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Benchmarking LanceDB Enterprise

LanceDB's architecture is designed to deliver 25ms vector search latency. Even with metadata filtering, our query latency remains as low as 50ms. It is important to note that we can support thousands of QPS with such query performance.

Percentile Vector Search Vector Search w. Filtering Full-Text Search
P50 25ms 30ms 26ms
P90 26ms 39ms 37ms
P99 35ms 50ms 42ms

Dataset

We used two datasets for this benchmark test: the dbpedia-entities-openai-1M for vector search, and a synthetic dataset for vector search with metadata filtering.

Name # Vectors Vector Dimension
dbpedia-entities-openai-1M 1,000,000 1536
synthetic dataset 15,000,000 256

We ran vector queries with dbpedia-entities-openai-1M with a warmed-up cache. The query latency is as follows:

Percentile Latency
P50 25ms
P90 26ms
P99 35ms
Max 49ms

With the same dataset and a warmed-up cache, the full-text search performance is as follows:

Percentile Latency
P50 26ms
P90 37ms
P99 42ms
Max 98ms

Vector Search with Metadata Filtering

We created a 15M-vector dataset with sufficient complexity to thoroughly test our complex metadata filtering capabilities. Such filtering can span a wide range of scalar columns, e.g., "find Sci-fi movies since 1900".

With a warmed-up cache, the query performance using slightly more selective filters, e.g., "find Sci-fi movies between the years 2000 and 2012", is as follows:

Percentile Latency
P50 30ms
P90 39ms
P99 50ms

The query performance using complex filters, e.g., "find Sci-fi movies since 1900", is as follows:

Percentile Latency
P50 65ms
P90 76ms
P99 100ms

Note

Our benchmarking tests provide consistent, up-to-date performance evaluations of LanceDB. We regularly update and re-run these benchmarks to ensure the data remains accurate and relevant.