#!/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-2"},
            "worker_config": {"num_instances": 2, "machine_type_uri": "n1-standard-2"},
        },
    }

    # 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]

#!/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-2"},
            "worker_config": {"num_instances": 2, "machine_type_uri": "n1-standard-2"},
        },
    }

    # 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]

#!/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> # Create the cluster client. # Create the cluster config. # Create the cluster. # Create the job client. # Create the job config. # 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. # Delete the cluster once the job has terminated. # [END dataproc_quickstart]