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

#
# 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 hadoop job.
"""
from __future__ import annotations

import os
from datetime import datetime

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.dataproc import (
    DataprocCreateClusterOperator,
    DataprocDeleteClusterOperator,
    DataprocSubmitJobOperator,
)
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_hadoop"
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT")
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]CLUSTER_NAME = f"cluster-{ENV_ID}-{DAG_ID}".replace("_", "-")
[docs]REGION = "europe-west1"
[docs]OUTPUT_FOLDER = "wordcount"
[docs]OUTPUT_PATH = f"gs://{BUCKET_NAME}/{OUTPUT_FOLDER}/"
# 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": 3, "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_hadoop_config]
[docs]HADOOP_JOB = { "reference": {"project_id": PROJECT_ID}, "placement": {"cluster_name": CLUSTER_NAME}, "hadoop_job": { "main_jar_file_uri": "file:///usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar", "args": ["wordcount", "gs://pub/shakespeare/rose.txt", OUTPUT_PATH], }, }
# [END how_to_cloud_dataproc_hadoop_config] with DAG( DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "dataproc", "hadoop"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator( task_id="create_bucket", bucket_name=BUCKET_NAME, project_id=PROJECT_ID )
create_cluster = DataprocCreateClusterOperator( task_id="create_cluster", project_id=PROJECT_ID, cluster_config=CLUSTER_CONFIG, region=REGION, cluster_name=CLUSTER_NAME, ) hadoop_task = DataprocSubmitJobOperator( task_id="hadoop_task", job=HADOOP_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, ) delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE ) ( # TEST SETUP [create_bucket, create_cluster] # TEST BODY >> hadoop_task # 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)

Was this entry helpful?