Flask Celery 任务锁定

Question

我正在将 Flask 与 Celery 一起使用，并且我正在尝试锁定特定任务，以便一次只能运行一个任务。在 celery 文档中，它给出了执行此Celery 文档的示例，确保一次只执行一个任务。给出的这个例子是针对 Django 的，但是我正在使用 Flask 我已尽力将其转换为与 Flask 一起使用，但是我仍然看到具有锁的 myTask1 可以多次运行。

我不清楚的一件事是我是否正确使用缓存，我以前从未使用过它，所以所有这些对我来说都是新的。提到但未解释的文档中的一件事是

文档注释：

In order for this to work correctly you need to be using a cache backend where the .add operation is atomic. memcached is known to work well for this purpose.

我不确定这意味着什么，我应该将缓存与数据库结合使用，如果是这样，我该怎么做？我正在使用 mongodb。在我的代码中，我只是为缓存设置了这个设置，cache = Cache(app, config={'CACHE_TYPE': 'simple'})因为这就是 Flask-Cache 文档的Flask-Cache Docs 中提到的

我不清楚的另一件事是，当我myTask1从 Flask 路线内打电话给我时，我是否需要做任何不同的事情task1

这是我正在使用的代码示例。

from flask import (Flask, render_template, flash, redirect,
                   url_for, session, logging, request, g, render_template_string, jsonify)
from flask_caching import Cache
from contextlib import contextmanager
from celery import Celery
from Flask_celery import make_celery
from celery.result import AsyncResult
from celery.utils.log import get_task_logger
from celery.five import monotonic
from flask_pymongo import PyMongo
from hashlib import md5
import pymongo
import time


app = Flask(__name__)

cache = Cache(app, config={'CACHE_TYPE': 'simple'})
app.config['SECRET_KEY']= 'super secret key for me123456789987654321'

######################
# MONGODB SETUP
#####################
app.config['MONGO_HOST'] = 'localhost'
app.config['MONGO_DBNAME'] = 'celery-test-db'
app.config["MONGO_URI"] = 'mongodb://localhost:27017/celery-test-db'


mongo = PyMongo(app)


##############################
# CELERY ARGUMENTS
##############################


app.config['CELERY_BROKER_URL'] = 'amqp://localhost//'
app.config['CELERY_RESULT_BACKEND'] = 'mongodb://localhost:27017/celery-test-db'

app.config['CELERY_RESULT_BACKEND'] = 'mongodb'
app.config['CELERY_MONGODB_BACKEND_SETTINGS'] = {
    "host": "localhost",
    "port": 27017,
    "database": "celery-test-db", 
    "taskmeta_collection": "celery_jobs",
}

app.config['CELERY_TASK_SERIALIZER'] = 'json'


celery = Celery('task',broker='mongodb://localhost:27017/jobs')
celery = make_celery(app)


LOCK_EXPIRE = 60 * 2  # Lock expires in 2 minutes


@contextmanager
def memcache_lock(lock_id, oid):
    timeout_at = monotonic() + LOCK_EXPIRE - 3
    # cache.add fails if the key already exists
    status = cache.add(lock_id, oid, LOCK_EXPIRE)
    try:
        yield status
    finally:
        # memcache delete is very slow, but we have to use it to take
        # advantage of using add() for atomic locking
        if monotonic() < timeout_at and status:
            # don't release the lock if we exceeded the timeout
            # to lessen the chance of releasing an expired lock
            # owned by someone else
            # also don't release the lock if we didn't acquire it
            cache.delete(lock_id)



@celery.task(bind=True, name='app.myTask1')
def myTask1(self):

    self.update_state(state='IN TASK')

    lock_id = self.name

    with memcache_lock(lock_id, self.app.oid) as acquired:
        if acquired:
            # do work if we got the lock
            print('acquired is {}'.format(acquired))
            self.update_state(state='DOING WORK')
            time.sleep(90)
            return 'result'

    # otherwise, the lock was already in use
    raise self.retry(countdown=60)  # redeliver message to the queue, so the work can be done later



@celery.task(bind=True, name='app.myTask2')
def myTask2(self):
    print('you are in task2')
    self.update_state(state='STARTING')
    time.sleep(120)
    print('task2 done')


@app.route('/', methods=['GET', 'POST'])
def index():

    return render_template('index.html')

@app.route('/task1', methods=['GET', 'POST'])
def task1():

    print('running task1')
    result = myTask1.delay()

    # get async task id
    taskResult = AsyncResult(result.task_id)


    # push async taskid into db collection job_task_id
    mongo.db.job_task_id.insert({'taskid': str(taskResult), 'TaskName': 'task1'})

    return render_template('task1.html')


