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"""
Example Airflow DAG for DataprocSubmitJobOperator with spark sql 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.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_sql" 
[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 
# 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
# [START how_to_cloud_dataproc_sparksql_config]
[docs]SPARK_SQL_JOB = {
    "reference": {"project_id": PROJECT_ID},
    "placement": {"cluster_name": CLUSTER_NAME},
    "spark_sql_job": {"query_list": {"queries": ["SHOW DATABASES;"]}},
} 
# [END how_to_cloud_dataproc_sparksql_config]
with DAG(
    DAG_ID,
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "dataproc"],
) 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,
    ) 
    spark_sql_task = DataprocSubmitJobOperator(
        task_id="spark_sql_task", job=SPARK_SQL_JOB, region=REGION, project_id=PROJECT_ID
    )
    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_sql_task
        # TEST TEARDOWN
        >> delete_cluster
    )
    from dev.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 dev.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)