Manage featurestores

To learn more, run the "Example Feature Store workflow with sample data" Jupyter notebook in one of the following environments:

• Run the notebook in Colab.
• Run the notebook in a Vertex AI Workbench user-managed notebooks instance.
• View the notebook on GitHub.

Learn how to create, list, describe, update, and delete featurestores.

Create a featurestore

Create a featurestore resource to contain entity types and features. The location of your featurestore must be in the same location as your source data. For example, if your featurestore is in us-central, you can ingest data from files in Cloud Storage buckets that are in us-central1 or in the US multi-region location. Similarly for BigQuery, you can ingest data from tables that are in us-central1 or in the US multi-region location. For more information about source data requirements, see Data source.

Note: Billing for a featurestore instance starts when it is created and continues as long as it is serving requests or idle. For details, see the pricing page.

Web UI

Use another method. You cannot create a featurestore from the Cloud Console.

REST & CMD LINE

To create a featurestore, send a POST request by using the featurestores.create method.

The following sample creates a featurestore with a fixed node count of 1. The node count specifies the number of online serving nodes, which affects the number of online serving requests that the featurestore can handle. The latency could increase if the number of nodes cannot support the number of incoming requests.

Before using any of the request data, make the following replacements:

• LOCATION: Region where the featurestore is created. For example, us-central1.
• PROJECT: Your project ID or project number.
• FEATURESTORE_ID: ID for the featurestore.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores?featurestoreId=FEATURESTORE_ID

Request JSON body:

{
  "online_serving_config": {
    "fixed_node_count": 1
  },
  "labels": {
    "environment": "testing"
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores?featurestoreId=FEATURESTORE_ID"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores?featurestoreId=FEATURESTORE_ID" | Select-Object -Expand Content

You should see output similar to the following. You can use the OPERATION_ID in the response to get the status of the operation.

{
"name": "projects/PROJECT_NUMBER/locations/LOCATION/featurestores/FEATURESTORE_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateFeaturestoreOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-02-26T00:44:40.722474Z",
      "updateTime": "2021-02-26T00:44:40.722474Z"
    }
  }
}

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

View on GitHub Feedback

from google.cloud import aiplatform


def create_featurestore_sample(
    project: str,
    featurestore_id: str,
    fixed_node_count: int = 1,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # The AI Platform services require regional API endpoints, which need to be
    # in the same region or multi-region overlap with the Feature Store location.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.FeaturestoreServiceClient(client_options=client_options)
    parent = f"projects/{project}/locations/{location}"
    create_featurestore_request = aiplatform.gapic.CreateFeaturestoreRequest(
        parent=parent,
        featurestore_id=featurestore_id,
        featurestore=aiplatform.gapic.Featurestore(
            online_serving_config=aiplatform.gapic.Featurestore.OnlineServingConfig(
                fixed_node_count=fixed_node_count,
            ),
        ),
    )
    lro_response = client.create_featurestore(request=create_featurestore_request)
    print("Long running operation:", lro_response.operation.name)
    create_featurestore_response = lro_response.result(timeout=timeout)
    print("create_featurestore_response:", create_featurestore_response)

Additional languages

You can install and use the following Vertex AI client libraries to call the Vertex AI API. Cloud Client Libraries provide an optimized developer experience by using each supported language's natural conventions and styles.

• Vertex AI SDK for Python
• Java
• Node.js

Create a featurestore that uses a CMEK

Before you begin, if you don't have an existing CMEK, use Cloud Key Management Service to configure a customer-managed encryption key and set up permissions. The following sample creates a featurestore that uses a CMEK.

Note: If Vertex AI Feature Store loses permission to the associated CMEK, any resources and values within featurestores that were encrypted by that key become inaccessible until Vertex AI Feature Store can use that key again.

Web UI

Use another method. You cannot create a featurestore from the Cloud Console.

REST & CMD LINE

Before using any of the request data, make the following replacements:

• LOCATION: Region where the featurestore is created. For example, us-central1.
• PROJECT: Your project ID or project number.
• FEATURESTORE_ID: ID for the featurestore.
• CMEK_PROJECT: The project ID or project number that contains your CMEK.
• KEY_RING: The name of the Cloud Key Management Service key ring that your encryption key is on.
• KEY_NAME: The name of the encryption key to use.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores?featurestoreId=FEATURESTORE_ID

Request JSON body:

{
  "online_serving_config": {
    "fixed_node_count": 1
  },
  "encryption_spec":{
    "kms_key_name": "projects/CMEK_PROJECT/locations/LOCATION/keyRings/KEY_RING/cryptoKeys/KEY_NAME"
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores?featurestoreId=FEATURESTORE_ID"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method POST `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores?featurestoreId=FEATURESTORE_ID" | Select-Object -Expand Content

You should see output similar to the following. You can use the OPERATION_ID in the response to get the status of the operation.

