celery安装：

pip install celery
pip install celery[redis] 

flower 安装：

docker pull placr/flower
docker run -d –p 5555:5555 --name flower --link redis:redis placr/flower

#######访问端口5555就可以看到web 界面

Celery 定时任务：

    项目目录结构：
    demo/
        celery_app/
            __init__.py
            celeryconfig.py
            tasks.py

init.py 内容

from celery import Celery

app = Celery('demo')                                # 创建 Celery 实例
app.config_from_object('celery_app.celeryconfig')      # 添加配置文件

celeryconfig.py 内容

# -*- coding: utf-8 -*-

from celery.schedules import timedelta
from celery.schedules import crontab

# Broker and Backend
BROKER_URL = 'redis://192.168.5.151:6379' # 指定 Broker
#BROKER_URL = 'redis://127.0.0.1:6379'
CELERY_RESULT_BACKEND = 'redis://192.168.5.151:6379/0' # 指定 Backend
#CELERY_RESULT_BACKEND = 'redis://127.0.0.1:6379/0'
CELERYD_PREFETCH_MULTIPLIER = 10  # 并发量
CELERY_TASK_RESULT_EXPIRES = 3600 # 结果过期
CELERY_TASK_ALWAYS_EAGER = False # 如果是这样True，所有任务将通过阻塞在本地执行，直到任务返回
CELERY_ENABLE_UTC = False
# Timezone
CELERY_TIMEZONE="Asia/Shanghai"    # 指定时区，不指定默认为 'UTC'
# CELERY_TIMEZONE='UTC'

# import # 指定导入的任务模块
CELERY_IMPORTS = (
'celery_app.tasks',
)

# schedules
CELERYBEAT_SCHEDULE = {
    'add-every-30-seconds': {
         'task': 'celery_app.tasks.add',
         'schedule': crontab(minute="*"),       # 每 60 秒执行一次
         'args': (5, 8)                           # 任务函数参数
},
}

Tasks.py 内容

# coding:utf-8

import time
from celery_app import app
import os

os.environ.setdefault('FORKED_BY_MULTIPROCESSING', '1')
@app.task
def add(x, y):
    time.sleep(2)
return x + y

启动一个worker

Celery –A celery_app worker –l info

启动一个 beat # 随时检查配置变化

Celery –A celery_app beat –l info

Celery 配合flask 异步发送邮件任务：

Flask-demo/
    Main.py
    Celeryconfig.py
    Celerytasks.py
    Celeryapp.py

Main.py 内容:

from flask import Flask, request, render_template, session, flash, redirect, \
    url_for
from flask_mail import Mail, Message
from celeryapp import make_celery
app = Flask(__name__)
app.config['SECRET_KEY'] = 'top-secret!'

# Flask-Mail configuration
app.config['MAIL_SERVER'] = "smtp.163.com"
app.config['MAIL_PORT'] = 25
app.config['MAIL_USE_TLS'] = True
app.config['MAIL_USERNAME'] = "xxxx@163.com"  # 配置自己的
app.config['MAIL_PASSWORD'] = "xxxx"            # 配置自己的邮箱授权码
app.config['MAIL_DEFAULT_SENDER'] = 'xxxx@163.com'

mail = Mail(app)
celery = make_celery(app)
from celerytasks import add, send_async_email  # 只有在 celery 配置后 才可以调task


@app.route('/', methods=['GET', 'POST'])
def index():
    
    title = 'Hello from Flask'
email = “to_email@xx.com”
body = 'This is a test email sent from a background Celery task.'
send_async_email.delay(title, email, body)
    return redirect(url_for('index'))
if __name__ == '__main__':
    app.run(debug=True)

celeryapp.py 内容：

from celery import Celery

def make_celery(app):
    celery = Celery(app.import_name)
    celery.config_from_object("celerytest")

    class ContextTask(celery.Task):
        def __call__(self, *args, **kwargs):
            with app.app_context():
                return self.run(*args, **kwargs)

    celery.Task = ContextTask
return celery

Celeryconfig.py 内容：

# -*- coding: utf-8 -*-
from celery.schedules import timedelta
from celery.schedules import crontab

# Broker and Backend
# BROKER_URL = 'redis://192.168.5.151:6379' # 指定 Broker
BROKER_URL = 'redis://127.0.0.1:6379'
# CELERY_RESULT_BACKEND = 'redis://192.168.5.151:6379/0' # 指定 Backend
CELERY_RESULT_BACKEND = 'redis://127.0.0.1:6379/2'
CELERYD_PREFETCH_MULTIPLIER = 1  # 并发量
CELERY_TASK_ALWAYS_EAGER = False  # 如果是这样True，所有任务将通过阻塞在本地执行，直到任务返回
CELERY_ENABLE_UTC = False # 如果消息中的已启用日期和时间将转换为使用UTC时区。

