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Sie können eine Featuregruppe aktualisieren, um eine BigQuery-Tabelle oder ‐Ansicht
als Datenquelle für diese Featuregruppe zu registrieren. Wenn der Featuregruppe bereits eine Datenquelle zugeordnet ist, können Sie eine andere BigQuery-Tabelle oder -Ansicht als Feature-Datenquelle verknüpfen.
Beim Erstellen oder Aktualisieren einer Featuregruppe haben Sie die Möglichkeit, benutzerdefinierte Metadaten in Form von Labels in die Featuregruppe einzufügen. Weitere Informationen zum Aktualisieren benutzerdefinierter Labels für eine Featuregruppe finden Sie unter Labels für eine Featuregruppe aktualisieren.
Hinweise
Authentifizieren Sie sich bei Vertex AI, sofern nicht bereits geschehen.
Verwenden Sie die von der gcloud CLI bereitgestellten Anmeldedaten, um die REST API-Beispiele auf dieser Seite in einer lokalen Entwicklungsumgebung zu verwenden.
Install the Google Cloud CLI.
After installation,
initialize the Google Cloud CLI by running the following command:
Ersetzen Sie diese Werte in den folgenden Anfragedaten:
LOCATION_ID: Die Region, in der sich die Featuregruppe befindet, z. B. us-central1.
PROJECT_ID ist die Projekt-ID.
FEATURE_GROUP_NAME: der Name der Featuregruppe, die Sie aktualisieren möchten.
ENTITY_ID_COLUMNS: Die Namen der Spalten mit den Entitäts-IDs. Sie können entweder eine oder mehrere Spalten angeben.
Wenn Sie nur eine Spalte für die Entitäts-ID angeben möchten, geben Sie den Spaltennamen im folgenden Format an: "entity_id_column_name".
Wenn Sie mehrere Entitäts-ID-Spalten angeben möchten, geben Sie die Spaltennamen im folgenden Format an: ["entity_id_column_1_name", "entity_id_column_2_name", ...]
BIGQUERY_SOURCE_URI: URI der BigQuery-Quelltabelle oder -ansicht, die Sie mit der Featuregruppe verknüpfen möchten.
[[["Leicht verständlich","easyToUnderstand","thumb-up"],["Mein Problem wurde gelöst","solvedMyProblem","thumb-up"],["Sonstiges","otherUp","thumb-up"]],[["Schwer verständlich","hardToUnderstand","thumb-down"],["Informationen oder Beispielcode falsch","incorrectInformationOrSampleCode","thumb-down"],["Benötigte Informationen/Beispiele nicht gefunden","missingTheInformationSamplesINeed","thumb-down"],["Problem mit der Übersetzung","translationIssue","thumb-down"],["Sonstiges","otherDown","thumb-down"]],["Zuletzt aktualisiert: 2025-09-02 (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)."]]
