airflow local_executor 源码
airflow local_executor 代码
文件路径：/airflow/executors/local_executor.py
#
# 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.
"""
LocalExecutor
.. seealso::
For more information on how the LocalExecutor works, take a look at the guide:
:ref:`executor:LocalExecutor`
"""
from __future__ import annotations
import logging
import os
import subprocess
from abc import abstractmethod
from multiprocessing import Manager, Process
from multiprocessing.managers import SyncManager
from queue import Empty, Queue
from typing import Any, Optional, Tuple
from setproctitle import getproctitle, setproctitle
from airflow import settings
from airflow.exceptions import AirflowException
from airflow.executors.base_executor import NOT_STARTED_MESSAGE, PARALLELISM, BaseExecutor, CommandType
from airflow.models.taskinstance import TaskInstanceKey, TaskInstanceStateType
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.state import State
# This is a work to be executed by a worker.
# It can Key and Command - but it can also be None, None which is actually a
# "Poison Pill" - worker seeing Poison Pill should take the pill and ... die instantly.
ExecutorWorkType = Tuple[Optional[TaskInstanceKey], Optional[CommandType]]
class LocalWorkerBase(Process, LoggingMixin):
"""
LocalWorkerBase implementation to run airflow commands. Executes the given
command and puts the result into a result queue when done, terminating execution.
:param result_queue: the queue to store result state
"""
def __init__(self, result_queue: Queue[TaskInstanceStateType]):
super().__init__(target=self.do_work)
self.daemon: bool = True
self.result_queue: Queue[TaskInstanceStateType] = result_queue
def run(self):
# We know we've just started a new process, so lets disconnect from the metadata db now
settings.engine.pool.dispose()
settings.engine.dispose()
setproctitle("airflow worker -- LocalExecutor")
return super().run()
def execute_work(self, key: TaskInstanceKey, command: CommandType) -> None:
"""
Executes command received and stores result state in queue.
:param key: the key to identify the task instance
:param command: the command to execute
"""
if key is None:
return
self.log.info("%s running %s", self.__class__.__name__, command)
setproctitle(f"airflow worker -- LocalExecutor: {command}")
if settings.EXECUTE_TASKS_NEW_PYTHON_INTERPRETER:
state = self._execute_work_in_subprocess(command)
else:
state = self._execute_work_in_fork(command)
self.result_queue.put((key, state))
# Remove the command since the worker is done executing the task
setproctitle("airflow worker -- LocalExecutor")
def _execute_work_in_subprocess(self, command: CommandType) -> str:
try:
subprocess.check_call(command, close_fds=True)
return State.SUCCESS
except subprocess.CalledProcessError as e:
self.log.error("Failed to execute task %s.", str(e))
return State.FAILED
def _execute_work_in_fork(self, command: CommandType) -> str:
pid = os.fork()
if pid:
# In parent, wait for the child
pid, ret = os.waitpid(pid, 0)
return State.SUCCESS if ret == 0 else State.FAILED
from airflow.sentry import Sentry
ret = 1
try:
import signal
from airflow.cli.cli_parser import get_parser
signal.signal(signal.SIGINT, signal.SIG_DFL)
signal.signal(signal.SIGTERM, signal.SIG_DFL)
signal.signal(signal.SIGUSR2, signal.SIG_DFL)
parser = get_parser()
# [1:] - remove "airflow" from the start of the command
args = parser.parse_args(command[1:])
args.shut_down_logging = False
setproctitle(f"airflow task supervisor: {command}")
args.func(args)
ret = 0
return State.SUCCESS
except Exception as e:
self.log.exception("Failed to execute task %s.", e)
return State.FAILED
finally:
Sentry.flush()
logging.shutdown()
os._exit(ret)
@abstractmethod
def do_work(self):
"""Called in the subprocess and should then execute tasks"""
raise NotImplementedError()
class LocalWorker(LocalWorkerBase):
"""
Local worker that executes the task.
:param result_queue: queue where results of the tasks are put.
:param key: key identifying task instance
:param command: Command to execute
"""
def __init__(
self, result_queue: Queue[TaskInstanceStateType], key: TaskInstanceKey, command: CommandType
):
super().__init__(result_queue)
self.key: TaskInstanceKey = key
self.command: CommandType = command
def do_work(self) -> None:
self.execute_work(key=self.key, command=self.command)
class QueuedLocalWorker(LocalWorkerBase):
"""
LocalWorker implementation that is waiting for tasks from a queue and will
continue executing commands as they become available in the queue.
It will terminate execution once the poison token is found.
:param task_queue: queue from which worker reads tasks
:param result_queue: queue where worker puts results after finishing tasks
"""
def __init__(self, task_queue: Queue[ExecutorWorkType], result_queue: Queue[TaskInstanceStateType]):
super().__init__(result_queue=result_queue)
self.task_queue = task_queue
def do_work(self) -> None:
while True:
try:
key, command = self.task_queue.get()
except EOFError:
self.log.info(
"Failed to read tasks from the task queue because the other "
"end has closed the connection. Terminating worker %s.",
self.name,
)
break
try:
if key is None or command is None:
# Received poison pill, no more tasks to run
break
self.execute_work(key=key, command=command)
finally:
self.task_queue.task_done()
class LocalExecutor(BaseExecutor):
"""
LocalExecutor executes tasks locally in parallel.
It uses the multiprocessing Python library and queues to parallelize the execution
of tasks.
