Add deferrable mode to DataprocInstantiateInlineWorkflowTemplateOpera…

…tor (#30878)

Co-authored-by: Beata Kossakowska <bkossakowska@google.com>

airflow/providers/google/cloud/operators/dataproc.py

@@ -1780,7 +1780,6 @@ def execute(self, context: Context):
         else:
             self.defer(
                 trigger=DataprocWorkflowTrigger(
-                    template_name=self.template_id,
                     name=operation.operation.name,
                     project_id=self.project_id,
                     region=self.region,
@@ -1843,6 +1842,8 @@ class DataprocInstantiateInlineWorkflowTemplateOperator(GoogleCloudBaseOperator)
         If set as a sequence, the identities from the list must grant
         Service Account Token Creator IAM role to the directly preceding identity, with first
         account from the list granting this role to the originating account (templated).
+    :param deferrable: Run operator in the deferrable mode.
+    :param polling_interval_seconds: Time (seconds) to wait between calls to check the run status.
     """
 
     template_fields: Sequence[str] = ("template", "impersonation_chain")
@@ -1861,9 +1862,13 @@ def __init__(
         metadata: Sequence[tuple[str, str]] = (),
         gcp_conn_id: str = "google_cloud_default",
         impersonation_chain: str | Sequence[str] | None = None,
+        deferrable: bool = False,
+        polling_interval_seconds: int = 10,
         **kwargs,
     ) -> None:
         super().__init__(**kwargs)
+        if deferrable and polling_interval_seconds <= 0:
+            raise ValueError("Invalid value for polling_interval_seconds. Expected value greater than 0")
         self.template = template
         self.project_id = project_id
         self.region = region
@@ -1874,13 +1879,15 @@ def __init__(
         self.metadata = metadata
         self.gcp_conn_id = gcp_conn_id
         self.impersonation_chain = impersonation_chain
+        self.deferrable = deferrable
+        self.polling_interval_seconds = polling_interval_seconds
 
     def execute(self, context: Context):
         self.log.info("Instantiating Inline Template")
         hook = DataprocHook(gcp_conn_id=self.gcp_conn_id, impersonation_chain=self.impersonation_chain)
         operation = hook.instantiate_inline_workflow_template(
             template=self.template,
-            project_id=self.project_id,
+            project_id=self.project_id or hook.project_id,
             region=self.region,
             request_id=self.request_id,
             retry=self.retry,
@@ -1891,9 +1898,34 @@ def execute(self, context: Context):
         DataprocLink.persist(
             context=context, task_instance=self, url=DATAPROC_WORKFLOW_LINK, resource=self.workflow_id
         )
-        self.log.info("Template instantiated. Workflow Id : %s", self.workflow_id)
-        operation.result()
-        self.log.info("Workflow %s completed successfully", self.workflow_id)
+        if not self.deferrable:
+            self.log.info("Template instantiated. Workflow Id : %s", self.workflow_id)
+            operation.result()
+            self.log.info("Workflow %s completed successfully", self.workflow_id)
+        else:
+            self.defer(
+                trigger=DataprocWorkflowTrigger(
+                    name=operation.operation.name,
+                    project_id=self.project_id or hook.project_id,
+                    region=self.region,
+                    gcp_conn_id=self.gcp_conn_id,
+                    impersonation_chain=self.impersonation_chain,
+                    polling_interval_seconds=self.polling_interval_seconds,
+                ),
+                method_name="execute_complete",
+            )
+
+    def execute_complete(self, context, event=None) -> None:
+        """
+        Callback for when the trigger fires - returns immediately.
+        Relies on trigger to throw an exception, otherwise it assumes execution was
+        successful.
+        """
+        if event["status"] == "failed" or event["status"] == "error":
+            self.log.exception("Unexpected error in the operation.")
+            raise AirflowException(event["message"])
+
+        self.log.info("Workflow %s completed successfully", event["operation_name"])
 
 
 class DataprocSubmitJobOperator(GoogleCloudBaseOperator):

airflow/providers/google/cloud/triggers/dataproc.py

@@ -290,16 +290,14 @@ class DataprocWorkflowTrigger(DataprocBaseTrigger):
     Implementation leverages asynchronous transport.
     """
 
-    def __init__(self, template_name: str, name: str, **kwargs: Any):
+    def __init__(self, name: str, **kwargs: Any):
         super().__init__(**kwargs)
-        self.template_name = template_name
         self.name = name
 
     def serialize(self):
         return (
             "airflow.providers.google.cloud.triggers.dataproc.DataprocWorkflowTrigger",
             {
-                "template_name": self.template_name,
                 "name": self.name,
                 "project_id": self.project_id,
                 "region": self.region,

docs/apache-airflow-providers-google/operators/cloud/dataproc.rst

@@ -264,7 +264,7 @@ Once a workflow is created users can trigger it using
 
 Also for all this action you can use operator in the deferrable mode:
 