{
"name": "projects/PROJECT_NUMBER/locations/LOCATION/featurestores/FEATURESTORE_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateFeaturestoreOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-02-26T00:44:40.722474Z",
      "updateTime": "2021-02-26T00:44:40.722474Z"
    }
  }
}

List featurestores

List all featurestores in a project.

Web UI

1. In the Vertex AI section of the Google Cloud Console, go to the Features page.

   Go to the Features page

2. Select a region from the Region drop-down list.
3. In the features table, view the Featurestore column to see the featurestores in your project for the selected region.

REST & CMD LINE

To list featurestores for a particular region in your project, send a GET request by using the featurestores.list method.

Before using any of the request data, make the following replacements:

• LOCATION: Region where the featurestore is located, such as us-central1.
• PROJECT: Your project ID or project number.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Execute the following command:

curl -X GET \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method GET `
  -Headers $headers `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "featurestores": [
    {
      "name": "projects/PROJECT_NUMBER/locations/us-central1/featurestores/test",
      "createTime": "2021-02-26T00:44:44.216805Z",
      "updateTime": "2021-02-26T00:44:44.364916Z",
      "etag": "AMEw9yNL0s7qZh8lZVZ5T3BEuhoEgFR7JmjbbCSAkRZjeKDXkkIYnxxA4POe5BWT8cCn",
      "labels": {
        "environment": "testing"
      },
      "onlineServingConfig": {
        "fixedNodeCount": 2
      },
      "state": "STABLE"
    },
    {
      "name": "projects/PROJECT_NUMBER/locations/us-central1/featurestores/featurestore_demo",
      "createTime": "2021-02-25T00:39:40.598781Z",
      "updateTime": "2021-02-25T00:39:40.744038Z",
      "etag": "AMEw9yO_e0vm-9W_yeCz4rJm-XnnEMYQ-vQesevxya_sz-FckuysnDwo3cEXHdWWSeda",
      "labels": {
        "environment": "testing"
      },
      "onlineServingConfig": {
        "fixedNodeCount": 3
      },
      "state": "STABLE"
    }
  ]
}

Additional languages

You can install and use the following Vertex AI client libraries to call the Vertex AI API. Cloud Client Libraries provide an optimized developer experience by using each supported language's natural conventions and styles.

• Vertex AI SDK for Python
• Java
• Node.js

View featurestore details

Get details about a featurestore such as its name and online serving configuration. If you use the Cloud Console, you can also view Cloud Monitoring metrics for featurestores.

Web UI

1. In the Vertex AI section of the Google Cloud Console, go to the Features page.

   Go to the Features page

2. Select a region from the Region drop-down list.
3. In the features table, view the Featurestore column and find the featurestore that you want to view information for.
4. Click the name of the featurestore to view its Monitoring metrics.
5. Click the Properties tab to view the featurestore's online serving configuration.

REST & CMD LINE

To get details about a single featurestore, send a GET request by using the featurestores.get method.

Before using any of the request data, make the following replacements:

• LOCATION: Region where the featurestore is located, such as us-central1.
• PROJECT: Your project ID or project number.
• FEATURESTORE_ID: ID of the featurestore.

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Execute the following command:

curl -X GET \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method GET `
  -Headers $headers `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/us-central1/featurestores/FEATURESTORE_ID",
  "createTime": "2021-02-25T00:39:40.598781Z",
  "updateTime": "2021-02-25T00:39:40.744038Z",
  "etag": "AMEw9yNy_b4IaMIvw1803ZT38cpUtjfwlyLkR709oBCY6pQrm6dHophLcqhrvsNqkQQZ",
  "onlineServingConfig": {
    "fixedNodeCount": 3
  },
  "state": "STABLE"
}

Additional languages

You can install and use the following Vertex AI client libraries to call the Vertex AI API. Cloud Client Libraries provide an optimized developer experience by using each supported language's natural conventions and styles.

