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Create system test for K8s and dataproc operators
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tests/providers/google/cloud/operators/test_kubernetes_engine_system.py
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tests/system/providers/google/dataproc/example_dataproc_gke.py
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| # | ||
| # 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. | ||
| """ | ||
|
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| import os | ||
| from datetime import datetime | ||
|
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| from airflow import models | ||
| 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 | ||
|
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| ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID") | ||
| DAG_ID = "dataproc-gke" | ||
| PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") | ||
|
|
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| REGION = "us-central1" | ||
| CLUSTER_NAME = f"cluster-test-build-in-gke{ENV_ID}" | ||
| GKE_CLUSTER_NAME = f"test-dataproc-gke-cluster-{ENV_ID}" | ||
| GKE_CLUSTER_CONFIG = { | ||
| "name": GKE_CLUSTER_NAME, | ||
| "workload_identity_config": { | ||
| "workload_pool": f"{PROJECT_ID}.svc.id.goog", | ||
| }, | ||
| "initial_node_count": 1, | ||
| } | ||
|
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| # [START how_to_cloud_dataproc_create_cluster_in_gke_config] | ||
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| 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", # noqa | ||
| "roles": ["DEFAULT"], | ||
| } | ||
| ], | ||
| }, | ||
| "kubernetes_software_config": {"component_version": {"SPARK": b'3'}}, | ||
| }, | ||
| "staging_bucket": "test-staging-bucket", | ||
| } | ||
|
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| # [END how_to_cloud_dataproc_create_cluster_in_gke_config] | ||
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| with models.DAG( | ||
| DAG_ID, | ||
| schedule_interval='@once', | ||
| start_date=datetime(2021, 1, 1), | ||
| catchup=False, | ||
| tags=['example'], | ||
| ) as dag: | ||
| create_gke_cluster = GKECreateClusterOperator( | ||
| task_id="create_gke_cluster", | ||
| project_id=PROJECT_ID, | ||
| location=REGION, | ||
| body=GKE_CLUSTER_CONFIG, | ||
| ) | ||
|
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| # [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, | ||
| run_in_gke_cluster=True, | ||
| virtual_cluster_config=VIRTUAL_CLUSTER_CONFIG, | ||
| ) | ||
| # [END how_to_cloud_dataproc_create_cluster_operator_in_gke] | ||
|
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| delete_dataproc_cluster = DataprocDeleteClusterOperator( | ||
| task_id="delete_dataproc_cluster", | ||
| project_id=PROJECT_ID, | ||
| cluster_name=CLUSTER_NAME, | ||
| region=REGION, | ||
| trigger_rule=TriggerRule.ALL_DONE, | ||
| ) | ||
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| delete_gke_cluster = GKEDeleteClusterOperator( | ||
| task_id="delete_gke_cluster", | ||
| name=GKE_CLUSTER_NAME, | ||
| project_id=PROJECT_ID, | ||
| location=REGION, | ||
| trigger_rule=TriggerRule.ALL_DONE, | ||
| ) | ||
|
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| create_gke_cluster >> create_cluster_in_gke >> [delete_dataproc_cluster, delete_gke_cluster] | ||
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| from tests.system.utils.watcher import watcher | ||
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| # 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() | ||
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| from tests.system.utils import get_test_run # noqa: E402 | ||
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| # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest) | ||
| test_run = get_test_run(dag) |
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