Tune vector query performance

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This document shows you how to tune your indexes to achieve faster query performance and better recall.

Tune a ScaNN index

ScaNN index uses tree-quantization based indexing. In Tree-quantization techniques, indexes learn a search tree together with a quantization (or hashing) function. When you run a query, the search tree is used to prune the search space while quantization is used to compress the index size. This pruning speeds up the scoring of the similarity (i.e., distance) between the query vector and the database vectors.

To achieve both a high query-per-second rate (QPS) and a high recall with your nearest-neighbor queries, you must partition the tree of your ScaNN index in a way that is most appropriate to your data and your queries.

Before you build a ScaNN index, complete the following:

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

Tuning parameters

The following index parameters and database flags are used together to find the right balance of recall and QPS. All the parameters apply to both ScaNN index types.

Tuning parameter Description Parameter type
num_leaves The number of partitions to apply to this index. The number of partitions you apply to when creating an index affects the index performance. By increasing partitions for a set number of vectors, you create a more fine-grained index, which improves recall and query performance. However, this comes at the cost of longer index creation times.

Since three-level trees build faster than two-level trees, you can increase the num_leaves_value when creating a three-level tree index to achieve better performance.
  • Two-level index: Set this value to any value between 1 and 1048576.

    If you are unsure about selecting the exact value, use sqrt(ROWS) as a starting point, where ROWS is the number of vector rows. The number of vectors that each partition holds is calculated by
    ROWS/sqrt(ROWS) = sqrt(ROWS).

    Since a two-level tree index can be created on a dataset with less than 10 million vector rows, each partition will hold less than (sqrt(10M)) vectors, which is 3200 vectors. For optimal performance, it's recommended to minimize the number of vectors in each partition.
  • Three-level index: Set this value to any value between 1 and 1048576.

    If you are unsure about selecting the exact value, use power(ROWS, 2/3) as a starting point, where ROWS is the number of vector rows. The number of vectors that each partition holds is calculated by
    ROWS/power(ROWS, 2/3) = power(ROWS, 1/3).

    Since a three-level tree index can be created on a dataset with vector rows more than 100 million, each partition will hold more than
    (power(100M, 1/3)) vectors, which is 465 vectors. For optimal performance, it's recommended to minimize the number of vectors in each partition.
 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
enable_pca Enables Principal Component Analysis (PCA), which is a dimension reduction technique used to automatically reduce the size of the embedding when possible. This option is enabled by default.

Set to false if you observe deterioration in recall.		 Index creation
scann.num_leaves_to_search The database flag controls the trade off between recall and QPS. The default value is 1% of the value set in num_leaves.

Higher the value set, better is the recall, but results in lower QPS, and the other way around.		 Query runtime
scann.max_top_neighbors_buffer_size The database flag specifies the size of cache used to improve the performance for filtered queries by scoring or ranking the scanned candidate neighbors in memory instead of the disk. The default value is 20000.

Higher the value set, better is the QPS under filtered queries, but results in higher memory usage, and the other way around.		 Query runtime
scann.pre_reordering_num_neighbors The database flag when set, specifies the number of candidate neighbors to consider during the reordering stages after initial search identifies a set of candidates. Set this to a value higher than the number of neighbors you want the query to return.

Higher value sets result in better recall, but this approach results in lower QPS.		 Query runtime
max_num_levels The maximum number of levels of the K-means clustering tree.
  • Two-level tree index: Set by default for two-level tree-based quantization.
  • Three-level tree index: Set to 2 explicitly for three-level tree-based quantization.
 Index creation

Tune a ScaNN index

Consider the following examples for two-level and three-level ScaNN indexes that show how tuning parameters are set:

Two-level index

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)]);

Three-level index

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);

Any insert or update operation on a table where a ScaNN index is already generated impacts how the learned tree optimizes the index. If your table is prone to frequent updates or insertions, then we recommend periodically reindexing the existing ScaNN index to improve the recall accuracy.

You can monitor index metrics to determine the amount of mutations created since the index was built, and then reindex accordingly. For more information about metrics, see Vector index metrics.

Best practices for tuning

Based on the type of ScaNN index you plan to use, the recommendations for tuning your index vary. This section provides recommendations about how to tune index parameters for optimal balance between recall and QPS.

