pgvecto.rs

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Benchmark ​

pgvecto.rs has been focused on performance from the beginning. We have continuously monitored the performance of pgvecto.rs and compared it with other vector search libraries. The following benchmark results are from January 2024.

The test is done on Google Cloud n2-standard-8 (8 vCPUs, 32 GB RAM) with the laion-768-5m-ip dataset.

pgvecto.rs v.s. pgvector ​

With the HNSW index, pgvecto.rs can achieve 2.5x responses per second as pgvector can do with a slightly better precision of around 97%. This advantage increases when higher precision is required.

pgvecto.rs_vs_pgvector

When the vbase mode is enabled, pgvecto.rs can achieve over 2x more responses per second compared to pgvector on various filter probabilities.

pgvecto.rs_vs_pgvector_filter

pgvecto.rs with different quantization methods ​

pgvecto.rs supports several quantization methods and different low precision indexing. These methods can help to reduce memory usage at different scales. The following figure shows the performance of pgvecto.rs with different quantization methods.

pgvecto.rs_quantization