#
# 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 sparkr 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.operators.gcs import (
GCSCreateBucketOperator,
GCSDeleteBucketOperator,
GCSSynchronizeBucketsOperator,
)
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_sparkr"
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT") or DEFAULT_GCP_SYSTEM_TEST_PROJECT_ID
[docs]BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
[docs]JOB_FILE = "dataproc-sparkr-job.r"
[docs]GCS_JOB_FILE = f"gs://{BUCKET_NAME}/dataproc/{JOB_FILE}"
[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},
},
"software_config": {
"image_version": "2.0.116-debian10",
},
}
# Jobs definitions
# [START how_to_cloud_dataproc_sparkr_config]
[docs]SPARKR_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"spark_r_job": {"main_r_file_uri": GCS_JOB_FILE},
}
# [END how_to_cloud_dataproc_sparkr_config]
with DAG(
DAG_ID,
schedule="@once",
start_date=datetime(2021, 1, 1),
catchup=False,
tags=["example", "dataproc", "sparkr"],
) as dag:
[docs] create_bucket = GCSCreateBucketOperator(
task_id="create_bucket", bucket_name=BUCKET_NAME, project_id=PROJECT_ID
)
move_job_file = GCSSynchronizeBucketsOperator(
task_id="move_job_file",
source_bucket=RESOURCE_DATA_BUCKET,
source_object="dataproc/sparkr",
destination_bucket=BUCKET_NAME,
destination_object="dataproc",
recursive=True,
)
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,
)
sparkr_task = DataprocSubmitJobOperator(
task_id="sparkr_task", job=SPARKR_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
>> move_job_file
>> create_cluster
# TEST BODY
>> sparkr_task
# TEST TEARDOWN
>> delete_cluster
>> delete_bucket
)
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)