Celery是一个简单,灵活且能处理异步任务,定时任务及大量消息的分布式系统
专注于实时处理的异步任务队列
同时也支持任务调度
Celery框架由三部分组成:消息中间件(AMQP broker),任务执行单元(celery workers),任务执行结果存储(task result store)组成
消息中间件
celery本身不提供消息服务,但是方便和第三方提供的消息中间件集成,包括RabbitMQ,redis等
任务执行单元
Worker是celery提供的任务执行单元worker并发的运行在任务节点中
任务执行结果存储
Task result store用来存储Worker执行的任务的结果，Celery支持以不同方式存储任务的结果，包括AMQP, redis等
版本支持情况
Celery version 4.0 runs on Python ❨2.7, 3.4, 3.5❩ PyPy ❨5.4, 5.5❩ This is the last version to support Python 2.7, and from the next version (Celery 5.x) Python 3.5 or newer is required. If you’re running an older version of Python, you need to be running an older version of Celery: Python 2.6: Celery series 3.1 or earlier. Python 2.5: Celery series 3.0 or earlier. Python 2.4 was Celery series 2.2 or earlier. Celery is a project with minimal funding, so we don’t support Microsoft Windows. Please don’t open any issues related to that platform.
2.使用场景
异步任务:将耗时操作任务提交给Celery去异步执行,比如发送短信/邮件,消息推送,音视频处理等等
定时任务:定时执行某件事情,比如每天数据统计
3.Celery的安装配置
pip install celery消息中间件:RabbitMQ/Redisapp=Celery('人物名',backend='xxx',broker='xxx')
4.Celery执行异步任务
基本使用
创建项目:celerytest
创建py文件:tasks.py
from celery import Celeryimport timebroker = 'redis://127.0.0.1:6379/1'backend = 'redis://127.0.0.1:6379/2'app = Celery('test',broker=broker,backend=backend)@app.taskdef add(x,y): return x+y
创建py文件:add_task.py,添加任务
from tasks import addresult = add.delay(4,5)print(result.id)
创建py文件,run.py,执行任务,或者使用命令执行:celery worker -A tasks -l info
注:windows下:celery worker -A tasks -l info -P eventlet
from tasks import addif __name__ == '__main__': add.worker_main() # cel.worker_main(argv=['--loglevel=info')
创建py文件:result.py ,查看任务执行结果
from celery.result import AsyncResultfrom tasks import addasync = AsyncResult(id="e919d97d-2938-4d0f-9265-fd8237dc2aa3", app=cel)if async.successful(): result = async.get() print(result) # result.forget() # 将结果删除elif async.failed(): print('执行失败')elif async.status == 'PENDING': print('任务等待中被执行')elif async.status == 'RETRY': print('任务异常后正在重试')elif async.status == 'STARTED': print('任务已经开始被执行')执行add_task.py,添加任务,并获取任务ID执行run.py ,或者执行命令:celery worker -A tasks -l info -P eventlet执行result.py 检查任务状态并获取结果
多任务结构
multi_celery ├── celery_task# celery相关文件夹 │ ├── celery.py # celery连接和配置相关文件,必须叫这个名字 │ └── tasks1.py # 所有任务函数 │ └── tasks2.py # 所有任务函数 ├── result.py # 检查结果 └── add_task.py # 触发任务
celery.py
from celery import Celery# broker:消息中间人用redisbroker='redis://127.0.0.1:6379/1'# 结果存储在redis中backend='redis://127.0.0.1:6379/2'# 第一个参数是别名,可以随便写# include=[]app=Celery('test',broker=broker,backend=backend,include=['celery_task.task1','celery_task.task2'])# 时区app.conf.timezone = 'Asia/Shanghai'# 是否使用UTCapp.conf.enable_utc = False
task1.py
from .celery import app@app.taskdef add(x,y): return x+y
taks2.py
from .celery import app@app.taskdef write_file(s): with open('a.txt','a',encoding='utf-8')as f: f.write(s) return '写成功'
result.py
