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回顾celery模块的文档结构
需求场景
假如我们有两个worker,一个worker专门用来处理邮件发送任务和图像处理任务，一个worker专门用来处理文件上传任务。
我们创建两个队列，一个专门用于存储邮件任务队列和图像处理，一个用来存储文件上传任务队列。
Celery支持AMQP(Advanced Message Queue)所有的路由功能，我们也可以使用简单的路由设置将指定的任务发送到指定的队列中.
路由配置示例
通过 celeryconfig.py 配置任务路由，来控制任务队列划分
我们需要配置在celeryconfig.py模块中配置 CELERY_ROUTES 项, tasks.py模块修改如下:
from celery_tasks.celery import app as celery_app
@celery_app.task
def my_task1(a, b):
print("my_task1任务正在执行....")
return a + b
@celery_app.task
def my_task2(a, b):
print("my_task2任务正在执行....")
return a + b
@celery_app.task
def my_task3(a, b):
print("my_task3任务正在执行....")
return a + b
@celery_app.task
def my_task4(a, b):
print("my_task3任务正在执行....")
return a + b
@celery_app.task
def my_task5():
print("my_task5任务正在执行....")
@celery_app.task
def my_task6():
print("my_task6任务正在执行....")
@celery_app.task
def my_task7():
print("my_task7任务正在执行....")
通过配置，将send_email和upload_file任务发送到queue1队列中，将image_process发送到queue2队列中。
修改celeryconfig.py:
# 设置结果存储
CELERY_RESULT_BACKEND = 'redis://127.0.0.1:6379/9'
# 设置代理人broker
BROKER_URL = 'redis://127.0.0.1:6379/8'
# 配置任务路由
CELERY_ROUTES=({
'celery_tasks.tasks.my_task5': {'queue': 'queue1'},
'celery_tasks.tasks.my_task6': {'queue': 'queue1'},
'celery_tasks.tasks.my_task7': {'queue': 'queue2'},
},
)
下面来测试看看：
In [1]: from celery_tasks.tasks import *
In [2]: my_task5.delay()
Out[2]: <AsyncResult: 16d2e1f7-745f-4f38-ab57-f18859a8ff8a>
In [3]: my_task6.delay()
Out[3]: <AsyncResult: 99b9f858-6bb8-47dc-b47f-d3b8b6b3f6bf>
In [4]: my_task7.delay()
Out[4]: <AsyncResult: f50c876b-f4c4-4a82-84cc-ad83c9314fa8>
开启两个worker服务器，分别处理两个队列：
celery -A celery_tasks worker -l info -P eventlet -Q queue1
celery -A celery_tasks worker -l info -P eventlet -Q queue2
启动了worker之后，任务立即按照队列进行处理，如下：
(venv) F:\pythonProject\django-pratice>celery -A celery_tasks worker -l info -P eventlet -Q queue1
-------------- celery@USC2VG2F9NPB650 v4.3.0 (rhubarb)
---- **** -----
--- * *** * -- Windows-10-10.0.17763-SP0 2019-08-04 01:42:00
-- * - **** ---
- ** ---------- [config]
- ** ---------- .> app: celery_tasks.tasks:0x155ee3036d8
- ** ---------- .> transport: redis://127.0.0.1:6379/8
- ** ---------- .> results: redis://127.0.0.1:6379/9
- *** --- * --- .> concurrency: 12 (eventlet)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
-------------- [queues]
.> queue1 exchange=queue1(direct) key=queue1
[tasks]
. celery_tasks.tasks.my_task1
. celery_tasks.tasks.my_task2
. celery_tasks.tasks.my_task3
. celery_tasks.tasks.my_task4
. celery_tasks.tasks.my_task5
. celery_tasks.tasks.my_task6
. celery_tasks.tasks.my_task7
[2019-08-04 01:42:00,230: INFO/MainProcess] Connected to redis://127.0.0.1:6379/8
[2019-08-04 01:42:00,271: INFO/MainProcess] mingle: searching for neighbors
[2019-08-04 01:42:01,422: INFO/MainProcess] mingle: all alone
[2019-08-04 01:42:01,547: INFO/MainProcess] celery@USC2VG2F9NPB650 ready.
[2019-08-04 01:42:01,551: INFO/MainProcess] pidbox: Connected to redis://127.0.0.1:6379/8.
[2019-08-04 01:42:01,567: INFO/MainProcess] Received task: celery_tasks.tasks.my_task5[16d2e1f7-745f-4f38-ab57-f18859a8ff8a]
[2019-08-04 01:42:01,568: WARNING/MainProcess] my_task5任务正在执行....
[2019-08-04 01:42:01,589: INFO/MainProcess] Received task: celery_tasks.tasks.my_task6[99b9f858-6bb8-47dc-b47f-d3b8b6b3f6bf]
[2019-08-04 01:42:01,590: WARNING/MainProcess] my_task6任务正在执行....
