feat: adding submit_job samples (#88)

Adding submit_job samples and updating quickstart samples.

dataproc/snippets/quickstart/quickstart.py

@@ -27,7 +27,7 @@
 """
 
 import argparse
-import time
+import re
 
 from google.cloud import dataproc_v1 as dataproc
 from google.cloud import storage
@@ -68,64 +68,23 @@ def quickstart(project_id, region, cluster_name, job_file_path):
         "pyspark_job": {"main_python_file_uri": job_file_path},
     }
 
-    job_response = job_client.submit_job(
+    operation = job_client.submit_job_as_operation(
         request={"project_id": project_id, "region": region, "job": job}
     )
-    job_id = job_response.reference.job_id
+    response = operation.result()
 
-    print('Submitted job "{}".'.format(job_id))
+    # 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)
 
-    # Termimal states for a job.
-    terminal_states = {
-        dataproc.JobStatus.State.ERROR,
-        dataproc.JobStatus.State.CANCELLED,
-        dataproc.JobStatus.State.DONE,
-    }
-
-    # Create a timeout such that the job gets cancelled if not in a
-    # terminal state after a fixed period of time.
-    timeout_seconds = 600
-    time_start = time.time()
-
-    # Wait for the job to complete.
-    while job_response.status.state not in terminal_states:
-        if time.time() > time_start + timeout_seconds:
-            job_client.cancel_job(
-                request={"project_id": project_id, "region": region, "job_id": job_id}
-            )
-            print(
-                "Job {} timed out after threshold of {} seconds.".format(
-                    job_id, timeout_seconds
-                )
-            )
-
-        # Poll for job termination once a second.
-        time.sleep(1)
-        job_response = job_client.get_job(
-            request={"project_id": project_id, "region": region, "job_id": job_id}
-        )
-
-    # Cloud Dataproc job output gets saved to a GCS bucket allocated to it.
-    cluster_info = cluster_client.get_cluster(
-        request={
-            "project_id": project_id,
-            "region": region,
-            "cluster_name": cluster_name,
-        }
+    output = (
+        storage.Client()
+        .get_bucket(matches.group(1))
+        .blob(f"{matches.group(2)}.000000000")
+        .download_as_string()
     )
 
-    storage_client = storage.Client()
-    bucket = storage_client.get_bucket(cluster_info.config.config_bucket)
-    output_blob = "google-cloud-dataproc-metainfo/{}/jobs/{}/driveroutput.000000000".format(
-        cluster_info.cluster_uuid, job_id
-    )
-    output = bucket.blob(output_blob).download_as_string()
-
-    print(
-        "Job {} finished with state {}:\n{}".format(
-            job_id, job_response.status.state.name, output
-        )
-    )
+    print(f"Job finished successfully: {output}")
 
     # Delete the cluster once the job has terminated.
     operation = cluster_client.delete_cluster(

dataproc/snippets/quickstart/quickstart_test.py

@@ -74,6 +74,5 @@ def test_quickstart(capsys):
     out, _ = capsys.readouterr()
 
     assert "Cluster created successfully" in out
-    assert "Submitted job" in out
-    assert "finished with state DONE:" in out
+    assert "Job finished successfully" in out
     assert "successfully deleted" in out

dataproc/snippets/submit_job.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+
+# Copyright 2020 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 using the Dataproc
+# client library.
+
+# Usage:
+#    python submit_job.py --project_id <PROJECT_ID> --region <REGION> \
+#        --cluster_name <CLUSTER_NAME>
+
+# [START dataproc_submit_job]
+import re
+# [END dataproc_submit_job]
+import sys
+# [START dataproc_submit_job]
+
+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': '{}-dataproc.googleapis.com:443'.format(region)
+    })
+
+    # 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']
+        }
+    }
+
+    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}")
+# [END dataproc_submit_job]
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        sys.exit('python submit_job.py project_id region cluster_name')
+
+    project_id = sys.argv[1]
+    region = sys.argv[2]
+    cluster_name = sys.argv[3]
+    submit_job(project_id, region, cluster_name)

dataproc/snippets/submit_job_test.py

@@ -0,0 +1,66 @@
+# Copyright 2020 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.
+
+import os
+import uuid
+
+from google.cloud import dataproc_v1 as dataproc
+import pytest
+
+import submit_job
+
+
+PROJECT_ID = os.environ['GOOGLE_CLOUD_PROJECT']
+REGION = 'us-central1'
+CLUSTER_NAME = 'py-sj-test-{}'.format(str(uuid.uuid4()))
+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'
+        }
+    }
+}
+
+
+@pytest.fixture(autouse=True)
+def setup_teardown():
+    cluster_client = dataproc.ClusterControllerClient(client_options={
+        'api_endpoint': '{}-dataproc.googleapis.com:443'.format(REGION)
+    })
+
+    # Create the cluster.
+    operation = cluster_client.create_cluster(
+        request={"project_id": PROJECT_ID, "region": REGION, "cluster": CLUSTER}
+    )
+    operation.result()
+
+    yield
+
+    cluster_client.delete_cluster(request={
+        "project_id": PROJECT_ID, "region": REGION, "cluster_name": CLUSTER_NAME
+    })
+
+
+def test_submit_job(capsys):
+    submit_job.submit_job(PROJECT_ID, REGION, CLUSTER_NAME)
+    out, _ = capsys.readouterr()
+
+    assert 'Job finished successfully' in out