Spike in the memory usage - Heroku

[devenbhooshan]

Our initial Configuration : 3 daphne dynos and 6 workers having 1/2 GB memory each.

We saw a sudden spike in the memory usage(198%) and we were supposed to increase the memory size to 1gb.

Not able to understand why workers needed more than 1/2 GB memory ?
What all factors should we consider while defining the size of memory required for each worker?

I saw one article in heroku about Gunicorn : https://devcenter.heroku.com/articles/optimizing-dyno-usage#basic-methodology-for-optimizing-memory

Couldn't find the same for daphne.

[andrewgodwin]

Memory usage of a worker is mostly dominated by Django and the app itself, though 500MB is quite a lot. Some questions:

• What version of Channels, Daphne, Django and Python are you using?
• Does the same thing happen with a normal WSGI process with, say, gunicorn with similar code?
• What sort of throughput are the workers getting?
• What channel layer are you using?

[devenbhooshan]

• What version of Channels, Daphne, Django and Python are you using?
  channels==0.14.0
  daphne==0.12.1
  Django==1.9.5
  Python : 2.7.1
• Does the same thing happen with a normal WSGI process with, say, gunicorn with similar code?
  Couldn't say for sure. I will try to reproduce the same load with gunicorn and will let you know
• What sort of throughput are the workers getting?
  throughput was around 200request per minute but I can assure you it was not that much.
• What channel layer are you using?
  Redis.

[andrewgodwin]

You're on old versions of channels and daphne; I'd at least try upgrading them first as some memory leaks were fixed a while ago.

[devenbhooshan]

Ok..will do that and if the problem still there, will update you. But even otherwise the memory footprint is too high.. On 9 Jan 2017 02:55, "Andrew Godwin" <notifications@github.com> wrote: You're on old versions of channels and daphne; I'd at least try upgrading them first as some memory leaks were fixed a while ago. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#65 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/ACTM4PsEbJltXuTbd0-O8bZWMjfhCRX6ks5rQVQzgaJpZM4LdYsp> .

[andrewgodwin]

I agree the footprint is too high, I want to make sure we can isolate it to the most recent release of channels and that it's not there when you run Django in normal WSGI mode, otherwise it's not a problem I can solve!

[andrewgodwin]

Closing due to lack of response.

[UbuntuEvangelist]

Deploying Python Applications with Gunicorn fix this problem for me-

pip install gunicorn
pip freeze requirements.txt

Or, you can manually add it to your requirements.txt file.

Next, revise your application’s Procfile to use Gunicorn. Here’s an example Procfile