Benchmarked on Apple M1, single-threaded, with auto-scaled buffer pool.
Operation Latency Throughput Target Status
Node lookup 0.13 us 7.9M ops/sec < 1 us PASS Node creation 0.65 us 1.5M ops/sec — — Edge traversal 9 us 111K ops/sec — — Full-text search (100 docs) 19 us 53K ops/sec — — 10-NN vector search (1M vectors) 0.83 ms 1.2K ops/sec < 10 ms @ 1M PASS
128-dimensional cosine vectors, M=16, ef_construction=200, ef_search=64, k=10.
Scale Mean Latency P99 Latency Recall@10 Memory
1,000 65 us 70 us 100% 1 MB 10,000 174 us 695 us 99% 10 MB 100,000 438 us 1.2 ms 99% 101 MB 1,000,000 832 us 1.8 ms 100% 1,040 MB
Search latency scales sub-linearly (O(log N)) with 99-100% recall@10. Uses heuristic neighbor selection (HNSW paper Algorithm 4) for diverse graph connectivity, connection page packing for ~4.5x memory reduction, and pre-normalized dot product for fast cosine distance.
ef_search Mean Latency Recall@10
16 506 us 57% 32 1.9 ms 79% 64 990 us 100% 128 3.2 ms 100% 256 11.6 ms 100%
Scale Insert Rate Search Mean Recall@10
1K ~91/sec 1.7ms 100% 10K ~42/sec 3.8ms 99% 100K ~23/sec 4.5ms 99% 1M ~14/sec 6.4ms 100%
Six optimizations applied: last_page tracking, pre-sized search structures, stack-buffer connection I/O, cached vectors in heuristic pruning, pre-normalize + dot product for cosine.
Scale Insert Rate Search Mean Recall@10
1K ~954/sec 65us 100% 10K ~726/sec 174us 99% 100K ~526/sec 438us 99% 1M ~248/sec 832us 100%
Scale Insert Speedup Search Speedup
1K 10.5x 26x 10K 17.5x 22x 100K 22.8x 10x 1M 17.7x 7.7x
zig build benchmark # Core operation benchmarks
zig build vector-benchmark -- --quick # Vector benchmarks (1K/10K/100K, ~7 min)
zig build vector-benchmark # Full vector benchmarks including 1M (~70 min)
zig build graph-benchmark -- --quick # Graph traversal benchmarks