@app.route('/task2', methods=['GET', 'POST'])
def task2():

    print('running task2')
    result = myTask2.delay()

    # get async task id
    taskResult = AsyncResult(result.task_id)

    # push async taskid into db collection job_task_id
    mongo.db.job_task_id.insert({'taskid': str(taskResult), 'TaskName': 'task2'})

    return render_template('task2.html') 


@app.route('/status', methods=['GET', 'POST'])
def status():

    taskid_list = []
    task_state_list = []
    TaskName_list = []

    allAsyncData = mongo.db.job_task_id.find()

    for doc in allAsyncData:
        try:
            taskid_list.append(doc['taskid'])
        except:
            print('error with db conneciton in asyncJobStatus')

        TaskName_list.append(doc['TaskName'])

    # PASS TASK ID TO ASYNC RESULT TO GET TASK RESULT FOR THAT SPECIFIC TASK
    for item in taskid_list:
        try:
            task_state_list.append(myTask1.AsyncResult(item).state)
        except:
            task_state_list.append('UNKNOWN')

    return render_template('status.html', data_list=zip(task_state_list, TaskName_list))

最终工作代码

from flask import (Flask, render_template, flash, redirect,
                   url_for, session, logging, request, g, render_template_string, jsonify)
from flask_caching import Cache
from contextlib import contextmanager
from celery import Celery
from Flask_celery import make_celery
from celery.result import AsyncResult
from celery.utils.log import get_task_logger
from celery.five import monotonic
from flask_pymongo import PyMongo
from hashlib import md5
import pymongo
import time
import redis
from flask_redis import FlaskRedis


app = Flask(__name__)

# ADDING REDIS
redis_store = FlaskRedis(app)

# POINTING CACHE_TYPE TO REDIS
cache = Cache(app, config={'CACHE_TYPE': 'redis'})
app.config['SECRET_KEY']= 'super secret key for me123456789987654321'

######################
# MONGODB SETUP
#####################
app.config['MONGO_HOST'] = 'localhost'
app.config['MONGO_DBNAME'] = 'celery-test-db'
app.config["MONGO_URI"] = 'mongodb://localhost:27017/celery-test-db'


mongo = PyMongo(app)


##############################
# CELERY ARGUMENTS
##############################

# CELERY USING REDIS
app.config['CELERY_BROKER_URL'] = 'redis://localhost:6379/0'
app.config['CELERY_RESULT_BACKEND'] = 'mongodb://localhost:27017/celery-test-db'

app.config['CELERY_RESULT_BACKEND'] = 'mongodb'
app.config['CELERY_MONGODB_BACKEND_SETTINGS'] = {
    "host": "localhost",
    "port": 27017,
    "database": "celery-test-db", 
    "taskmeta_collection": "celery_jobs",
}

app.config['CELERY_TASK_SERIALIZER'] = 'json'


celery = Celery('task',broker='mongodb://localhost:27017/jobs')
celery = make_celery(app)


LOCK_EXPIRE = 60 * 2  # Lock expires in 2 minutes


@contextmanager
def memcache_lock(lock_id, oid):
    timeout_at = monotonic() + LOCK_EXPIRE - 3
    print('in memcache_lock and timeout_at is {}'.format(timeout_at))
    # cache.add fails if the key already exists
    status = cache.add(lock_id, oid, LOCK_EXPIRE)
    try:
        yield status
        print('memcache_lock and status is {}'.format(status))
    finally:
        # memcache delete is very slow, but we have to use it to take
        # advantage of using add() for atomic locking
        if monotonic() < timeout_at and status:
            # don't release the lock if we exceeded the timeout
            # to lessen the chance of releasing an expired lock
            # owned by someone else
            # also don't release the lock if we didn't acquire it
            cache.delete(lock_id)



@celery.task(bind=True, name='app.myTask1')
def myTask1(self):

    self.update_state(state='IN TASK')
    print('dir is {} '.format(dir(self)))

    lock_id = self.name
    print('lock_id is {}'.format(lock_id))

    with memcache_lock(lock_id, self.app.oid) as acquired:
        print('in memcache_lock and lock_id is {} self.app.oid is {} and acquired is {}'.format(lock_id, self.app.oid, acquired))
        if acquired:
            # do work if we got the lock
            print('acquired is {}'.format(acquired))
            self.update_state(state='DOING WORK')
            time.sleep(90)
            return 'result'

