VECTOR_INDEXES view
The INFORMATION_SCHEMA.VECTOR_INDEXES
view contains one row for each vector
index in a dataset.
Required permissions
To see vector index metadata, you need the
bigquery.tables.get
or bigquery.tables.list
Identity and Access Management (IAM)
permission on the table with the index. Each of the following predefined
IAM roles includes at least one of these permissions:
roles/bigquery.admin
roles/bigquery.dataEditor
roles/bigquery.dataOwner
roles/bigquery.dataViewer
roles/bigquery.metadataViewer
roles/bigquery.user
For more information about BigQuery permissions, see Access control with IAM.
Schema
When you query theINFORMATION_SCHEMA.VECTOR_INDEXES
view, the query results
contain one row for each vector index in a dataset.
The INFORMATION_SCHEMA.VECTOR_INDEXES
view has the following schema:
Column name | Data type | Value |
---|---|---|
index_catalog |
STRING |
The name of the project that contains the dataset. |
index_schema |
STRING |
The name of the dataset that contains the index. |
table_name |
STRING |
The name of the table that the index is created on. |
index_name |
STRING |
The name of the vector index. |
index_status |
STRING |
The status of the index: ACTIVE , PENDING
DISABLEMENT , TEMPORARILY DISABLED , or
PERMANENTLY DISABLED .
|
creation_time |
TIMESTAMP |
The time the index was created. |
last_modification_time |
TIMESTAMP |
The last time the index configuration was modified. For example, deleting an indexed column. |
last_refresh_time |
TIMESTAMP |
The last time the table data was indexed. A NULL value
means the index is not yet available. |
disable_time |
TIMESTAMP |
The time the status of the index was set to DISABLED . The
value is NULL if the index status is not
DISABLED . |
disable_reason |
STRING |
The reason the index was disabled. NULL if the index
status is not DISABLED . |
DDL |
STRING |
The data definition language (DDL) statement used to create the index. |
coverage_percentage |
INTEGER |
The approximate percentage of table data that has been indexed.
0% means the index is not usable in a VECTOR_SEARCH query,
even if some data has already been indexed.
|
unindexed_row_count |
INTEGER |
The number of rows in the table that have not been indexed. |
total_logical_bytes |
INTEGER |
The number of billable logical bytes for the index. |
total_storage_bytes |
INTEGER |
The number of billable storage bytes for the index. |
Scope and syntax
Queries against this view must have a dataset qualifier. The following table explains the region scope for this view:
View Name | Resource scope | Region scope |
---|---|---|
[PROJECT_ID.]DATASET_ID.INFORMATION_SCHEMA.VECTOR_INDEXES |
Dataset level | Dataset location |
- Optional:
PROJECT_ID
: the ID of your Google Cloud project. If not specified, the default project is used.
DATASET_ID
: the ID of your dataset. For more information, see Dataset qualifier.
Example
-- Returns metadata for vector indexes in a single dataset.
SELECT * FROM myDataset.INFORMATION_SCHEMA.VECTOR_INDEXES;
Example
The following example shows all active vector indexes on tables in the dataset
my_dataset
, located in the project my_project
. It includes their names, the
DDL statements used to create them, and their coverage percentage. If an
indexed base table is less than 10 MB, then its index is not populated, in
which case the coverage_percentage
value is 0.
SELECT table_name, index_name, ddl, coverage_percentage FROM my_project.my_dataset.INFORMATION_SCHEMA.VECTOR_INDEXES WHERE index_status = 'ACTIVE';
The result is similar to the following:
+------------+------------+-------------------------------------------------------------------------------------------------+---------------------+ | table_name | index_name | ddl | coverage_percentage | +------------+------------+-------------------------------------------------------------------------------------------------+---------------------+ | table1 | indexa | CREATE VECTOR INDEX `indexa` ON `my_project.my_dataset.table1`(embeddings) | 100 | | | | OPTIONS (distance_type = 'EUCLIDEAN', index_type = 'IVF', ivf_options = '{"num_lists": 100}') | | +------------+------------+-------------------------------------------------------------------------------------------------+---------------------+ | table2 | indexb | CREATE VECTOR INDEX `indexb` ON `my_project.my_dataset.table2`(vectors) | 42 | | | | OPTIONS (distance_type = 'COSINE', index_type = 'IVF', ivf_options = '{"num_lists": 500}') | | +------------+------------+-------------------------------------------------------------------------------------------------+---------------------+ | table3 | indexc | CREATE VECTOR INDEX `indexc` ON `my_project.my_dataset.table3`(vectors) | 98 | | | | OPTIONS (distance_type = 'DOT_PRODUCT', index_type = 'TREE_AH', | | | | | tree_ah_options = '{"leaf_node_embedding_count": 1000, "normalization_type": "NONE"}') | | +------------+------------+-------------------------------------------------------------------------------------------------+---------------------+