This page provides a detailed reference for the tuning parameters available for Scalable Nearest Neighbors (ScaNN) indexes in AlloyDB for PostgreSQL.
For a step-by-step tutorial on how to implement vector search from start to finish, see the guide on how to Perform a vector search .
Tuning parameters
The following index and query parameters are used to find the right balance of recall and queries per second (QPS).
Tuning parameter
Description
Option type
max_num_levels
The maximum number of centroid levels of the K-means clustering tree.
- Two-level tree index: Set to
1by default for a two-level tree (1 centroid level + bottom leaf level). - Three-level tree index: Set to
2by default for a three-level tree (2 centroid levels + bottom leaf level) - Set the value to
2if the number of vector rows exceeds 100 million rows. - Set the value to
1if the number of vector rows are less than 10 million rows. - Set to either
1or2if the number of vector rows lie between 10 million and 100 million rows to optimize for index build time (set to 2) or optimize for search recall (set to 1).
Index creation
(optional)
(optional)
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
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
1and1048576.
For an index that balances fast index build and good search performance, usesqrt(ROWS)as a starting point, whereROWSis 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 is3200vectors. For optimal vector search quality, it's recommended to minimize the number of vectors in each partition. The recommended partition size is about 100 vectors per partition, so setnum_leavestoROWS/100. If you have 10 million vectors you would setnum_leavesto 100,000. - Three-level index: Set this value to any value between
1and1048576.
If you are unsure about selecting the exact value, usepower(ROWS, 2/3)as a starting point, whereROWSis 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 is465vectors. For optimal performance, it's recommended to minimize the number of vectors in each partition. The recommended partition size is about 100 vectors per partition, so setnum_leavestoROWS/100. If you have 100 million vectors you would setnum_leavesto 1 million.
Index creation
(required)
(required)
quantizer
The type of quantizer you want to use for the K-means tree. The default value is set to
Set it to
SQ8
which provides better query performance with minimal recall loss (typically less than 1-2%).Set it to
FLAT
if a recall of 99% or higher is required.Index creation
(optional)
(optional)
scann.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
Set to
false
if you observe deterioration in recall.Index creation
(optional)
(optional)
scann.pct_leaves_to_search ( Preview
)
This database flag lets you automatically manage the number of leaves or partitions to search
. Set this value to the current number of partitions. For example, to search 1% of of current number of partitions, set this value to
You can set this parameter to any value between
1
.You can set this parameter to any value between
0
to 100
. The default value is 0
, which disables this parameter and uses the scann.num_leaves_to_search
to calculate the number of leaves to search. The parameter is disabled by default.scann.num_leaves_to_search
This database flag controls the absolute number of leaves or partitions to search which lets you trade off between recall and QPS. The default value is 1% of the value set in
A higher value will result in better recall but lower QPS. Similarly, a lower value will result in lower recall but higher QPS.
num_leaves
.A higher value will result in better recall but lower QPS. Similarly, a lower value will result in lower recall but higher QPS.
Query runtime
(optional)
(optional)
scann.pre_reordering_num_neighbors
The database flag, when set, specifies the number of candidate neighbors to consider during the reordering stages after the initial search identifies a set of candidates. Set this parameter to a value higher than the number of neighbors you want the query to return.
A higher value results in better recall, but a lower QPS. Set this value to
A higher value results in better recall, but a lower QPS. Set this value to
0
to disable reordering. The default is 0
if PCA is not enabled during index creation. Otherwise, the default is 50 x K
, where K
is the LIMIT specified in the query.Query runtime
(optional)
(optional)
scann.num_search_threads
The number of searcher threads for multi-thread search. This can help reduce single query latency by using more than one thread for ScaNN ANN search in latency-sensitive applications. This setting doesn't improve single query latency if the database is already cpu-bound. The default value is
2
.Query runtime
(optional)
(optional)
