Apache Airflow on Steroids with Java, Scala and Python Spark Jobs

This project orchestrates Spark jobs written in different programming languages using Apache Airflow, all within a Dockerized environment. The DAG sparking_flow is designed to submit Spark jobs written in Python, Scala, and Java, ensuring that data processing is handled efficiently and reliably on a daily schedule.

Project Structure

The DAG sparking_flow includes the following tasks:

start: A PythonOperator that prints "Jobs started".
python_job: A SparkSubmitOperator that submits a Python Spark job.
scala_job: A SparkSubmitOperator that submits a Scala Spark job.
java_job: A SparkSubmitOperator that submits a Java Spark job.
end: A PythonOperator that prints "Jobs completed successfully".

These tasks are executed in a sequence where the start task triggers the Spark jobs in parallel, and upon their completion, the end task is executed.

Prerequisites

Before setting up the project, ensure you have the following:

Docker and Docker Compose installed on your system.
Apache Airflow Docker image or a custom image with Airflow installed.
Apache Spark Docker image or a custom image with Spark installed and configured to work with Airflow.
Docker volumes for Airflow DAGs, logs, and Spark jobs are properly set up.

Docker Setup

To run this project using Docker, follow these steps:

Clone this repository to your local machine.
Navigate to the directory containing the docker-compose.yml file.
Build and run the containers using Docker Compose:

docker-compose up -d --build

This command will start the necessary services defined in your docker-compose.yml, such as Airflow webserver, scheduler, Spark master, and worker containers.

Directory Structure for Jobs

Ensure your Spark job files are placed in the following directories and are accessible to the Airflow container:

Python job: jobs/python/wordcountjob.py
Scala job: jobs/scala/target/scala-2.12/word-count_2.12-0.1.jar
Java job: jobs/java/spark-job/target/spark-job-1.0-SNAPSHOT.jar

These paths should be relative to the mounted Docker volume for Airflow DAGs.

Usage

After the Docker environment is set up, the sparking_flow DAG will be available in the Airflow web UI localhost:8080, where it can be triggered manually or run on its daily schedule.

The DAG will execute the following steps:

Print "Jobs started" in the Airflow logs.
Submit the Python Spark job to the Spark cluster.
Submit the Scala Spark job to the Spark cluster.
Submit the Java Spark job to the Spark cluster.
Print "Jobs completed successfully" in the Airflow logs after all jobs have finished.

Note:

You must add the spark cluster url to the spark connection in the configuration on Airflow UI

abdelhaqs/SparkingFlow