今天介紹一下如何在django項目中使用celery搭建一個有兩個節點的任務隊列(一個主節點一個子節點;主節點發布任務,子節點收到任務並執行。搭建3個或者以上的節點就類似了),使用到了celery,rabbitmq。這裏不會單獨介紹celery和rabbitmq中的知識了。
1.項目基礎環境:
兩個ubuntu18.04虛擬機、python3.6.5、django2.0.4、celery3.1.26post2
2.主節點django項目結構:
3.settings.py中關於celery的配置:
import djcelery
# 此處的Queue和Exchange都涉及到RabbitMQ中的概念,這裏不做介紹
from kombu import Queue, Exchange
djcelery.setup_loader()
BROKER_URL = 'amqp://test:[email protected]:5672/testhost'
CELERY_RESULT_BACKEND = 'amqp://test:[email protected]:5672/testhost'
CELERY_TASK_RESULT_EXPIRES=3600
CELERY_TASK_SERIALIZER='json'
CELERY_RESULT_SERIALIZER='json'
# CELERY_ACCEPT_CONTENT = ['json', 'pickle', 'msgpack', 'yaml']
CELERY_DEFAULT_EXCHANGE = 'train'
CELERY_DEFAULT_EXCHANGE_TYPE = 'direct'
CELERY_IMPORTS = ("proj.celery1.tasks", )
CELERY_QUEUES = (
Queue('train', routing_key='train'),
Queue('predict', routing_key='predict'),
)
4.celery.py中的配置:
# coding:utf8
from __future__ import absolute_import
import os
from celery import Celery
from django.conf import settings
# set the default Django settings module for the 'celery' program.
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'proj.settings')
app = Celery('proj')
# Using a string here means the worker will not have to
# pickle the object when using Windows.
app.config_from_object('django.conf:settings')
# app.autodiscover_tasks(lambda: settings.INSTALLED_APPS)
app.autodiscover_tasks(settings.INSTALLED_APPS)
@app.task(bind=True)
def debug_task(self):
print('Request: {0!r}'.format(self.request))
5.proj/init.py中的配置:
from __future__ import absolute_import
from .celery import app as celery_app
6.celery1/tasks.py:(主節點中的任務不會執行,只執行子節點中的任務)
from __future__ import absolute_import
from celery import task
@task
def do_train(x, y):
return x + y
7.celery1/views.py:
from .tasks import do_train
class Test1View(APIView):
def get(self, request):
try:
# 這裏的queue和routing_key也涉及到RabiitMQ中的知識
# 關鍵,在這裏控制向哪個queue中發送任務,子節點通過這個執行對應queue中的任務
ret = do_train.apply_async(args=[4, 2], queue="train", routing_key="train")
# 獲取結果
data = ret.get()
except Exception as e:
return Response(dict(msg=str(e), code=10001))
return Response(dict(msg="OK", code=10000, data=data))
8.子節點目錄結構:
9.子節點中celery1/celery.py:
from __future__ import absolute_import
from celery import Celery
CELERY_IMPORTS = ("celery1.tasks", )
app = Celery('myapp',
# 此處涉及到RabbitMQ的知識,RabbitMQ是對應主節點上的
broker='amqp://test:[email protected]:5672/testhost',
backend='amqp://test:[email protected]:5672/testhost',
include=['celery1.tasks'])
app.config_from_object('celery1.config')
if __name__ == '__main__':
app.start()
10.子節點中celery1/config.py:
from __future__ import absolute_import
from kombu import Queue,Exchange
from datetime import timedelta
CELERY_TASK_RESULT_EXPIRES=3600
CELERY_TASK_SERIALIZER='json'
CELERY_RESULT_SERIALIZER='json'
CELERY_ACCEPT_CONTENT = ['json','pickle','msgpack','yaml']
CELERY_DEFAULT_EXCHANGE = 'train'
# exchange type可以看RabbitMQ中的相關內容
CELERY_DEFAULT_EXCHANGE_TYPE = 'direct'
CELERT_QUEUES = (
Queue('train',exchange='train',routing_key='train'),
)
11.子節點celery1/tasks.py:(這個是要真正執行的task,每個節點可以不同)
from __future__ import absolute_import
from celery1.celery import app
import time
from celery import task
@task
def do_train(x, y):
"""
訓練
:param data:
:return:
"""
time.sleep(3)
return dict(data=str(x+y),msg="train")
12.啓動子節點中的celery:
celery1是項目,-Q train表示從train這個queue中接收任務
celery -A celery1 worker -l info -Q train
13.啓動主節點中的django項目:
python manage.py runserver
14.使用Postman請求對應的view
請求url:http://127.0.0.1:8000/api/v1/celery1/test/
返回的結果是:
{
"msg": "OK",
"code": 10000,
"data": {
"data": "6",
"msg": "train"
}
}
15.遇到的問題:
1)celery隊列報錯: AttributeError: ‘str’ object has no attribute ‘items’
解決:將redis庫從3.0回退到了2.10,pip install redis==2.10
解決方法參考鏈接:https://stackoverflow.com/que...
今天就說到這裏,如有疑問,歡迎交流。