CoordiNode

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Benchmarks ​

CoordiNode benchmarks use industry-standard suites (ann-benchmarks, LDBC SNB, YCSB, TSBS, Search Benchmark Game); every result is JSON-recorded with hardware fingerprint + Git SHA so the timeline is reproducible end-to-end.

Live data

The charts below are generated from JSON files at bench-results/ on every commit. CoordiNode results are produced automatically by CI on a dedicated bench host (Intel i9-9900K, 8C/16T) on every push to main — accumulating a full timeline. Competitor baselines (hnswlib, Faiss, MongoDB, etc.) are pinned to a specific version, run manually on the same host, and replaced when we re-bench against a newer build — no competitor history is kept in-repo, only the current head-to-head.

How to read these charts ​

  • Pareto frontier — higher and to the right is better. Recall (or correctness proxy) on the x-axis, throughput on the y-axis (log scale). Each curve is one engine.
  • Recall / accuracy timeline — peak score across CoordiNode commits. The CoordiNode line moves on every push to main; competitor lines stay flat between manual re-baselines.
  • Throughput @ target quality — the canonical "useful operating point" cell (e.g. QPS @ recall ≥ 0.95 for ANN). The taller the bar, the better the engine handles that point.

Hardware fingerprint ​

Every bench JSON records the host:

Intel(R) Core(TM) i9-9900K CPU @ 3.60GHz  (8 cores / 16 threads, 64 GB RAM)

This is intentionally modest desktop-class hardware — when CoordiNode reports a number on this CPU, the equivalent server-class run is going to be faster, not slower. The point is comparability: every engine runs on the same box.

Why every modality is on one page ​

CoordiNode is a single engine, single transaction, single query language across all six modalities. A real workload mixes them — graph traversal feeding a vector search filtered by a time-series predicate over geo-indexed documents. Benchmarking each modality in isolation is the first step; the cross-modality workloads land as the per-modality binaries mature. The bench harness (crates/coordinode-bench) is modality-agnostic — adding a new dataset is a JSON + chart-spec addition.