INFORMATION_SCHEMA.VECTOR_INDEXES 뷰에는 데이터 세트에 있는 각 벡터에 대해 하나의 행이 포함되어 있습니다.
필수 권한
벡터 색인 메타데이터를 보려면 색인이 있는 테이블에 대한 bigquery.tables.get 또는 bigquery.tables.list Identity and Access Management(IAM) 권한이 필요합니다. 다음과 같은 사전 정의된 IAM 역할에는 이러한 권한 중 하나 이상이 포함되어 있습니다.
선택사항: PROJECT_ID: Google Cloud 프로젝트의 ID입니다. 지정하지 않으면 기본 프로젝트가 사용됩니다.
DATASET_ID: 데이터 세트의 ID입니다. 자세한 내용은 데이터 세트 한정자를 참조하세요.
예시
-- Returns metadata for vector indexes in a single dataset.SELECT*FROMmyDataset.INFORMATION_SCHEMA.VECTOR_INDEXES;
예
다음 예시는 my_project 프로젝트에 위치한 데이터 세트 my_dataset에 있는 테이블의 모든 활성 벡터 색인을 보여줍니다. 여기에는 이름, 벡터 색인을 만드는 데 사용된 DDL 문, 적용 범위 비율이 포함됩니다. 색인이 생성된 기본 테이블이 10MB 미만이면 색인이 채워지지 않으며, 이 경우 coverage_percentage 값은 0입니다.
[[["이해하기 쉬움","easyToUnderstand","thumb-up"],["문제가 해결됨","solvedMyProblem","thumb-up"],["기타","otherUp","thumb-up"]],[["이해하기 어려움","hardToUnderstand","thumb-down"],["잘못된 정보 또는 샘플 코드","incorrectInformationOrSampleCode","thumb-down"],["필요한 정보/샘플이 없음","missingTheInformationSamplesINeed","thumb-down"],["번역 문제","translationIssue","thumb-down"],["기타","otherDown","thumb-down"]],["최종 업데이트: 2025-09-04(UTC)"],[[["\u003cp\u003eThe \u003ccode\u003eINFORMATION_SCHEMA.VECTOR_INDEXES\u003c/code\u003e view provides metadata for each vector index within a dataset, with each row representing a unique index.\u003c/p\u003e\n"],["\u003cp\u003eAccessing vector index metadata requires \u003ccode\u003ebigquery.tables.get\u003c/code\u003e or \u003ccode\u003ebigquery.tables.list\u003c/code\u003e IAM permissions, which are included in several predefined roles such as \u003ccode\u003eroles/bigquery.admin\u003c/code\u003e and \u003ccode\u003eroles/bigquery.dataViewer\u003c/code\u003e.\u003c/p\u003e\n"],["\u003cp\u003eThe \u003ccode\u003eVECTOR_INDEXES\u003c/code\u003e schema includes details like \u003ccode\u003eindex_name\u003c/code\u003e, \u003ccode\u003eindex_status\u003c/code\u003e (\u003ccode\u003eACTIVE\u003c/code\u003e, \u003ccode\u003ePENDING DISABLEMENT\u003c/code\u003e, \u003ccode\u003eTEMPORARILY DISABLED\u003c/code\u003e, or \u003ccode\u003ePERMANENTLY DISABLED\u003c/code\u003e), \u003ccode\u003ecreation_time\u003c/code\u003e, \u003ccode\u003ecoverage_percentage\u003c/code\u003e, and the \u003ccode\u003eDDL\u003c/code\u003e statement used to create the index.\u003c/p\u003e\n"],["\u003cp\u003eQueries on this view must include a dataset qualifier and the query execution location must match the region of the \u003ccode\u003eINFORMATION_SCHEMA\u003c/code\u003e view.\u003c/p\u003e\n"],["\u003cp\u003eAn index might not be populated if the indexed base table is less than 10MB, in which case the \u003ccode\u003ecoverage_percentage\u003c/code\u003e is 0, meaning it is not usable.\u003c/p\u003e\n"]]],[],null,["# VECTOR_INDEXES view\n===================\n\nThe `INFORMATION_SCHEMA.VECTOR_INDEXES` view contains one row for each vector\nindex in a dataset.\n\nRequired permissions\n--------------------\n\nTo see [vector index](/bigquery/docs/vector-index) metadata, you need the\n`bigquery.tables.get` or `bigquery.tables.list` Identity and Access Management (IAM)\npermission on the table with the index. Each of the following predefined\nIAM roles includes at least one of these permissions:\n\n- `roles/bigquery.admin`\n- `roles/bigquery.dataEditor`\n- `roles/bigquery.dataOwner`\n- `roles/bigquery.dataViewer`\n- `roles/bigquery.metadataViewer`\n- `roles/bigquery.user`\n\nFor more information about BigQuery permissions, see\n[Access control with IAM](/bigquery/docs/access-control).