Tune vector query performance in AlloyDB for PostgreSQL

Learn how to tune the following vector indexes to achieve faster query performance and better recall in AlloyDB for PostgreSQL:

You can also analyze your queries and view vector index metrics to monitor and improve query performance.

Tune an IVFFlat index

Tuning the values you set for the lists and theivfflat.probes parameters can help optimize application 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
ivfflat.probes The number of nearest lists to explore during search. The starting point for this value is
sqrt(lists).		 Query runtime

Before you build an IVFFlat index, make sure that your database's max_parallel_maintenance_workers flag is set to a value sufficient to expedite the index creation on large tables.

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

SET LOCAL ivfflat.probes = 10;

CREATE INDEX my-ivfflat-index ON my-table
  USING ivfflat (vector_column cosine)
  WITH (lists = 100);

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 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;

You see output similar to the following:

-[ RECORD 1 ]----------+---------------------------------------------------------------------------
relid                  | 271236
indexrelid             | 271242
schemaname             | public
relname                | t1
indexrelname           | t1_ix1
indextype              | scann
indexconfig            | {num_leaves=100,quantizer=SQ8}
indexsize              | 832 kB
indexscan              | 0
insertcount            | 250
deletecount            | 0
updatecount            | 0
partitioncount         | 100
distribution           | {"average": 3.54, "maximum": 37, "minimum": 0, "outliers": [37, 12, 11, 10, 10, 9, 9, 9, 9, 9]}
distributionpercentile |{"10": { "num_vectors": 0, "num_partitions": 0 }, "25": { "num_vectors": 0, "num_partitions": 30 }, "50": { "num_vectors": 3, "num_partitions": 30 }, "75": { "num_vectors": 5, "num_partitions": 19 }, "90": { "num_vectors": 7, "num_partitions": 11 }, "95": { "num_vectors": 9, "num_partitions": 5 }, "99": { "num_vectors": 12, "num_partitions": 4 }, "100": { "num_vectors": 37, "num_partitions": 1 }}

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

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