Tune vector query performance in AlloyDB Omni

This page describes how to tune your indexes to achieve faster query performance and better recall in AlloyDB Omni.

Tune an IVF index

Tuning the values you set for the lists, ivf.probes, and the quantizer parameters might help optimize your application's performance:

Tuning parameter Description Parameter type
lists The number of lists created during index building. The starting point for setting this value is (rows)/1000 for up to one million rows, and sqrt(rows) for more than one million rows. Index creation
quantizer The type of quantizer you want to use for the K-means tree. The default value is SQ8 for better query performance. Set it to FLAT for better recall. Index creation
ivf.probes the number of nearest lists to explore during search. The starting point for this value is
sqrt(lists).		 Query runtime

Consider the following example that shows an IVF index with the tuning parameters set:

SET LOCAL ivf.probes = 10;

CREATE INDEX my-ivf-index ON my-table
  USING ivf (vector_column cosine)
  WITH (lists = 100, quantizer = 'SQ8');

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 <-> embedding('text-embedding-005', 'What is a database?')::vector
  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('text-embedding-005', '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.

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