def retrieve_job(self):
serving_service_stub = Serving.ServingServiceStub(
grpc.insecure_channel(""))
job_proto = JobProto(
id="123",
type=JobType.JOB_TYPE_DOWNLOAD,
status=JobStatus.JOB_STATUS_RUNNING,
)
return RetrievalJob(job_proto, serving_service_stub)
def get_batch_features(
self,
feature_refs: List[str],
entity_rows: Union[pd.DataFrame, str],
default_project: str = None,
) -> RetrievalJob:
"""
Retrieves historical features from a Feast Serving deployment.
Args:
feature_refs (List[str]):
List of feature references that will be returned for each entity.
Each feature reference should have the following format
"project/feature:version".
entity_rows (Union[pd.DataFrame, str]):
Pandas dataframe containing entities and a 'datetime' column.
Each entity in a feature set must be present as a column in this
dataframe. The datetime column must contain timestamps in
datetime64 format.
default_project: Default project where feature values will be found.
Returns:
feast.job.RetrievalJob:
Returns a retrival job object that can be used to monitor retrieval
progress asynchronously, and can be used to materialize the
results.
Examples:
>>> from feast import Client
>>> from datetime import datetime
>>>
>>> feast_client = Client(core_url="localhost:6565", serving_url="localhost:6566")
>>> feature_refs = ["my_project/bookings_7d:1", "booking_14d"]
>>> entity_rows = pd.DataFrame(
>>> {
>>> "datetime": [pd.datetime.now() for _ in range(3)],
>>> "customer": [1001, 1002, 1003],
>>> }
>>> )
>>> feature_retrieval_job = feast_client.get_batch_features(
>>> feature_refs, entity_rows, default_project="my_project")
>>> df = feature_retrieval_job.to_dataframe()
>>> print(df)
"""
self._connect_serving()
feature_references = _build_feature_references(
feature_refs=feature_refs, default_project=default_project)
# Retrieve serving information to determine store type and
# staging location
serving_info = self._serving_service_stub.GetFeastServingInfo(
GetFeastServingInfoRequest(),
timeout=self._config.getint(
CONFIG_GRPC_CONNECTION_TIMEOUT_DEFAULT_KEY),
) # type: GetFeastServingInfoResponse
if serving_info.type != FeastServingType.FEAST_SERVING_TYPE_BATCH:
raise Exception(
f'You are connected to a store "{self._serving_url}" which '
f"does not support batch retrieval ")
if isinstance(entity_rows, pd.DataFrame):
# Pandas DataFrame detected
# Remove timezone from datetime column
if isinstance(entity_rows["datetime"].dtype,
pd.core.dtypes.dtypes.DatetimeTZDtype):
entity_rows["datetime"] = pd.DatetimeIndex(
entity_rows["datetime"]).tz_localize(None)
elif isinstance(entity_rows, str):
# String based source
if not entity_rows.endswith((".avro", "*")):
raise Exception(
f"Only .avro and wildcard paths are accepted as entity_rows"
)
else:
raise Exception(f"Only pandas.DataFrame and str types are allowed"
f" as entity_rows, but got {type(entity_rows)}.")
# Export and upload entity row DataFrame to staging location
# provided by Feast
staged_files = export_source_to_staging_location(
entity_rows, serving_info.job_staging_location) # type: List[str]
request = GetBatchFeaturesRequest(
features=feature_references,
dataset_source=DatasetSource(file_source=DatasetSource.FileSource(
file_uris=staged_files,
data_format=DataFormat.DATA_FORMAT_AVRO)),
)
# Retrieve Feast Job object to manage life cycle of retrieval
response = self._serving_service_stub.GetBatchFeatures(request)
return RetrievalJob(response.job, self._serving_service_stub)