_MINGLE = Arg(
    ("--without-mingle",),
    default=False,
    help="Don't synchronize with other workers at start-up",
    action="store_true",
)



ARG_WITHOUT_GOSSIP = Arg(
    ("--without-gossip",),
    default=False,
    help="Don't subscribe to other workers events",
    action="store_true",
)




CELERY_CLI_COMMAND_PATH = "airflow.providers.celery.cli.celery_command"




CELERY_COMMANDS = (
    ActionCommand(
        name="worker",
        help="Start a Celery worker node",
        func=lazy_load_command(f"{CELERY_CLI_COMMAND_PATH}.worker"),
        args=(
            ARG_QUEUES,
            ARG_CONCURRENCY,
            ARG_CELERY_HOSTNAME,
            ARG_PID,
            ARG_DAEMON,
            ARG_UMASK,
            ARG_STDOUT,
            ARG_STDERR,
            ARG_LOG_FILE,
            ARG_AUTOSCALE,
            ARG_SKIP_SERVE_LOGS,
            ARG_WITHOUT_MINGLE,
            ARG_WITHOUT_GOSSIP,
            ARG_VERBOSE,
        ),
    ),
    ActionCommand(
        name="flower",
        help="Start a Celery Flower",
        func=lazy_load_command(f"{CELERY_CLI_COMMAND_PATH}.flower"),
        args=(
            ARG_FLOWER_HOSTNAME,
            ARG_FLOWER_PORT,
            ARG_FLOWER_CONF,
            ARG_FLOWER_URL_PREFIX,
            ARG_FLOWER_BASIC_AUTH,
            ARG_BROKER_API,
            ARG_PID,
            ARG_DAEMON,
            ARG_STDOUT,
            ARG_STDERR,
            ARG_LOG_FILE,
            ARG_VERBOSE,
        ),
    ),
    ActionCommand(
        name="stop",
        help="Stop the Celery worker gracefully",
        func=lazy_load_command(f"{CELERY_CLI_COMMAND_PATH}.stop_worker"),
        args=(ARG_PID, ARG_VERBOSE),
    ),
)





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



    supports_sentry: bool = True


    if TYPE_CHECKING and AIRFLOW_V_3_0_PLUS:
        # In the v3 path, we store workloads, not commands as strings.
        # TODO: TaskSDK: move this type change into BaseExecutor


        queued_tasks: dict[TaskInstanceKey, workloads.All]  # type: ignore[assignment]


    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)
        from airflow.providers.celery.executors.celery_executor_utils import BulkStateFetcher



        self.bulk_state_fetcher = BulkStateFetcher(self._sync_parallelism)



        self.tasks = {}



        self.task_publish_retries: Counter[TaskInstanceKey] = Counter()



        self.task_publish_max_retries = conf.getint("celery", "task_publish_max_retries")




    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
        """
        return max(1, math.ceil(to_send_count / self._sync_parallelism))

    def _process_tasks(self, task_tuples: list[TaskTuple]) -> None:
        # Airflow V2 version
        from airflow.providers.celery.executors.celery_executor_utils import execute_command

        task_tuples_to_send = [task_tuple[:3] + (execute_command,) for task_tuple in task_tuples]

        self._send_tasks(task_tuples_to_send)

    def _process_workloads(self, input: list[workloads.All]) -> None:
        # Airflow V3 version -- have to delay imports until we know we are on v3
        from airflow.executors import workloads
        from airflow.providers.celery.executors.celery_executor_utils import execute_workload

        tasks = [
            (workload.ti.key, workload, workload.ti.queue, execute_workload)
            for workload in input
            if isinstance(workload, workloads.ExecuteTask)
        ]
        if len(tasks) != len(input):
            invalid = list(workload for workload in input if not isinstance(workload, workloads.ExecuteTask))
            raise ValueError(f"{type(self)}._process_workloads cannot handle {invalid}")

        self._send_tasks(tasks)

    def _send_tasks(self, task_tuples_to_send: Sequence[TaskInstanceInCelery]):
        first_task = next(t[-1] 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.")
        from airflow.providers.celery.executors.celery_executor_utils import ExceptionWithTraceback

