def start_stream_to_online_ingestion(
self, ingestion_job_params: StreamIngestionJobParameters
) -> StreamIngestionJob:
"""
Starts a stream ingestion job on a Spark cluster.
Returns:
StreamIngestionJob: wrapper around remote job that can be used to check on the job.
"""
jar_s3_path = _upload_jar(self._staging_location,
ingestion_job_params.get_main_file_path())
extra_jar_paths: List[str] = []
for extra_jar in ingestion_job_params.get_extra_jar_paths():
if extra_jar.startswith("s3://"):
extra_jar_paths.append(extra_jar)
else:
extra_jar_paths.append(
_upload_jar(self._staging_location, extra_jar))
job_hash = ingestion_job_params.get_job_hash()
step = _stream_ingestion_step(
jar_s3_path,
extra_jar_paths,
ingestion_job_params.get_feature_table_name(),
args=ingestion_job_params.get_arguments(),
job_hash=job_hash,
)
job_ref = self._submit_emr_job(step)
return EmrStreamIngestionJob(self._emr_client(), job_ref, job_hash)
def start_stream_to_online_ingestion(
self, ingestion_job_params: StreamIngestionJobParameters
) -> StreamIngestionJob:
job, refresh_fn, cancel_fn = self.dataproc_submit(ingestion_job_params)
job_hash = ingestion_job_params.get_job_hash()
return DataprocStreamingIngestionJob(job, refresh_fn, cancel_fn,
job_hash)
def start_stream_to_online_ingestion(
self, ingestion_job_params: StreamIngestionJobParameters
) -> StreamIngestionJob:
job, refresh_fn, cancel_fn = self.dataproc_submit(ingestion_job_params, {})
job_hash = ingestion_job_params.get_job_hash()
return DataprocStreamingIngestionJob(
job=job,
refresh_fn=refresh_fn,
cancel_fn=cancel_fn,
project=self.project_id,
region=self.region,
job_hash=job_hash,
)
def start_stream_to_online_ingestion(
self, ingestion_job_params: StreamIngestionJobParameters
) -> StreamIngestionJob:
job_id = str(uuid.uuid4())
ui_port = _find_free_port()
job = StandaloneClusterStreamingIngestionJob(
job_id,
ingestion_job_params.get_name(),
self.spark_submit(ingestion_job_params, ui_port),
ui_port,
ingestion_job_params.get_job_hash(),
)
global_job_cache.add_job(job)
return job
def start_stream_to_online_ingestion(
self, ingestion_job_params: StreamIngestionJobParameters
) -> StreamIngestionJob:
"""
Starts a stream ingestion job to a Spark cluster.
Raises:
SparkJobFailure: The spark job submission failed, encountered error
during execution, or timeout.
Returns:
StreamIngestionJob: wrapper around remote job.
"""
jar_s3_path = self._upload_jar(ingestion_job_params.get_main_file_path())
extra_jar_paths: List[str] = []
for extra_jar in ingestion_job_params.get_extra_jar_paths():
extra_jar_paths.append(self._upload_jar(extra_jar))
job_hash = ingestion_job_params.get_job_hash()
job_id = _generate_job_id()
resource = _prepare_job_resource(
job_template=self._resource_template,
job_id=job_id,
job_type=STREAM_TO_ONLINE_JOB_TYPE,
main_application_file=jar_s3_path,
main_class=ingestion_job_params.get_class_name(),
packages=[BQ_SPARK_PACKAGE],
jars=extra_jar_paths,
extra_metadata={METADATA_JOBHASH: job_hash},
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(StreamIngestionJob, self._job_from_job_info(job_info))
