How much memory django

I know. What does it means? In short, the article says that the Django process has an initial space for loading objects into memory when requests come. However, if the request is too big, the process will increase and never shrinks. Consequently, if more than one BIG request come at roughly the same time, Django process will inflate not just one process, but a few of those. Therefore, these Django process compete for space on the server.

Ok, to solve this problem we need to restart workers in some conditions. As you can see, before deploying the new Docker image, the memory usage blue line grew as the counts of request Red line increased and never decreased. After that, the memory usage goes down after consuming request. Hope this article can help you to solve your problem :. Final Edit: Well I have been discussing this with Webfaction to see if they could assist with recompiling Apache and this is their word on the matter:.

This brings me back to my original question which I am still none the wiser about. How does one go about identifying where the problems lies? It's a well known maxim that you don't optimize without testing to see where you need to optimize but there is very little in the way of tutorials on measuring Python memory usage and none at all specific to Django.

I asked this on the django-users list and got some very helpful replies. This was just released. Could be the best solution yet: Profiling Django object size and memory usage with Pympler. Make sure you are not keeping global references to data. That prevents the python garbage collector from releasing the memory. It loads an interpreter inside apache. It is not tricky to switch.

It is very easy. If you can remove apache from your requirements, that would be even better to your memory. It should be a very easy task. Please elaborate on the problem you are having with the switch.

Improve this question. Devang Devang 1, 2 2 gold badges 22 22 silver badges 36 36 bronze badges. Add a comment. Active Oldest Votes. Improve this answer. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Fortunately, we finally solved this problem by modifying uswgi. Ok, to solve this problem we need to restart workers in some conditions. From heroku's memory metrics, you can see that the memory is slowly creeping up.

For this simple example, the memory went from 35 to 90 MB in 90 minutes. Just been deploying to Heroku and using their memory metrics. Looking into it today after some IPC improvements though. Using these techniques together led to a roughly constant memory usage in my script.

Improved speed might be the most obvious aim for a program, but sometimes other performance improvements might be sought, such as lower memory consumption or fewer demands on the database or network. Your own time is a valuable resource, more precious than CPU time. Some improvements might be too difficult to be worth implementing, or might affect the portability or maintainability of the code.

Not all performance improvements are worth the effort. Successive releases have offered a number of improvements across the system, but you should still check the real-world performance of your application, because in some cases you may find that changes mean it performs worse rather than better.