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

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"""
Example Airflow DAG for DataprocSubmitJobOperator with async spark job.
"""

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,
    DataprocSubmitJobOperator,
)
from airflow.providers.google.cloud.sensors.dataproc import DataprocJobSensor
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_spark_async"


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



[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]REGION = "europe-west1"


# Cluster definition

[docs]CLUSTER_CONFIG = {
    "master_config": {
        "num_instances": 1,
        "machine_type_uri": "n1-standard-4",
        "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 32},
    },
    "worker_config": {
        "num_instances": 2,
        "machine_type_uri": "n1-standard-4",
        "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 32},
    },
}


# Jobs definitions

[docs]SPARK_JOB = {
    "reference": {"project_id": PROJECT_ID},
    "placement": {"cluster_name": CLUSTER_NAME},
    "spark_job": {
        "jar_file_uris": ["file:///usr/lib/spark/examples/jars/spark-examples.jar"],
        "main_class": "org.apache.spark.examples.SparkPi",
    },
}



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

[docs]    create_cluster = DataprocCreateClusterOperator(
        task_id="create_cluster",
        project_id=PROJECT_ID,
        cluster_config=CLUSTER_CONFIG,
        region=REGION,
        cluster_name=CLUSTER_NAME,
        retry=Retry(maximum=100.0, initial=10.0, multiplier=1.0),
        num_retries_if_resource_is_not_ready=3,
    )


    # [START cloud_dataproc_async_submit_sensor]
    spark_task_async = DataprocSubmitJobOperator(
        task_id="spark_task_async", job=SPARK_JOB, region=REGION, project_id=PROJECT_ID, asynchronous=True
    )

    spark_task_async_sensor = DataprocJobSensor(
        task_id="spark_task_async_sensor_task",
        region=REGION,
        project_id=PROJECT_ID,
        dataproc_job_id=spark_task_async.output,
        poke_interval=10,
    )
    # [END cloud_dataproc_async_submit_sensor]

    delete_cluster = DataprocDeleteClusterOperator(
        task_id="delete_cluster",
        project_id=PROJECT_ID,
        cluster_name=CLUSTER_NAME,
        region=REGION,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    (
        # TEST SETUP
        create_cluster
        # TEST BODY
        >> spark_task_async
        >> spark_task_async_sensor
        # TEST TEARDOWN
        >> delete_cluster
    )

    from 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 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)

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