Fix: updating submit_job_to_cluster.py (#387)

aman-ebay · web-flow · commit 0719d2b69661 · 2022-03-31T12:25:23.000-07:00
* Fix lint errors

* Update submit_job_to_cluster.py
diff --git a/samples/snippets/submit_job_to_cluster.py b/samples/snippets/submit_job_to_cluster.py
@@ -1,307 +1,140 @@
 #!/usr/bin/env python
+
+# Copyright 2022 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
+#    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
 
+# [START dataproc_quickstart]
 """
+Command-line program to create a Dataproc cluster,
+run a PySpark job located in Cloud Storage on the cluster,
+then delete the cluster after the job completes.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+Usage:
+    python submit_job_to_cluster --project_id <PROJECT_ID> --region <REGION> \
+        --cluster_name <CLUSTER_NAME> --job_file_path <GCS_JOB_FILE_PATH>
+"""
 
 import argparse
-import os
+import re
 
 from google.cloud import dataproc_v1
 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
+# [START dataproc_create_cluster]
+def quickstart(project_id, region, cluster_name, job_file_path):
+    # Create the cluster client.
+    cluster_client = dataproc_v1.ClusterControllerClient(
+        client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
     )
-    return bucket.blob(output_blob).download_as_string()
-
 
-# [START dataproc_submit_job_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,
+    # Create the cluster config.
+    cluster = {
+        "project_id": project_id,
         "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"},
+            "master_config": {"num_instances": 1, "machine_type_uri": "n1-standard-2"},
+            "worker_config": {"num_instances": 2, "machine_type_uri": "n1-standard-2"},
         },
     }
 
-    cluster = dataproc.create_cluster(
-        request={"project_id": project, "region": region, "cluster": cluster_data}
+    # Create the cluster.
+    operation = cluster_client.create_cluster(
+        request={"project_id": project_id, "region": region, "cluster": cluster}
     )
-    cluster.add_done_callback(callback)
-    global waiting_callback
-    waiting_callback = True
-
-
-# [END dataproc_submit_job_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(
-        request={"project_id": project, "region": region}
-    ):
-        print(("{} - {}".format(cluster.cluster_name, cluster.status.state.name,)))
-
+    result = operation.result()
 
-# [END dataproc_list_clusters_with_detail]
+    print("Cluster created successfully: {}".format(result.cluster_name))
 
+# [END dataproc_create_cluster]
 
-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(
-        request={"project_id": project_id, "region": region}
-    ):
-        if cluster.cluster_name == cluster_name:
-            return cluster.cluster_uuid, cluster.config.config_bucket
-
+# [START dataproc_submit_job]
+    # Create the job client.
+    job_client = dataproc_v1.JobControllerClient(
+        client_options={"api_endpoint": "{}-dataproc.googleapis.com:443".format(region)}
+    )
 
-# [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 = {
+    # Create the job config.
+    job = {
         "placement": {"cluster_name": cluster_name},
-        "pyspark_job": {
-            "main_python_file_uri": "gs://{}/{}".format(bucket_name, filename)
-        },
+        "pyspark_job": {"main_python_file_uri": job_file_path},
     }
 
-    result = dataproc.submit_job(
-        request={"project_id": project, "region": region, "job": job_details}
+    operation = job_client.submit_job_as_operation(
+        request={"project_id": project_id, "region": region, "job": job}
     )
-    job_id = result.reference.job_id
-    print("Submitted job ID {}.".format(job_id))
-    return job_id
-
+    response = operation.result()
 
-# [END dataproc_submit_pyspark_job]
+    # Dataproc job output is saved to the Cloud Storage bucket
+    # allocated to the job. Use regex to obtain the bucket and blob info.
+    matches = re.match("gs://(.*?)/(.*)", response.driver_output_resource_uri)
 
-
-# [START dataproc_delete]
-def delete_cluster(dataproc, project, region, cluster):
-    """Delete the cluster."""
-    print("Tearing down cluster.")
-    result = dataproc.delete_cluster(
-        request={"project_id": project, "region": region, "cluster_name": cluster}
+    output = (
+        storage.Client()
+        .get_bucket(matches.group(1))
+        .blob(f"{matches.group(2)}.000000000")
+        .download_as_string()
     )
-    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(
-            request={"project_id": project, "region": region, "job_id": job_id}
-        )
-        # Handle exceptions
-        if job.status.State(job.status.state).name == "ERROR":
-            raise Exception(job.status.details)
-        if job.status.State(job.status.state).name == "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
-        dataproc_cluster_client = dataproc_v1.ClusterControllerClient(
-            client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
-        )
-        dataproc_job_client = dataproc_v1.ClusterControllerClient(
-            client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
-        )
-    # [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]
+    print(f"Job finished successfully: {output}\r\n")
+    # [END dataproc_submit_job]
+
+    # [START dataproc_delete_cluster]
+    # 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()
 
-        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()
+    print("Cluster {} successfully deleted.".format(cluster_name))
+# [END dataproc_delete_cluster]
 
 
 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
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
     )
     parser.add_argument(
-        "--cluster_name", help="Name of the cluster to create/connect to", required=True
+        "--project_id",
+        type=str,
+        required=True,
+        help="Project to use for creating resources.",
     )
     parser.add_argument(
-        "--gcs_bucket", help="Bucket to upload Pyspark file to", required=True
+        "--region",
+        type=str,
+        required=True,
+        help="Region where the resources should live.",
     )
     parser.add_argument(
-        "--pyspark_file", help="Pyspark filename. Defaults to pyspark_sort.py"
+        "--cluster_name",
+        type=str,
+        required=True,
+        help="Name to use for creating a cluster.",
     )
     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",
+        "--job_file_path",
+        type=str,
+        required=True,
+        help="Job in Cloud Storage to run on the cluster.",
     )
 
     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,
-    )
+    quickstart(args.project_id, args.region, args.cluster_name, args.job_file_path)
+# [END dataproc_quickstart]
