Re: Django Channels : High CPU usage by workers and 503 responses.

(self.name, content) >> File "/usr/local/lib/python2.7/dist-packages/asgi_redis/core.py", line >> 184, in send >> raise self.ChannelFull >> asgiref.base_layer.ChannelFull >> 4.) Running 28 workers on 10 cores. >> 5.) For very less number of concurrent users,

Re: Django Channels : High CPU usage by workers and 503 responses.

10 cores. > 5.) For very less number of concurrent users, the configuration is running > fine.But as already said, I did a load testing on tornado server (4Cores) > and was running fine, but with django-daphne i am giving 10Cores to workers > and still facing 503 error code and 504 err

Django Channels : High CPU usage by workers and 503 responses.

ss number of concurrent users, the configuration is running fine.But as already said, I did a load testing on tornado server (4Cores) and was running fine, but with django-daphne i am giving 10Cores to workers and still facing 503 error code and 504 error code by haproxy.May be I misunderstood some

Re: CPU usage by workers

For those who have same CPU usage issue — my mistake was in isage RedisLocalChannelLayer in combo with delay server. Looks like many delayed messages just hangs on nodes. I've thought it should be executed on same node, but seems partially it was consumed, but partially hangs. Then i've tried

Re: CPU usage by workers

Thanks for suggestions but i have found nothing suspicious. But i've installed pyinotify and switch to running workers in separate processes manually instead of using --threads option. Not sure which of this actions helps more but now CPU usage floating around 15-25% On Wednesday, May 10,

Re: CPU usage by workers

On Tuesday 09 May 2017 23:21:23 qnub wrote: > Also it's may be my fault somewhere (and i pretty sure it is). But i > not sure where to start my investigation. I would start with strace[1] - a common cause for this is expecting a resource that does not exist and keep trying. A filename or

Re: CPU usage by workers

Thanks for answer! python manage.py runworker --threads=4 This process consume CPU resources (daphne and delay processes seems not meaningful here). I'll try describe step by step: 1. daphne, workers and delay started and workers consume about 10% CPU 2. open new tab in chrome and worker's

Re: CPU usage by workers

Daphne does tend to idle hot, but this is so it performs better under high load. It's not clear from your description which of the processes is using more CPU as connections come through and then disconnect - is it Daphne or is it runworker? Andrew On Mon, May 8, 2017 at 5:46 AM, qnub

CPU usage by workers

We have executed cluster of 3 docker containers (on separated machines) with daphne and workers --threads=4 in each container. And this container consube about 15% machine's CPU after start. Then it start consume addtional 4-5% of CPU per new connection and seems like not free this resources