[2019-08-04 01:42:01,596: INFO/MainProcess] Task celery_tasks.tasks.my_task5[16d2e1f7-745f-4f38-ab57-f18859a8ff8a] succeeded in 0.030999999959021807s: None
[2019-08-04 01:42:01,619: INFO/MainProcess] Task celery_tasks.tasks.my_task6[99b9f858-6bb8-47dc-b47f-d3b8b6b3f6bf] succeeded in 0.03100000007543713s: None
(venv) F:\pythonProject\django-pratice>celery -A celery_tasks worker -l info -P eventlet -Q queue2
-------------- celery@USC2VG2F9NPB650 v4.3.0 (rhubarb)
---- **** -----
--- * *** * -- Windows-10-10.0.17763-SP0 2019-08-04 01:42:54
-- * - **** ---
- ** ---------- [config]
- ** ---------- .> app: celery_tasks.tasks:0x17e72b83668
- ** ---------- .> transport: redis://127.0.0.1:6379/8
- ** ---------- .> results: redis://127.0.0.1:6379/9
- *** --- * --- .> concurrency: 12 (eventlet)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
-------------- [queues]
.> queue2 exchange=queue2(direct) key=queue2
[tasks]
. celery_tasks.tasks.my_task1
. celery_tasks.tasks.my_task2
. celery_tasks.tasks.my_task3
. celery_tasks.tasks.my_task4
. celery_tasks.tasks.my_task5
. celery_tasks.tasks.my_task6
. celery_tasks.tasks.my_task7
[2019-08-04 01:42:54,990: INFO/MainProcess] Connected to redis://127.0.0.1:6379/8
[2019-08-04 01:42:55,025: INFO/MainProcess] mingle: searching for neighbors
[2019-08-04 01:42:56,197: INFO/MainProcess] mingle: all alone
[2019-08-04 01:42:56,260: INFO/MainProcess] pidbox: Connected to redis://127.0.0.1:6379/8.
[2019-08-04 01:42:56,323: INFO/MainProcess] celery@USC2VG2F9NPB650 ready.
[2019-08-04 01:42:56,484: INFO/MainProcess] Received task: celery_tasks.tasks.my_task7[f50c876b-f4c4-4a82-84cc-ad83c9314fa8]
[2019-08-04 01:42:56,487: WARNING/MainProcess] my_task7任务正在执行....
[2019-08-04 01:42:56,520: INFO/MainProcess] Task celery_tasks.tasks.my_task7[f50c876b-f4c4-4a82-84cc-ad83c9314fa8] succeeded in 0.030999999959021807s: None
可以从上面两个日志看出，queue1的worker执行了tast5和tast6，queue2的worker执行了task7
这是前面在路由配置好的，那么如果没有配置好的任务，能否动态直接调用到不同的queue队列中呢？
通过apply_aynsc()方法动态划分任务至队列中
可以通过apply_aynsc()方法来设置任务发送到那个队列中
In [6]: my_task1.apply_async(args=(10,20),queue='queue1')
Out[6]: <AsyncResult: b3eb2fe8-791a-40fd-bcd5-d538afedbf81>
查看queue1的worker日志，如下：
可以看到queue1的worker已经执行了my_task1的任务了。
双/多队列worker
我们也可设置一个worker服务器处理两个队列中的任务:
celery -A celery_tasks worker -l info -P eventlet -Q queue1,queue2
启动日志如下：
(venv) F:\pythonProject\django-pratice>celery -A celery_tasks worker -l info -P eventlet -Q queue1,queue2
-------------- celery@USC2VG2F9NPB650 v4.3.0 (rhubarb)
---- **** -----
--- * *** * -- Windows-10-10.0.17763-SP0 2019-08-04 01:49:45
-- * - **** ---
- ** ---------- [config]
- ** ---------- .> app: celery_tasks.tasks:0x1d0bb4227b8
- ** ---------- .> transport: redis://127.0.0.1:6379/8
- ** ---------- .> results: redis://127.0.0.1:6379/9
- *** --- * --- .> concurrency: 12 (eventlet)
-- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker)
--- ***** -----
-------------- [queues]
.> queue1 exchange=queue1(direct) key=queue1
.> queue2 exchange=queue2(direct) key=queue2
[tasks]
. celery_tasks.tasks.my_task1
. celery_tasks.tasks.my_task2
. celery_tasks.tasks.my_task3
. celery_tasks.tasks.my_task4
. celery_tasks.tasks.my_task5
. celery_tasks.tasks.my_task6
. celery_tasks.tasks.my_task7
[2019-08-04 01:49:45,805: INFO/MainProcess] Connected to redis://127.0.0.1:6379/8
[2019-08-04 01:49:45,838: INFO/MainProcess] mingle: searching for neighbors
[2019-08-04 01:49:47,054: WARNING/MainProcess] g:\python3\python371\lib\site-packages\celery\app\control.py:54: DuplicateNodenameWarning: Received multiple replies from node name:
celery@USC2VG2F9NPB650.
Please make sure you give each node a unique nodename using
the celery worker `-n` option.
pluralize(len(dupes), 'name'), ', '.join(sorted(dupes)),
[2019-08-04 01:49:47,055: INFO/MainProcess] mingle: all alone
[2019-08-04 01:49:47,196: INFO/MainProcess] celery@USC2VG2F9NPB650 ready.
[2019-08-04 01:49:47,229: INFO/MainProcess] pidbox: Connected to redis://127.0.0.1:6379/8.
现在让my_task1在queue1和queue2都调用一下：
In [7]: my_task1.apply_async(args=(10,20),queue='queue1')
Out[7]: <AsyncResult: 3e07792e-6d82-44e2-8825-6a9cdb0034f5>
In [8]: my_task1.apply_async(args=(10,20),queue='queue2')
Out[8]: <AsyncResult: 757f62b6-01c8-4f2d-a3f2-54956b962a0c>
查看双队列worker的运行日志，如下：
可以从日志中看出，两个队列的任务该worker都可以执行。