from celery.result import AsyncResult# 导入celery对象from celery_task.celery import appasync = AsyncResult(id="ac2a7e52-ef66-4caa-bffd-81414d869f85", app=app)if async.successful(): # 任务执行的结果,也就是返回值 result = async.get() print(result) # result.forget() # 将结果删除elif async.failed(): print('执行失败')elif async.status == 'PENDING': print('任务等待中被执行')elif async.status == 'RETRY': print('任务异常后正在重试')elif async.status == 'STARTED': print('任务已经开始被执行')
add_task.py
from celery_task import task1from celery_task import task2# 往队列中添加一个2+3的任务result=task1.add.delay(2,3)print(result.id)# 往队列中添加一个写文件的任务result=task2.write_file.delay('lqz')print(result.id)
添加任务(执行add_task.py),开启worker:celery worker -A celery_task -l info -P eventlet,检查任务执行结果(执行result.py)
5 Celery执行定时任务
设定时间让celery执行一个任务
add_task.py
from celery_app_task import addfrom datetime import datetime# 方式一# v1 = datetime(2019, 2, 13, 18, 19, 56)# print(v1)# v2 = datetime.utcfromtimestamp(v1.timestamp())# print(v2)# result = add.apply_async(args=[1, 3], eta=v2)# print(result.id)# 方式二ctime = datetime.now()# 默认用utc时间utc_ctime = datetime.utcfromtimestamp(ctime.timestamp())from datetime import timedeltatime_delay = timedelta(seconds=10)task_time = utc_ctime + time_delay# 使用apply_async并设定时间result = add.apply_async(args=[4, 3], eta=task_time)print(result.id)
类似于contab的定时任务
多任务结构中celery.py修改如下
from datetime import timedeltafrom celery import Celeryfrom celery.schedules import crontabcel = Celery('tasks', broker='redis://127.0.0.1:6379/1', backend='redis://127.0.0.1:6379/2', include=[ 'celery_task.tasks1', 'celery_task.tasks2',])cel.conf.timezone = 'Asia/Shanghai'cel.conf.enable_utc = Falsecel.conf.beat_schedule = { # 名字随意命名 'add-every-10-seconds': { # 执行tasks1下的test_celery函数 'task': 'celery_task.tasks1.test_celery', # 每隔2秒执行一次 # 'schedule': 1.0, # 'schedule': crontab(minute="*/1"), 'schedule': timedelta(seconds=2), # 传递参数 'args': ('test',) }, # 'add-every-12-seconds': { # 'task': 'celery_task.tasks1.test_celery', # 每年4月11号，8点42分执行 # 'schedule': crontab(minute=42, hour=8, day_of_month=11, month_of_year=4), # 'schedule': crontab(minute=42, hour=8, day_of_month=11, month_of_year=4), # 'args': (16, 16) # },}
启动一个beat：celery beat -A celery_task -l info
启动work执行：celery worker -A celery_task -l info -P eventlet
6.Django中使用Celery
在项目中创建celeryconfig.py
import djcelerydjcelery.setup_loader()CELERY_IMPORTS = ( 'app01.tasks',)# 有些情况可以防止死锁CELERYD_FORCE_EXECV = True# 设置并发worker数量CELERYD_CONCURRENCY = 4# 允许重试CELERY_ACKS_LATE = True# 每个worker最多执行100个任务被销毁，可以防止内存泄漏CELERYD_MAX_TASKS_PER_CHILD = 100# 超时时间CELERYD_TASK_TIME_LIMIT = 12 * 30
在app01目录下面创建tasks.py
from celery import taskimport time@taskdef add(x,y): time.sleep(3) return x+y
视图函数views.py
from django.shortcuts import render,HttpResponse# Create your views here.from app01 import tasksdef test(request): result=tasks.add.delay(2,4) print(result.id) return HttpResponse('ok')
settings.py
INSTALLED_APPS = [ ... 'djcelery', 'app01']...from djagocele import celeryconfigBROKER_BACKEND='redis'BROKER_URL='redis://127.0.0.1:6379/1'CELERY_RESULT_BACKEND='redis://127.0.0.1:6379/2'
起worker:
python3 manage.py celery worker