:param parallelism: how many parallel processes are run in the executor
"""
def __init__(self, parallelism: int = PARALLELISM):
super().__init__(parallelism=parallelism)
if self.parallelism < 0:
raise AirflowException("parallelism must be bigger than or equal to 0")
self.manager: SyncManager | None = None
self.result_queue: Queue[TaskInstanceStateType] | None = None
self.workers: list[QueuedLocalWorker] = []
self.workers_used: int = 0
self.workers_active: int = 0
self.impl: None | (LocalExecutor.UnlimitedParallelism | LocalExecutor.LimitedParallelism) = None
class UnlimitedParallelism:
"""
Implements LocalExecutor with unlimited parallelism, starting one process
per each command to execute.
:param executor: the executor instance to implement.
"""
def __init__(self, executor: LocalExecutor):
self.executor: LocalExecutor = executor
def start(self) -> None:
"""Starts the executor."""
self.executor.workers_used = 0
self.executor.workers_active = 0
def execute_async(
self,
key: TaskInstanceKey,
command: CommandType,
queue: str | None = None,
executor_config: Any | None = None,
) -> None:
"""
Executes task asynchronously.
:param key: the key to identify the task instance
:param command: the command to execute
:param queue: Name of the queue
:param executor_config: configuration for the executor
"""
if not self.executor.result_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
local_worker = LocalWorker(self.executor.result_queue, key=key, command=command)
self.executor.workers_used += 1
self.executor.workers_active += 1
local_worker.start()
def sync(self) -> None:
"""Sync will get called periodically by the heartbeat method."""
if not self.executor.result_queue:
raise AirflowException("Executor should be started first")
while not self.executor.result_queue.empty():
results = self.executor.result_queue.get()
self.executor.change_state(*results)
self.executor.workers_active -= 1
def end(self) -> None:
"""
This method is called when the caller is done submitting job and
wants to wait synchronously for the job submitted previously to be
all done.
"""
while self.executor.workers_active > 0:
self.executor.sync()
class LimitedParallelism:
"""
Implements LocalExecutor with limited parallelism using a task queue to
coordinate work distribution.
:param executor: the executor instance to implement.
"""
def __init__(self, executor: LocalExecutor):
self.executor: LocalExecutor = executor
self.queue: Queue[ExecutorWorkType] | None = None
def start(self) -> None:
"""Starts limited parallelism implementation."""
if not self.executor.manager:
raise AirflowException(NOT_STARTED_MESSAGE)
self.queue = self.executor.manager.Queue()
if not self.executor.result_queue:
raise AirflowException(NOT_STARTED_MESSAGE)
self.executor.workers = [
QueuedLocalWorker(self.queue, self.executor.result_queue)
for _ in range(self.executor.parallelism)
]
self.executor.workers_used = len(self.executor.workers)
for worker in self.executor.workers:
worker.start()
def execute_async(
self,
key: TaskInstanceKey,
command: CommandType,
queue: str | None = None,
executor_config: Any | None = None,
) -> None:
"""
Executes task asynchronously.
:param key: the key to identify the task instance
:param command: the command to execute
:param queue: name of the queue
:param executor_config: configuration for the executor
"""
if not self.queue:
raise AirflowException(NOT_STARTED_MESSAGE)
self.queue.put((key, command))
def sync(self):
"""Sync will get called periodically by the heartbeat method."""
while True:
try:
results = self.executor.result_queue.get_nowait()
try:
self.executor.change_state(*results)
finally:
self.executor.result_queue.task_done()
except Empty:
break
def end(self):
"""Ends the executor. Sends the poison pill to all workers."""
for _ in self.executor.workers:
self.queue.put((None, None))
# Wait for commands to finish
self.queue.join()
self.executor.sync()
def start(self) -> None:
"""Starts the executor"""
old_proctitle = getproctitle()
setproctitle("airflow executor -- LocalExecutor")
self.manager = Manager()
setproctitle(old_proctitle)
self.result_queue = self.manager.Queue()
self.workers = []
self.workers_used = 0
self.workers_active = 0
self.impl = (
LocalExecutor.UnlimitedParallelism(self)
if self.parallelism == 0
else LocalExecutor.LimitedParallelism(self)
)
self.impl.start()
def execute_async(
self,
key: TaskInstanceKey,
command: CommandType,
queue: str | None = None,
executor_config: Any | None = None,
) -> None:
"""Execute asynchronously."""
if not self.impl:
raise AirflowException(NOT_STARTED_MESSAGE)
self.validate_airflow_tasks_run_command(command)
self.impl.execute_async(key=key, command=command, queue=queue, executor_config=executor_config)
def sync(self) -> None:
"""Sync will get called periodically by the heartbeat method."""
if not self.impl:
raise AirflowException(NOT_STARTED_MESSAGE)
self.impl.sync()
def end(self) -> None:
"""
Ends the executor.
:return:
"""
if not self.impl:
raise AirflowException(NOT_STARTED_MESSAGE)
if not self.manager:
raise AirflowException(NOT_STARTED_MESSAGE)
self.log.info(
"Shutting down LocalExecutor"
"; waiting for running tasks to finish. Signal again if you don't want to wait."
)
self.impl.end()
self.manager.shutdown()
def terminate(self):
"""Terminate the executor is not doing anything."""
相关信息
相关文章
airflow celery_kubernetes_executor 源码
0
赞
热门推荐
-
2、 - 优质文章
-
3、 gate.io
-
6、 golang
-
7、 openharmony