问题描述
在我的 /etc/defaults/celeryd 配置文件中，我设置了:
In my /etc/defaults/celeryd config file, I've set:
CELERYD_NODES="agent1 agent2 agent3 agent4 agent5 agent6 agent7 agent8"
CELERYD_OPTS="--autoscale=10,3 --concurrency=5"
我知道守护进程产生了 8 个 celery worker，但我完全不确定 autoscale 和 concurrency 一起做什么.我认为并发是一种指定工作人员可以使用的最大线程数的方法，自动缩放是工作人员在必要时扩大和缩小子工作人员的一种方式.
I understand that the daemon spawns 8 celery workers, but I'm fully not sure what autoscale and concurrency do together. I thought that concurrency was a way to specify the max number of threads that a worker can use and autoscale was a way for the worker to scale up and down child workers, if necessary.
任务具有较大的有效负载(大约 20-50kB)，并且有大约 2-3 百万个这样的任务，但每个任务的运行时间不到一秒.我看到内存使用量激增，因为代理将任务分配给每个工作人员，从而多次复制有效负载.
The tasks have a largish payload (some 20-50kB) and there are like 2-3 million such tasks, but each task runs in less than a second. I'm seeing memory usage spike up because the broker distributes the tasks to every worker, thus replicating the payload multiple times.
我认为问题出在配置上，worker + 并发 + 自动缩放的组合过多，我想更好地了解这三个选项的作用.
I think the issue is in the config and that the combination of workers + concurrency + autoscaling is excessive and I would like to get a better understanding of what these three options do.
推荐答案
让我们区分工作进程和工作进程.您生成一个 celery worker，然后生成多个进程(取决于 --concurrency 和 --autoscale 之类的东西，默认是生成与内核一样多的进程在机器上).除非您想进行路由，否则在特定机器上运行多个 worker 是没有意义的.
Let's distinguish between workers and worker processes. You spawn a celery worker, this then spawns a number of processes (depending on things like --concurrency and --autoscale, the default is to spawn as many processes as cores on the machine). There is no point in running more than one worker on a particular machine unless you want to do routing.
我建议每台机器只运行 1 个工人，并使用默认的进程数.这将通过消除工作人员之间的数据重复来减少内存使用.
I would suggest running only 1 worker per machine with the default number of processes. This will reduce memory usage by eliminating the duplication of data between workers.
如果您仍然有内存问题，则将数据保存到存储中并仅将 id 传递给工作人员.
If you still have memory issues then save the data to a store and pass only an id to the workers.
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