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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eThe \u003ccode\u003epg_stat_ann_indexes\u003c/code\u003e view provides metrics for vector indexes generated in AlloyDB Omni, accessible via the \u003ccode\u003ealloydb_scann\u003c/code\u003e extension.\u003c/p\u003e\n"],["\u003cp\u003eUsability metrics, including \u003ccode\u003eindexconfig\u003c/code\u003e, \u003ccode\u003eindexsize\u003c/code\u003e, and \u003ccode\u003eindexscan\u003c/code\u003e, help users understand the current state and utilization of their vector indexes.\u003c/p\u003e\n"],["\u003cp\u003eTuning metrics, such as \u003ccode\u003einsertcount\u003c/code\u003e, \u003ccode\u003eupdatecount\u003c/code\u003e, \u003ccode\u003edeletecount\u003c/code\u003e, and \u003ccode\u003edistribution\u003c/code\u003e, offer insights into the optimization and performance of vector indexes.\u003c/p\u003e\n"],["\u003cp\u003eHigh mutation rates, as indicated by \u003ccode\u003einsertcount\u003c/code\u003e, \u003ccode\u003eupdatecount\u003c/code\u003e, and \u003ccode\u003edeletecount\u003c/code\u003e, or uneven vector distributions, as detailed in the \u003ccode\u003edistribution\u003c/code\u003e metric, may signal the need to reindex to address potential performance issues.\u003c/p\u003e\n"]]],[],null,["# Vector index metrics\n\nSelect a documentation version: 15.7.0keyboard_arrow_down\n\n- [Current (16.8.0)](/alloydb/omni/current/docs/reference/vector-index-metrics)\n- [16.8.0](/alloydb/omni/16.8.0/docs/reference/vector-index-metrics)\n- [16.3.0](/alloydb/omni/16.3.0/docs/reference/vector-index-metrics)\n- [15.12.0](/alloydb/omni/15.12.0/docs/reference/vector-index-metrics)\n- [15.7.1](/alloydb/omni/15.7.1/docs/reference/vector-index-metrics)\n- [15.7.0](/alloydb/omni/15.7.0/docs/reference/vector-index-metrics)\n\n\u003cbr /\u003e\n\nThis 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](/alloydb/omni/15.7.0/docs/ai/store-index-query-vectors).\n\n\u003cbr /\u003e\n\nFor more information about viewing the metrics, see [View vector index metrics](/alloydb/omni/15.7.0/docs/ai/tune-indexes#vector-index-metrics).\n\nUsability metrics\n-----------------\n\nThe usability metrics include metrics that help you understand the state of\nindex utilization with metrics, such as index configuration and number of index\nscans.\n\nTuning metrics\n--------------\n\nTuning metrics provide insights into your current index optimization, allowing you to apply recommendations for faster query performance.\n\n### Tuning recommendation based on the metrics\n\nMutation\n: The `insertcount`, `updatecount`,\n and `deletecount` metrics together show the changes or mutations in\n the vector for the index.\n: 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.\n: 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.\n\nDistribution\n: The `distribution` metric shows the vector distributions across all partitions.\n: 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.\n: 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."]]