This is a well-written Apache Airflow Directed Acyclic Graph (DAG) script for running a Hadoop wordcount example on a Google Cloud Dataproc cluster. Here’s a brief explanation of what the script does:
Imports necessary modules: The script starts by importing necessary modules such as datetime, os, models, dataproc_operator, and trigger_rule from the Airflow library.
Defines variables and arguments: It then defines several variables and arguments that will be used in the DAG. These include the output file path, the path to the Hadoop wordcount example jar file, arguments for the Cloud Dataproc job, and default DAG arguments.
Creates a DAG: A DAG named ‘composer_sample_quickstart’ is created with a schedule interval of one day and the previously defined default arguments.
Defines tasks: Within this DAG, three tasks are defined using operators from the dataproc_operator module:
create_dataproc_cluster: This task creates a Cloud Dataproc cluster. run_dataproc_hadoop: This task runs the Hadoop wordcount example on the master node of the Cloud Dataproc cluster. delete_dataproc_cluster: This task deletes the Cloud Dataproc cluster once the Hadoop job is done. Sets task dependencies: Finally, it sets up dependencies between these tasks using the bitshift operators. The create_dataproc_cluster task is performed first, followed by the run_dataproc_hadoop task, and finally the delete_dataproc_cluster task.
This script is a good example of how to automate data processing workflows with Apache Airflow and Google Cloud Dataproc. It demonstrates how to create a cluster, run a job, and clean up resources in an organized and efficient manner.