Source code for tests.system.providers.google.cloud.dataproc.example_dataproc_cluster_generator

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"""
Example Airflow DAG testing Dataproc
operators for managing a cluster and submitting jobs.
"""
from __future__ import annotations

import os
from datetime import datetime
from pathlib import Path

from airflow import models
from airflow.providers.google.cloud.operators.dataproc import (
    ClusterGenerator,
    DataprocCreateClusterOperator,
    DataprocDeleteClusterOperator,
)
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]DAG_ID = "dataproc_cluster_generation"
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "")
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]CLUSTER_NAME = f"dataproc-cluster-gen-{ENV_ID}"
[docs]REGION = "europe-west1"
[docs]ZONE = "europe-west1-b"
[docs]INIT_FILE_SRC = str(Path(__file__).parent / "resources" / "pip-install.sh")
# Cluster definition: Generating Cluster Config for DataprocCreateClusterOperator # [START how_to_cloud_dataproc_create_cluster_generate_cluster_config]
[docs]INIT_FILE = "pip-install.sh"
[docs]CLUSTER_GENERATOR_CONFIG = ClusterGenerator( project_id=PROJECT_ID, zone=ZONE, master_machine_type="n1-standard-4", worker_machine_type="n1-standard-4", num_workers=2, storage_bucket=BUCKET_NAME, init_actions_uris=[f"gs://{BUCKET_NAME}/{INIT_FILE}"], metadata={"PIP_PACKAGES": "pyyaml requests pandas openpyxl"},
).make() # [END how_to_cloud_dataproc_create_cluster_generate_cluster_config]
[docs]TIMEOUT = {"seconds": 1 * 24 * 60 * 60}
with models.DAG( DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "dataproc"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator( task_id="create_bucket", bucket_name=BUCKET_NAME, project_id=PROJECT_ID
) upload_file = LocalFilesystemToGCSOperator( task_id="upload_file", src=INIT_FILE_SRC, dst=INIT_FILE, bucket=BUCKET_NAME, ) # [START how_to_cloud_dataproc_create_cluster_generate_cluster_config_operator] create_dataproc_cluster = DataprocCreateClusterOperator( task_id="create_dataproc_cluster", cluster_name=CLUSTER_NAME, project_id=PROJECT_ID, region=REGION, cluster_config=CLUSTER_GENERATOR_CONFIG, ) # [END how_to_cloud_dataproc_create_cluster_generate_cluster_config_operator] delete_cluster = DataprocDeleteClusterOperator( task_id="delete_cluster", project_id=PROJECT_ID, cluster_name=CLUSTER_NAME, region=REGION, trigger_rule=TriggerRule.ALL_DONE, ) delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE ) create_bucket >> upload_file >> create_dataproc_cluster >> [delete_cluster, delete_bucket] from tests.system.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.system.utils 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)

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