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[meta] EventCache storage #3280

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14 of 23 tasks
Hywan opened this issue Mar 27, 2024 · 0 comments
Open
14 of 23 tasks

[meta] EventCache storage #3280

Hywan opened this issue Mar 27, 2024 · 0 comments

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@Hywan
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Hywan commented Mar 27, 2024

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matrix_sdk::EventCache is a new module that has been introduced with #3058. The idea is to manage all events inside the Rust SDK in a single place. An important user of EventCache is the matrix_sdk_ui::Timeline.

EventCache uses a new data structure to organize all events, LinkedChunk, since:

This issue describes the plan to get a persistent storage for EventCache, along with the ability to make it reactive.

Persistent storage

In order to create a persistent storage for EventCache, we need a mechanism that listens to LinkedChunk updates, and map these updates to database operations (like INSERT, DELETE and so on).

We initially went to using a reactive mechanism (like ObservableLinkedChunk), but like any reactive mechanisms, we need to handle the lag.

Note

What is a lag? When new updates are generated, they are usually accumulated in a buffer. This buffer is drained by subscribers of the observed value. When the buffer is full, which can happen because subscribers lag to consume updates, the buffer is reset.

The lag, in this case, is pretty problematic. In case of a lag, the database will be out of sync —data will be missing—, and there is no easy way to get them again. In case of a lag, we might imagine to reset the database and to rewrite everything again, but not all events are loaded in memory. Alternatively, we may want to recompute all the differences between what is in memory and what is inside the database, but again, it's not an easy problem. Anyway, it implies more guards and more complexity.

Instead, we've decided to define a LinkedChunkListener trait, used by LinkedChunk, onto which methods will be called on some particular operations, like insert_chunk, remove_chunk, insert_events and remove_events, that's probably all we need.

The cons:

  • It's a new API to manage,
  • It cannot be re-used by something else. It exists for one usage only.

The pros:

  • It's immediate: it removes the problem of the lag introduced by the initial reactive approach,
  • It's easily unit-testable!
  • The API is quite small and clearly not complex at all, it's straighforward.

Tasks

  • Create LinkedChunkListener: feat(sdk): Introduce LinkedChunkUpdate #3281
  • Create a persistent storage for EventCache; write mode: from memory to persistent storage,
  • Create a LinkedChunk from the storage; read mode: from persistent storage to memory,
  • Disable the persistent storage for EventCache.

Reactive EventCache

Right now, there is no satisfying way to get a stream of EventCache updates. The only mechanism that exists so far is RoomEventCache::subscribe. It returns a tokio::sync::mpsc::Receiver<RoomEventCacheUpdate>. RoomEventCacheUpdate is defined like so:

/// An update related to events happened in a room.
#[derive(Debug, Clone)]
pub enum RoomEventCacheUpdate {
/// The room has been cleared from events.
Clear,
/// The room has new events.
Append {
/// All the new events that have been added to the room's timeline.
events: Vec<SyncTimelineEvent>,
/// XXX: this is temporary, until account data lives in the event cache
/// — or will it live there?
account_data: Vec<Raw<AnyRoomAccountDataEvent>>,
/// XXX: this is temporary, until read receipts are handled in the event
/// cache
ephemeral: Vec<Raw<AnySyncEphemeralRoomEvent>>,
/// Collection of ambiguity changes that room member events trigger.
///
/// This is a map of event ID of the `m.room.member` event to the
/// details of the ambiguity change.
ambiguity_changes: BTreeMap<OwnedEventId, AmbiguityChange>,
},
}

Constraint: Prepare for reconciliation

This is OK-ish for the moment (at the time of writing), but it will quickly show limitations, in particular with the reconciliation.

Note

When events are received in different orders —e.g. between /messages, /context or /sync—, it's important to re-order them. We call that reconciliation. It's far, far, faaar from trivial. Actually, there is no solution to this problem, but we will try to make the best heuristics as possible.

Why reconciliation is going to create a problem here? Because it's impossible to represent an update like: “remove item at position $p$, and insert item at position $q$”. The only possible updates so far are “clear” and “append”.

The assiduous reader (oh, hi1) will think: “How does it work with back- or front-pagination?”. Thanks for asking. Let's make a detour.

Constraint: Pagination

Frontpagination isn't implemented yet (not something hard, just not here yet). Backpagination is done with RoomEventCache::backpaginate. It returns a BackPaginationOutcome, defined like so:

/// The result of a single back-pagination request.
#[derive(Debug)]
pub enum BackPaginationOutcome {
/// The back-pagination succeeded, and new events have been found.
Success {
/// Did the back-pagination reach the start of the timeline?
reached_start: bool,
/// All the events that have been returned in the back-pagination
/// request.
///
/// Events are presented in reverse order: the first element of the vec,
/// if present, is the most "recent" event from the chunk (or
/// technically, the last one in the topological ordering).
///
/// Note: they're not deduplicated (TODO: smart reconciliation).
events: Vec<TimelineEvent>,
},
/// The back-pagination token was unknown to the event cache, and the caller
/// must retry after obtaining a new back-pagination token.
UnknownBackpaginationToken,
}

Ah. Isn't it using RoomEventCacheUpdate? Well, no, because of the Timeline! The API from EventCache has been extracted from the Timeline. The Timeline had and still has 2 sources of updates: /sync and /messages for pagination.

