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"""
Example Airflow DAG for asynchronous mode of Google Kubernetes Engine.
"""
from __future__ import annotations
import os
from datetime import datetime
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.kubernetes_engine import (
GKECreateClusterOperator,
GKEDeleteClusterOperator,
GKEStartPodOperator,
)
from airflow.providers.standard.operators.bash import BashOperator
from airflow.utils.trigger_rule import TriggerRule
from providers.tests.system.google import DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]DAG_ID = "kubernetes_engine_async"
[docs]GCP_PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
[docs]GCP_LOCATION = "europe-north1-a"
[docs]CLUSTER_NAME_BASE = f"cluster-{DAG_ID}".replace("_", "-")
[docs]CLUSTER_NAME_FULL = CLUSTER_NAME_BASE + f"-{ENV_ID}".replace("_", "-")
[docs]CLUSTER_NAME = CLUSTER_NAME_BASE if len(CLUSTER_NAME_FULL) >= 33 else CLUSTER_NAME_FULL
[docs]CLUSTER = {"name": CLUSTER_NAME, "initial_node_count": 1}
with DAG(
DAG_ID,
schedule="@once", # Override to match your needs
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example"],
) as dag:
# [START howto_operator_gke_create_cluster_async]
[docs] create_cluster = GKECreateClusterOperator(
task_id="create_cluster",
project_id=GCP_PROJECT_ID,
location=GCP_LOCATION,
body=CLUSTER,
deferrable=True,
)
# [END howto_operator_gke_create_cluster_async]
pod_task = GKEStartPodOperator(
task_id="pod_task",
project_id=GCP_PROJECT_ID,
location=GCP_LOCATION,
cluster_name=CLUSTER_NAME,
namespace="default",
image="perl",
name="test-pod-async",
in_cluster=False,
on_finish_action="delete_pod",
get_logs=True,
deferrable=True,
)
# [START howto_operator_gke_start_pod_xcom_async]
pod_task_xcom_async = GKEStartPodOperator(
task_id="pod_task_xcom_async",
project_id=GCP_PROJECT_ID,
location=GCP_LOCATION,
cluster_name=CLUSTER_NAME,
namespace="default",
image="alpine",
cmds=["sh", "-c", "mkdir -p /airflow/xcom/;echo '[1,2,3,4]' > /airflow/xcom/return.json"],
name="test-pod-xcom-async",
in_cluster=False,
on_finish_action="delete_pod",
do_xcom_push=True,
deferrable=True,
get_logs=True,
)
# [END howto_operator_gke_start_pod_xcom_async]
# [START howto_operator_gke_xcom_result_async]
pod_task_xcom_result = BashOperator(
bash_command="echo \"{{ task_instance.xcom_pull('pod_task_xcom_async')[0] }}\"",
task_id="pod_task_xcom_result",
)
# [END howto_operator_gke_xcom_result_async]
# [START howto_operator_gke_delete_cluster_async]
delete_cluster = GKEDeleteClusterOperator(
task_id="delete_cluster",
name=CLUSTER_NAME,
project_id=GCP_PROJECT_ID,
location=GCP_LOCATION,
deferrable=True,
)
# [END howto_operator_gke_delete_cluster_async]
delete_cluster.trigger_rule = TriggerRule.ALL_DONE
create_cluster >> [pod_task, pod_task_xcom_async] >> delete_cluster
pod_task_xcom_async >> pod_task_xcom_result
from tests_common.test_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_common.test_utils.system_tests import get_test_run # noqa: E402
# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)