submit_job_to_cluster.py

#!/usr/bin/env python
# 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.
r"""Sample command-line program to run a pyspark job on a new or existing
cluster.

Global region clusters are supported with --global_region flag.

Example Usage to run the pyspark job on a new cluster:
python submit_job_to_cluster.py --project_id=$PROJECT --gcs_bucket=$BUCKET \
  --create_new_cluster --cluster_name=$CLUSTER --zone=$ZONE

Example Usage to run the pyspark job on an existing global region cluster:
python submit_job_to_cluster.py --project_id=$PROJECT --gcs_bucket=$BUCKET \
  --global_region --cluster_name=$CLUSTER --zone=$ZONE

"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import os

from google.cloud import dataproc_v1
from google.cloud.dataproc_v1.gapic.transports import (
    cluster_controller_grpc_transport)
from google.cloud.dataproc_v1.gapic.transports import (
    job_controller_grpc_transport)
from google.cloud import storage

DEFAULT_FILENAME = 'pyspark_sort.py'
waiting_callback = False


def get_pyspark_file(pyspark_file=None):
    if pyspark_file:
        f = open(pyspark_file, "rb")
        return f, os.path.basename(pyspark_file)
    else:
        """Gets the PySpark file from current directory."""
        current_dir = os.path.dirname(os.path.abspath(__file__))
        f = open(os.path.join(current_dir, DEFAULT_FILENAME), "rb")
        return f, DEFAULT_FILENAME


def get_region_from_zone(zone):
    try:
        region_as_list = zone.split('-')[:-1]
        return '-'.join(region_as_list)
    except (AttributeError, IndexError, ValueError):
        raise ValueError('Invalid zone provided, please check your input.')


def upload_pyspark_file(project, bucket_name, filename, spark_file):
    """Uploads the PySpark file in this directory to the configured input
    bucket."""
    print('Uploading pyspark file to Cloud Storage.')
    client = storage.Client(project=project)
    bucket = client.get_bucket(bucket_name)
    blob = bucket.blob(filename)
    blob.upload_from_file(spark_file)


def download_output(project, cluster_id, output_bucket, job_id):
    """Downloads the output file from Cloud Storage and returns it as a
    string."""
    print('Downloading output file.')
    client = storage.Client(project=project)
    bucket = client.get_bucket(output_bucket)
    output_blob = (
        ('google-cloud-dataproc-metainfo/{}/jobs/{}/driveroutput.000000000'.
            format(cluster_id, job_id)))
    return bucket.blob(output_blob).download_as_string()


# [START dataproc_create_cluster]
def create_cluster(dataproc, project, zone, region, cluster_name):
    """Create the cluster."""
    print('Creating cluster...')
    zone_uri = \
        'https://www.googleapis.com/compute/v1/projects/{}/zones/{}'.format(
            project, zone)
    cluster_data = {
        'project_id': project,
        'cluster_name': cluster_name,
        'config': {
            'gce_cluster_config': {
                'zone_uri': zone_uri
            },
            'master_config': {
                'num_instances': 1,
                'machine_type_uri': 'n1-standard-1'
            },
            'worker_config': {
                'num_instances': 2,
                'machine_type_uri': 'n1-standard-1'
            }
        }
    }

    cluster = dataproc.create_cluster(project, region, cluster_data)
    cluster.add_done_callback(callback)
    global waiting_callback
    waiting_callback = True
# [END dataproc_create_cluster]


def callback(operation_future):
    # Reset global when callback returns.
    global waiting_callback
    waiting_callback = False


def wait_for_cluster_creation():
    """Wait for cluster creation."""
    print('Waiting for cluster creation...')

    while True:
        if not waiting_callback:
            print("Cluster created.")
            break


# [START dataproc_list_clusters_with_detail]
def list_clusters_with_details(dataproc, project, region):
    """List the details of clusters in the region."""
    for cluster in dataproc.list_clusters(project, region):
        print(('{} - {}'.format(cluster.cluster_name,
                                cluster.status.State.Name(
                                    cluster.status.state))))
# [END dataproc_list_clusters_with_detail]


def get_cluster_id_by_name(dataproc, project_id, region, cluster_name):
    """Helper function to retrieve the ID and output bucket of a cluster by
    name."""
    for cluster in dataproc.list_clusters(project_id, region):
        if cluster.cluster_name == cluster_name:
            return cluster.cluster_uuid, cluster.config.config_bucket


