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#!/usr/bin/env python
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# [START dataproc_quickstart]
"""
This quickstart sample walks a user through creating a Cloud Dataproc
cluster, submitting a PySpark job from Google Cloud Storage to the
cluster, reading the output of the job and deleting the cluster, all
using the Python client library.
Usage:
python quickstart.py --project_id <PROJECT_ID> --region <REGION> \
--cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
"""
import argparse
import time
from google.cloud import dataproc_v1 as dataproc
from google.cloud import storage
def quickstart(project_id, region, cluster_name, job_file_path):
# Create the cluster client.
cluster_client = dataproc.ClusterControllerClient(client_options={
'api_endpoint': '{}-dataproc.googleapis.com:443'.format(region)
})
# Create the cluster config.
cluster = {
'project_id': project_id,
'cluster_name': cluster_name,
'config': {
'master_config': {
'num_instances': 1,
'machine_type_uri': 'n1-standard-1'
},
'worker_config': {
'num_instances': 2,
'machine_type_uri': 'n1-standard-1'
}
}
}
# Create the cluster.
operation = cluster_client.create_cluster(project_id, region, cluster)
result = operation.result()
print('Cluster created successfully: {}'.format(result.cluster_name))
# Create the job client.
job_client = dataproc.JobControllerClient(client_options={
'api_endpoint': '{}-dataproc.googleapis.com:443'.format(region)
})
# Create the job config.
job = {
'placement': {
'cluster_name': cluster_name
},
'pyspark_job': {
'main_python_file_uri': job_file_path
}
}
job_response = job_client.submit_job(project_id, region, job)
job_id = job_response.reference.job_id
print('Submitted job \"{}\".'.format(job_id))
# Termimal states for a job.
terminal_states = {
dataproc.types.JobStatus.ERROR,
dataproc.types.JobStatus.CANCELLED,
dataproc.types.JobStatus.DONE
}
# Create a timeout such that the job gets cancelled if not in a
# terminal state after a fixed period of time.
timeout_seconds = 600
time_start = time.time()
# Wait for the job to complete.
while job_response.status.state not in terminal_states:
if time.time() > time_start + timeout_seconds:
job_client.cancel_job(project_id, region, job_id)
print('Job {} timed out after threshold of {} seconds.'.format(
job_id, timeout_seconds))
# Poll for job termination once a second.
time.sleep(1)
job_response = job_client.get_job(project_id, region, job_id)
# Cloud Dataproc job output gets saved to a GCS bucket allocated to it.
cluster_info = cluster_client.get_cluster(
project_id, region, cluster_name)
storage_client = storage.Client()
bucket = storage_client.get_bucket(cluster_info.config.config_bucket)
output_blob = (
'google-cloud-dataproc-metainfo/{}/jobs/{}/driveroutput.000000000'
.format(cluster_info.cluster_uuid, job_id))
output = bucket.blob(output_blob).download_as_string()
print('Job {} finished with state {}:\n{}'.format(
job_id,
job_response.status.State.Name(job_response.status.state),
output))
# Delete the cluster once the job has terminated.
operation = cluster_client.delete_cluster(project_id, region, cluster_name)
operation.result()
print('Cluster {} successfully deleted.'.format(cluster_name))
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter,
)
parser.add_argument(
'--project_id',
type=str,
required=True,
help='Project to use for creating resources.')
parser.add_argument(
'--region',
type=str,
required=True,
help='Region where the resources should live.')
parser.add_argument(
'--cluster_name',
type=str,
required=True,
help='Name to use for creating a cluster.')
parser.add_argument(
'--job_file_path',
type=str,
required=True,
help='Job in GCS to execute against the cluster.')
args = parser.parse_args()
quickstart(args.project_id, args.region,
args.cluster_name, args.job_file_path)
# [END dataproc_quickstart]