python celery多worker多队列定时任务
Posted Chang-kuk
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多worker、多队列
celery是一个分布式的任务调度模块，那么怎么实现它的分布式功能呢，celery可以支持多台不同的计算机执行不同的任务或者相同的任务。
如果要说celery的分布式应用的话，就要提到celery的消息路由机制，提到AMQP协议。
简单理解：
可以有多个"消息队列"（message Queue），不同的消息可以指定发送给不同的Message Queue，
而这是通过Exchange来实现的，发送消息到"消息队列"中时，可以指定routiing_key，Exchange通过routing_key来吧消息路由（routes）到不同的"消息队列"中去。
exchange 对应 一个消息队列(queue)，即：通过"消息路由"的机制使exchange对应queue，每个queue对应每个worker。
下面我们来看一个列子：
vi tasks.py
#!/usr/bin/env python #-*- coding:utf-8 -*- from celery import Celery app = Celery() app.config_from_object("celeryconfig") # 指定配置文件 @app.task def taskA(x,y): return x + y @app.task def taskB(x,y,z): return x + y + z @app.task def add(x,y): return x + y
编写配置文件，配置文件一般单独写在一个文件中。
vi celeryconfig.py
#!/usr/bin/env python #-*- coding:utf-8 -*- from kombu import Exchange,Queue BROKER_URL = "redis://47.106.106.220:5000/1" CELERY_RESULT_BACKEND = "redis://47.106.106.220:5000/2" CELERY_QUEUES = ( Queue("default",Exchange("default"),routing_key="default"), Queue("for_task_A",Exchange("for_task_A"),routing_key="for_task_A"), Queue("for_task_B",Exchange("for_task_B"),routing_key="for_task_B") ) # 路由 CELERY_ROUTES = { \'tasks.taskA\':{"queue":"for_task_A","routing_key":"for_task_A"}, \'tasks.taskB\':{"queue":"for_task_B","routing_key":"for_task_B"} }
远程客户端上编写测试脚本
vi test.py from tasks import * re1 = taskA.delay(100, 200) print(re1.result) re2 = taskB.delay(1, 2, 3) print(re2.result) re3 = add.delay(1, 2) print(re3.status)
启动两个worker来分别指定taskA、taskB，开两个窗口分别执行下面语句。
celery -A tasks worker -l info -n workerA.%h -Q for_task_A celery -A tasks worker -l info -n workerB.%h -Q for_task_B
远程客户端上执行脚本可以看到如下输出：
python test.py 300 6 PENDING
在taskA所在窗口可以看到如下输出：
....... ....... ....... task_A [tasks] . tasks.add . tasks.taskA . tasks.taskB [2018-05-27 19:23:49,235: INFO/MainProcess] Connected to redis://47.106.106.220:5000/1 [2018-05-27 19:23:49,253: INFO/MainProcess] mingle: searching for neighbors [2018-05-27 19:23:50,293: INFO/MainProcess] mingle: all alone [2018-05-27 19:23:50,339: INFO/MainProcess] celery@workerA.izwz920j4zsv1q15yhii1qz ready. [2018-05-27 19:23:56,051: INFO/MainProcess] sync with celery@workerB.izwz920j4zsv1q15yhii1qz [2018-05-27 19:24:28,855: INFO/MainProcess] Received task: tasks.taskA[8860e78a-b82b-4715-980c-ae125dcab2f9] [2018-05-27 19:24:28,872: INFO/ForkPoolWorker-1] Task tasks.taskA[8860e78a-b82b-4715-980c-ae125dcab2f9] succeeded in 0.0162177120219s: 300
在taskB所在窗口可以看到如下输出：
....... ....... ....... task_B [tasks] . tasks.add . tasks.taskA . tasks.taskB [2018-05-27 19:23:56,012: INFO/MainProcess] Connected to redis://47.106.106.220:5000/1 [2018-05-27 19:23:56,022: INFO/MainProcess] mingle: searching for neighbors [2018-05-27 19:23:57,064: INFO/MainProcess] mingle: sync with 1 nodes [2018-05-27 19:23:57,064: INFO/MainProcess] mingle: sync complete [2018-05-27 19:23:57,112: INFO/MainProcess] celery@workerB.izwz920j4zsv1q15yhii1qz ready. [2018-05-27 19:24:33,885: INFO/MainProcess] Received task: tasks.taskB[5646d0b7-3dd5-4b7f-8994-252c5ef03973] [2018-05-27 19:24:33,910: INFO/ForkPoolWorker-1] Task tasks.taskB[5646d0b7-3dd5-4b7f-8994-252c5ef03973] succeeded in 0.0235358460341s: 6
我们看到状态是PENDING，表示没有执行，这个是因为没有celeryconfig.py文件中指定改route到哪一个Queue中，所以会被发动到默认的名字celery的Queue中，但是我们还没有启动worker执行celery中的任务。下面，我们来启动一个worker来执行celery队列中的任务。
celery -A tasks worker -l info -n worker.%h -Q celery
