Multiprocessing memory leak, processes that stay around forever

I'm trying to solve a multiprocessing memory leak and am trying to fully understand where the problem is. My architecture is looking for the following: A main process that delegates tasks to a few sub-processes. Right now there are only 3 sub-processes. I'm using Queues to send data to these sub-processes and it's working just fine except the memory leak.

It seems most issues people are having with memory leaks involve people either forgetting to join/exit/terminate their processes after completion. My case is a bit different. I want these processes to stay around forever for the entire duration of the application. So the main process will launch these 3 sub-processes, and they will never die until the entire app dies.

Do I still need to join them for any reason?

Is this a bad idea to keep processes around forever? Should I consider killing them and re-launching them at some point despite me not wanting to do that?

Should I not be using multiprocessing.Process for this use case?

I'm making a lot of API calls and generating a lot of dictionaries and arrays of data within my sub processes. I'm assuming my memory leak comes from not properly cleaning that up. Maybe my problem is entirely there and not related to the way I'm using multiprocessing.Process?

from multiprocessing import Process

# This is how I'm creating my 3 sub processes
procs = []
for name in names:
    proc = Process(target=print_func, args=(name,))
    procs.append(proc)
    proc.start()

# Then I want my sub-processes to live forever for the remainder of the application's life
# But memory leaks until I run out of memory

Update 1:

• I'm seeing this memory growth/leaking on MacOS 10.15.5 as well as Ubuntu 16.04. It behaves the same way in both OSs. I've tried python 3.6 and python 3.8 and have seen the same results
• I never had this leak before going multiprocess. So that's why I was thinking this was related to multiprocess. So when I ran my code on one single process -> no leaking. Once I went multiprocess running the same code -> leaking/bloating memory.
• The data that's actually bloating are lists of data (floats & strings). I confirmed this using the python package pympler, which is a memory profiler.
• The biggest thing that changed since my multiprocess feature was added is, my data is gathered in the subprocesses then sent to the main process using Pyzmq. So I'm wondering if there are new pointers hanging around somehow preventing python from garbage collecting and fully releasing this lists of floats and strings.
• I do have a feature that every ~30 seconds clears "old" data that I no longer need (since my data is time-sensitive). I'm currently investigating this to see if it's working as expected.

Update 2:

I've improved the way I'm deleting old dicts and lists. It seems to have helped but the problem still persists. The python package pympler is showing that I'm no longer leaking memory which is great. When I run it on mac, my activity monitor is showing a consistent increase of memory usage. When I run it on Ubuntu, the free -m command is also showing consistent memory bloating.

Here's what my memory looks like shortly after running the script:

ubuntu:~/Folder/$ free -m
              total        used        free      shared  buff/cache   available
Mem:           7610        3920        2901           0         788        3438
Swap:             0           0           0

After running for a while, memory bloats according to free -m:

ubuntu:~/Folder/$ free -m
              total        used        free      shared  buff/cache   available
Mem:           7610        7385         130           0          93          40
Swap:             0           0           0
ubuntu:~/Folder/$ 

It eventually crashes from using too much memory.

To test where the leak comes from, I've turned off my feature where my subprocess send data to my main processes via Pyzmq. So the subprocesses are still making API calls and collecting data, just not doing anything with it. The memory leak completely goes away when I do this. So clearly the process of sending data from my subprocesses and then handling the data on my main process is where the leak is happening. I'll continue to debug.

Update 3 POSSIBLY SOLVED:

I may have resolved the issue. Still testing more thoroughly. I did some extra memory clean up on my dicts and lists that contained data. I also gave my EC2 instances ~20 GB of memory. My apps memory usage timeline looks like this:

Runtime after 1 minutes: ~4 GB Runtime after 2 minutes: ~5 GB Runtime after 3 minutes: ~6 GB Runtime after 5 minutes: ~7 GB Runtime after 10 minutes: ~9 GB Runtime after 6 hours: ~9 GB Runtime after 10 hours: ~9 GB

What's odd is that slow increment. Based on how my code works, I don't understand how it slowly increases memory usage from minute 2 to minute 10. It should be using max memory by around minute 2 or 3. Also, previously when I was running ALL of this logic on one single process, my memory usage was pretty low. I don't recall exactly what it was, but it was much much lower than 9 GB.

I've done some reading on Pyzmq and it appears to use a ton of memory. I think the massive memory usage increase comes from Pyzmq. Since I'm using it to send a massive amount of data between processes. I've read that Pyzmq is incredibly slow to release memory from large data messages. So it's very possible that my memory leak was not really a memory leak, it was just me using way way more memory due to Pyzmq and multi-processing sending data around.. I could confirm this by running my code from before my recent changes on a machine with ~20GB of memory.

Update 4 SOLVED:

My previous theory checked out. There was never a memory leak to begin with. The usage of Pyzmq with massive amounts of data dramatically increases memory usage to the point to where I had to ~6x my memory on my EC2 instance. So Pyzmq seems to either use a ton of memory or be very slow at releasing memory or both. Regardless, this has been resolved.

Given that you are on Linux, I'd suggest using https://github.com/vmware/chap to understand why the processes are growing.

To do that, first use ps to figure out the process IDs for each of your processes (the main and the child processes) then use "gcore " for each process to gather a live core. Gather cores again for each process after they have grown a bit.

For each core, you can open it in chap and use the following commands:

redirect on
describe used

The result will be files named like the original cores, followed by ".describe_used".

You can compare them to see which allocations are new.

Once you have identified some interesting new allocations for a process, try using "describe incoming" repeatedly from the chap prompt until you have seen how those allocations are used.