# 任务序列化和反序列化使用msgpack方案
# CELERY_TASK_SERIALIZER = 'json'  # 可以是 json（默认），pickle，yaml，msgpack

# 读取任务结果一般性能要求不高，所以使用了可读性更好的JSON
# CELERY_RESULT_SERIALIZER = 'json'

# 任务过期时间，这样写更加明显
CELERY_TASK_RESULT_EXPIRES = 60 * 60 * 24 # 结果过期


# Timezone
CELERY_TIMEZONE = "Asia/Shanghai"  # 指定时区，不指定默认为 'UTC'

# import # 指定导入的任务模块
CELERY_IMPORTS = ['test3',"celerydemo"]

Celerytasks.py 内容

# coding:utf-8
import os
from main import celery,mail,app
from flask_mail import Message
os.environ.setdefault('FORKED_BY_MULTIPROCESSING','1')


@celery.task
def add(x, y):
    return x + y


@celery.task
def send_async_email(title,email,body):
    """Background task to send an email with Flask-Mail."""

    msg = Message(title,sender="xx@163.com",
                  recipients=[email])
    msg.body = body
    with app.app_context():
        return mail.send(msg)

启动一个worker

Celery –A main.celery worker –l info

启动flask项目

Python main.py

5.celery 多队列、多路由

5.0 多队列、多worker流程图

如果要说celery的分布式应用的话，我觉得就要提到celery的消息路由机制，就要提一下AMQP协议。具体的可以查看AMQP的文档。简单地说就是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Message Queue中时，可以指定routiing_key，Exchange通过routing_key来把消息路由(routes)到不同的Message Queue中去，

image.png

5.1 celeryconfig.py 配置

#!/usr/bin/env python

# -*- coding:utf-8 -*-

from kombu import Exchange, Queue

BROKER_URL = "redis://127.0.0.1:6379/1"

CELERY_RESULT_BACKEND = "redis://127.0.0.1:6379/2"

# 多队列

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 通过CELERY_ROUTES来为每一个task指定队列，如果有任务到达时，通过任务的名字来让指定的worker来处理。

CELERY_ROUTES = {

'tasks.taskA': {"queue": "for_task_A", "routing_key": "for_task_A"},

'tasks.taskB': {"queue": "for_task_B", "routing_key": "for_task_B"}

}

5.2 tasks.py 内容

#!/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

5.3 测试文件 run_redis_queue.py

# coding:utf-8

from redis_queue.tasks import *

re1 = taskA.delay(100, 200)

re2 = taskB.delay(1, 2, 3)

re3 = add.delay(1, 2)

5.4 启动

# windows

celery -A tasks worker -l info -n workerA.%h -Q for_task_A -P eventlet

celery -A tasks worker -l info -n workerB.%h -Q for_task_B -P eventlet

celery -A tasks worker -l info -n worker.%h -Q celery -P eventlet

# linux

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

linux 直接运行 python run_redis_queue.py

windows 无法使用  运行后 无效果  只能直接在python命令窗口中调用

各个任务 只在指定队列中的worker中运行

6. celery multi

### **6.1** **后台启动worker**

celery multi start w1 -A proj -l info  # 启动

celery multi restart w1 -A proj -l info  # 重新启动

celery multi stop w1 -A proj -l info # 异步关闭 立即返回

celery multi stopwait w1 -A proj -l info # 等待关闭操作完成

 # 默认情况下，celery会在当前目录下创建pidfile和logfile.为了防止多个worker在启动时相互影响，你可以指定一个特定的目录。

$ mkdir -p /var/run/celery

$ mkdir -p /var/log/celery

$ celery multi start w1 -A proj -l info --pidfile=/var/run/celery/%n.pid \

 --logfile=/var/log/celery/%n%I.log

在linux下执行 windows有报错