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.

Before you begin

Before you build a ScaNN index, complete the following:

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

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:

  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 
 )]); 
 

  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 
  
< - 
>  
 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