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。在 celery 文档中，它给出了一个这样做的例子Celery docs, Ensuring a task is only executed one at a time。给出的这个示例是针对 Django 的，但是我使用的是烧瓶，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，如果我正确使用缓存，我以前从未使用过它，所以所有这些对我来说都是新的。文档中提到但未解释的一件事是

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，我是否应该将缓存与数据库结合使用，如果是，我将如何做到这一点？我正在使用 mongodb。在我的代码中，我只是为缓存cache = Cache(app, config={'CACHE_TYPE': 'simple'}) 设置了这个设置，因为这是 Flask-Cache 文档的Flask-Cache Docs 中提到的内容@

我不清楚的另一件事是我是否需要做任何不同的事情，因为我在我的 Flask 路由 task1 中调用我的 myTask1

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')

这也是一个屏幕截图，你可以看到我运行了两次 myTask1 和一次 myTask2。现在我有了 myTask1 的预期行为。现在myTask1 将由单个工作人员运行，如果另一个工作人员尝试接听它，它将根据我定义的内容继续重试。

Answer 1

我还发现这是一个令人惊讶的难题。主要受Sebastian's work 在redis 中实现分布式锁定算法的启发，我写了一个decorator function。

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的参数空间级别，例如我们允许多个游戏更新/处理订单任务同时运行，但每个游戏只能运行一个。这就是argument_signature 在下面的代码中实现的。您可以在this gist 处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，还有一个关于如何设置redis_cache 的快速示例）

Answer 2

在您的问题中，您从您使用的 Celery 示例中指出了这个警告：

为了使其正常工作，您需要使用.add 操作是原子操作的缓存后端。众所周知，memcached 可以很好地实现此目的。

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

考虑这段代码：

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

您想要的是acquired 一次只对一个线程有效。如果两个线程同时进入with 块，则只有一个线程应该“获胜”并且acquired 为真。具有acquired true 的线程可以继续其工作，而另一个线程必须跳过工作并稍后再试以获取锁。 为了确保只有一个线程可以拥有acquired true，.add 必须是原子的。

这是.add(key, value) 所做的一些伪代码：

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.add 的执行不是原子的，那么如果两个线程A 和B 执行.add("foo", "bar")，就会发生这种情况。假设一开始有一个空缓存。

1. 线程 A 执行 1. if "foo" is already in the cache 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，also 发现“foo”不在缓存中。因此它跳到第 3 行，然后执行第 4 行和第 5 行，将键 "foo" 设置为值 "bar"，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，还将键 "foo" 设置为值 "bar"，并返回 True。

这里有两个返回True 的.add 调用，如果这些.add 调用是在memcache_lock 内进行的，这意味着两个线程可以使acquired 为真。所以两个线程可以同时工作，而你的memcache_lock 并没有做它应该做的事情，即一次只允许一个线程工作。

您没有使用确保.add 是原子的缓存。你像这样初始化它：

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

simple backend 作用于单个进程，没有线程安全性，并且有一个非原子的.add 操作。 （顺便说一句，这根本不涉及 Mongo。如果您希望缓存由 Mongo 支持，则必须指定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 是原子的。您可以按照 Celery 示例的引导并使用 memcached backend，它确实具有原子 .add 操作。我不使用 Flask，但我基本上已经完成了您对 Django 和 Celery 所做的工作，并且成功地使用了 Redis 后端来提供您在此处使用的那种锁定。

Answer 3

使用此设置，您应该仍然可以看到工作人员收到任务，因为锁是在任务本身内部检查的。唯一的区别是，如果锁被另一个工作人员获取，则不会执行工作。
在文档中给出的示例中，这是所需的行为；如果锁已经存在，则任务将不做任何事情并成功完成。你想要的略有不同；你希望工作被排队而不是被忽略。

为了获得预期的效果，您需要确保该任务将由工作人员接手并在未来某个时间执行。实现此目的的一种方法是重试。

@task(bind=True, name='my-task')
def my_task(self):
    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))
            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