Possible memory leak

[sergii-mamedov]

Hi @JosepSampe,

I think I found two memory leaks in lithops:

1. FaaSInvoker and self.executor
   You create a ThreadPoolExecutor
   

   lithops/lithops/invokers.py

   Line 305 in c4ecc74

   self.executor = ThreadPoolExecutor(invoke_pool_threads)

   
   and use it here
   

   lithops/lithops/invokers.py

   Line 456 in c4ecc74

   future = self.executor.submit(self._invoke_task, job, call_ids_range)

   
   but you don't do self.executor.shutdown() so the threads remain hanging.
   In our case, the worker using lithops is a daemon. This means that for each new dataset for AWS Lambda, we create 64 new threads because we are reusing an existing Serverless Executor. This leads to hundreds of hanging threads after a couple of hours .

2. FaaSInvoker and _start_async_invokers
   This is much less of a problem, but also creates two threads each time
   

   lithops/lithops/invokers.py

   Lines 332 to 336 in c4ecc74

   	for inv_id in range(self.ASYNC_INVOKERS):
   	p = threading.Thread(target=invoker_process, args=(inv_id,))
   	self.invokers.append(p)
   	p.daemon = True
   	p.start()

   
   Yes, there is a stop() method below but it automatically works only for class FaaSRemoteInvoker. In case I call executor = lithops.ServerlessExecutor(config=lithops_config, ...) then the stop() method only works in the case of a context manager:
   

   lithops/lithops/executors.py

   Lines 159 to 163 in fdb6432

   	def __exit__(self, exc_type, exc_value, traceback):
   	""" Context manager method """
   	self.job_monitor.stop()
   	self.invoker.stop()
   	self.compute_handler.clear()

   
   I'm not insisting that there is a memory leak here, but even in the documentation there are many examples where FunctionExecutor is used without with.

P.S. Now the main developer of METASPACE is @lmacielvieira, so I'm tagging him as well @Bisho2122

[JosepSampe]

Hi @sergii-mamedov @lmacielvieira

After extensive testing of Lithops, I’ve identified the following findings:

The self.executor = ThreadPoolExecutor(invoke_pool_threads) is initialized once, creating a fixed-size pool of reusable threads. These threads persist throughout the execution, and do not grow at any time.

The self.invoker.stop() method is used to stop the async invokers. However, as you pointed out, it is only invoked when using the context manager. Despite this, the thread pool created in the async invokers remains fixed in size as in the previos case.

After several experiments running map() operations for hours, I verified that the number of threads do not grow without bounds. However, I found that it can grow to more than 500 due to this hardcoded line:

lithops/lithops/invokers.py

Line 318 in c4ecc74

with ThreadPoolExecutor(max_workers=250) as executor:

For the tests I used the py-spy tool, for example with: py-spy top --pid $(pgrep -f "python3 examples/map.py")

To improve this, I created this PR (#1414) with fixes that will substantially reduce the number of threads, so that the number of threads in the async invokers will depend on your configuration.

I also verified that in the rest of the components of the code where we create threads, they are properly closed.