Run this DAG

1. Install Astronomer CLISkip if you already have the CLI

2. Initate the project:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your requirements.txt file:

5. Run the DAG:

#
# 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 that show how to use various Dataproc
operators to manage a cluster and submit jobs.
"""
import os
from airflow import models
from airflow.providers.google.cloud.operators.dataproc import (
DataprocCreateClusterOperator,
DataprocCreateWorkflowTemplateOperator,
DataprocDeleteClusterOperator,
DataprocInstantiateWorkflowTemplateOperator,
DataprocSubmitJobOperator,
DataprocUpdateClusterOperator,
)
from airflow.providers.google.cloud.sensors.dataproc import DataprocJobSensor
from airflow.utils.dates import days_ago
PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "an-id")
CLUSTER_NAME = os.environ.get("GCP_DATAPROC_CLUSTER_NAME", "example-cluster")
REGION = os.environ.get("GCP_LOCATION", "europe-west1")
ZONE = os.environ.get("GCP_REGION", "europe-west1-b")
BUCKET = os.environ.get("GCP_DATAPROC_BUCKET", "dataproc-system-tests")
OUTPUT_FOLDER = "wordcount"
OUTPUT_PATH = f"gs://{BUCKET}/{OUTPUT_FOLDER}/"
PYSPARK_MAIN = os.environ.get("PYSPARK_MAIN", "hello_world.py")
PYSPARK_URI = f"gs://{BUCKET}/{PYSPARK_MAIN}"
SPARKR_MAIN = os.environ.get("SPARKR_MAIN", "hello_world.R")
SPARKR_URI = f"gs://{BUCKET}/{SPARKR_MAIN}"
# Cluster definition
# [START how_to_cloud_dataproc_create_cluster]
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},
},
}
# [END how_to_cloud_dataproc_create_cluster]
# Update options
# [START how_to_cloud_dataproc_updatemask_cluster_operator]
CLUSTER_UPDATE = {
"config": {"worker_config": {"num_instances": 3}, "secondary_worker_config": {"num_instances": 3}}
}
UPDATE_MASK = {
"paths": ["config.worker_config.num_instances", "config.secondary_worker_config.num_instances"]
}
# [END how_to_cloud_dataproc_updatemask_cluster_operator]
TIMEOUT = {"seconds": 1 * 24 * 60 * 60}
# Jobs definitions
# [START how_to_cloud_dataproc_pig_config]
PIG_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"pig_job": {"query_list": {"queries": ["define sin HiveUDF('sin');"]}},
}
# [END how_to_cloud_dataproc_pig_config]
# [START how_to_cloud_dataproc_sparksql_config]
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]
# [START how_to_cloud_dataproc_spark_config]
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]
# [START how_to_cloud_dataproc_pyspark_config]
PYSPARK_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"pyspark_job": {"main_python_file_uri": PYSPARK_URI},
}
# [END how_to_cloud_dataproc_pyspark_config]
# [START how_to_cloud_dataproc_sparkr_config]
SPARKR_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"spark_r_job": {"main_r_file_uri": SPARKR_URI},
}
# [END how_to_cloud_dataproc_sparkr_config]
# [START how_to_cloud_dataproc_hive_config]
HIVE_JOB = {
"reference": {"project_id": PROJECT_ID},
"placement": {"cluster_name": CLUSTER_NAME},
"hive_job": {"query_list": {"queries": ["SHOW DATABASES;"]}},
}
# [END how_to_cloud_dataproc_hive_config]
# [START how_to_cloud_dataproc_hadoop_config]
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]
WORKFLOW_NAME = "airflow-dataproc-test"
WORKFLOW_TEMPLATE = {
"id": WORKFLOW_NAME,
"placement": {
"managed_cluster": {
"cluster_name": CLUSTER_NAME,
"config": CLUSTER_CONFIG,
}
},
"jobs": [{"step_id": "pig_job_1", "pig_job": PIG_JOB["pig_job"]}],
}
