Memory leak in synchrotron worker

[kosmos]

Description

I have the following synchrotron worker configuration:

# Sync initial/normal
location ~ ^/_matrix/client/(r0|v3)/sync$ {
  proxy_pass https://$sync;
}


# Synchrotron
location ~ ^/_matrix/client/(api/v1|r0|v3)/events$ {
  proxy_pass https://synapse_sync;
}


# Initial_sync
location ~ ^/_matrix/client/(api/v1|r0|v3)/initialSync$ {
  proxy_pass https://synapse_initial_sync;
}

location ~ ^/_matrix/client/(api/v1|r0|v3)/rooms/[^/]+/initialSync$ {
  proxy_pass https://synapse_initial_sync;
}

And the following cache settings for this worker:

event_cache_size: 150K
caches:
  global_factor: 1
  expire_caches: true
  cache_entry_ttl: 30m
  sync_response_cache_duration: 2m
  cache_autotuning:
    max_cache_memory_usage: 2048M
    target_cache_memory_usage: 1792M
    min_cache_ttl: 1m
  per_cache_factors:
    stateGroupCache: 1
    stateGroupMembersCache: 5
    get_users_in_room: 20
    get_users_who_share_room_with_user: 10
    _get_linearized_receipts_for_room: 20
    get_presence_list_observers_accepted: 5
    get_user_by_access_token: 2
    get_user_filter: 2
    is_support_user: 2
    state_cache: 5
    get_current_state_ids: 5
    get_forgotten_rooms_for_user: 5

All other worker types work without problems, but it is a memory leak in synchrotrons, which leads to the exhaustion of all memory.

It seems that the cache_autotuning settings are not working. The environment variable PYTHONMALLOC=malloc is set at the operating system level.

According to my impressions, the problem became relevant after updating to 1.114 of Synapse and remains relevant in 1.116.

Steps to reproduce

To reproduce the problem, you need a homeserver with a heavy load and dedicated synchrotron workers.

Homeserver

Synapse 1.116.0

Synapse Version

Synapse 1.116.0

Installation Method

Docker (matrixdotorg/synapse)

Database

PostgreSQL

Workers

Multiple workers

Platform

Configuration

No response

Relevant log output

-

Anything else that would be useful to know?

No response

[anoadragon453]

Hi @kosmos, thanks for filing an issue. Can I just double-check that you're using jemalloc in your configuration? Doing so is required for the cache_autotuning option.

[kosmos]

@anoadragon453 I'm using the official docker image, and this feature (jemalloc) is enabled by default.

[anoadragon453]

@kosmos Did you upgrade from 1.1130 before being on 1.114.0? I don't see any changes that are particularly related to caches in 1.114.0.

Around the time of the memory ballooning, are you seeing lots of initial syncs at once? Those requests are known to be memory intensive, especially for users with a large amount of rooms.

Do you have monitoring set up for your Synapse instance? If so, could you have a look at the Caches -> Cache Size graph around the time of the memory ballooning to see what cache might be overinflating? I'm also happy to poke through your Grafana metrics if you can make them privately or publicly available. Feel free to DM me at @andrewm:element.io if you'd like to share them via that route.

[kosmos]

@anoadragon453 Our update history was as follows: 113->114->116. I will write to you in private messages.

[anoadragon453]

@kosmos and I exchanged some DMs to try and get to the bottom of this. The conclusion was that @kosmos does have jemalloc enabled in their deployment, yet the memory-based cache eviction doesn't seem to be kicking in. They had no mention of this log line in their logs:

synapse/synapse/util/caches/lrucache.py

Line 151 in 05576f0

logger.info("Begin memory-based cache eviction.")

This is odd, as the memory of the process (2500M) is going over their configured maximum (2048M).

However, only part of the total memory allocations of the application are being carried out by jemalloc. Over half(!) are still being performed by the built-in python allocator. Looking at the stats @kosmos provided me, jemalloc's stats showed that only ~700M of RAM was allocated at the time of the OOM, with 1.75G being allocated otherwise.

This resulted in the cache autotuning not kicking in. The real question is what is actually taking up all that memory, and why isn't it being allocated through jemalloc?

Native code (Synapse contains rust code) won't use jemalloc to allocate memory, so this could be one explanation. Native code in imports that use C/C++ extensions could also be a contender. Short of getting out a python memory profiler however, it's hard to say.

As a workaround, I'd recommend reducing your max_cache_memory_usage and target_cache_memory_usage values by 1/2 to allow the autotuning to kick in.

[kosmos]

This approach won't work because the jmalloc memory graph is not at its maximum at the moment of physical memory growth. That is, there is no criterion by which we can understand when to clear the memory.

In addition, if it's not the memory allocated by jmalloc that is growing, then clearing the allocated jmalloc memory won't save me.

[anoadragon453]

Good point, yes. I think the next step is tracking down what exactly is taking up the memory in your homeserver when it OOM's, and from there figuring out why it's not being allocated by jemalloc.

Alternatively, having the ability to discard caches based on total application memory versus only jemalloc-allocated memory could work.

[kosmos]

I still feel that there is a memory leak somewhere in the native libs, and it is important to find it.

[flesueur]

Hi,

I think we may suffer from the same problem. We run synapse 1.113 on a deployment with 2000 daily active users and memory usage grows indefinitely (?). We use a single process, no workers, with the docker image and the following configuration :

event_cache_size: 15K
caches:
  global_factor: 2.0
  sync_response_cache_duration: 2m
  cache_autotuning:
    max_cache_memory_usage: 1500M
    target_cache_memory_usage: 1000M
    min_cache_ttl: 5m

We had the same behavior without the cache_autotuning part (we added this autotuning part as part of our experiments to solve this, without improvement). The memory-drop on 04/10 is a reboot of the container.

I attach a few graphs :

RAM usage

GC time

Objects counts :

Reactor

We observe that RAM grows, number of objects stays quite similar day after day but GC time becomes longer and longer. At some point, Synapse becomes quite unresponsive, probably because of this, and we need to restart it.

Cheers
François