How To Measure Memory Usage Of A Web Request When Using Werkzeug/flask?

Is there a way to measure the amount of memory allocated by an arbitrary web request in a Flask/Werkzeug app? By arbitrary, I mean I'd prefer a technique that lets me instrument co

Solution 1:

First of all, one should understand the main difference between PHP and Python requests processing. Roughly speaking, each PHP worker accepts only one request, handle it and then die (or reinit interpreter). PHP was designed directly for it, it's request processing language by its nature. So, it's pretty simple to measure per request memory usage. Request's peak memory usage is equal to the worker peak memory usage. It's a language feature.

At the same time, Python usually uses another approach to handle requests. There are two main models - synchronous and asynchronous request processing. However, both of them have the same difficulty when it comes to measure per request memory usage. The reason is that one Python worker handles plenty of requests (concurrently or sequentially) during his life. So, it's hard to get memory usage exactly for a request.

However, one can adapt an underlying framework and application code to accomplish collecting memory usage task. One possible solution is to use some kind of events. For example, one can raise an abstract mem_usage event on: before request, at the beginning of a view function, at the end of a view function, in some important places within the business logic and so on. Then it should exists a subscriber for such events, doing the next thing:

importresourcemem_usage= resource.getrusage(resource.RUSAGE_SELF).ru_maxrss

This subscriber have to accumulate such usage data and on the app_request_teardown/after_request send it to the metrics collection system with information about current request.endpoint or route or whatever.

Also, using a memory profiler is a good idea, but usually not for a production usage.

Further reading about request processing models:

• CGI
• FastCGI
• PHP specific

Solution 2:

Another possible solution is to use sys.setrace. Using this tool one can measure memory usage even per each line of code. Usage examples can be found in the memory_profiler project. Of course, it will slowdown the code significantly.