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

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"""
Example Airflow DAG for Dataproc batch operators.
"""

from __future__ import annotations

import os
from datetime import datetime

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.dataproc import (
    ClusterGenerator,
    DataprocCreateBatchOperator,
    DataprocCreateClusterOperator,
    DataprocDeleteClusterOperator,
)
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator
from airflow.utils.trigger_rule import TriggerRule


[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")


[docs]DAG_ID = "dataproc_batch_ps"


[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")


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


[docs]REGION = "europe-west1"


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


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



[docs]CLUSTER_GENERATOR_CONFIG_FOR_PHS = ClusterGenerator(
    project_id=PROJECT_ID,
    region=REGION,
    master_machine_type="n1-standard-4",
    worker_machine_type="n1-standard-4",
    num_workers=0,
    properties={
        "spark:spark.history.fs.logDirectory": f"gs://{BUCKET_NAME}/logging",
    },
    enable_component_gateway=True,
).make()


[docs]BATCH_CONFIG_WITH_PHS = {
    "spark_batch": {
        "jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
        "main_class": "org.apache.spark.examples.SparkPi",
    },
    "environment_config": {
        "peripherals_config": {
            "spark_history_server_config": {
                "dataproc_cluster": f"projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}"
            }
        }
    },
}



with DAG(
    DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "dataproc", "batch", "persistent"],
) as dag:

[docs]    create_bucket = GCSCreateBucketOperator(
        task_id="create_bucket", bucket_name=BUCKET_NAME, project_id=PROJECT_ID
    )


    # [START how_to_cloud_dataproc_create_cluster_for_persistent_history_server]
    create_cluster = DataprocCreateClusterOperator(
        task_id="create_cluster_for_phs",
        project_id=PROJECT_ID,
        cluster_config=CLUSTER_GENERATOR_CONFIG_FOR_PHS,
        region=REGION,
        cluster_name=CLUSTER_NAME,
    )
    # [END how_to_cloud_dataproc_create_cluster_for_persistent_history_server]

    # [START how_to_cloud_dataproc_create_batch_operator_with_persistent_history_server]
    create_batch = DataprocCreateBatchOperator(
        task_id="create_batch_with_phs",
        project_id=PROJECT_ID,
        region=REGION,
        batch=BATCH_CONFIG_WITH_PHS,
        batch_id=BATCH_ID,
    )
    # [END how_to_cloud_dataproc_create_batch_operator_with_persistent_history_server]

    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
        >> create_cluster
        # TEST BODY
        >> create_batch
        # 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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