AsyncWebsocketClient uses ever increasing memory with subsequent requests

[ubuddemeier]

I think the problem is that received messages are enqueued (in WebsocketBase._handler) regardless of whether the message corresponds to a request or not:

https://github.com/XRPLF/xrpl-py/blob/main/xrpl/asyncio/clients/websocket_base.py#L139

This should probably be inside of an else-clause to the preceding if-clause.

[ckeshava]

Hello, thanks for pointing this out.

How did you observe the increasing memory load? Are you using any profiler?

[Jbekker]

@ckeshava , I'm noticing the same, it's best seen when you do a larger requests like requesting BookOffers. If you don't iterate over the client, the queue continuously grows and therefor also the memory usage.

Would it make sense to also have a limit on the amount of messages in the queue for the case when a client can't keep up?

import asyncio

from xrpl.asyncio.clients import AsyncWebsocketClient

from xrpl.models.requests import BookOffers
from xrpl.models.currencies import XRP, IssuedCurrency

async def main():
    async with AsyncWebsocketClient("wss://s1.ripple.com") as client:
                
                while True:
                    orderbook_asks_info = await client.request(
                        BookOffers(
                            ledger_index="current",
                            taker_gets=XRP(),
                            taker_pays=IssuedCurrency(
                                  currency="USD",
                                  issuer="rhub8VRN55s94qWKDv6jmDy1pUykJzF3wq"
                            ),
                            limit=500,
                        )
                    )

                    orderbook_bids_info = await client.request(
                        BookOffers(
                            ledger_index="current",
                            taker_gets=IssuedCurrency(
                                  currency="USD",
                                  issuer="rhub8VRN55s94qWKDv6jmDy1pUykJzF3wq"
                            ),
                            taker_pays=XRP(),
                            limit=500,
                        )
                    )

                    print(f"Queued items: {client._messages.qsize()}")
                    await asyncio.sleep(2)

if __name__ == "__main__":
    asyncio.run(main())

[ckeshava]

thanks, this is helpful @Jbekker

[ckeshava]

@Jbekker I have incorporated your test case at the tip of this branch: https://github.com/XRPLF/xrpl-py/pull/713 -- many thanks, this is insightful.

Regarding placing limits on the queue size: A client might want to send in a large number of requests to a rippled node. For instance - We don't want to rate-limit access to a personal rippled process. (Hopefully with the resolution of this bug, there won't be any unforeseen bumps in compute/memory usage). Users can always set limits on this Python process through the Operating System tools.

But, I understand this approach is contrary to the message queues inside the OS network buffers. I don't have a strong opinion, I'm open to ideas.

[mvadari]

Regarding placing limits on the queue size: A client might want to send in a large number of requests to a rippled node. For instance - We don't want to rate-limit access to a personal rippled process.

There could be some sort of config option added.

[ckeshava]

Regarding placing limits on the queue size: A client might want to send in a large number of requests to a rippled node. For instance - We don't want to rate-limit access to a personal rippled process.

There could be some sort of config option added.

yeah, that's true. To determine a good default value, we might have to pull some statistics (for an upper bound on the queue size), do you have any ideas for that? I don't think the Data team currently tracks the client libraries.

Adding a config option might increase the complexity of the client libraries, but I think it's an acceptable compromise.

[ubuddemeier]

Question: does the response need to go into the message queue if it corresponds to a pending request and is handled by fulfilling the future? I did a quick fix on my end to work around the problem in this way: # if this response corresponds to request, fulfill the Future if "id" in response_dict and response_dict["id"] in self._open_requests: self._open_requests[response_dict["id"]].set_result(response_dict) else: # enqueue the response for the message queue cast(_MESSAGES_TYPE, self._messages).put_nowait(response_dict) Am I missing something here?

[ubuddemeier]

Another problem I noticed earlier in that same file, is that _inject_request_id might cause a collision when generating a random ID with a lot of outstanding requests. It should check whether the randomly generated ID is already used.

[ckeshava]

@ubuddemeier You are correct, the fix is similar: https://github.com/XRPLF/xrpl-py/pull/713

Regarding the request-IDs, please refer to this discussion: https://github.com/XRPLF/xrpl-py/issues/718#issuecomment-2195457626

For a collision to occur, the random math library would need to produce the same output within a given span of time. I think math libraries have a stronger guarantee insofar as psuedo-random number generation is concerned.

The requests are fulfilled very quickly (except for certain heavy workloads). I don't think we'll see collisions within such a short span of time

[ledhed2222]

hey author here. i think you're probably right about collisions with request IDs. we should be using a dictionary to determine what IDs are already in use, although i'm surprised if you've seen a collision in practice.

however when it comes to the message queue we must keep them all because this is how you are able to read all responses. why would you want to drop messages received? perhaps I don't understand.

[ckeshava]

hey author here. i think you're probably right about collisions with request IDs. we should be using a dictionary to determine what IDs are already in use, although i'm surprised if you've seen a collision in practice.

You are right, I have not seen collisions in my experience.