Tune vector query performance

This document shows you how to tune your indexes to achieve faster query performance and better recall.

Before you build a ScaNN index, complete the following:

  • Make sure that a table with your data is already created.
  • Make sure that the value you set for the maintenance_work_mem and the shared_buffers flag is less than total machine memory to avoid issues while generating the index.

Tune a ScaNN index

Use the following guidance to choose between a two-level and three-level ScaNN index:

  • Choose a two-level index if the number of vector rows is less than 10 million rows.
  • Choose a three-level index if the number of vector rows exceeds 100 million rows.
  • Choose a three-level index to optimize for index build time or choose a two-level index to optimize for search recall if the number of vector rows lies between 10 million and 100 million rows.

Consider the following examples for two-level and three-level ScaNN indexes that show how tuning parameters are set for a table with 1000000 rows:

Two-level index

SET LOCAL scann.num_leaves_to_search = 1;
SET LOCAL scann.pre_reordering_num_neighbors=50;

CREATE INDEX my-scann-index ON my-table
  USING scann (vector_column cosine)
  WITH (num_leaves = [power(1000000, 1/2)]);

Three-level index

SET LOCAL scann.num_leaves_to_search = 10;
SET LOCAL scann.pre_reordering_num_neighbors=50;

CREATE INDEX my-scann-index ON my-table
  USING scann (vector_column cosine)
  WITH (num_leaves = [power(1000000, 2/3)], max_num_levels = 2);

Analyze your queries

Use the EXPLAIN ANALYZE command to analyze your query insights as shown in the following example SQL query.

  EXPLAIN ANALYZE SELECT result-column FROM my-table
    ORDER BY EMBEDDING_COLUMN ::vector
    USING INDEX my-scann-index
    <-> embedding('textembedding-gecko@003', 'What is a database?')
    LIMIT 1;

The example response QUERY PLAN includes information such as the time taken, the number of rows scanned or returned, and the resources used.

Limit  (cost=0.42..15.27 rows=1 width=32) (actual time=0.106..0.132 rows=1 loops=1)
  ->  Index Scan using my-scann-index on my-table  (cost=0.42..858027.93 rows=100000 width=32) (actual time=0.105..0.129 rows=1 loops=1)
        Order By: (embedding_column <-> embedding('textgecko@003', 'What is a database?')::vector(768))
        Limit value: 1
Planning Time: 0.354 ms
Execution Time: 0.141 ms

View vector index metrics

You can use the vector index metrics to review performance of your vector index, identify areas for improvement, and tune your index based on the metrics, if needed.

To view all vector index metrics, run the following SQL query, which uses the pg_stat_ann_indexes view:

SELECT * FROM pg_stat_ann_indexes;

For more information about the complete list of metrics, see Vector index metrics.

What's next