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Databricks benchmarks Lakebase Search vector search on 1M to 4M passages

1 article1 sourcesince Nov 3Last article 1h ago ·

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Databricks benchmarked Lakebase Search on 1M to 4M passages and reports that cache size relative to the vector index mattered more than row count.

When the cache roughly matched the index, 95–98% of page reads avoided storage, and warm vector search p50 latency stayed between 11 and 23 ms, according to the company's post on X.

Written by AI from the articles below · updated Nov 3, 3:49 PM ET

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Latest developmentNov 3, 3:38 PM ET
Databricks benchmarks Lakebase Search vector latency at 1M to 4M passages

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Nov 3
  1. DatabricksOfficial
    Databricks benchmarks Lakebase Search vector latency at 1M to 4M passages

    AIDatabricks benchmarked Lakebase Search on 1M to 4M passages and found cache size relative to the vector index mattered more than row count. When the cache roughly matched the index, 95–98% of page reads avoided storage and warm vector search p50 latency stayed between 11 and 23 ms.

    Image from @databricks's post

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