Mantenha tudo organizado com as coleções
Salve e categorize o conteúdo com base nas suas preferências.
É possível atualizar um grupo de recursos para registrar uma tabela ou visualização do BigQuery
como fonte de dados de recursos desse grupo. Se o grupo de recursos já tiver
uma fonte de dados associada, será possível associar uma tabela ou visualização
diferente do BigQuery como a fonte de dados de elementos.
Ao criar ou atualizar um grupo de recursos, você tem a opção de adicionar metadados definidos
pelo usuário na forma de rótulos ao grupo de recursos. Para mais informações sobre
como atualizar rótulos definidos pelo usuário em um grupo de recursos, consulte
Atualizar rótulos para um grupo de recursos.
Antes de começar
Faça a autenticação na
Vertex AI, a menos que já tenha feito isso.
Para usar as amostras da API REST nesta página em um ambiente de desenvolvimento local, use as
credenciais fornecidas para a CLI gcloud.
Instale a CLI do Google Cloud.
Após a instalação,
inicialize a Google Cloud CLI executando o seguinte comando:
Antes de usar os dados da solicitação abaixo, faça as substituições a seguir:
LOCATION_ID: região em que o grupo de recursos está localizado, como us-central1.
PROJECT_ID: o ID do projeto.
FEATURE_GROUP_NAME: o nome do grupo de recursos que você quer atualizar.
ENTITY_ID_COLUMNS: os nomes das colunas que contêm os IDs das entidades. É possível especificar uma ou várias colunas.
Para especificar apenas uma coluna de IDs das entidades, especifique o nome da coluna no seguinte formato: "entity_id_column_name".
Para especificar várias colunas de IDs das entidades, especifique os nomes das colunas no seguinte formato: ["entity_id_column_1_name", "entity_id_column_2_name", ...].
BIGQUERY_SOURCE_URI: URI da tabela ou visualização de origem do BigQuery que você quer associar ao grupo de recursos.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-08-19 UTC."],[],[],null,["# Update a feature group\n\n| **Preview**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\nYou can update a feature group to register a BigQuery table or view\nas the feature data source for that feature group. If the feature group already has\nan associated data source, you can associate a different BigQuery\ntable or view as the feature data source.\n| **Caution:** You can update a feature group even if there are feature views and features associated with it. If the updated data source excludes a column that's being used for online serving or is associated with a feature view or feature, then you also need to update that [feature view](/vertex-ai/docs/featurestore/latest/update-featureview) or [feature](/vertex-ai/docs/featurestore/latest/update-feature).\n\nWhile creating or updating a feature group, you have the option to add user-defined\nmetadata in the form of labels to the feature group. For more information about\nhow to update user-defined labels for a feature group, see\n[Update labels for a feature group](/vertex-ai/docs/featurestore/latest/feature-labels#feature_group).\n\nBefore you begin\n----------------\n\n\nto\nVertex AI, unless you've done so already.\n\n\nTo use the REST API samples on this page in a local development environment, you use the\ncredentials you provide to the gcloud CLI.\n\n1. [Install](/sdk/docs/install) the Google Cloud CLI. After installation, [initialize](/sdk/docs/initializing) the Google Cloud CLI by running the following command: \n\n```bash\ngcloud init\n```\n2. If you're using an external identity provider (IdP), you must first [sign in to the gcloud CLI with your federated identity](/iam/docs/workforce-log-in-gcloud).\n\n\nFor more information, see\n[Authenticate for using REST](/docs/authentication/rest)\nin the Google Cloud authentication documentation.\n\nUpdate a feature group\n----------------------\n\nUse the following sample to update a feature group. \n\n### REST\n\n\nTo update a [`FeatureGroup`](/vertex-ai/docs/reference/rest/v1/projects.locations.featureGroups#resource:-featuregroup)\nresource, send a `PATCH` request by using the\n[featureGroups.patch](/vertex-ai/docs/reference/rest/v1/projects.locations.featureGroups/patch)\nmethod.\n\n\nBefore using any of the request data,\nmake the following replacements:\n\n- \u003cvar translate=\"no\"\u003eLOCATION_ID\u003c/var\u003e: Region where the feature group is located, such as `us-central1`.\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eFEATURE_GROUP_NAME\u003c/var\u003e: The name of the feature group that you want to update.\n- \u003cvar translate=\"no\"\u003eENTITY_ID_COLUMNS\u003c/var\u003e: The names of the column(s) containing the entity IDs. You can specify either one column or multiple columns.\n - To specify only one entity ID column, specify the column name in the following format: \n `\"entity_id_column_name\"`.\n - To specify multiple entity ID columns, specify the column names in the following format: \n `[\"entity_id_column_1_name\", \"entity_id_column_2_name\", ...]`.\n- \u003cvar translate=\"no\"\u003eBIGQUERY_SOURCE_URI\u003c/var\u003e: URI of the BigQuery source table or view that you want to associate with the feature group.\n\n\nHTTP method and URL:\n\n```\nPATCH https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureGroups?feature_group_id=FEATURE_GROUP_NAME\n```\n\n\nRequest JSON body:\n\n```\n{\n \"big_query\": {\n \"entity_id_columns\": \"ENTITY_ID_COLUMNS\",\n \"big_query_source\": {\n \"input_uri\": \"BIGQUERY_SOURCE_URI\"\n }\n }\n}\n```\n\nTo send your request, choose one of these options: \n\n#### curl\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) , or by using [Cloud Shell](/shell/docs), which automatically logs you into the `gcloud` CLI . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\ncurl -X PATCH \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n -H \"Content-Type: application/json; charset=utf-8\" \\\n -d @request.json \\\n \"https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureGroups?feature_group_id=FEATURE_GROUP_NAME\"\n```\n\n#### PowerShell\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nSave the request body in a file named `request.json`,\nand execute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\" }\n\nInvoke-WebRequest `\n -Method PATCH `\n -Headers $headers `\n -ContentType: \"application/json; charset=utf-8\" `\n -InFile request.json `\n -Uri \"https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureGroups?feature_group_id=FEATURE_GROUP_NAME\" | Select-Object -Expand Content\n```\n\nYou should receive a JSON response similar to the following:\n\n```\n{\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/featureGroups/FEATURE_GROUP_NAME/operations/OPERATION_ID\",\n \"metadata\": {\n \"@type\": \"type.googleapis.com/google.cloud.aiplatform.v1.UpdateFeatureGroupOperationMetadata\",\n \"genericMetadata\": {\n \"createTime\": \"2023-09-18T03:00:13.060636Z\",\n \"updateTime\": \"2023-09-18T03:00:13.060636Z\"\n }\n },\n \"done\": true,\n \"response\": {\n \"@type\": \"type.googleapis.com/google.cloud.aiplatform.v1.FeatureGroup\",\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/featureGroups/FEATURE_GROUP_NAME\"\n }\n}\n```\n\n\u003cbr /\u003e\n\nWhat's next\n-----------\n\n- Learn how to [update a feature view](/vertex-ai/docs/featurestore/latest/update-featureview).\n\n- Learn how to [update a feature](/vertex-ai/docs/featurestore/latest/update-feature).\n\n- Learn how to [delete a feature group](/vertex-ai/docs/featurestore/latest/update-featureview)."]]
