Gunicorn worker timeout error

Question

I have setup gunicorn with 3 workers 30 worker connections and using eventlet worker class  It is setup behind Nginx  After every few requests  I see this in the logs    ERROR  gunicorn error  WORKER TIMEOUT  pid 23475  None  INFO  gunicorn error  Booting worker with pid  23514   Why is this happening  How can I figure out whats going wrong   thanks

User · Answer

For me  the solution was to add --timeout 90 to my entrypoint  but it wasn t working because I had TWO entrypoints defined  one in app yaml  and another in my Dockerfile  I deleted the unused entrypoint and added --timeout 90 in the other

User · Answer

This worked for me   gunicorn app app -b  8080 --timeout 120 --workers 3 --threads 3 --worker-connections 1000   If you have eventlet add   --worker-class eventlet   If you have gevent add   --worker-class gevent

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The Microsoft Azure official documentation for running Flask Apps on Azure App Services  Linux App  states the use of timeout as 600 gunicorn --bind 0 0 0 0 --timeout 600 application app  https   docs microsoft com en-us azure app-service configure-language-python flask-app

User · Answer

We had the same problem using Django nginx gunicorn  From Gunicorn documentation we have configured the graceful-timeout that made almost no difference   After some testings  we found the solution  the parameter to configure is  timeout  And not graceful timeout   It works like a clock    So  Do   1  open the gunicorn configuration file  2  set the TIMEOUT to what ever you need - the value is in seconds  NUM WORKERS 3 TIMEOUT 120  exec gunicorn   DJANGO WSGI MODULE  application   --name  NAME   --workers  NUM WORKERS   --timeout  TIMEOUT   --log-level debug   --bind 127 0 0 1 9000   --pid  PIDFILE

User · Answer

timeout is a key parameter to this problem   however it s not suit for me    i found there is not gunicorn timeout error when i set workers 1    when i look though my code  i found some socket connect  socket send  amp  socket recv  in server init   socket recv will block my code and that s why it always timeout when workers 1  hope to give some ideas to the people who have some problem with me

User · Answer

Run Gunicorn with --log-level debug  It should give you an app stack trace

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Frank s answer pointed me in the right direction   I have a Digital Ocean droplet accessing a managed Digital Ocean Postgresql database   All I needed to do was add my droplet to the database s  quot Trusted Sources quot    click on database in DO console  then click on settings   Edit Trusted Sources and select droplet name  click in editable area and it will be suggested to you

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I ve got the same problem in Docker   In Docker I keep trained LightGBM model   Flask serving requests  As HTTP server I used gunicorn 19 9 0  When I run my code locally on my Mac laptop everything worked just perfect  but when I ran the app in Docker my POST JSON requests were freezing for some time  then gunicorn worker had been failing with  CRITICAL  WORKER TIMEOUT exception    I tried tons of different approaches  but the only one solved my issue was adding worker class gthread    Here is my complete config     import multiprocessing  workers   multiprocessing cpu count     2   1 accesslog    -    STDOUT access log format      h s   l s   u s   t s    r s    s s   b s    q s     D s   bind    0 0 0 0 5000  keepalive   120 timeout   120 worker class    gthread  threads   3

User · Answer

Is this endpoint taking too many time  Maybe you are using flask without asynchronous support  so every request will block the call  To create async support without make difficult  add the gevent worker  With gevent  a new call will spawn a new thread  and you app will be able to receive more requests pip install gevent gunicon      --worker-class gevent

User · Answer

You need to used an other worker type class an async one like gevent or tornado see this for more explanation   First explantion       You may also want to install Eventlet or Gevent if you expect that your application code may need to pause for extended periods of time during request processing   Second one       The default synchronous workers assume that your application is resource bound in terms of CPU and network bandwidth  Generally this means that your application shouldn   t do anything that takes an undefined amount of time  For instance  a request to the internet meets this criteria  At some point the external network will fail in such a way that clients will pile up on your servers

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On Google Cloud  Just add --timeout 90 to entrypoint in app yaml  entrypoint  gunicorn -b   PORT main app --timeout 90

User · Answer

Could it be this   http   docs gunicorn org en latest settings html timeout  Other possibilities could be your response is taking too long or is stuck waiting

User · Answer

If you are using GCP then you have to set workers per instance type    Link to GCP best practices https   cloud google com appengine docs standard python3 runtime

User · Answer

WORKER TIMEOUT means your application cannot response to the request in a defined amount of time  You can set this using gunicorn timeout settings  Some application need more time to response than another   Another thing that may affect this is choosing the worker type     The default synchronous workers assume that your application is resource-bound in terms of CPU and network bandwidth  Generally this means that your application shouldn   t do anything that takes an undefined amount of time  An example of something that takes an undefined amount of time is a request to the internet  At some point the external network will fail in such a way that clients will pile up on your servers  So  in this sense  any web application which makes outgoing requests to APIs will benefit from an asynchronous worker    When I got the same problem as yours  I was trying to deploy my application using Docker Swarm   I ve tried to increase the timeout and using another type of worker class  But all failed   And then I suddenly realised I was limitting my resource too low for the service inside my compose file  This is the thing slowed down the application in my case  deploy    replicas  5   resources      limits        cpus   0 1        memory  50M   restart policy      condition  on-failure   So I suggest you to check what thing slowing down your application in the first place

User · Answer

For me  it was because I forgot to setup firewall rule on database server for my Django

User · Answer

I had very similar problem  I also tried using  runserver  to see if I could find anything but all I had was a message Killed  So I thought it could be resource problem  and I went ahead to give more RAM to the instance  and it worked

[gunicorn] Gunicorn worker timeout error

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