一：准备工作
（1）安装虚拟环境工具
（2）创建虚拟环境：会在当前路径下创建一个虚拟环境，由于放置在conf里面，移出后再上层重新创建一个mooc的虚拟环境
（3）查看虚拟环境
（4）激活虚拟环境
（5）退出虚拟环境
（6）安装virtualenvwrapper
修改 ~/.bashrc，里面增加一个 source /usr/local/bin/virtualenvwrapper.sh
source ~/.bashrc 立即生效
使用virtualenvwrapper创建一个虚拟环境，其实virtualenvwrapper就是virtualenv封装的一个工具类，更加方便的通过命令，创建和执行虚拟环境
mkvirtualenv mooc_4：创建好之后，直接进入虚拟环境，不用 source xxxx/active这种方式了
deactive 退出 workon 进入虚拟环境
（7）安装pyenv
$curl -L https://github.com/pyenv/pyenv-installer/raw/master/bin/pyenv-installer | bash
提示：-bash: -L: command not found
环境变量没有配置好
pyenv 是python的版本管理包
这里先不修改，直接用系统的python
（8）安装celery
查看安装结果
pip3 install celery[redis] 会安装celery需要的依赖包，还会安装适配的redis
在指定文件夹下创建一个目录，用来编写代码
在celery_test下面创建一个app.py的文件
import time def test(): print("enter") time.sleep(10) print("haha") print("out") if __name__ == "__main__": test()
执行后会发现，等待10s后才能输出 haha和out，如果我们想直接不等待10s直接执行呢？
在celery_test下再创建一个task.py的文件
import time from celery import Celery broker = "redis://localhost:6379/1" backend = "redis://localhost:6379/2" app = Celery("haha",broker=broker,backend=backend) @app.task def add(): time.sleep(10) return 100
修改app.py
from task import add print("enter app.py") if __name__ == "__main__": result = add.delay() print(result)
运行app.py
打印的这个结果不是返回值100的原因是，这里打印的是将任务信息发送到redis里面的信息，启动worker后可以发现打印了内容
(mooc_4) [root@VM_0_13_centos celery_test]# celery worker -A task -l INFO /root/.virtualenvs/mooc_4/lib/python3.7/site-packages/celery/platforms.py:801: RuntimeWarning: You're running the worker with superuser privileges: this is absolutely not recommended! Please specify a different user using the --uid option. User information: uid=0 euid=0 gid=0 egid=0 uid=uid, euid=euid, gid=gid, egid=egid, -------------- celery@VM_0_13_centos v4.4.2 (cliffs) --- ***** ----- -- ******* ---- Linux-3.10.0-862.el7.x86_64-x86_64-with-centos-7.5.1804-Core 2020-04-19 21:54:43 - *** --- * --- - ** ---------- [config] - ** ---------- .> app: haha:0x7f8458755160 - ** ---------- .> transport: redis://localhost:6379/1 - ** ---------- .> results: redis://localhost:6379/2 - *** --- * --- .> concurrency: 1 (prefork) -- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker) --- ***** ----- -------------- [queues] .> celery exchange=celery(direct) key=celery [tasks] . task.add [2020-04-19 21:54:43,503: INFO/MainProcess] Connected to redis://localhost:6379/1 [2020-04-19 21:54:43,509: INFO/MainProcess] mingle: searching for neighbors [2020-04-19 21:54:44,531: INFO/MainProcess] mingle: all alone [2020-04-19 21:54:44,540: INFO/MainProcess] celery@VM_0_13_centos ready. [2020-04-19 21:54:44,686: INFO/MainProcess] Received task: task.add[f73a4d9f-dc3a-4da8-a538-a138160b2fab] [2020-04-19 21:54:44,688: INFO/MainProcess] Received task: task.add[18d0feeb-6bd1-494f-bf68-27df49a2597c] [2020-04-19 21:54:44,689: INFO/MainProcess] Received task: task.add[72e77356-f81e-48d9-84f3-1638954d4dea] [2020-04-19 21:54:44,691: INFO/MainProcess] Received task: task.add[f3cd0d0b-c9cd-4fc6-9099-8f3e1e75b99b] [2020-04-19 21:54:44,691: INFO/MainProcess] Received task: task.add[02261dcf-9e60-49e5-8649-7eea15bc14c8] [2020-04-19 21:54:54,698: INFO/ForkPoolWorker-1] Task task.add[f73a4d9f-dc3a-4da8-a538-a138160b2fab] succeeded in 10.009396089008078s: 100 [2020-04-19 21:55:04,706: INFO/ForkPoolWorker-1] Task task.add[18d0feeb-6bd1-494f-bf68-27df49a2597c] succeeded in 10.006045571062714s: 100 [2020-04-19 21:55:14,717: INFO/ForkPoolWorker-1] Task task.add[72e77356-f81e-48d9-84f3-1638954d4dea] succeeded in 10.010685473913327s: 100 [2020-04-19 21:55:24,727: INFO/ForkPoolWorker-1] Task task.add[f3cd0d0b-c9cd-4fc6-9099-8f3e1e75b99b] succeeded in 10.009241034975275s: 100 [2020-04-19 21:55:34,730: INFO/ForkPoolWorker-1] Task task.add[02261dcf-9e60-49e5-8649-7eea15bc14c8] succeeded in 10.001496333163232s: 100
python3 app.py 就相当于发送了一条信息，等待消费，可以看出刚才 app.py这个文件别执行了5次，就相当于发送了5次信息，当我们启动 celery worker -A task -l INFO的时候，就相当于启动了消费者去消费刚才发布的五条信息。并将结果打印了。
（9）项目情况下的celery使用情况
celery_test
# 明天解决这个问题
# TODO