Create submit_spark_job_to_driver_node_group_cluster.py

Sample code to submit a Spark job to a Dataproc driver node group cluster.

Author: aman-ebay

dataproc/snippets/submit_spark_job_to_driver_node_group_cluster.py

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+#!/usr/bin/env python
+
+# Copyright 2025 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.
+
+# This sample walks a user through submitting a Spark job to a 
+# Dataproc driver node group cluster using the Dataproc
+# client library.
+
+# Usage:
+#    python submit_spark_job_to_driver_node_group_cluster.py \
+#        --project_id <PROJECT_ID> --region <REGION> \
+#        --cluster_name <CLUSTER_NAME>
+
+# [START dataproc_submit_spark_job_to_driver_node_group_cluster]
+
+import re
+ 
+from google.cloud import dataproc_v1 as dataproc
+from google.cloud import storage
+ 
+def submit_job(project_id, region, cluster_name):
+    # Create the job client.
+    job_client = dataproc.JobControllerClient(
+        client_options={"api_endpoint": f"{region}-dataproc.googleapis.com:443"}
+    )
+ 
+    driver_scheduling_config = dataproc.DriverSchedulingConfig(
+    memory_mb=2048, # Example memory in MB
+    vcores=2, # Example number of vcores
+    )
+ 
+    # Create the job config. 'main_jar_file_uri' can also be a
+    # Google Cloud Storage URL.
+    job = {
+        "placement": {"cluster_name": cluster_name},
+        "spark_job": {
+            "main_class": "org.apache.spark.examples.SparkPi",
+            "jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
+            "args": ["1000"],
+        },
+        "driver_scheduling_config": driver_scheduling_config
+    }
+ 
+    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 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_bytes()
+        .decode("utf-8")
+    )
+ 
+    print(f"Job finished successfully: {output}")
+
+
+# [END dataproc_submit_spark_job_to_driver_node_group_cluster]
+
+
+if __name__ == "__main__":
+ 
+    my_project_id = "your_cluster"  # <-- REPLACE THIS
+    my_region = "us-central1"        # <-- REPLACE THIS
+    my_cluster_name = "your-node-group-cluster" # <-- REPLACE THIS
+ 
+    submit_job(my_project_id, my_region, my_cluster_name)