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Posted to commits@airflow.apache.org by po...@apache.org on 2026/05/10 03:48:25 UTC
(airflow) branch main updated: Add system tests for Dataproc trigger on_kill cancel behavior (#65982)
This is an automated email from the ASF dual-hosted git repository.
potiuk pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/airflow.git
The following commit(s) were added to refs/heads/main by this push:
new f38982cf3dd Add system tests for Dataproc trigger on_kill cancel behavior (#65982)
f38982cf3dd is described below
commit f38982cf3dd59dc3654cc70a6c6615b67c590880
Author: Sadha Chilukoori <sr...@gmail.com>
AuthorDate: Sat May 9 20:48:18 2026 -0700
Add system tests for Dataproc trigger on_kill cancel behavior (#65982)
* Add system tests for Dataproc trigger on_kill cancel behavior
Follow-up to #65742 (requested by @shahar1). Adds a system test DAG
that exercises the cancel_on_kill plumbing introduced when
DataprocSubmitTrigger and DataprocSubmitJobDirectTrigger migrated
to BaseTrigger.on_kill().
Test A: deferrable submit with cancel_on_kill=True completes normally.
Test B: submits a long-running job, cancels it via DataprocHook.cancel_job()
(the same call on_kill delegates to), and asserts CANCELLED state.
* Fix MyPy failure: use version-compat import pattern for task decorator
The bare `from airflow.decorators import task` import fails MyPy in
the providers check because `airflow.decorators` does not export `task`
in Airflow 3.x. Switch to the standard `AIRFLOW_V_3_0_PLUS` conditional
import pattern used by all other system tests.
* Gate cancel_on_kill system test behind opt-in env var
Per reviewer feedback (#65982), exclude the cancel_on_kill system test
from automated Google CI runs. Test now skips at collection time unless
RUN_MANUAL_DATAPROC_CANCEL_ON_KILL_TEST=1 is set, allowing manual
end-to-end runs without burdening automated infrastructure.
* Replace module-level pytest.skip with pytestmark skipif
pytest.skip(allow_module_level=True) raises a Skipped exception at
import time that DagBag records as an import error, breaking the
test_should_be_importable parametrized test in DB-core CI jobs.
pytestmark = pytest.mark.skipif(...) lets the file import cleanly
(DagBag happy, importability test passes) while still skipping
test_run when RUN_MANUAL_DATAPROC_CANCEL_ON_KILL_TEST is unset.
* Rename env flag to RUN_MANUAL_GOOGLE_SYSTEM_TESTS for generalization
---------
Co-authored-by: Sadha Chilukoori <ss...@gmail.com>
Co-authored-by: Shahar Epstein <60...@users.noreply.github.com>
---
.../dataproc/example_dataproc_cancel_on_kill.py | 201 +++++++++++++++++++++
1 file changed, 201 insertions(+)
diff --git a/providers/google/tests/system/google/cloud/dataproc/example_dataproc_cancel_on_kill.py b/providers/google/tests/system/google/cloud/dataproc/example_dataproc_cancel_on_kill.py
new file mode 100644
index 00000000000..457ad240798
--- /dev/null
+++ b/providers/google/tests/system/google/cloud/dataproc/example_dataproc_cancel_on_kill.py
@@ -0,0 +1,201 @@
+#
+# 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 that tests cancel_on_kill behavior for Dataproc triggers.
+
+Test A (happy path): Submits a Spark job in deferrable mode with cancel_on_kill=True
+and verifies it completes successfully.
+
+Test B (cancel path): Submits a long-running Spark job asynchronously, cancels it
+via DataprocHook.cancel_job() — the same call that trigger.on_kill() delegates to —
+and verifies the job reaches CANCELLED state.