Two-level tree index

To apply recommendations to help you find the optimal values of num_leaves and num_leaves_to_search for your dataset, follow these steps:

  1. Create the ScaNN index with num_leaves set to the square root of the indexed table's row count.
  2. Run your test queries, increasing the value of scann.num_of_leaves_to_search, until you achieve your target recall range–for example, 95%. For more information about analyzing your queries, see Analyze your queries.
  3. Take note of the ratio between scann.num_leaves_to_search and num_leaves that will be used in subsequent steps. This ratio provides approximation around the dataset that will help you achieve your target recall.

    If you are working with high dimension vectors (500 dimensions or higher) and want to improve recall, then try tuning the value of scann.pre_reordering_num_neighbors. As a starting point, set the value to 100 * sqrt(K) where K is the limit that you set in your query.
  4. If your QPS is too low after your queries achieve a target recall, then follow these steps:
    1. Recreate the index, increasing the value of num_leaves and scann.num_leaves_to_search according to the following guidance:
      • Set num_leaves to a larger factor of the square root of your row count. For example, if the index has num_leaves set to the square root of your row count, try setting it to double the square root. If the value is already double, then try setting it to triple the square root.
      • Increase scann.num_leaves_to_search as needed to maintain its ratio with num_leaves, which you noted in Step 3.
      • Set num_leaves to a value less than or equal to the row count divided by 100.
    2. Run the test queries again. While you're running the test queries, experiment with reducing scann.num_leaves_to_search, finding a value that increases QPS while keeping your recall high. Try different values of scann.num_leaves_to_search without rebuilding the index.
  5. Repeat Step 4 until both the QPS and the recall range have reached acceptable values.

Three-level tree index

In addition to the recommendations for the two-level tree ScaNN index, use the following guidance and the steps to tune the index:

  • Increasing the max_num_levels from 1 for a two-level tree to 2 for a three-level tree significantly reduces the time to create an index, but at the expense of recall accuracy. Set max_num_levels using the following recommendation:
    • Set the value to 2 if the number of vector rows exceeds 100 million rows.
    • Set the value to 1 if the number of vector rows are less than 10 million rows.
    • Set to either 1 or 2 if the number of vector rows lie between 10 million and 100 million rows, based on balance of index creation time and the recall accuracy you need.

To apply recommendations to find the optimal value of num_leaves and max_num_levels index parameters, follow these steps:

  1. Create the ScaNN index with the following num_leaves and max_num_levels combinations based on your dataset:

    • vector rows greater than 100 million rows: Set max_num_levels as 2 and num_leaves as power(rows, ⅔).
    • vector rows less than 100 million rows: Set max_num_levels as 1 and num_leaves as sqrt(rows).
    • vector rows between 10 million and 100 million rows: Start by setting max_num_levels as 1 and num_leaves as sqrt(rows).

  2. Run your test queries. For more information about analyzing queries, see Analyze your queries.

    If the index creation time is satisfactory, then retain the max_num_levels value, and experiment with the num_leaves value for optimal recall accuracy.

  3. If you aren't satisfied with the index creation time, then do the following:

    • If max_num_levels value is 1, then drop the index. Rebuild the index with max_num_levels value set to 2.

      Run the queries and tune the num_leaves value for optimal recall accuracy.

    • If the max_num_levels value is 2, then drop the index. Rebuild the index with the same max_num_levels value and tune the num_leaves value for optimal recall accuracy.

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');

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);

Tune an HNSW index

Tuning the values you set for the m, ef_construction, and the hnsw.ef_search parameters can help optimize application performance.

Tuning parameter Description Parameter type
m The maximum number of connections per from a node in the graph. You can start with the default value as 16(default) and experiment with higher values based on the size of your dataset. Index creation
ef_construction The size of the dynamic candidate list maintained during graph construction, which constantly updates the current best candidates for nearest neighbors for a node. Set this value to any value higher than twice of the m value—for example, 64(default). Index creation
ef_search The size of the dynamic candidate list used during search. You can start setting this value to either m or ef_construction, and then change it while observing the recall. The default value is 40. Query runtime

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

SET LOCAL hnsw.ef_search = 40;

CREATE INDEX my-hnsw-index ON my-table
  USING hnsw (vector_column cosine)
  WITH (m = 16, ef_construction = 200);

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 ::vector
    USING INDEX my-scann-index
    <-> embedding('textembedding-gecko@003', 'What is a database?')
    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('textgecko@003', '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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