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.models.dag import DAG
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", "default")



[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"


[docs]CLUSTER_NAME = f"cluster-{ENV_ID}-{DAG_ID}".replace("_", "-")


[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",
    master_disk_size=32,
    worker_machine_type="n1-standard-4",
    worker_disk_size=32,
    num_workers=2,
    storage_bucket=BUCKET_NAME,
    init_actions_uris=[f"gs://{BUCKET_NAME}/{INIT_FILE}"],
    metadata={"PIP_PACKAGES": "pyyaml requests pandas openpyxl"},
    num_preemptible_workers=1,
    preemptibility="PREEMPTIBLE",
).make()


# [END how_to_cloud_dataproc_create_cluster_generate_cluster_config]


with 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
    )

    (
        # TEST SETUP
        create_bucket
        >> upload_file
        # TEST BODY
        >> create_dataproc_cluster
        # TEST TEARDOWN
        >> [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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