def dataproc_submit(self, job_params: SparkJobParameters) -> Operation:
local_job_id = str(uuid.uuid4())
main_file_uri = self._stage_files(job_params.get_main_file_path(),
local_job_id)
job_config: Dict[str, Any] = {
"reference": {
"job_id": local_job_id
},
"placement": {
"cluster_name": self.cluster_name
},
}
if job_params.get_class_name():
job_config.update({
"spark_job": {
"jar_file_uris": [main_file_uri],
"main_class": job_params.get_class_name(),
"args": job_params.get_arguments(),
}
})
else:
job_config.update({
"pyspark_job": {
"main_python_file_uri": main_file_uri,
"args": job_params.get_arguments(),
}
})
return self.job_client.submit_job_as_operation(
request={
"project_id": self.project_id,
"region": self.region,
"job": job_config,
})
def spark_submit(self,
job_params: SparkJobParameters,
ui_port: int = None) -> subprocess.Popen:
submission_cmd = [
self.spark_submit_script_path,
"--master",
self.master_url,
"--name",
job_params.get_name(),
]
if job_params.get_class_name():
submission_cmd.extend(["--class", job_params.get_class_name()])
if ui_port:
submission_cmd.extend(["--conf", f"spark.ui.port={ui_port}"])
# Workaround for https://github.com/apache/spark/pull/26552
# Fix running spark job with bigquery connector (w/ shadowing) on JDK 9+
submission_cmd.extend([
"--conf",
"spark.executor.extraJavaOptions="
"-Dcom.google.cloud.spark.bigquery.repackaged.io.netty.tryReflectionSetAccessible=true -Duser.timezone=GMT",
"--conf",
"spark.driver.extraJavaOptions="
"-Dcom.google.cloud.spark.bigquery.repackaged.io.netty.tryReflectionSetAccessible=true -Duser.timezone=GMT",
"--conf",
"spark.sql.session.timeZone=UTC", # ignore local timezone
"--packages",
f"com.google.cloud.spark:spark-bigquery-with-dependencies_{self.BQ_CONNECTOR_VERSION}",
"--jars",
"https://storage.googleapis.com/hadoop-lib/gcs/gcs-connector-hadoop2-latest.jar,"
"https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/2.7.3/hadoop-aws-2.7.3.jar,"
"https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk/1.7.4/aws-java-sdk-1.7.4.jar",
"--conf",
"spark.hadoop.fs.s3a.impl=org.apache.hadoop.fs.s3a.S3AFileSystem",
"--conf",
"spark.hadoop.fs.gs.impl=com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
])
if self.additional_options is not None:
for option, value in self.additional_options.items():
submission_cmd.extend(["--conf", f'"{option}"="{value}"'])
submission_cmd.append(job_params.get_main_file_path())
submission_cmd.extend(job_params.get_arguments())
return subprocess.Popen(submission_cmd)
def spark_submit(self, job_params: SparkJobParameters) -> subprocess.Popen:
submission_cmd = [
self.spark_submit_script_path,
"--master",
self.master_url,
"--name",
job_params.get_name(),
]
if job_params.get_class_name():
submission_cmd.extend(["--class", job_params.get_class_name()])
submission_cmd.append(job_params.get_main_file_path())
submission_cmd.extend(job_params.get_arguments())
return subprocess.Popen(submission_cmd)
def dataproc_submit(
self, job_params: SparkJobParameters
) -> Tuple[Job, Callable[[], Job], Callable[[], None]]:
local_job_id = str(uuid.uuid4())
main_file_uri = self._stage_file(job_params.get_main_file_path(),
local_job_id)
job_config: Dict[str, Any] = {
"reference": {
"job_id": local_job_id
},
"placement": {
"cluster_name": self.cluster_name
},
"labels": {
self.JOB_TYPE_LABEL_KEY:
job_params.get_job_type().name.lower()
},
}
# Add job hash to labels only for the stream ingestion job
if isinstance(job_params, StreamIngestionJobParameters):
job_config["labels"][
self.JOB_HASH_LABEL_KEY] = job_params.get_job_hash()
if job_params.get_class_name():
job_config.update({
"spark_job": {
"jar_file_uris": [main_file_uri] + self.EXTERNAL_JARS,
"main_class": job_params.get_class_name(),
"args": job_params.get_arguments(),
"properties": {
"spark.yarn.user.classpath.first": "true"
},
}
})
else:
job_config.update({
"pyspark_job": {
"main_python_file_uri": main_file_uri,
"jar_file_uris": self.EXTERNAL_JARS,
