This page describes how to tune your indexes to achieve faster query
performance and better recall in AlloyDB Omni.
Analyze your queries
Use the EXPLAIN ANALYZE command to analyze your query insights as shown in the following example SQL query.
EXPLAINANALYZESELECTresult-columnFROMmy-tableORDERBYEMBEDDING_COLUMN<->embedding('text-embedding-005','What is a database?')::vectorLIMIT1;
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*FROMpg_stat_ann_indexes;
For more information about the complete list of metrics, see Vector index
metrics.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eThis document provides instructions on how to optimize indexes for enhanced query speed and improved recall.\u003c/p\u003e\n"],["\u003cp\u003eUtilize the \u003ccode\u003eEXPLAIN ANALYZE\u003c/code\u003e command with a sample SQL query to examine query performance insights, including execution time and resource usage.\u003c/p\u003e\n"],["\u003cp\u003eReview vector index metrics using the \u003ccode\u003epg_stat_ann_indexes\u003c/code\u003e view to assess performance and identify areas for index improvement.\u003c/p\u003e\n"],["\u003cp\u003eThe document includes the usage of ScaNN, IVF, IVFFlat and HNSW.\u003c/p\u003e\n"]]],[],null,["# Tune vector query performance in AlloyDB Omni\n\nSelect a documentation version: 16.3.0keyboard_arrow_down\n\n- [Current (16.8.0)](/alloydb/omni/current/docs/ai/tune-indexes)\n- [16.8.0](/alloydb/omni/16.8.0/docs/ai/tune-indexes)\n- [16.3.0](/alloydb/omni/16.3.0/docs/ai/tune-indexes)\n- [15.12.0](/alloydb/omni/15.12.0/docs/ai/tune-indexes)\n- [15.7.1](/alloydb/omni/15.7.1/docs/ai/tune-indexes)\n- [15.7.0](/alloydb/omni/15.7.0/docs/ai/tune-indexes)\n\n\u003cbr /\u003e\n\nThis page describes how to tune your indexes to achieve faster query\nperformance and better recall in AlloyDB Omni. \nScaNN IVF IVFFlat HNSW\n\nAnalyze your queries\n--------------------\n\nUse the `EXPLAIN ANALYZE` command to analyze your query insights as shown in the following example SQL query. \n\n EXPLAIN ANALYZE SELECT result-column\n FROM my-table\n ORDER BY EMBEDDING_COLUMN \u003c-\u003e embedding('text-embedding-005', 'What is a database?')::vector\n LIMIT 1;\n\nThe example response `QUERY PLAN` includes information such as the time taken, the number of rows scanned or returned, and the resources used. \n\n Limit (cost=0.42..15.27 rows=1 width=32) (actual time=0.106..0.132 rows=1 loops=1)\n -\u003e 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)\n Order By: (embedding_column \u003c-\u003e embedding('text-embedding-005', 'What is a database?')::vector(768))\n Limit value: 1\n Planning Time: 0.354 ms\n Execution Time: 0.141 ms\n\nView vector index metrics\n-------------------------\n\nYou can use the vector index metrics to review performance of your vector index,\nidentify areas for improvement, and tune your index based on the metrics, if\nneeded.\n\nTo view all vector index metrics, run the following SQL query, which uses the\n`pg_stat_ann_indexes` view: \n\n SELECT * FROM pg_stat_ann_indexes;\n\nFor more information about the complete list of metrics, see [Vector index\nmetrics](/alloydb/omni/16.3.0/docs/reference/vector-index-metrics).\n\nWhat's next\n-----------\n\n- [An example embedding workflow](/alloydb/omni/16.3.0/docs/ai/example-embeddings)"]]