Mit Sammlungen den Überblick behalten
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Sie können eine Liste aller Featureansicht-Instanzen abrufen, die in einem Onlinespeicher in Ihrem Google Cloud-Projekt erstellt wurden. Für jede Feature-Ansicht können Sie auch die Feature-Datenquelle aufrufen. Dabei kann es sich um eine der folgenden Datenquellen handeln:
Eine oder mehrere Featuregruppen und ihre zugehörigen Features. Jede Featuregruppe ist einer Feature-Datenquelle zugeordnet, z. B. einer BigQuery-Tabelle oder -Ansicht. Jedes Feature entspricht einer Spalte in der BigQuery-Datenquelle.
Eine BigQuery-Tabelle oder -Ansicht, die direkt mit der Feature-Ansicht verknüpft ist.
Hinweise
Authentifizieren Sie sich bei Vertex AI, sofern nicht bereits geschehen.
Select the tab for how you plan to use the samples on this page:
Console
When you use the Google Cloud console to access Google Cloud services and
APIs, you don't need to set up authentication.
REST
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:
Verwenden Sie die folgenden Beispiele, um eine Liste der Featureansichten abzurufen, die für einen Onlineshop in Ihrem Projekt für einen bestimmten Standort erstellt wurden.
Console
So rufen Sie mit der Google Cloud Console die Liste der Featureansichten in einem Onlineshop auf:
Rufen Sie im Abschnitt „Vertex AI“ der Google Cloud Console die Seite Feature Store auf.
Klicken Sie auf den Namen des Onlineshops, um die zugehörigen Details auf dessen Detailseite aufzurufen.
Im Bereich Featureansichten finden Sie eine Liste aller Onlineshops für den ausgewählten Standort.
REST
Wenn Sie eine Liste aller FeatureView-Instanzen abrufen möchten, die in einem bestimmten Onlinespeicher in Ihrem Projekt erstellt wurden, senden Sie eine GET-Anfrage mit featureViews.list-Methode.
Ersetzen Sie diese Werte in den folgenden Anfragedaten:
LOCATION_ID: Die Region, in der sich der Onlinespeicher befindet, z. B. us-central1.
PROJECT_ID: Ihre Projekt-ID.
FEATUREONLINESTORE_NAME: Der Name des Onlineshops, für den Sie die Liste der Featureansichten aufrufen möchten.
HTTP-Methode und URL:
GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews
Senden Sie die Anfrage mithilfe einer der folgenden Optionen:
[[["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,["# List feature views\n\nYou can retrieve a list of all the feature view instances created within an online store\nin your Google Cloud project. For each feature view, you can also view the feature\ndata source, which can be either of the following:\n\n- One or more feature groups and their constituent features. Each feature group\n is associated with a feature data source, such as a BigQuery table\n or view. Each feature designates a column in the BigQuery\n data source.\n\n- A BigQuery table or view directly associated with the feature view.\n\nIf a feature view is configured to use a dedicated service account, then the\ndetails for that feature view also include the associated service account email\naddress. For more information about creating feature views with dedicated\nservice account configurations, see\n[Configure the service account for a feature view](/vertex-ai/docs/featurestore/latest/create-featureview#serviceaccount).\n\nBefore you begin\n----------------\n\n\nto\nVertex AI, unless you've done so already.\n\nSelect the tab for how you plan to use the samples on this page: \n\n### Console\n\n\nWhen you use the Google Cloud console to access Google Cloud services and\nAPIs, you don't need to set up authentication.\n\n### REST\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\nList feature views in an online store\n-------------------------------------\n\nUse the following samples to retrieve a list of feature views created for an\nonline store in your project for a specific location. \n\n### Console\n\nUse the following instructions to view the list of feature views in an online store using the Google Cloud console.\n\n1. In the Vertex AI section of the Google Cloud console, go\n to the **Feature Store** page.\n\n [Go to the Feature Store page](https://console.cloud.google.com/vertex-ai/feature-store)\n2. Click **Online store**.\n\n3. Click the name of the online store to view its details on the **Online store details** page.\n\n4. In the **Feature views** section, you can view the list of all the online stores for the selected location.\n\n### REST\n\n\nTo retrieve a list of all the [`FeatureView`](/vertex-ai/docs/reference/rest/v1/projects.locations.featureOnlineStores.featureViews#resource:-featureView) instances created within a specific online\nstore in your project, send a `GET` request by using the\n[featureViews.list](/vertex-ai/docs/reference/rest/v1/projects.locations.featureOnlineStores.featureViews/list)\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 online store is located, such as `us-central1`.\n- \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e: Your project ID.\n- \u003cvar translate=\"no\"\u003eFEATUREONLINESTORE_NAME\u003c/var\u003e: The name of the online store for which you want to view the list of feature views.\n\n\nHTTP method and URL:\n\n```\nGET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews\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\nExecute the following command:\n\n```\ncurl -X GET \\\n -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n \"https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews\"\n```\n\n#### PowerShell\n\n| **Note:** The following command assumes that you have logged in to the `gcloud` CLI with your user account by running [`gcloud init`](/sdk/gcloud/reference/init) or [`gcloud auth login`](/sdk/gcloud/reference/auth/login) . You can check the currently active account by running [`gcloud auth list`](/sdk/gcloud/reference/auth/list).\n\n\nExecute the following command:\n\n```\n$cred = gcloud auth print-access-token\n$headers = @{ \"Authorization\" = \"Bearer $cred\" }\n\nInvoke-WebRequest `\n -Method GET `\n -Headers $headers `\n -Uri \"https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews\" | Select-Object -Expand Content\n```\nYou should receive a JSON response similar to the following. If any of the feature views listed in the response has a dedicated service account configuration, then the service account email address is also listed in its details. In this example, \u003cvar translate=\"no\"\u003eSERVICE_ACCOUNT_EMAIL\u003c/var\u003e is the service account email address associated with the feature view \u003cvar translate=\"no\"\u003eFEATUREVIEW_NAME_1\u003c/var\u003e.\n\n```\n{\n \"featureViews\": [\n {\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews/FEATUREVIEW_NAME_1\",\n \"createTime\": \"2023-09-06T23:46:49.936284Z\",\n \"updateTime\": \"2023-09-06T23:46:49.936284Z\",\n \"etag\": \"sample_etag\",\n \"featureRegistrySource\": {\n \"featureGroups\": [\n {\n \"featureGroupId\": \"FEATUREGROUP_ID\",\n \"featureIds\": [\n \"FEATURE_ID_1\",\n \"FEATURE_ID_2\",\n ]\n }\n ]\n }\n \"serviceAccountEmail\": \"SERVICE_ACCOUNT_EMAIL\"\n },\n {\n \"name\": \"projects/PROJECT_NUMBER/locations/LOCATION_ID/featureOnlineStores/FEATUREONLINESTORE_NAME/featureViews/FEATUREVIEW_NAME_2\",\n \"createTime\": \"2024-02-05T23:48:49.936284Z\",\n \"updateTime\": \"2024-02-05T23:48:49.936284Z\",\n \"etag\": \"sample_etag\",\n \"featureRegistrySource\": {\n \"featureGroups\": [\n {\n \"featureGroupId\": \"FEATUREGROUP_ID\",\n \"featureIds\": [\n \"FEATURE_ID_3\",\n \"FEATURE_ID_4\",\n ]\n }\n ]\n }\n }\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 [delete a feature view](/vertex-ai/docs/featurestore/latest/delete-featureview).\n\n- [Add more feature views](/vertex-ai/docs/featurestore/latest/create-featureview)."]]
