[BUG] - Memory leaks during environment creation tasks

[peytondmurray]

Describe the bug

See https://github.com/nebari-dev/nebari/issues/2418 and #840 for context. TLDR: action_add_conda_prefix_packages is leaking memory, causing problems on various nebari deployments.

edit by @trallard: It seems action_add_conda_prefix_packages is not the main or at least not the sole culprit of memory leaks so I adjusted the title

Memray flamegraph:

Expected behavior

No memory leaks.

How to Reproduce the problem?

See https://github.com/nebari-dev/nebari/issues/2418 for a description.

Output

No response

Versions and dependencies used.

No response

Anything else?

No response

[Adam-D-Lewis]

• The way I generated the flamegraph was to run docker compose up --build -d to start up the docker containers after I made a few changes including adding memray to the docker container and setting concurrency=1 in conda-store/tests/assets/conda_store_config.py.
• I then ran docker compose exec -it conda-store-worker bash to get a shell in the container. I waited until the worker seemed idle then I attached memray to the running process with memray attach --trace-python-allocators --native <pid> -o myfile.bin to the process which seemed to be growing the most in memory.
• I then logged into the UI at localhost:8080/conda-store/admin and created a bunch of environments with very little in them (just the nothing package installed by pip used in some of the tests). I watched as the memory raises with each new build.
• I then copied the myfile.bin out of the docker container with docker cp <container_id>:/path/to/myfile.bin ..
• I then generated the flamegraph with memray flamegraph --leaks myfile.bin.

The conda store worker docker container usage rises by ~18Mb with each new env build according to docker compose stats, but the memory usage at the top of the flamegraph (see below) seems to show an increase of about 7Mb with each new env build (each spike is a new conda env build). I then ran a quick test bypassing list_conda_prefix_packages and the memory seemed to only increase by about ~11Mb with each new env so it seems likely that there may be multiple sources of memory growth (perhaps the rest is in one of the other subprocesses started by conda store that I didn't track), but I'm not sure where the other increase(s) are currently.

[trallard]

Per @Adam-D-Lewis's comment above about seeing memory increases even after bypassing the initially flagged action, we need to do a more in-depth profiling analysis.

[Adam-D-Lewis]

I tried restricting the docker container to 1 GiB of memory usage, and the memory growth per build was then less than 18Mb per build, but did increase, and eventually the celery worker was restarted due to memory usage. I'm wondering if the excess 11Mb of memory growth in the docker container when I had no memory limit specified were just due to python being a memory managed language and that the memory usage from garbage collected objects is not always given back to the OS.

That being said, I think it should still be possible to write a test showing the memory usage grows to the point where a celery worker runs out of memory.

[trallard]

@peytondmurray is this still relevant. IIRC we could not demonstrate that there was in fact a leak happening but we will be doing some profiling soon too. Shall we close this issue

[peytondmurray]

That sounds fine; closing now. Anyway, even if there was a leak we are now restarting workers regularly so the symptom is in principle avoided even if any underlying leak (if any) isn't solved.