r/computerarchitecture

Horosvec is an embedded approximate-nearest-neighbor index in pure Go — no CGO, single dependency (modernc.org/sqlite). Vamana graph + RaBitQ binary quantization + exact rerank; the index persists as a SQLite file, and at scale the raw vectors live in an fp16 mmap "arena" outside the Go heap: 26.7M vectors (all of Hacker News) served at p50 7.8 ms with ~14 GB of heap.

Two things this crowd might find interesting beyond the engine itself:

1. The concurrency result. Under 32 closed-loop clients on a real 512-dim corpus, the pure-Go engine sustains ~1.9x the throughput of hnswlib (C++ through a cgo binding) at equal-or-better recall:

• ef 64: 39,599 QPS vs 20,501 (recall 0.947 vs 0.914)
• ef 128: 22,638 vs 11,525 (recall 0.977 vs 0.961)
• ef 512: 6,582 vs 3,423 (recall 0.994 vs 0.989)

Single-client, hnswlib still wins ~2x. On 128-dim SIFT at iso-recall it wins ~5x (1-bit codes are information-starved in low dimension). All numbers, including the ones we lose, are published as raw JSONL.

2. The GC lesson. Our benchmark initially showed a throughput cliff (5.7x collapse past a beam-width threshold) and dead concurrency scaling. perf on the query window: 41% gcBgMarkWorker/gcDrain, 22% bgsweep, 21.6% database/sql.withLock. Root cause: the harness was silently running the non-production code path where each rerank candidate is a row-by-row SQL blob read — an allocation storm whose GC cost grows with the live heap (that's why the cliff moved with corpus size) behind a process-wide pool lock (that's why client scaling died). The production path — mmap arena, zero SQL in the hot loop, per-query state from a sync.Pool — has neither problem: the cliff flattened and 32-client throughput multiplied by 56. Lesson: a bench harness that picks its configuration through a silent env var lies by default; the measured mode now belongs in the output record.

Build side: memory-bounded streaming build from the arena, parallel Vamana construction with sharded neighborhood mutexes, and an import path that consumes an externally-built graph (GPU cuVS/CAGRA builds the 26.7M graph in 17 minutes, re-encoded into horosvec in 22).

Repo: https://github.com/hazyhaar/horosvec (v0.7.0, MIT). Benchmark write-up with the full story: https://github.com/hazyhaar/horosvec/blob/main/docs/BENCHMARK-2026-07.md — including an honest section on why we're deliberately not on ann-benchmarks.