"args": job_params.get_arguments(),
}
})
job = self.job_client.submit_job(
request={
"project_id": self.project_id,
"region": self.region,
"job": job_config,
})
refresh_fn = partial(
self.job_client.get_job,
project_id=self.project_id,
region=self.region,
job_id=job.reference.job_id,
)
cancel_fn = partial(self.dataproc_cancel, job.reference.job_id)
return job, refresh_fn, cancel_fn
def dataproc_submit(self, job_params: SparkJobParameters) -> Operation:
local_job_id = str(uuid.uuid4())
pyspark_gcs = self._stage_files(job_params.get_main_file_path(),
local_job_id)
job_config = {
"reference": {
"job_id": local_job_id
},
"placement": {
"cluster_name": self.cluster_name
},
"pyspark_job": {
"main_python_file_uri": pyspark_gcs,
"args": job_params.get_arguments(),
},
}
return self.job_client.submit_job_as_operation(
request={
"project_id": self.project_id,
"region": self.region,
"job": job_config,
})
def spark_submit(self,
job_params: SparkJobParameters,
ui_port: int = None) -> subprocess.Popen:
submission_cmd = [
self.spark_submit_script_path,
"--master",
self.master_url,
"--name",
job_params.get_name(),
]
if job_params.get_class_name():
submission_cmd.extend(["--class", job_params.get_class_name()])
if ui_port:
submission_cmd.extend(["--conf", f"spark.ui.port={ui_port}"])
if job_params.get_extra_options():
submission_cmd.extend(job_params.get_extra_options().split(" "))
submission_cmd.append(job_params.get_main_file_path())
submission_cmd.extend(job_params.get_arguments())
return subprocess.Popen(submission_cmd)
def dataproc_submit(
self, job_params: SparkJobParameters, extra_properties: Dict[str, str]
) -> Tuple[Job, Callable[[], Job], Callable[[], None]]:
local_job_id = str(uuid.uuid4())
main_file_uri = self._stage_file(job_params.get_main_file_path(),
local_job_id)
job_config: Dict[str, Any] = {
"reference": {
"job_id": local_job_id
},
"placement": {
"cluster_name": self.cluster_name
},
"labels": {
self.JOB_TYPE_LABEL_KEY:
job_params.get_job_type().name.lower()
},
}
maven_package_properties = {
"spark.jars.packages": ",".join(job_params.get_extra_packages())
}
common_properties = {
"spark.executor.instances": self.executor_instances,
"spark.executor.cores": self.executor_cores,
"spark.executor.memory": self.executor_memory,
}
if isinstance(job_params, StreamIngestionJobParameters):
job_config["labels"][
self.FEATURE_TABLE_LABEL_KEY] = _truncate_label(
job_params.get_feature_table_name())
# Add job hash to labels only for the stream ingestion job
job_config["labels"][
self.JOB_HASH_LABEL_KEY] = job_params.get_job_hash()
if isinstance(job_params, BatchIngestionJobParameters):
job_config["labels"][
self.FEATURE_TABLE_LABEL_KEY] = _truncate_label(
job_params.get_feature_table_name())
if job_params.get_class_name():
scala_job_properties = {
"spark.yarn.user.classpath.first": "true",
"spark.executor.instances": self.executor_instances,
"spark.executor.cores": self.executor_cores,
"spark.executor.memory": self.executor_memory,
"spark.pyspark.driver.python": "python3.7",
"spark.pyspark.python": "python3.7",
}
job_config.update({
"spark_job": {
"jar_file_uris": [main_file_uri] + self.EXTERNAL_JARS,
"main_class": job_params.get_class_name(),
"args": job_params.get_arguments(),
"properties": {
**scala_job_properties,
**common_properties,
**maven_package_properties,
**extra_properties,
},
}
})
else:
job_config.update({
"pyspark_job": {
"main_python_file_uri": main_file_uri,
"jar_file_uris": self.EXTERNAL_JARS,
"args": job_params.get_arguments(),
"properties": {
**common_properties,
**maven_package_properties,
**extra_properties,
},
}
})
job = self.job_client.submit_job(
request={
"project_id": self.project_id,
"region": self.region,
"job": job_config,
})
refresh_fn = partial(
self.job_client.get_job,
project_id=self.project_id,
region=self.region,
job_id=job.reference.job_id,
)
cancel_fn = partial(self.dataproc_cancel, job.reference.job_id)
return job, refresh_fn, cancel_fn
