Source code for tests.system.google.cloud.dataproc.example_dataproc_gke

#
# 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 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]DAG_ID = "dataproc_gke"


[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID



[docs]REGION = "us-central1"


[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 dev.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 dev.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)

Was this entry helpful?