Tune vector query performance in AlloyDB Omni

This document 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.

   
 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 .

What's next

• An example embedding workflow