feat: add Dataproc-Composer tutorial (#4485)

* feat: initial composer dataproc DAG for composer-dataproc tutorial

* fix: run linting

* fix: add code region tags

* fix: linting

* chore: add more descriptive workflow name

* chore: more descriptive dag title

Co-authored-by: Brad Miro <[email protected]>

Author: sofisl

composer/workflows/dataproc_workflow_template_instantiate_operator_tutorial.py

@@ -0,0 +1,61 @@
+# 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
+#
+#     https://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 composer_dataproc_workflow_instantiate_operator_tutorial]
+
+"""Example Airflow DAG that kicks off a Cloud Dataproc Template that runs a
+Spark Pi Job.
+
+This DAG relies on an Airflow variable
+https://airflow.apache.org/concepts.html#variables
+* project_id - Google Cloud Project ID to use for the Cloud Dataproc Template.
+"""
+
+import datetime
+
+from airflow import models
+from airflow.contrib.operators import dataproc_operator
+from airflow.utils.dates import days_ago
+
+project_id = models.Variable.get("project_id")
+
+
+default_args = {
+    # Tell airflow to start one day ago, so that it runs as soon as you upload it
+    "start_date": days_ago(1),
+    "project_id": project_id,
+}
+
+# Define a DAG (directed acyclic graph) of tasks.
+# Any task you create within the context manager is automatically added to the
+# DAG object.
+with models.DAG(
+    # The id you will see in the DAG airflow page
+    "dataproc_workflow_dag",
+    default_args=default_args,
+    # The interval with which to schedule the DAG
+    schedule_interval=datetime.timedelta(days=1),  # Override to match your needs
+) as dag:
+
+    start_template_job = dataproc_operator.DataprocWorkflowTemplateInstantiateOperator(
+        # The task id of your job
+        task_id="dataproc_workflow_dag",
+        # The template id of your workflow
+        template_id="sparkpi",
+        project_id=project_id,
+        # The region for the template
+        region="us-central1",
+    )
+
+# [END composer_dataproc_workflow_instantiate_operator_tutorial]

composer/workflows/dataproc_workflow_template_instantiate_operator_tutorial_test.py

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+# 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
+#
+#     https://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.
+
+from airflow import models
+
+from . import unit_testing
+
+
+def test_dag_import():
+    """Test that the DAG file can be successfully imported.
+
+    This tests that the DAG can be parsed, but does not run it in an Airflow
+    environment. This is a recommended sanity check by the official Airflow
+    docs: https://airflow.incubator.apache.org/tutorial.html#testing
+    """
+    models.Variable.set("project_id", "example-project")
+    from . import dataproc_workflow_template_instantiate_operator_tutorial as module
+
+    unit_testing.assert_has_valid_dag(module)