    # otherwise, the lock was already in use
    raise self.retry(countdown=60)  # redeliver message to the queue, so the work can be done later



@celery.task(bind=True, name='app.myTask2')
def myTask2(self):
    print('you are in task2')
    self.update_state(state='STARTING')
    time.sleep(120)
    print('task2 done')


@app.route('/', methods=['GET', 'POST'])
def index():

    return render_template('index.html')

@app.route('/task1', methods=['GET', 'POST'])
def task1():

    print('running task1')
    result = myTask1.delay()

    # get async task id
    taskResult = AsyncResult(result.task_id)


    # push async taskid into db collection job_task_id
    mongo.db.job_task_id.insert({'taskid': str(taskResult), 'TaskName': 'myTask1'})

    return render_template('task1.html')


@app.route('/task2', methods=['GET', 'POST'])
def task2():

    print('running task2')
    result = myTask2.delay()

    # get async task id
    taskResult = AsyncResult(result.task_id)

    # push async taskid into db collection job_task_id
    mongo.db.job_task_id.insert({'taskid': str(taskResult), 'TaskName': 'task2'})

    return render_template('task2.html')

@app.route('/status', methods=['GET', 'POST'])
def status():

    taskid_list = []
    task_state_list = []
    TaskName_list = []

    allAsyncData = mongo.db.job_task_id.find()

    for doc in allAsyncData:
        try:
            taskid_list.append(doc['taskid'])
        except:
            print('error with db conneciton in asyncJobStatus')

        TaskName_list.append(doc['TaskName'])

    # PASS TASK ID TO ASYNC RESULT TO GET TASK RESULT FOR THAT SPECIFIC TASK
    for item in taskid_list:
        try:
            task_state_list.append(myTask1.AsyncResult(item).state)
        except:
            task_state_list.append('UNKNOWN')

    return render_template('status.html', data_list=zip(task_state_list, TaskName_list))


if __name__ == '__main__':
    app.secret_key = 'super secret key for me123456789987654321'
    app.run(port=1234, host='localhost')

这里还有一个屏幕截图，您可以看到我运行了myTask1两次，而 myTask2 运行了一次。现在我有了 myTask1 的预期行为。现在myTask1将由一个工作人员运行，如果另一个工作人员尝试接它，它将根据我定义的任何内容继续重试。

Answer 1

在您的问题中，您从您使用的 Celery 示例中指出了此警告：

为了使其正常工作，您需要使用.add操作是原子的缓存后端。memcached已知可以很好地用于此目的。

你提到你并不真正理解这意味着什么。实际上，您展示的代码表明您没有注意该警告，因为您的代码使用了不适当的后端。

考虑这个代码：

with memcache_lock(lock_id, self.app.oid) as acquired:
    if acquired:
        # do some work

你在这里想要的是acquired一次只对一个线程是真的。如果两个线程同时进入with块，只有一个应该“赢”并且acquired为真。具有acquiredtrue 的线程可以继续其工作，而另一个线程必须跳过该工作并稍后重试以获取锁。为了保证只有一个线程可以有acquired真，.add必须是原子的。

这是一些伪代码.add(key, value)：

1. if <key> is already in the cache:
2.   return False    
3. else:
4.   set the cache so that <key> has the value <value>
5.   return True

如果 的执行.add不是原子的，那么这可能会在两个线程 A 和 B 执行时发生.add("foo", "bar")。假设开始时缓存为空。

1. 线程 A 执行1. if "foo" is already in the cache并发现"foo"不在缓存中，并跳转到第 3 行，但线程调度程序将控制权切换到线程 B。
2. 线程B也执行了1. if "foo" is already in the cache，也发现“foo”不在缓存中。所以它跳到第 3 行，然后执行第 4 行和第 5 行，将键"foo"设置为值"bar"，然后调用返回True。
3. 最终，调度程序将控制权交还给线程 A，线程 A 继续执行 3、4、5，并将键"foo"设置为值"bar"并返回True。

您在这里拥有的是两个.add返回的调用True，如果这些.add调用是在memcache_lockthis 中进行的，则意味着两个线程可能acquired为真。所以两个线程可以同时工作，而你memcache_lock没有做它应该做的事情，一次只允许一个线程工作。

您没有使用确保它.add是 atomic的缓存。你像这样初始化它：

cache = Cache(app, config={'CACHE_TYPE': 'simple'})

的simple后端的作用范围是一个单一的过程，不具有线程安全，并且具有.add操作这是不原子。（顺便说一下，这根本不涉及 Mongo。如果您希望缓存由 Mongo 支持，则必须指定一个专门用于将数据发送到 Mongo 数据库的支持。）