-.. exampleinclude:: /../../tests/system/providers/google/cloud/dataproc/example_dataproc_workflow.py
+.. exampleinclude:: /../../tests/system/providers/google/cloud/dataproc/example_dataproc_workflow_deferrable.py
     :language: python
     :dedent: 4
     :start-after: [START how_to_cloud_dataproc_trigger_workflow_template_async]
@@ -279,6 +279,15 @@ The inline operator is an alternative. It creates a workflow, run it, and delete
     :start-after: [START how_to_cloud_dataproc_instantiate_inline_workflow_template]
     :end-before: [END how_to_cloud_dataproc_instantiate_inline_workflow_template]
 
+Also for all this action you can use operator in the deferrable mode:
+
+.. exampleinclude:: /../../tests/system/providers/google/cloud/dataproc/example_dataproc_workflow_deferrable.py
+    :language: python
+    :dedent: 4
+    :start-after: [START how_to_cloud_dataproc_instantiate_inline_workflow_template_async]
+    :end-before: [END how_to_cloud_dataproc_instantiate_inline_workflow_template_async]
+
+
 Create a Batch
 --------------
 

tests/providers/google/cloud/operators/test_dataproc.py

@@ -1464,6 +1464,33 @@ def test_execute(self, mock_hook):
             metadata=METADATA,
         )
 
+    @mock.patch(DATAPROC_PATH.format("DataprocHook"))
+    @mock.patch(DATAPROC_TRIGGERS_PATH.format("DataprocAsyncHook"))
+    def test_execute_call_defer_method(self, mock_trigger_hook, mock_hook):
+        operator = DataprocInstantiateInlineWorkflowTemplateOperator(
+            task_id=TASK_ID,
+            template={},
+            region=GCP_REGION,
+            project_id=GCP_PROJECT,
+            request_id=REQUEST_ID,
+            retry=RETRY,
+            timeout=TIMEOUT,
+            metadata=METADATA,
+            gcp_conn_id=GCP_CONN_ID,
+            impersonation_chain=IMPERSONATION_CHAIN,
+            deferrable=True,
+        )
+
+        with pytest.raises(TaskDeferred) as exc:
+            operator.execute(mock.MagicMock())
+
+        mock_hook.assert_called_once_with(gcp_conn_id=GCP_CONN_ID, impersonation_chain=IMPERSONATION_CHAIN)
+
+        mock_hook.return_value.instantiate_inline_workflow_template.assert_called_once()
+
+        assert isinstance(exc.value.trigger, DataprocWorkflowTrigger)
+        assert exc.value.method_name == GOOGLE_DEFAULT_DEFERRABLE_METHOD_NAME
+
 
 @pytest.mark.need_serialized_dag
 @mock.patch(DATAPROC_PATH.format("DataprocHook"))

tests/providers/google/cloud/triggers/test_dataproc.py

@@ -44,7 +44,6 @@
 TEST_CLUSTER_NAME = "cluster_name"
 TEST_POLL_INTERVAL = 5
 TEST_GCP_CONN_ID = "google_cloud_default"
-TEST_TEMPLATE_NAME = "template_name"
 TEST_OPERATION_NAME = "name"
 
 
@@ -76,7 +75,6 @@ def batch_trigger():
 @pytest.fixture
 def workflow_trigger():
     return DataprocWorkflowTrigger(
-        template_name=TEST_TEMPLATE_NAME,
         name=TEST_OPERATION_NAME,
         project_id=TEST_PROJECT_ID,
         region=TEST_REGION,
@@ -291,7 +289,6 @@ def test_async_cluster_trigger_serialization_should_execute_successfully(self, w
         classpath, kwargs = workflow_trigger.serialize()
         assert classpath == "airflow.providers.google.cloud.triggers.dataproc.DataprocWorkflowTrigger"
         assert kwargs == {
-            "template_name": TEST_TEMPLATE_NAME,
             "name": TEST_OPERATION_NAME,
             "project_id": TEST_PROJECT_ID,
             "region": TEST_REGION,

tests/system/providers/google/cloud/dataproc/example_dataproc_workflow.py

@@ -31,7 +31,7 @@
 
 ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
 DAG_ID = "dataproc_workflow"
-PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "")
+PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT")
 
 REGION = "europe-west1"
 CLUSTER_NAME = f"cluster-dataproc-workflow-{ENV_ID}"
@@ -83,28 +83,13 @@
     )
     # [END how_to_cloud_dataproc_trigger_workflow_template]
 