+"""
+
+from __future__ import annotations
+
+import os
+import time
+from datetime import datetime
+
+import pytest
+from google.api_core.retry import Retry
+from google.cloud.dataproc_v1 import JobStatus
+
+from airflow.models.dag import DAG
+from airflow.providers.google.cloud.hooks.dataproc import DataprocHook
+from airflow.providers.google.cloud.operators.dataproc import (
+ DataprocCreateClusterOperator,
+ DataprocDeleteClusterOperator,
+ DataprocSubmitJobOperator,
+)
+
+from system.google import DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
+from tests_common.test_utils.version_compat import AIRFLOW_V_3_0_PLUS
+
+if AIRFLOW_V_3_0_PLUS:
+ from airflow.sdk import TriggerRule, task
+else:
+ from airflow.decorators import task # type: ignore[attr-defined,no-redef]
+ from airflow.utils.trigger_rule import TriggerRule # type: ignore[no-redef,attr-defined]
+
+pytestmark = pytest.mark.skipif(
+ not os.environ.get("RUN_MANUAL_GOOGLE_SYSTEM_TESTS"),
+ reason="Manual-only system test: set RUN_MANUAL_GOOGLE_SYSTEM_TESTS=1 to run.",
+)
+
+ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
+DAG_ID = "dataproc_cancel_on_kill"
+PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
+
+CLUSTER_NAME_BASE = f"cluster-{DAG_ID}".replace("_", "-")
+CLUSTER_NAME_FULL = CLUSTER_NAME_BASE + f"-{ENV_ID}".replace("_", "-")
+CLUSTER_NAME = CLUSTER_NAME_BASE if len(CLUSTER_NAME_FULL) >= 33 else CLUSTER_NAME_FULL
+
+REGION = "europe-west1"
+
+CLUSTER_CONFIG = {
+ "master_config": {
+ "num_instances": 1,
+ "machine_type_uri": "n1-standard-4",
+ "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 32},
+ },
+ "worker_config": {
+ "num_instances": 2,
+ "machine_type_uri": "n1-standard-4",
+ "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 32},
+ },
+}
+
+SPARK_JOB = {
+ "reference": {"project_id": PROJECT_ID},
+ "placement": {"cluster_name": CLUSTER_NAME},
+ "spark_job": {
+ "jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
+ "main_class": "org.apache.spark.examples.SparkPi",
+ },
+}
+
+# [START how_to_cloud_dataproc_cancel_on_kill_config]
+LONG_RUNNING_SPARK_JOB = {
+ "reference": {"project_id": PROJECT_ID},
+ "placement": {"cluster_name": CLUSTER_NAME},
+ "spark_job": {
+ "jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
+ "main_class": "org.apache.spark.examples.SparkPi",
+ "args": ["1000000"],
+ },
+}
+# [END how_to_cloud_dataproc_cancel_on_kill_config]
+
+
+with DAG(
+ DAG_ID,
+ schedule="@once",
+ start_date=datetime(2021, 1, 1),
+ catchup=False,
+ tags=["example", "dataproc", "cancel_on_kill", "deferrable"],
+) as dag:
+ create_cluster = DataprocCreateClusterOperator(
+ task_id="create_cluster",
+ project_id=PROJECT_ID,
+ cluster_config=CLUSTER_CONFIG,
+ region=REGION,
+ cluster_name=CLUSTER_NAME,
+ retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
+ num_retries_if_resource_is_not_ready=3,
+ )
+
+ # Test A: deferrable submit with cancel_on_kill=True completes normally
+ # [START how_to_cloud_dataproc_deferrable_cancel_on_kill]
+ spark_task_deferrable = DataprocSubmitJobOperator(
+ task_id="spark_task_deferrable",
+ job=SPARK_JOB,
+ region=REGION,
+ project_id=PROJECT_ID,
+ deferrable=True,
+ cancel_on_kill=True,
+ )
+ # [END how_to_cloud_dataproc_deferrable_cancel_on_kill]
+
+ # Test B: submit a long-running job, cancel it, verify CANCELLED state
+ submit_long_job = DataprocSubmitJobOperator(
+ task_id="submit_long_job",
+ job=LONG_RUNNING_SPARK_JOB,
+ region=REGION,
+ project_id=PROJECT_ID,
+ asynchronous=True,
+ )
+
+ @task(task_id="cancel_and_verify")
+ def cancel_and_verify_job(job_id: str, project_id: str, region: str):
+ """Cancel a running Dataproc job and verify it reaches CANCELLED state.
+
+ Exercises the same DataprocHook.cancel_job() call that
+ DataprocSubmitTrigger.on_kill() and DataprocSubmitJobDirectTrigger.on_kill()
+ delegate to.
+ """
+ hook = DataprocHook(gcp_conn_id="google_cloud_default")
+
+ hook.cancel_job(job_id=job_id, project_id=project_id, region=region)
+
+ for _ in range(30):
+ job = hook.get_job(job_id=job_id, project_id=project_id, region=region)
+ state = job.status.state
+ if state in (JobStatus.State.DONE, JobStatus.State.CANCELLED, JobStatus.State.ERROR):
+ break
+ time.sleep(5)
+ else:
+ raise RuntimeError(f"Job {job_id} did not reach terminal state within 150s")
+
+ assert job.status.state == JobStatus.State.CANCELLED, (
+ f"Expected CANCELLED, got {JobStatus.State(job.status.state).name}"
+ )
+
+ cancel_task = cancel_and_verify_job(
+ job_id=submit_long_job.output,
+ project_id=PROJECT_ID,
+ region=REGION,
+ )
+
+ delete_cluster = DataprocDeleteClusterOperator(
+ task_id="delete_cluster",
+ project_id=PROJECT_ID,
+ cluster_name=CLUSTER_NAME,
+ region=REGION,
+ trigger_rule=TriggerRule.ALL_DONE,
+ )
+
+ (
+ # TEST SETUP
+ create_cluster
+ # TEST BODY
+ >> spark_task_deferrable
+ >> submit_long_job
+ >> cancel_task
+ # TEST TEARDOWN
+ >> delete_cluster
+ )
+
+ from tests_common.test_utils.watcher import watcher
+
+ list(dag.tasks) >> watcher()
+
+
+from tests_common.test_utils.system_tests import get_test_run # noqa: E402
+
+test_run = get_test_run(dag)