所以你必须切换到另一个后端，一个保证.add原子性的后端。您可以按照 Celery 示例的指导并使用memcachedbackend，它确实具有原子.add操作。我不使用 Flask，但我基本上做了你在 Django 和 Celery 上所做的事情，并成功地使用了 Redis 后端来提供你在这里使用的那种锁定。

Answer 2

我还发现这是一个令人惊讶的难题。主要受到Sebastian在 redis 中实现分布式锁定算法的工作的启发，我编写了一个装饰器函数。

关于这种方法要记住的一个关键点是，我们将任务锁定在任务参数空间的级别，例如，我们允许多个游戏更新/处理顺序任务同时运行，但每个游戏只能运行一个。这就是argument_signature下面代码中实现的效果。您可以在以下要点中查看有关我们如何在堆栈中使用它的文档：

import base64
from contextlib import contextmanager
import json
import pickle as pkl
import uuid

from backend.config import Config
from redis import StrictRedis
from redis_cache import RedisCache
from redlock import Redlock

rds = StrictRedis(Config.REDIS_HOST, decode_responses=True, charset="utf-8")
rds_cache = StrictRedis(Config.REDIS_HOST, decode_responses=False, charset="utf-8")
redis_cache = RedisCache(redis_client=rds_cache, prefix="rc", serializer=pkl.dumps, deserializer=pkl.loads)
dlm = Redlock([{"host": Config.REDIS_HOST}])

TASK_LOCK_MSG = "Task execution skipped -- another task already has the lock"
DEFAULT_ASSET_EXPIRATION = 8 * 24 * 60 * 60  # by default keep cached values around for 8 days
DEFAULT_CACHE_EXPIRATION = 1 * 24 * 60 * 60  # we can keep cached values around for a shorter period of time

REMOVE_ONLY_IF_OWNER_SCRIPT = """
if redis.call("get",KEYS[1]) == ARGV[1] then
    return redis.call("del",KEYS[1])
else
    return 0
end
"""


@contextmanager
def redis_lock(lock_name, expires=60):
    # https://breadcrumbscollector.tech/what-is-celery-beat-and-how-to-use-it-part-2-patterns-and-caveats/
    random_value = str(uuid.uuid4())
    lock_acquired = bool(
        rds.set(lock_name, random_value, ex=expires, nx=True)
    )
    yield lock_acquired
    if lock_acquired:
        rds.eval(REMOVE_ONLY_IF_OWNER_SCRIPT, 1, lock_name, random_value)


def argument_signature(*args, **kwargs):
    arg_list = [str(x) for x in args]
    kwarg_list = [f"{str(k)}:{str(v)}" for k, v in kwargs.items()]
    return base64.b64encode(f"{'_'.join(arg_list)}-{'_'.join(kwarg_list)}".encode()).decode()


def task_lock(func=None, main_key="", timeout=None):
    def _dec(run_func):
        def _caller(*args, **kwargs):
            with redis_lock(f"{main_key}_{argument_signature(*args, **kwargs)}", timeout) as acquired:
                if not acquired:
                    return TASK_LOCK_MSG
                return run_func(*args, **kwargs)
        return _caller
    return _dec(func) if func is not None else _dec

在我们的任务定义文件中实现：

@celery.task(name="async_test_task_lock")
@task_lock(main_key="async_test_task_lock", timeout=UPDATE_GAME_DATA_TIMEOUT)
def async_test_task_lock(game_id):
    print(f"processing game_id {game_id}")
    time.sleep(TASK_LOCK_TEST_SLEEP)

我们如何针对本地 celery 集群进行测试：

from backend.tasks.definitions import async_test_task_lock, TASK_LOCK_TEST_SLEEP
from backend.tasks.redis_handlers import rds, TASK_LOCK_MSG
class TestTaskLocking(TestCase):
    def test_task_locking(self):
        rds.flushall()
        res1 = async_test_task_lock.delay(3)
        res2 = async_test_task_lock.delay(5)
        self.assertFalse(res1.ready())
        self.assertFalse(res2.ready())
        res3 = async_test_task_lock.delay(5)
        res4 = async_test_task_lock.delay(5)
        self.assertEqual(res3.get(), TASK_LOCK_MSG)
        self.assertEqual(res4.get(), TASK_LOCK_MSG)
        time.sleep(TASK_LOCK_TEST_SLEEP)
        res5 = async_test_task_lock.delay(3)
        self.assertFalse(res5.ready())

（作为一个好东西，还有一个如何设置的快速示例redis_cache）