再次在远程客户端执行test.py，可以看到结果执行成功，并且刚新启动的worker窗口有如下输出：
....... ....... ....... [tasks] . tasks.add . tasks.taskA . tasks.taskB [2018-05-27 19:25:44,596: INFO/MainProcess] Connected to redis://47.106.106.220:5000/1 [2018-05-27 19:25:44,611: INFO/MainProcess] mingle: searching for neighbors [2018-05-27 19:25:45,660: INFO/MainProcess] mingle: sync with 2 nodes [2018-05-27 19:25:45,660: INFO/MainProcess] mingle: sync complete [2018-05-27 19:25:45,711: INFO/MainProcess] celery@worker.izwz920j4zsv1q15yhii1qz ready. [2018-05-27 19:25:45,868: INFO/MainProcess] Received task: tasks.add[f9c5ca2b-623e-4c0a-9c45-a99fb0b79ed5] [2018-05-27 19:25:45,880: INFO/ForkPoolWorker-1] Task tasks.add[f9c5ca2b-623e-4c0a-9c45-a99fb0b79ed5] succeeded in 0.0107084610499s: 3
Celery与定时任务
在celery中执行定时任务非常简单，只需要设置celery对象中的CELERYBEAT_SCHEDULE属性即可。
下面我们接着在celeryconfig.py中添加CELERYBEAT_SCHEDULE变量：
cat celeryconfig.py
#!/usr/bin/env python
#-*- coding:utf-8 -*-
from kombu import Exchange,Queue
BROKER_URL = "redis://47.106.106.220:5000/1"
CELERY_RESULT_BACKEND = "redis://47.106.106.220:5000/2"
CELERY_QUEUES = (
Queue("default",Exchange("default"),routing_key="default"),
Queue("for_task_A",Exchange("for_task_A"),routing_key="for_task_A"),
Queue("for_task_B",Exchange("for_task_B"),routing_key="for_task_B")
)
CELERY_ROUTES = {
\'tasks.taskA\':{"queue":"for_task_A","routing_key":"for_task_A"},
\'tasks.taskB\':{"queue":"for_task_B","routing_key":"for_task_B"}
}
# 新增加的定时任务部分
CELERY_TIMEZONE = \'UTC\'
CELERYBEAT_SCHEDULE = {
\'taskA_schedule\' : {
\'task\':\'tasks.taskA\',
\'schedule\':2,
\'args\':(5,6)
},
\'taskB_scheduler\' : {
\'task\':"tasks.taskB",
"schedule":10,
"args":(10,20,30)
},
\'add_schedule\': {
"task":"tasks.add",
"schedule":5,
"args":(1,2)
}
}
还是按之前启动三个worker
celery -A tasks worker -l info -n workerA.%h -Q for_task_A celery -A tasks worker -l info -n workerB.%h -Q for_task_B celery -A tasks worker -l info -n worker.%h -Q celery
启动定时任务
[root@izwz920j4zsv1q15yhii1qz scripts]# celery -A tasks beat
celery beat v4.1.1 (latentcall) is starting.
__ - ... __ - _
LocalTime -> 2018-05-27 19:39:29
Configuration ->
. broker -> redis://47.106.106.220:5000/1
. loader -> celery.loaders.app.AppLoader
. scheduler -> celery.beat.PersistentScheduler
. db -> celerybeat-schedule
. logfile -> [stderr]@%WARNING
. maxinterval -> 5.00 minutes (300s)
在之前启动worker的三个窗口分别可以看到定时任务正在运行：
celery -A tasks worker -l info -n workerA.%h -Q for_task_A [2018-05-27 19:41:27,432: INFO/ForkPoolWorker-1] Task tasks.taskA[60f41780-c9a2-477b-be46-6620ef07631f] succeeded in 0.00289130600868s: 11 [2018-05-27 19:41:29,428: INFO/MainProcess] Received task: tasks.taskA[27220f52-dde2-471a-a87c-3f533d67217c] ......
...... celery -A tasks worker -l info -n workerB.%h -Q for_task_B [2018-05-27 19:41:18,420: INFO/ForkPoolWorker-1] Task tasks.taskB[b6f9aee3-e6b4-4f10-9428-457d9bb844cf] succeeded in 0.00282042898471s: 60 [2018-05-27 19:41:28,416: INFO/MainProcess] Received task: tasks.taskB[44dfea0b-b725-4874-bea2-9b66e8da573b]
......
...... celery -A tasks worker -l info -n worker.%h -Q celery [2018-05-27 19:41:23,428: INFO/ForkPoolWorker-1] Task tasks.add[315a9cca-3c95-4517-9289-2ece15cd46a4] succeeded in 0.00355823297286s: 3 [2018-05-27 19:41:28,423: INFO/MainProcess] Received task: tasks.add[c4a1b2c7-ecb7-4af4-85c1-a341b3ec6726]
......
......
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