Using the ANN-Benchmarks suite
ANN-Benchmarks is an open-source framework for evaluating approximate nearest neighbor (ANN) algorithms and libraries on standard datasets. It produces recall-vs.-latency curves so you can compare algorithms and parameter settings fairly.
Summary
- ANN-Benchmarks is an open-source framework for evaluating ANN algorithms and libraries on standard datasets. Produces recall-vs.-latency curves to compare algorithms and parameter settings fairly.
- Runs multiple algorithms (e.g. Faiss, HNSW, Annoy) on the same datasets; measures recall@k and query latency at various settings. Results plotted as recall vs. QPS or latency—the recall–latency trade-off per method. Install suite, add your algorithm as a wrapper (build index, run queries), run evaluation scripts.
- Focuses on single-machine, in-memory ANN; does not cover distributed VDBs, filtering, or hybrid search—use alongside application-level benchmarks for full-system decisions. Pipeline: install, add algorithm wrapper, run scripts. Practical tip: run on same dataset and K as your production to compare algorithms.
What the suite does
The suite runs multiple algorithms (e.g. Faiss, HNSW, Annoy) on the same datasets (e.g. GloVe, SIFT, random vectors) and measures recall@k and query latency at various index build and search parameter settings. Results are plotted as recall versus queries per second (or latency), showing the recall–latency trade-off for each method.
How to use it
To use it: install the suite, add your algorithm as a wrapper that implements the expected interface (build index, run queries), then run the evaluation scripts. Keep in mind it focuses on single-machine, in-memory ANN; it does not cover distributed VDBs, filtering, or hybrid search, so use it alongside application-level benchmarks for full-system decisions.
Pipeline: install, add algorithm wrapper, run scripts. Practical tip: run on same dataset and K as your production to compare algorithms.
Frequently Asked Questions
What is ANN-Benchmarks?
An open-source framework for evaluating approximate nearest neighbor (ANN) algorithms and libraries on standard datasets. Produces recall-vs.-latency curves so you can compare algorithms and parameter settings fairly. See recall@K and latency.
What does it measure?
Recall@k and query latency at various index build and search parameter settings. Results are plotted as recall versus queries per second (or latency), showing the recall–latency trade-off for each method (e.g. Faiss, HNSW, Annoy).
How do I add my algorithm?
Implement the expected interface: build index and run queries. Run the evaluation scripts on standard datasets (e.g. GloVe, SIFT, random vectors). Keep in mind it focuses on single-machine, in-memory ANN.
What does ANN-Benchmarks not cover?
Distributed VDBs, metadata filtering, or hybrid search. Use it alongside application-level benchmarks for full-system decisions. See benchmarking distance metrics.