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("_", "-")
# 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_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)