Would it be hard to switch to a single Stream<Item = SyncTimelineEvent>? Well. Yes and no.

  • For /sync, the Timeline listens to RoomEventCache::subscribe:

// Subscribe the event cache to sync responses, in case we hadn't done it yet.
event_cache.subscribe()?;
let (room_event_cache, event_cache_drop) = room.event_cache().await?;
let (events, mut event_subscriber) = room_event_cache.subscribe().await?;

  • For /messages, the Timeline has a dedicated mechanism that calls /messages in a loop until $n$ TimelineItems are received. This is different of $n$ SyncTimelineItem, because some events are state events and cannot be displayed to the user:

while let Some(batch_size) = options.next_event_limit(outcome) {
loop {
match self.event_cache.backpaginate(batch_size, token).await? {

What do we want? A single source of data for Timeline.
What does it require? Change the backpagination mechanism. Not a big deal, it's doable. The biggest difficulty is that the Timeline will ask for data that will be injected to another place (Timeline::paginate_backwards will see the results of RoomEventCache::backpaginate via RoomEventCache::subscribe). It's not easy to connect both.
How to do it? Glad you ask.

The solution: Step 1

LinkedChunk must expose a Stream<Item = Vec<VectorDiff<SyncTimelineEvent>>>-like API, something like:

impl LinkedChunk {
    fn subscribe_as_vector(&self) -> (Vec<SyncTimelineEvent>, impl Stream<Item = Vec<VectorDiff<SyncTimelineEvent>>>) {
        todo!()
    }

Easy right? Well. No. LinkedChunk is not a Vector. The algorithm is going to be fun here. LinkedChunk::subscribe_as_vector should fake it's a Vector and should emit VectorDiff, à la eyeball_im::ObservableVector.

Of course, RoomEventCache::subscribe must be rewritten to use LinkedChunk::subscribe_as_vector.

The solution: Step 2

Timeline must listen to RoomEventCache::subscribe but for all updates. Then, Timeline will map Stream<Item = Vec<VectorDiff<SyncTimelineItem>>> into TimelineItems that will be inserted/deleted/moved in the correct places inside its own ObservableVector<TimelineItem> inside TimelineInnerState:

#[derive(Debug)]
pub(in crate::timeline) struct TimelineInnerState {
pub items: ObservableVector<Arc<TimelineItem>>,
pub meta: TimelineInnerMetadata,
}

This is going to be delicate.

Tasks

The 2 following lists can be done in parallel:

Tasks on EventCache:

Tasks on EventCache and Timeline:

  • Improve/refactor the pagination mechanism of Timeline to EventCache so that EventCache is the only place to have pagination logics. Timeline must entirely depend on EventCache.

The following list must be done after the 2 previous lists:

Lazy EventCache: Combine pagination and persistent storage

Before being able to enable persistent storage in EventCache, one last problem must be addressed.

RoomEventCache will use RoomEvents (which uses LinkedChunk) to load events from the persistent storage. That's fine. However, we don't want to load all events from the persistent storage. Imagine rooms with 1'000'000 events: do we want to load all the events in memory? Absolutely no! RoomEventCache must be lazy: it must load only the $n$ newest chunks from the persistent storage.

OK, but what happens when we backpaginate?

  • We must load events from the persistent storage first,
  • We must run a proper backpagination (with /messages) to see if events aren't missing from the federation that could have missed by /sync,
  • We must reconciliate both.

Note

A more detailed plan is being drafted by @bnjbvr and @Hywan to define when running a network request (/messages) is necessary or not necessary. The heuristic is not trivial, all edge-cases must be well-defined and considered carefully.

Once we have this mechanism in-place, we can enable the persistent storage for EventCache.

Tasks

  • RoomEventCache is lazy: load chunks on-demand,
  • RoomEventCache is able to backpaginate from its persistent storage and from /messages at the same time,
  • Finally, enable the persistent storage for EventCache 🎉.

Naive reconciliation

To start with, we can implement a super native reconciliation algorithm that will simply remove duplicated events. For a first shot, it's totally fine. A better reconciliation algorithm can be defined later. For the record, what is present now is the remove duplicated events approach.

What we want to ensure is that the reconciliation algorithm will modify events in LinkedChunk, which will propagate to the persistent storage via LinkedChunkListener and to the Timeline via RoomEventCache::subscribe. That's one of the main goal of this proposed designed.

Tasks

Conclusion

This plan provides a solution to support a reconciliation mechanism in a single place. It will benefit to other users, like Timeline. The Timeline needs to be refactored due to having 2 source of updates, one for /sync (live events), and one for /messages (front- and back-paginations of old events).

Because EventCache will be reactive, it will simplify a lot all the testing aspects:

  • EventCache will be easy to test: assert a stream,
  • Timeline will be super easy to test without having to mock a server will all its possible requests: simply generate a stream that will emit what we want to test exactly, it's a matter of writing the following code for the inputs of the Timeline:
let inputs = stream! {
    yield vec![VectorDiff::Append { values: events![,,] }];
    yield vec![VectorDiff::Remove { index: … }];
    yield vec![VectorDiff::Insert { index: …, value: … }];
};
  • Components will be fully decoupled. Each part will be unit-tested. It will increase the robustness of the Timeline.

Bonus


Footnotes

  1. Mark?

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