-    # [START how_to_cloud_dataproc_trigger_workflow_template_async]
-    trigger_workflow_async = DataprocInstantiateWorkflowTemplateOperator(
-        task_id="trigger_workflow_async",
-        region=REGION,
-        project_id=PROJECT_ID,
-        template_id=WORKFLOW_NAME,
-        deferrable=True,
-    )
-    # [END how_to_cloud_dataproc_trigger_workflow_template_async]
-
     # [START how_to_cloud_dataproc_instantiate_inline_workflow_template]
     instantiate_inline_workflow_template = DataprocInstantiateInlineWorkflowTemplateOperator(
         task_id="instantiate_inline_workflow_template", template=WORKFLOW_TEMPLATE, region=REGION
     )
     # [END how_to_cloud_dataproc_instantiate_inline_workflow_template]
 
-    (
-        create_workflow_template
-        >> trigger_workflow
-        >> instantiate_inline_workflow_template
-        >> trigger_workflow_async
-    )
+    (create_workflow_template >> trigger_workflow >> instantiate_inline_workflow_template)
 
     from tests.system.utils.watcher import watcher
 

tests/system/providers/google/cloud/dataproc/example_dataproc_workflow_deferrable.py

@@ -0,0 +1,109 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+"""
+Example Airflow DAG for Dataproc workflow operators.
+"""
+from __future__ import annotations
+
+import os
+from datetime import datetime
+
+from airflow import models
+from airflow.providers.google.cloud.operators.dataproc import (
+    DataprocCreateWorkflowTemplateOperator,
+    DataprocInstantiateInlineWorkflowTemplateOperator,
+    DataprocInstantiateWorkflowTemplateOperator,
+)
+
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
+DAG_ID = "dataproc_workflow"
+PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT")
+
+REGION = "europe-west1"
+CLUSTER_NAME = f"cluster-dataproc-workflow-{ENV_ID}"
+CLUSTER_CONFIG = {
+    "master_config": {
+        "num_instances": 1,
+        "machine_type_uri": "n1-standard-4",
+        "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 1024},
+    },
+    "worker_config": {
+        "num_instances": 2,
+        "machine_type_uri": "n1-standard-4",
+        "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 1024},
+    },
+}
+PIG_JOB = {"query_list": {"queries": ["define sin HiveUDF('sin');"]}}
+WORKFLOW_NAME = "airflow-dataproc-test"
+WORKFLOW_TEMPLATE = {
+    "id": WORKFLOW_NAME,
+    "placement": {
+        "managed_cluster": {
+            "cluster_name": CLUSTER_NAME,
+            "config": CLUSTER_CONFIG,
+        }
+    },
+    "jobs": [{"step_id": "pig_job_1", "pig_job": PIG_JOB}],
+}
+
+
+with models.DAG(
+    DAG_ID,
+    schedule="@once",
+    start_date=datetime(2021, 1, 1),
+    catchup=False,
+    tags=["example", "dataproc"],
+) as dag:
+    create_workflow_template = DataprocCreateWorkflowTemplateOperator(
+        task_id="create_workflow_template",
+        template=WORKFLOW_TEMPLATE,
+        project_id=PROJECT_ID,
+        region=REGION,
+    )
+
+    # [START how_to_cloud_dataproc_trigger_workflow_template_async]
+    trigger_workflow_async = DataprocInstantiateWorkflowTemplateOperator(
+        task_id="trigger_workflow_async",
+        region=REGION,
+        project_id=PROJECT_ID,
+        template_id=WORKFLOW_NAME,
+        deferrable=True,
+    )
+    # [END how_to_cloud_dataproc_trigger_workflow_template_async]
+
+    # [START how_to_cloud_dataproc_instantiate_inline_workflow_template_async]
+    instantiate_inline_workflow_template_async = DataprocInstantiateInlineWorkflowTemplateOperator(
+        task_id="instantiate_inline_workflow_template_async",
+        template=WORKFLOW_TEMPLATE,
+        region=REGION,
+        deferrable=True,
+    )
+    # [END how_to_cloud_dataproc_instantiate_inline_workflow_template_async]
+
+    (create_workflow_template >> trigger_workflow_async >> instantiate_inline_workflow_template_async)
+
+    from tests.system.utils.watcher import watcher
+
+    # This test needs watcher in order to properly mark success/failure
+    # when "teardown" task with trigger rule is part of the DAG
+    list(dag.tasks) >> watcher()
+
+
+from tests.system.utils import get_test_run  # noqa: E402
+
+# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
+test_run = get_test_run(dag)