• Vertex AI SDK for Python
• Java
• Node.js

Update a featurestore

Update a featurestore, for example, to change the number of online serving nodes or update labels on a featurestore.

Web UI

You can update only the number of online serving nodes. To update labels, use the API.

1. In the Vertex AI section of the Google Cloud Console, go to the Features page.

   Go to the Features page

2. Select a region from the Region drop-down list.
3. In the features table, view the Featurestore column and click the name of the featurestore to update.
4. Click Edit configuration to open the Edit featurestore configuration pane.
5. Edit the featurestore configuration.
6. Click Update to apply your changes.

REST & CMD LINE

To update a featurestore, send a PATCH request by using the featurestores.patch method.

The following sample updates the number of online serving nodes to 2 for the featurestore. All other settings remain the same.

Before using any of the request data, make the following replacements:

• LOCATION: Region where the featurestore is located, such as us-central1.
• PROJECT: Your project ID or project number.
• FEATURESTORE_ID: ID of the featurestore.

HTTP method and URL:

PATCH https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID

Request JSON body:

{
  "online_serving_config": {
    "fixed_node_count": 2
  }
}

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

curl -X PATCH \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method PATCH `
  -Headers $headers `
  -ContentType: "application/json; charset=utf-8" `
  -InFile request.json `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID" | Select-Object -Expand Content

You should see output similar to the following. You can use the OPERATION_ID in the response to get the status of the operation.

{
"name": "projects/PROJECT_NUMBER/locations/LOCATION/featurestores/FEATURESTORE_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.UpdateFeaturestoreOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-03-18T21:12:08.373664Z",
      "updateTime": "2021-03-18T21:12:08.373664Z"
    }
  }
}

Additional languages

You can install and use the following Vertex AI client libraries to call the Vertex AI API. Cloud Client Libraries provide an optimized developer experience by using each supported language's natural conventions and styles.

• Vertex AI SDK for Python
• Java
• Node.js

Delete a featurestore

Delete a featurestore. If the featurestore includes existing entity types and features, enable the force query parameter to delete the featurestore and all of its contents.

Web UI

Use another method. You cannot delete a featurestore from the Cloud Console.

REST & CMD LINE

To delete a featurestore and all of its content, send a DELETE request by using the featurestores.delete method.

Before using any of the request data, make the following replacements:

• LOCATION: Region where the featurestore is located, such as us-central1.
• PROJECT: Your project ID or project number.
• FEATURESTORE_ID: ID of the featurestore.
• BOOLEAN: Whether to delete the featurestore even if it contains entity types and features. The force query parameter is optional and is false by default.

HTTP method and URL:

DELETE https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID?force=BOOLEAN

To send your request, choose one of these options:

curl

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Execute the following command:

curl -X DELETE \
-H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID?force=BOOLEAN"

PowerShell

Note: Ensure you have set the GOOGLE_APPLICATION_CREDENTIALS environment variable to the path for your service account private key file.

Execute the following command:

$cred = gcloud auth application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
  -Method DELETE `
  -Headers $headers `
  -Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/featurestores/FEATURESTORE_ID?force=BOOLEAN" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/operations/OPERATIONS_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeleteOperationMetadata",
    "genericMetadata": {
      "createTime": "2021-05-03T17:50:21.813112Z",
      "updateTime": "2021-05-03T17:50:21.813112Z"
    }
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

View on GitHub Feedback

from google.cloud import aiplatform


def delete_featurestore_sample(
    project: str,
    featurestore_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # The AI Platform services require regional API endpoints, which need to be
    # in the same region or multi-region overlap with the Feature Store location.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.FeaturestoreServiceClient(client_options=client_options)
    name = client.featurestore_path(
        project=project, location=location, featurestore=featurestore_id
    )
    response = client.delete_featurestore(name=name)
    print("Long running operation:", response.operation.name)
    delete_featurestore_response = response.result(timeout=timeout)
    print("delete_featurestore_response:", delete_featurestore_response)

Additional languages

You can install and use the following Vertex AI client libraries to call the Vertex AI API. Cloud Client Libraries provide an optimized developer experience by using each supported language's natural conventions and styles.

• Vertex AI SDK for Python
• Java
• Node.js

What's next

• Learn how to manage entity types and features.
• Troubleshoot common Vertex AI Feature Store issues.