def offline_to_online_ingestion(
self, ingestion_job_params: BatchIngestionJobParameters
) -> BatchIngestionJob:
"""
Submits a batch ingestion job to a Spark cluster.
Raises:
SparkJobFailure: The spark job submission failed, encountered error
during execution, or timeout.
Returns:
BatchIngestionJob: wrapper around remote job that can be used to check when job completed.
"""
jar_s3_path = _upload_jar(self._staging_location,
ingestion_job_params.get_main_file_path())
step = _sync_offline_to_online_step(
jar_s3_path,
ingestion_job_params.get_feature_table_name(),
args=ingestion_job_params.get_arguments(),
)
job_ref = self._submit_emr_job(step)
return EmrBatchIngestionJob(self._emr_client(), job_ref)
def offline_to_online_ingestion(
self, ingestion_job_params: BatchIngestionJobParameters
) -> BatchIngestionJob:
"""
Submits a batch ingestion job to a Spark cluster.
Raises:
SparkJobFailure: The spark job submission failed, encountered error
during execution, or timeout.
Returns:
BatchIngestionJob: wrapper around remote job that can be used to check when job completed.
"""
jar_s3_path = self._upload_jar(ingestion_job_params.get_main_file_path())
job_id = _generate_job_id()
resource = _prepare_job_resource(
job_template=self._resource_template,
job_id=job_id,
job_type=OFFLINE_TO_ONLINE_JOB_TYPE,
main_application_file=jar_s3_path,
main_class=ingestion_job_params.get_class_name(),
packages=[BQ_SPARK_PACKAGE],
jars=[],
extra_metadata={},
azure_credentials=self._get_azure_credentials(),
arguments=ingestion_job_params.get_arguments(),
namespace=self._namespace,
extra_labels={
LABEL_FEATURE_TABLE: ingestion_job_params.get_feature_table_name()
},
)
job_info = _submit_job(
api=self._api, resource=resource, namespace=self._namespace,
)
return cast(BatchIngestionJob, self._job_from_job_info(job_info))