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

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
"""
Example Airflow DAG for DataprocSubmitJobOperator with spark job.
"""
from __future__ import annotations

import os
from datetime import datetime

from airflow import models
from airflow.providers.google.cloud.operators.dataproc import (
    DataprocCreateClusterOperator,
    DataprocDeleteClusterOperator,
    DataprocSubmitJobOperator,
)
from airflow.utils.trigger_rule import TriggerRule


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


[docs]DAG_ID = "dataproc_spark"


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



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


[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": 1024},
    },
    "worker_config": {
        "num_instances": 2,
        "machine_type_uri": "n1-standard-4",
        "disk_config": {"boot_disk_type": "pd-standard", "boot_disk_size_gb": 1024},
    },
}


# Jobs definitions
# [START how_to_cloud_dataproc_spark_config]

[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",
    },
}

# [END how_to_cloud_dataproc_spark_config]


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

[docs]    create_cluster = DataprocCreateClusterOperator(
        task_id="create_cluster",
        project_id=PROJECT_ID,
        cluster_config=CLUSTER_CONFIG,
        region=REGION,
        cluster_name=CLUSTER_NAME,
    )


    spark_task = DataprocSubmitJobOperator(
        task_id="spark_task", job=SPARK_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_task
        # TEST TEARDOWN
        >> delete_cluster
    )

    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)

Was this entry helpful?