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quickstart.py
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quickstart.py
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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 re
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(
request={"project_id": project_id, "region": region, "cluster": 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},
}
operation = job_client.submit_job_as_operation(
request={"project_id": project_id, "region": region, "job": job}
)
response = operation.result()
# Dataproc job output gets saved to the Google Cloud Storage bucket
# allocated to the job. Use a regex to obtain the bucket and blob info.
matches = re.match("gs://(.*?)/(.*)", response.driver_output_resource_uri)
output = (
storage.Client()
.get_bucket(matches.group(1))
.blob(f"{matches.group(2)}.000000000")
.download_as_string()
)
print(f"Job finished successfully: {output}")
# Delete the cluster once the job has terminated.
operation = cluster_client.delete_cluster(
request={
"project_id": project_id,
"region": region,
"cluster_name": 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]