with models.DAG("example_gcp_dataproc", start_date=days_ago(1), schedule_interval=None) as dag:
# [START how_to_cloud_dataproc_create_cluster_operator]
create_cluster = DataprocCreateClusterOperator(
task_id="create_cluster",
project_id=PROJECT_ID,
cluster_config=CLUSTER_CONFIG,
region=REGION,
cluster_name=CLUSTER_NAME,
)
# [END how_to_cloud_dataproc_create_cluster_operator]
# [START how_to_cloud_dataproc_update_cluster_operator]
scale_cluster = DataprocUpdateClusterOperator(
task_id="scale_cluster",
cluster_name=CLUSTER_NAME,
cluster=CLUSTER_UPDATE,
update_mask=UPDATE_MASK,
graceful_decommission_timeout=TIMEOUT,
project_id=PROJECT_ID,
location=REGION,
)
# [END how_to_cloud_dataproc_update_cluster_operator]
# [START how_to_cloud_dataproc_create_workflow_template]
create_workflow_template = DataprocCreateWorkflowTemplateOperator(
task_id="create_workflow_template",
template=WORKFLOW_TEMPLATE,
project_id=PROJECT_ID,
location=REGION,
)
# [END how_to_cloud_dataproc_create_workflow_template]
# [START how_to_cloud_dataproc_trigger_workflow_template]
trigger_workflow = DataprocInstantiateWorkflowTemplateOperator(
task_id="trigger_workflow", region=REGION, project_id=PROJECT_ID, template_id=WORKFLOW_NAME
)
# [END how_to_cloud_dataproc_trigger_workflow_template]
pig_task = DataprocSubmitJobOperator(
task_id="pig_task", job=PIG_JOB, location=REGION, project_id=PROJECT_ID
)
spark_sql_task = DataprocSubmitJobOperator(
task_id="spark_sql_task", job=SPARK_SQL_JOB, location=REGION, project_id=PROJECT_ID
)
spark_task = DataprocSubmitJobOperator(
task_id="spark_task", job=SPARK_JOB, location=REGION, project_id=PROJECT_ID
)
# [START cloud_dataproc_async_submit_sensor]
spark_task_async = DataprocSubmitJobOperator(
task_id="spark_task_async", job=SPARK_JOB, location=REGION, project_id=PROJECT_ID, asynchronous=True
)
spark_task_async_sensor = DataprocJobSensor(
task_id='spark_task_async_sensor_task',
location=REGION,
project_id=PROJECT_ID,
dataproc_job_id="{{task_instance.xcom_pull(task_ids='spark_task_async')}}",
poke_interval=10,
)
# [END cloud_dataproc_async_submit_sensor]
# [START how_to_cloud_dataproc_submit_job_to_cluster_operator]
pyspark_task = DataprocSubmitJobOperator(
task_id="pyspark_task", job=PYSPARK_JOB, location=REGION, project_id=PROJECT_ID
)
# [END how_to_cloud_dataproc_submit_job_to_cluster_operator]
sparkr_task = DataprocSubmitJobOperator(
task_id="sparkr_task", job=SPARKR_JOB, location=REGION, project_id=PROJECT_ID
)
hive_task = DataprocSubmitJobOperator(
task_id="hive_task", job=HIVE_JOB, location=REGION, project_id=PROJECT_ID
)
hadoop_task = DataprocSubmitJobOperator(
task_id="hadoop_task", job=HADOOP_JOB, location=REGION, project_id=PROJECT_ID
)
# [START how_to_cloud_dataproc_delete_cluster_operator]
delete_cluster = DataprocDeleteClusterOperator(
task_id="delete_cluster", project_id=PROJECT_ID, cluster_name=CLUSTER_NAME, region=REGION
)
# [END how_to_cloud_dataproc_delete_cluster_operator]
create_cluster >> scale_cluster
scale_cluster >> create_workflow_template >> trigger_workflow >> delete_cluster
scale_cluster >> hive_task >> delete_cluster
scale_cluster >> pig_task >> delete_cluster
scale_cluster >> spark_sql_task >> delete_cluster
scale_cluster >> spark_task >> delete_cluster
scale_cluster >> spark_task_async >> spark_task_async_sensor >> delete_cluster
scale_cluster >> pyspark_task >> delete_cluster
scale_cluster >> sparkr_task >> delete_cluster
scale_cluster >> hadoop_task >> delete_cluster