def streaming_read_feature_values(
self,
request: Union[
featurestore_online_service.StreamingReadFeatureValuesRequest,
dict] = None,
*,
entity_type: str = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> Iterable[featurestore_online_service.ReadFeatureValuesResponse]:
r"""Reads Feature values for multiple entities. Depending
on their size, data for different entities may be broken
up across multiple responses.
.. code-block:: python
from google.cloud import aiplatform_v1beta1
def sample_streaming_read_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreOnlineServingServiceClient()
# Initialize request argument(s)
feature_selector = aiplatform_v1beta1.FeatureSelector()
feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']
request = aiplatform_v1beta1.StreamingReadFeatureValuesRequest(
entity_type="entity_type_value",
entity_ids=['entity_ids_value_1', 'entity_ids_value_2'],
feature_selector=feature_selector,
)
# Make the request
stream = client.streaming_read_feature_values(request=request)
# Handle the response
for response in stream:
print(response)
Args:
request (Union[google.cloud.aiplatform_v1beta1.types.StreamingReadFeatureValuesRequest, dict]):
The request object. Request message for
[FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].
entity_type (str):
Required. The resource name of the entities' type. Value
format:
``projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}``.
For example, for a machine learning model predicting
user clicks on a website, an EntityType ID could be
``user``.
This corresponds to the ``entity_type`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
Iterable[google.cloud.aiplatform_v1beta1.types.ReadFeatureValuesResponse]:
Response message for
[FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreOnlineServingService.ReadFeatureValues].
"""
# Create or coerce a protobuf request object.
# Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([entity_type])
if request is not None and has_flattened_params:
raise ValueError("If the `request` argument is set, then none of "
"the individual field arguments should be set.")
# Minor optimization to avoid making a copy if the user passes
# in a featurestore_online_service.StreamingReadFeatureValuesRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(
request,
featurestore_online_service.StreamingReadFeatureValuesRequest):
request = featurestore_online_service.StreamingReadFeatureValuesRequest(
request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if entity_type is not None:
request.entity_type = entity_type
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[
self._transport.streaming_read_feature_values]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("entity_type", request.entity_type), )), )
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def streaming_read_feature_values(
self,
request: featurestore_online_service.
StreamingReadFeatureValuesRequest = None,
*,
entity_type: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> Awaitable[AsyncIterable[
featurestore_online_service.ReadFeatureValuesResponse]]:
r"""Reads Feature values for multiple entities. Depending
on their size, data for different entities may be broken
up across multiple responses.
Args:
request (:class:`google.cloud.aiplatform_v1beta1.types.StreamingReadFeatureValuesRequest`):
The request object. Request message for
[FeaturestoreOnlineServingService.StreamingFeatureValuesRead][].
entity_type (:class:`str`):
Required. The resource name of the entities' type. Value
format:
``projects/{project}/locations/{location}/featurestores/ {featurestore}/entityTypes/{entityType}``.
For example, for a machine learning model predicting
user clicks on a website, an EntityType ID could be
"user".
This corresponds to the ``entity_type`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
AsyncIterable[google.cloud.aiplatform_v1beta1.types.ReadFeatureValuesResponse]:
Response message for
[FeaturestoreOnlineServingService.ReadFeatureValues][google.cloud.aiplatform.v1beta1.FeaturestoreOnlineServingService.ReadFeatureValues].
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([entity_type])
if request is not None and has_flattened_params:
raise ValueError("If the `request` argument is set, then none of "
"the individual field arguments should be set.")
request = featurestore_online_service.StreamingReadFeatureValuesRequest(
request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if entity_type is not None:
request.entity_type = entity_type
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = gapic_v1.method_async.wrap_method(
self._client._transport.streaming_read_feature_values,
default_timeout=5.0,
client_info=DEFAULT_CLIENT_INFO,
)
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (gapic_v1.routing_header.to_grpc_metadata(
(("entity_type", request.entity_type), )), )
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
