烧瓶芹菜任务锁定 [英] Flask Celery task locking

问题描述

我正在将Flask与Celery结合使用，并且试图锁定一个特定任务，以便一次只能运行一个任务.在celery文档中，给出了执行此操作的示例

解决方案

我还发现这是一个非常困难的问题.灵感主要来自装饰器函数.

要牢记这种方法的一个关键点是，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/流程订单任务同时运行，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.您可以在此要点:

中查看有关如何在堆栈中使用此文档的文档.

 导入base64从contextlib导入contextmanager导入json以pkl的方式导入泡菜导入uuid从backend.config导入配置从redis导入StrictRedis从redis_cache导入RedisCache从redlock导入Redlockrds = StrictRedis(Config.REDIS_HOST，decode_responses =真，字符集="utf-8")rds_cache = StrictRedis(Config.REDIS_HOST，decode_responses = False，字符集="utf-8")redis_cache = RedisCache(redis_client = rds_cache，前缀="rc"，序列化器= pkl.dumps，解串器= pkl.loads)dlm = Redlock([[{"host":Config.REDIS_HOST}])TASK_LOCK_MSG =跳过任务执行-另一个任务已经具有锁".DEFAULT_ASSET_EXPIRATION =默认为8 * 24 * 60 * 60#将缓存的值保留8天左右DEFAULT_CACHE_EXPIRATION = 1 * 24 * 60 * 60#我们可以将缓存的值保留较短的时间REMOVE_ONLY_IF_OWNER_SCRIPT =""如果redis.call("get"，KEYS [1])== ARGV [1]，则返回redis.call("del"，KEYS [1])别的返回0结尾"@contextmanagerdef redis_lock(锁定名称，过期= 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))产生lock_acquired如果lock_acquired:rds.eval(REMOVE_ONLY_IF_OWNER_SCRIPT，1，lock_name，random_value)def arguments_signature(* args，** kwargs):arg_list = [str(x)for args中的x]kwarg_list = [f''{str(k)}:{str(v)}"对于k，v，在kwargs.items()中返回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):具有获得的redis_lock(f''{main_key} _ {argument_signature(* args，** kwargs)}''，超时):如果没有获得:返回TASK_LOCK_MSG返回run_func(* args，** kwargs)返回_caller如果func不为其他则返回_dec(func) 

我们的任务定义文件中的实现:

  @ celery.task(name =" async_test_task_lock")@task_lock(main_key ="async_test_task_lock"，超时= UPDATE_GAME_DATA_TIMEOUT)def async_test_task_lock(game_id):打印(f正在处理game_id {game_id}")time.sleep(TASK_LOCK_TEST_SLEEP) 

我们如何针对本地芹菜丛进行测试:

从backend.tasks.definitions中的

 导入async_test_task_lock，TASK_LOCK_TEST_SLEEP从backend.tasks.redis_handlers导入rds，TASK_LOCK_MSG类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，还有一个简单的示例，说明如何设置 redis_cache )

I am using Flask with Celery and I am trying to lock a specific task so that it can only be run one at a time. In the celery docs it gives a example of doing this Celery docs, Ensuring a task is only executed one at a time. This example that was given was for Django however I am using flask I have done my best to convert this to work with Flask however I still see myTask1 which has the lock can be run multiple times.

One thing that is not clear to me is if I am using the cache correctly, I have never used it before so all of it is new to me. One thing from the doc's that is mentioned but not explained is this

Doc Notes:

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.

Im not truly sure what that means, should i be using the cache in conjunction with a database and if so how would I do that? I am using mongodb. In my code I just have this setup for the cache cache = Cache(app, config={'CACHE_TYPE': 'simple'}) as that is what was mentioned in the Flask-Cache doc's Flask-Cache Docs

Another thing that is not clear to me is if there is anything different I need to do as I am calling my myTask1 from within my Flask route task1

Here is an example of my code that I am using.

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))

Final Working Code

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')

Here is also a screen shot you can see that I ran myTask1 two times and myTask2 a single time. Now I have the expected behavior for myTask1. Now myTask1 will be run by a single worker if another worker attempt to pick it up it will just keep retrying based on whatever i define.

解决方案

I also found this to be a surprisingly hard problem. Inspired mainly by Sebastian's work on implementing a distributed locking algorithm in redis I wrote up a decorator function.

A key point to bear in mind about this approach is that we lock tasks at the level of the task's argument space, e.g. we allow multiple game update/process order tasks to run concurrently, but only one per game. That's what argument_signature achieves in the code below. You can see documentation on how we use this in our stack at this gist:

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

Implementation in our task definitions file:

@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)

How we test against a local celery cluster:

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())

(as a goodie there's also a quick example of how to setup a redis_cache)

这篇关于烧瓶芹菜任务锁定的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！