def historical_feature_retrieval(
self, job_params: RetrievalJobParameters) -> RetrievalJob:
with open(job_params.get_main_file_path()) as f:
pyspark_script = f.read()
pyspark_script_path = urlunparse(
get_staging_client("s3").upload_fileobj(
BytesIO(pyspark_script.encode("utf8")),
local_path="historical_retrieval.py",
remote_path_prefix=self._staging_location,
remote_path_suffix=".py",
))
step = _historical_retrieval_step(
pyspark_script_path,
args=job_params.get_arguments(),
output_file_uri=job_params.get_destination_path(),
)
job_ref = self._submit_emr_job(step)
return EmrRetrievalJob(
self._emr_client(),
job_ref,
job_params.get_destination_path(),
)
def historical_feature_retrieval(
self, job_params: RetrievalJobParameters) -> RetrievalJob:
job, refresh_fn, cancel_fn = self.dataproc_submit(
job_params,
{"dev.feast.outputuri": job_params.get_destination_path()})
return DataprocRetrievalJob(job, refresh_fn, cancel_fn,
job_params.get_destination_path())
def historical_feature_retrieval(
self, job_params: RetrievalJobParameters) -> RetrievalJob:
job, refresh_fn, cancel_fn = self.dataproc_submit(
job_params,
{"dev.feast.outputuri": job_params.get_destination_path()})
return DataprocRetrievalJob(
job=job,
refresh_fn=refresh_fn,
cancel_fn=cancel_fn,
project=self.project_id,
region=self.region,
output_file_uri=job_params.get_destination_path(),
)
def historical_feature_retrieval(
self, job_params: RetrievalJobParameters
) -> RetrievalJob:
with open(job_params.get_main_file_path()) as f:
pyspark_script = f.read()
pyspark_script_path = _s3_upload(
BytesIO(pyspark_script.encode("utf8")),
local_path="historical_retrieval.py",
remote_path_prefix=self._staging_location,
remote_path_suffix=".py",
)
step = _historical_retrieval_step(
pyspark_script_path, args=job_params.get_arguments()
)
job_ref = self._submit_emr_job(step)
return EmrRetrievalJob(
self._emr_client(),
job_ref,
os.path.join(job_params.get_destination_path(), _random_string(8)),
)
def historical_feature_retrieval(
self, job_params: RetrievalJobParameters) -> RetrievalJob:
job_id = str(uuid.uuid4())
return StandaloneClusterRetrievalJob(
job_id,
job_params.get_name(),
self.spark_submit(job_params),
job_params.get_destination_path(),
)
def historical_feature_retrieval(
self, job_params: RetrievalJobParameters) -> RetrievalJob:
"""
Submits a historical feature retrieval job to a Spark cluster.
Raises:
SparkJobFailure: The spark job submission failed, encountered error
during execution, or timeout.
Returns:
RetrievalJob: wrapper around remote job that returns file uri to the result file.
"""
with open(job_params.get_main_file_path()) as f:
pyspark_script = f.read()
pyspark_script_path = urlunparse(
self._get_staging_client().upload_fileobj(
BytesIO(pyspark_script.encode("utf8")),
local_path="historical_retrieval.py",
remote_path_prefix=self._staging_location,
remote_path_suffix=".py",
))
job_id = _generate_job_id()
resource = _prepare_job_resource(
job_template=self._resource_template,
job_id=job_id,
job_type=HISTORICAL_RETRIEVAL_JOB_TYPE,
main_application_file=pyspark_script_path,
main_class=None,
packages=[],
jars=[],
extra_metadata={
METADATA_OUTPUT_URI: job_params.get_destination_path()
},
arguments=job_params.get_arguments(),
namespace=self._namespace,
)
job_info = _submit_job(
api=self._api,
resource=resource,
namespace=self._namespace,
)
return cast(RetrievalJob, self._job_from_job_info(job_info))
def historical_feature_retrieval(
self, job_params: RetrievalJobParameters) -> RetrievalJob:
operation = self.dataproc_submit(job_params)
cancel_fn = partial(self.dataproc_cancel, operation.metadata.job_id)
return DataprocRetrievalJob(operation, cancel_fn,
job_params.get_destination_path())
def historical_feature_retrieval(
self, job_params: RetrievalJobParameters) -> RetrievalJob:
return DataprocRetrievalJob(self.dataproc_submit(job_params),
job_params.get_destination_path())
