Vector index metrics

Preview — AlloyDB Omni

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This page lists the metrics related to the vector indexes that you generate in AlloyDB Omni. You can view these metrics using the pg_stat_ann_indexes view that is available when you install the alloydb_scann extension.

For more information about viewing the metrics, see View vector index metrics.

Usability metrics

The usability metrics include metrics that help you understand the state of index utilization with metrics, such as index configuration and number of index scans.

Metric name	Data type	Description
relid	OID	Unique identifier of the table that contains the vector index
indexrelid	OID	Unique identifier of the vector index
schemaname	NAME	Name of the schema that owns the index
relname	NAME	Name of the table that contains the index
indexrelname	NAME	Name of the index
indextype	NAME	Type of the index. This value is always set to alloydb_scann
indexconfig	TEXT[]	Configuration, such as leaves count and quantizer, defined for the index when it was created
indexsize	TEXT	Size of the index
indexscan	BIGINT	Number of index scans initiated on the index

Tuning metrics

Tuning metrics provide insights into your current index optimization, allowing you to apply recommendations for faster query performance.

Metric name	Data type	Description
insertcount	BIGINT	Number of insert operations on the index. This metric also includes the number of rows that existed before the index was created.
updatecount	BIGINT	Number of update operations on the index. This metric doesn't take into account any HOT updates.
deletecount	BIGINT	Number of delete operations on the index.
distribution	JSONB	Vector distributions across all partitions for the index. The following fields show the distribution: maximum (INT8): Maximum number of vectors across all partitions. minimum (INT8): Minimum number of vectors across all partitions. average (FLOAT) : Average number of vectors across all partitions. outliers (INT8[]): Top outliers across all partitions. This value shows the top 20 outliers. Note: Due to the inherent characteristics of the K-means clustering algorithm, there will always be some degree of variance in the distribution of vectors across partitions, even when the index is initially created.

Tuning recommendation based on the metrics

Mutation

The insertcount, updatecount, and deletecount metrics together show the changes or mutations in the vector for the index.

The index is created with a specific number of vectors and partitions. When operations such as insert, update, or delete are performed on the vector index, it only affects the initial set of partitions where the vectors reside. Consequently, the number of vectors in each partition fluctuates over time, potentially impacting recall, QPS, or both.

If you encounter slowness or accuracy issues such as low QPS or poor recall, in your ANN search queries over time, then consider reviewing these metrics. A high number of mutations relative to the total number of vectors could indicate the need for reindexing.

Distribution

The distribution metric shows the vector distributions across all partitions.

When you create an index, it is created with a specific number of vectors and fixed partitions. The partitioning process and subsequent distribution occurs based on this consideration. If additional vectors are added, they are partitioned among the existing partitions, resulting in a different distribution compared to the distribution when the index was created. Since the final distribution does not consider all vectors simultaneously, the recall, QPS, or both might be affected.

If you observe a gradual decline in the performance of your ANN search queries, such as slower response times or reduced accuracy in the results (measured by QPS or recall), then consider checking this metric and reindexing.