rv.append(item)returnrvif__name__ =="__main__":# 使用sys.argv参数运行# app.worker_main()# 使用自定义参数运行# --beat同时开启beat模式,即运行按计划发送task的实例# 应确保全局只有一份同样的beatapp.worker_main(["worker","--beat","--loglevel=debug","-n","jobfind.%h","-s","./sc...
# def test_worker_main(self):# from celery.bin import worker as worker_bin## class worker(worker_bin.worker):## def execute_from_commandline(self, argv):# return argv## prev, worker_bin.worker = worker_bin.worker, worker# try:# ret = self.app.worker_main(argv=['--version'])# ...
# from celery.bin import worker as worker_bin # # class worker(worker_bin.worker): # # def execute_from_commandline(self, argv): # return argv # # prev, worker_bin.worker = worker_bin.worker, worker # try: # ret = self.app.worker_main(argv=['--version']) # assert ret == [...
objects.py:43setup_queues, worker.py:172setup_instance, worker.py:106__init__, worker.py:99worker, worker.py:326caller, base.py:132new_func, decorators.py:21invoke, core.py:610invoke, core.py:1066invoke, core.py:1259main, core.py:782start, base.py:358worker_main, base.py:374 ...
所以我们可以模仿来进行,使用如下启动worker,进行调试。 代码语言:javascript 复制 from celeryimportCelery app=Celery('tasks',broker='redis://localhost:6379')@app.task()defadd(x,y):returnx+yif__name__=='__main__':app.worker_main(argv=['worker']) ...
10. 11. 12. 13. 14. 15. 16. 启动celery worker 执行demo.py from apps.task1 import add from apps.task2 import subs if __name__ == '__main__': add.delay(3,5) subs.apply_async(args=[55,33]) 1. 2. 3. 4. 5. 6.
前文提到了,我们经过一系列过程,正式来到了 Worker 的逻辑。我们在本文将接下来继续看后续 work as a program 的启动过程。 代码语言:javascript 复制 +---++---+|@cached_property||User||Worker|+---+---++--->|||worker_main||Worker application|||celery/app/base.py|v|+---++---+---+||...
启动celery worker 执行demo.py
在celery 中,多进程主要指的是一个主进程负责调度,然后多个从进程负责消费具体的任务。也就是前文中我说的调度器(Main process)和工作进程(Worker processes)。我们今天主要就是看来一下 celery 这部分的源码。 我先说明下版本,celery 是 4.2.0,broker 和 result backend 都是用的 redis。
celery是一个基于分布式消息传输的异步任务队列,它专注于实时处理,同时也支持任务调度。它的执行单元为任务(task),利用多线程,如Eventlet,gevent等,它们能被并发地执行在单个或多个职程服务器(worker servers)上。任务能异步执行(后台运行)或同步执行(等待任务完成)。