# [START dataproc_submit_pyspark_job]
def submit_pyspark_job(dataproc, project, region, cluster_name, bucket_name,
                       filename):
    """Submit the Pyspark job to the cluster (assumes `filename` was uploaded
    to `bucket_name."""
    job_details = {
        'placement': {
            'cluster_name': cluster_name
        },
        'pyspark_job': {
            'main_python_file_uri': 'gs://{}/{}'.format(bucket_name, filename)
        }
    }

    result = dataproc.submit_job(
        project_id=project, region=region, job=job_details)
    job_id = result.reference.job_id
    print('Submitted job ID {}.'.format(job_id))
    return job_id
# [END dataproc_submit_pyspark_job]


# [START dataproc_delete]
def delete_cluster(dataproc, project, region, cluster):
    """Delete the cluster."""
    print('Tearing down cluster.')
    result = dataproc.delete_cluster(
        project_id=project, region=region, cluster_name=cluster)
    return result
# [END dataproc_delete]


# [START dataproc_wait]
def wait_for_job(dataproc, project, region, job_id):
    """Wait for job to complete or error out."""
    print('Waiting for job to finish...')
    while True:
        job = dataproc.get_job(project, region, job_id)
        # Handle exceptions
        if job.status.State.Name(job.status.state) == 'ERROR':
            raise Exception(job.status.details)
        elif job.status.State.Name(job.status.state) == 'DONE':
            print('Job finished.')
            return job
# [END dataproc_wait]


def main(project_id,
         zone,
         cluster_name,
         bucket_name,
         pyspark_file=None,
         create_new_cluster=True,
         global_region=True):

    # [START dataproc_get_client]
    if global_region:
        region = 'global'
        # Use the default gRPC global endpoints.
        dataproc_cluster_client = dataproc_v1.ClusterControllerClient()
        dataproc_job_client = dataproc_v1.JobControllerClient()
    else:
        region = get_region_from_zone(zone)
        # Use a regional gRPC endpoint. See:
        # https://cloud.google.com/dataproc/docs/concepts/regional-endpoints
        client_transport = (
            cluster_controller_grpc_transport.ClusterControllerGrpcTransport(
                address='{}-dataproc.googleapis.com:443'.format(region)))
        job_transport = (
            job_controller_grpc_transport.JobControllerGrpcTransport(
                address='{}-dataproc.googleapis.com:443'.format(region)))
        dataproc_cluster_client = dataproc_v1.ClusterControllerClient(
            client_transport)
        dataproc_job_client = dataproc_v1.JobControllerClient(job_transport)
    # [END dataproc_get_client]

    try:
        spark_file, spark_filename = get_pyspark_file(pyspark_file)
        if create_new_cluster:
            create_cluster(dataproc_cluster_client, project_id, zone, region,
                           cluster_name)
            wait_for_cluster_creation()
        upload_pyspark_file(project_id, bucket_name, spark_filename,
                            spark_file)

        list_clusters_with_details(dataproc_cluster_client, project_id,
                                   region)

        (cluster_id, output_bucket) = (
                get_cluster_id_by_name(dataproc_cluster_client, project_id,
                                       region, cluster_name))

        # [START dataproc_call_submit_pyspark_job]
        job_id = submit_pyspark_job(dataproc_job_client, project_id, region,
                                    cluster_name, bucket_name, spark_filename)
        # [END dataproc_call_submit_pyspark_job]

        wait_for_job(dataproc_job_client, project_id, region, job_id)
        output = download_output(project_id, cluster_id, output_bucket, job_id)
        print('Received job output {}'.format(output))
        return output
    finally:
        if create_new_cluster:
            delete_cluster(dataproc_cluster_client, project_id, region,
                           cluster_name)
            spark_file.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description=__doc__,
                                     formatter_class=argparse.
                                     RawDescriptionHelpFormatter)
    parser.add_argument(
        '--project_id', help='Project ID you want to access.', required=True)
    parser.add_argument('--zone',
                        help='Zone to create clusters in/connect to',
                        required=True)
    parser.add_argument('--cluster_name',
                        help='Name of the cluster to create/connect to',
                        required=True)
    parser.add_argument('--gcs_bucket',
                        help='Bucket to upload Pyspark file to',
                        required=True)
    parser.add_argument('--pyspark_file',
                        help='Pyspark filename. Defaults to pyspark_sort.py')
    parser.add_argument('--create_new_cluster',
                        action='store_true',
                        help='States if the cluster should be created')
    parser.add_argument('--global_region',
                        action='store_true',
                        help='If cluster is in the global region')

    args = parser.parse_args()
    main(args.project_id, args.zone, args.cluster_name, args.gcs_bucket,
         args.pyspark_file, args.create_new_cluster, args.global_region)