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"""
Example Airflow DAG that show how to create a Dataproc cluster in Google Kubernetes Engine.
Required environment variables:
GKE_NAMESPACE = os.environ.get("GKE_NAMESPACE", f"{CLUSTER_NAME}")
A GKE cluster can support multiple DP clusters running in different namespaces.
Define a namespace or assign a default one.
Notice: optional kubernetes_namespace parameter in VIRTUAL_CLUSTER_CONFIG should be the same as GKE_NAMESPACE
"""
from __future__ import annotations
import os
from datetime import datetime
from google.api_core.retry import Retry
from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.dataproc import (
DataprocCreateClusterOperator,
DataprocDeleteClusterOperator,
)
from airflow.providers.google.cloud.operators.kubernetes_engine import (
GKECreateClusterOperator,
GKEDeleteClusterOperator,
)
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]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
[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]GKE_CLUSTER_NAME = f"cluster-{ENV_ID}-{DAG_ID}-gke".replace("_", "-")
[docs]WORKLOAD_POOL = f"{PROJECT_ID}.svc.id.goog"
[docs]GKE_CLUSTER_CONFIG = {
"name": GKE_CLUSTER_NAME,
"workload_identity_config": {
"workload_pool": WORKLOAD_POOL,
},
"initial_node_count": 1,
}
[docs]GKE_NAMESPACE = os.environ.get("GKE_NAMESPACE", f"{CLUSTER_NAME}")
# [START how_to_cloud_dataproc_create_cluster_in_gke_config]
[docs]VIRTUAL_CLUSTER_CONFIG = {
"kubernetes_cluster_config": {
"gke_cluster_config": {
"gke_cluster_target": f"projects/{PROJECT_ID}/locations/{REGION}/clusters/{GKE_CLUSTER_NAME}",
"node_pool_target": [
{
"node_pool": f"projects/{PROJECT_ID}/locations/{REGION}/clusters/{GKE_CLUSTER_NAME}/nodePools/dp",
"roles": ["DEFAULT"],
"node_pool_config": {
"config": {
"preemptible": False,
"machine_type": "e2-standard-4",
}
},
}
],
},
"kubernetes_software_config": {"component_version": {"SPARK": b"3"}},
},
"staging_bucket": "test-staging-bucket",
}
# [END how_to_cloud_dataproc_create_cluster_in_gke_config]
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "dataproc", "gke"],
) as dag:
[docs] create_gke_cluster = GKECreateClusterOperator(
task_id="create_gke_cluster",
project_id=PROJECT_ID,
location=REGION,
body=GKE_CLUSTER_CONFIG,
)
# [START how_to_cloud_dataproc_create_cluster_operator_in_gke]
create_cluster_in_gke = DataprocCreateClusterOperator(
task_id="create_cluster_in_gke",
project_id=PROJECT_ID,
region=REGION,
cluster_name=CLUSTER_NAME,
virtual_cluster_config=VIRTUAL_CLUSTER_CONFIG,
retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
num_retries_if_resource_is_not_ready=3,
)
# [END how_to_cloud_dataproc_create_cluster_operator_in_gke]
delete_dataproc_cluster = DataprocDeleteClusterOperator(
task_id="delete_dataproc_cluster",
project_id=PROJECT_ID,
cluster_name=CLUSTER_NAME,
region=REGION,
trigger_rule=TriggerRule.ALL_DONE,
)
delete_gke_cluster = GKEDeleteClusterOperator(
task_id="delete_gke_cluster",
name=GKE_CLUSTER_NAME,
project_id=PROJECT_ID,
location=REGION,
trigger_rule=TriggerRule.ALL_DONE,
)
(
# TEST SETUP
create_gke_cluster
# TEST BODY
>> create_cluster_in_gke
# TEST TEARDOWN
>> delete_dataproc_cluster
>> delete_gke_cluster
)
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)