\n\nSchema\n------\n\nWhen you query the `INFORMATION_SCHEMA.VECTOR_INDEXES` view, the query results contain one row for each vector index in a dataset.\n\n\u003cbr /\u003e\n\nThe `INFORMATION_SCHEMA.VECTOR_INDEXES` view has the following schema:\n\nScope and syntax\n----------------\n\nQueries against this view must have a [dataset qualifier](/bigquery/docs/information-schema-intro#syntax). The\nfollowing table explains the region scope for this view:\n\n\u003cbr /\u003e\n\nReplace the following:\n\n- Optional: \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: the ID of your Google Cloud project. If not specified, the default project is used.\n- \u003cvar translate=\"no\"\u003eDATASET_ID\u003c/var\u003e: the ID of your dataset. For more information, see [Dataset qualifier](/bigquery/docs/information-schema-intro#dataset_qualifier).\n\n \u003cbr /\u003e\n\n \u003cbr /\u003e\n\n | **Note:** You must use [a region qualifier](/bigquery/docs/information-schema-intro#region_qualifier) to query `INFORMATION_SCHEMA` views. The location of the query execution must match the region of the `INFORMATION_SCHEMA` view.\n\n\u003cbr /\u003e\n\n**Example** \n\n -- Returns metadata for vector indexes in a single dataset.\n SELECT * FROM myDataset.INFORMATION_SCHEMA.VECTOR_INDEXES;\n\nExample\n-------\n\nThe following example shows all active vector indexes on tables in the dataset\n`my_dataset`, located in the project `my_project`. It includes their names, the\nDDL statements used to create them, and their coverage percentage. If an\nindexed base table is less than 10 MB, then its index is not populated, in\nwhich case the `coverage_percentage` value is 0. \n\n```googlesql\nSELECT table_name, index_name, ddl, coverage_percentage\nFROM my_project.my_dataset.INFORMATION_SCHEMA.VECTOR_INDEXES\nWHERE index_status = 'ACTIVE';\n```\n\nThe result is similar to the following: \n\n```\n+------------+------------+-------------------------------------------------------------------------------------------------+---------------------+\n| table_name | index_name | ddl | coverage_percentage |\n+------------+------------+-------------------------------------------------------------------------------------------------+---------------------+\n| table1 | indexa | CREATE VECTOR INDEX `indexa` ON `my_project.my_dataset.table1`(embeddings) | 100 |\n| | | OPTIONS (distance_type = 'EUCLIDEAN', index_type = 'IVF', ivf_options = '{\"num_lists\": 100}') | |\n+------------+------------+-------------------------------------------------------------------------------------------------+---------------------+\n| table2 | indexb | CREATE VECTOR INDEX `indexb` ON `my_project.my_dataset.table2`(vectors) | 42 |\n| | | OPTIONS (distance_type = 'COSINE', index_type = 'IVF', ivf_options = '{\"num_lists\": 500}') | |\n+------------+------------+-------------------------------------------------------------------------------------------------+---------------------+\n| table3 | indexc | CREATE VECTOR INDEX `indexc` ON `my_project.my_dataset.table3`(vectors) | 98 |\n| | | OPTIONS (distance_type = 'DOT_PRODUCT', index_type = 'TREE_AH', | |\n| | | tree_ah_options = '{\"leaf_node_embedding_count\": 1000, \"normalization_type\": \"NONE\"}') | |\n+------------+------------+-------------------------------------------------------------------------------------------------+---------------------+\n```"]]
