Class FeaturestoreServiceAsyncClient (1.71.1)

The service that handles CRUD and List for resources for Featurestore.

Return the API endpoint used by the client instance.

Returns the transport used by the client instance.

Return the universe domain used by the client instance.

Instantiates the featurestore service async client.

Creates a batch of Features in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_batch_create_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    requests = aiplatform_v1.CreateFeatureRequest()
    requests.parent = "parent_value"
    requests.feature_id = "feature_id_value"

    request = aiplatform_v1.BatchCreateFeaturesRequest(
        parent="parent_value",
        requests=requests,
    )

    # Make the request
    operation = client.batch_create_features(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Batch reads Feature values from a Featurestore.

This API enables batch reading Feature values, where each read instance in the batch may read Feature values of entities from one or more EntityTypes. Point-in-time correctness is guaranteed for Feature values of each read instance as of each instance's read timestamp.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_batch_read_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    csv_read_instances = aiplatform_v1.CsvSource()
    csv_read_instances.gcs_source.uris = ['uris_value1', 'uris_value2']

    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    entity_type_specs = aiplatform_v1.EntityTypeSpec()
    entity_type_specs.entity_type_id = "entity_type_id_value"
    entity_type_specs.feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

    request = aiplatform_v1.BatchReadFeatureValuesRequest(
        csv_read_instances=csv_read_instances,
        featurestore="featurestore_value",
        destination=destination,
        entity_type_specs=entity_type_specs,
    )

    # Make the request
    operation = client.batch_read_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Creates a new EntityType in a given Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateEntityTypeRequest(
        parent="parent_value",
        entity_type_id="entity_type_id_value",
    )

    # Make the request
    operation = client.create_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Creates a new Feature in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateFeatureRequest(
        parent="parent_value",
        feature_id="feature_id_value",
    )

    # Make the request
    operation = client.create_feature(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Creates a new Featurestore in a given project and location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.CreateFeaturestoreRequest(
        parent="parent_value",
        featurestore_id="featurestore_id_value",
    )

    # Make the request
    operation = client.create_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a single EntityType. The EntityType must not have any Features or force must be set to true for the request to succeed.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteEntityTypeRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_entity_type(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeatureRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_feature(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Delete Feature values from Featurestore.

The progress of the deletion is tracked by the returned operation. The deleted feature values are guaranteed to be invisible to subsequent read operations after the operation is marked as successfully done.

If a delete feature values operation fails, the feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same delete request again and wait till the new operation returned is marked as successfully done.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    select_entity = aiplatform_v1.SelectEntity()
    select_entity.entity_id_selector.csv_source.gcs_source.uris = ['uris_value1', 'uris_value2']

    request = aiplatform_v1.DeleteFeatureValuesRequest(
        select_entity=select_entity,
        entity_type="entity_type_value",
    )

    # Make the request
    operation = client.delete_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a single Featurestore. The Featurestore must not contain any EntityTypes or force must be set to true for the request to succeed.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteFeaturestoreRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified entity_type string.

Exports Feature values from all the entities of a target EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_export_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    destination = aiplatform_v1.FeatureValueDestination()
    destination.bigquery_destination.output_uri = "output_uri_value"

    feature_selector = aiplatform_v1.FeatureSelector()
    feature_selector.id_matcher.ids = ['ids_value1', 'ids_value2']

    request = aiplatform_v1.ExportFeatureValuesRequest(
        entity_type="entity_type_value",
        destination=destination,
        feature_selector=feature_selector,
    )

    # Make the request
    operation = client.export_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Returns a fully-qualified feature string.

Returns a fully-qualified featurestore string.

Creates an instance of this client using the provided credentials file.

Creates an instance of this client using the provided credentials info.

Creates an instance of this client using the provided credentials file.

Gets details of a single EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetEntityTypeRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_entity_type(request=request)

    # Handle the response
    print(response)

Gets details of a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeatureRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_feature(request=request)

    # Handle the response
    print(response)

Gets details of a single Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetFeaturestoreRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_featurestore(request=request)

    # Handle the response
    print(response)

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Gets information about a location.

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variable is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Gets the latest state of a long-running operation.

Returns an appropriate transport class.

Imports Feature values into the Featurestore from a source storage. The progress of the import is tracked by the returned operation. The imported features are guaranteed to be visible to subsequent read operations after the operation is marked as successfully done.

If an import operation fails, the Feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same import request again and wait till the new operation returned is marked as successfully done.

There are also scenarios where the caller can cause inconsistency.

• Source data for import contains multiple distinct Feature values for the same entity ID and timestamp.
• Source is modified during an import. This includes adding, updating, or removing source data and/or metadata. Examples of updating metadata include but are not limited to changing storage location, storage class, or retention policy.
• Online serving cluster is under-provisioned.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_import_feature_values():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    avro_source = aiplatform_v1.AvroSource()
    avro_source.gcs_source.uris = ['uris_value1', 'uris_value2']

    feature_specs = aiplatform_v1.FeatureSpec()
    feature_specs.id = "id_value"

    request = aiplatform_v1.ImportFeatureValuesRequest(
        avro_source=avro_source,
        feature_time_field="feature_time_field_value",
        entity_type="entity_type_value",
        feature_specs=feature_specs,
    )

    # Make the request
    operation = client.import_feature_values(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Lists EntityTypes in a given Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_entity_types():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListEntityTypesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_entity_types(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists Features in a given EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_features(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists Featurestores in a given project and location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_featurestores():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListFeaturestoresRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_featurestores(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists information about the supported locations for this service.

Lists operations that match the specified filter in the request.

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a entity_type path into its component segments.

Parses a feature path into its component segments.

Parses a featurestore path into its component segments.

Searches Features matching a query in a given project.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_search_features():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.SearchFeaturesRequest(
        location="location_value",
    )

    # Make the request
    page_result = client.search_features(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Tests the specified IAM permissions against the IAM access control policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Updates the parameters of a single EntityType.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_entity_type():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateEntityTypeRequest(
    )

    # Make the request
    response = await client.update_entity_type(request=request)

    # Handle the response
    print(response)

Updates the parameters of a single Feature.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_feature():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateFeatureRequest(
    )

    # Make the request
    response = await client.update_feature(request=request)

    # Handle the response
    print(response)

Updates the parameters of a single Featurestore.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_featurestore():
    # Create a client
    client = aiplatform_v1.FeaturestoreServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.UpdateFeaturestoreRequest(
    )

    # Make the request
    operation = client.update_featurestore(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.