        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,
                        tuple(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("%s: %s\n%s\n", CELERY_SEND_ERR_MSG_HEADER, result.exception, result.traceback)
                self.event_buffer[key] = (TaskInstanceState.FAILED, None)
            elif result is not None:
                result.backend = cached_celery_backend
                self.running.add(key)
                self.tasks[key] = result

                # 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] = (TaskInstanceState.QUEUED, result.task_id)

    def _send_tasks_to_celery(self, task_tuples_to_send: Sequence[TaskInstanceInCelery]):
        from airflow.providers.celery.executors.celery_executor_utils import send_task_to_executor

        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()




    def debug_dump(self) -> None:
        """Debug dump; 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()))
        )




    def update_all_task_states(self) -> None:
        """Update 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: TaskInstanceState, info=None, remove_running=True
    ) -> None:
        try:
            super().change_state(key, state, info, remove_running=remove_running)
        except (AttributeError, TypeError):
            # Earlier versions of the BaseExecutor don't accept the remove_running parameter for this method
            # TODO: Remove when min airflow version >= 2.9.2
            super().change_state(key, state, info)
        self.tasks.pop(key, None)




    def update_task_state(self, key: TaskInstanceKey, state: str, info: Any) -> None:
        """Update 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 in (celery_states.STARTED, celery_states.PENDING, celery_states.RETRY):
                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.)
        from celery.result import AsyncResult

        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
            if isinstance(result.result, BaseException):
                e = result.result
                # Log the exception we got from the remote end
                self.log.warning("Task %s failed with error", ti.key, exc_info=e)

            # Set the correct elements of the state dicts, then update this
            # like we just queried it.
            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


    @deprecated(
        reason="Replaced by function `revoke_task`. Upgrade airflow core to make this go away.",
        category=AirflowProviderDeprecationWarning,
    )


    def cleanup_stuck_queued_tasks(self, tis: list[TaskInstance]) -> list[str]:
        """
        Remove tasks stuck in queued from executor and fail them.

        This method is deprecated. Use `cleanup_tasks_stuck_in_queued` instead.
        """
        reprs = []
        for ti in tis:
            reprs.append(repr(ti))
            self.revoke_task(ti=ti)
            self.fail(ti.key)
        return reprs




    def revoke_task(self, *, ti: TaskInstance):
        from airflow.providers.celery.executors.celery_executor_utils import app

        celery_async_result = self.tasks.pop(ti.key, None)
        if celery_async_result:
            try:
                app.control.revoke(celery_async_result.task_id)
            except Exception:
                self.log.exception("Error revoking task instance %s from celery", ti.key)
        self.running.discard(ti.key)
        self.queued_tasks.pop(ti.key, None)


    @staticmethod


    def get_cli_commands() -> list[GroupCommand]:
        return [
            GroupCommand(
                name="celery",
                help="Celery components",
                description=(
                    "Start celery components. Works only when using CeleryExecutor. For more information, "
                    "see https://airflow.apache.org/docs/apache-airflow/stable/executor/celery.html"
                ),
                subcommands=CELERY_COMMANDS,
            ),
        ]




    def queue_workload(self, workload: workloads.All, session: Session | None) -> None:
        from airflow.executors import workloads

        if not isinstance(workload, workloads.ExecuteTask):
            raise RuntimeError(f"{type(self)} cannot handle workloads of type {type(workload)}")
        ti = workload.ti
        self.queued_tasks[ti.key] = workload




def _get_parser() -> argparse.ArgumentParser:
    """
    Generate documentation; used by Sphinx.

    :meta private:
    """
    return CeleryExecutor._get_parser()

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