Verifies the FilterIndex path in LiveEventStore: each event matches
exactly ONE of N subscribers, so without an index the relay walks
all N subs per event (O(N)); with the author-keyed index the lookup
is O(1) and per-event latency stays flat as N grows.
Different from fanoutLatency (broadcast: 1 event reaches every
sub). Here per-event work is lookup-bound rather than
delivery-bound. Configurable via -DfanoutScalingSubs (comma list)
and -DfanoutScalingEvents.
Measured (laptop, JDK 21, full sweep):
100 subs, 2k events: p50=1.35ms p99=5.71ms
1000 subs, 2k events: p50=1.05ms p99=3.05ms
5000 subs, 1k events: p50=1.01ms p99=2.96ms
p50 ~1ms across N — the predicted O(1) scaling. Default events count
adapts downward at high N to stay below WebSocketSessionPump's
8192-frame outbound cap (test-client read side is the bottleneck
above ~5k subs, not the relay).
Plan doc updated with the measured numbers in the "How to verify"
section.
Replace the SharedFlow-based broadcast in LiveEventStore with an
inverted index over filter-bearing subscribers. Each REQ registers
its filters into a per-store FilterIndex<LiveSubscription>; insert()
calls index.candidatesFor(event) and only delivers to candidates
whose Filter.match still passes. Cuts the per-event walk from
O(N_subs * N_filters) to a few hash lookups plus match() over a
small candidate set.
FilterIndex itself lives next to Filter.kt (commonMain, KMP-friendly,
AtomicReference + COW) so other call sites with the same shape
(LocalCache.observables, ObservableEventStore.changes) can reuse it.
Each filter contributes entries on its single most-selective dimension
(ids > authors > tags > tagsAll > kinds > unindexed) to keep buckets
narrow and avoid Set-dedupe work in candidatesFor.
The historical-replay race the previous SharedFlow + onSubscription
handoff closed is preserved by registering BEFORE replay starts and
deduping seen ids until EOSE.
Four sketches, queued by impact, each grounded in current code paths
and observed benchmark numbers:
- event-ingestion-batching: SQLite group commit + EVENT pipelining +
off-thread Schnorr verify. Targets 5–10× EPS on a fast SSD.
- live-broadcast-fanout-index: indexed filter matching to replace the
O(N_subs × N_filters) per-event walk in LiveEventStore. Targets
flat fanout p99 up to high subscriber counts.
- connection-scaling: shrink the per-session outQueue footprint
(currently the dominant per-conn cost), tune Ktor CIO group sizes,
reduce JSON parse allocations. Targets 10 000+ concurrent conns.
- negentropy-large-corpus: id-and-time-only snapshot path so NEG-OPEN
on a 5M-event store doesn't materialise full Event objects, plus
bounded-window defaults and concurrent-session caps.
Each plan names the verification benchmark to add. Plans are queued,
not committed work — README orders them by expected impact.
Claude