armada-spark

Run Apache Spark workloads seamlessly on Armada, a multi-cluster Kubernetes batch scheduler

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Overview

armada-spark is an open-source integration designed to streamline deployment and management of Apache Spark workloads on Armada. It provides preconfigured Docker images, tooling for efficient image management, and example workflows to simplify local and production deployments.

Getting Started

Prerequisites

• Java 8/11/17
• Scala 2.12/2.13
• Apache Maven 3.9.6+
• (Optional) kind for local clusters
• An accessible Armada Server and Lookout endpoint (check Armada Operator for the Quickstart guide)

Versions

By default, the project targets Spark 3.5.3 and Scala 2.13.15. To change versions:

./scripts/set-version.sh <spark-version> <scala-version>

Example:

./scripts/set-version.sh 3.5.3 2.13.15

Building Armada Spark

After setting your desired Spark and Scala versions, build the Armada Spark project with Maven by running the following command:

mvn clean package

Building Docker Images

Once your project is built, create the Docker image using:

./scripts/createImage.sh [-i image-name] [-m armada-master-url] [-q armada-queue] [-l armada-lookout-url]

Options:

Flag	Description	Example
-i	Docker image name	spark:armada
-m	Armada master URL	armada://localhost:30002
-q	Armada queue	default
-l	Armada Lookout URL	http://localhost:30000
-p	Include python
-h	Display help

To simplify, you may store these values in scripts/config.sh:

export IMAGE_NAME="spark:armada"
export ARMADA_MASTER="armada://localhost:30002"
export ARMADA_QUEUE="default"
export ARMADA_LOOKOUT_URL="http://localhost:30000"
export INCLUDE_PYTHON=true

Deployment

We recommend using kind for local testing. If you are using the Armada Operator Quickstart, it is already based on kind.

Run the following command to load the Armada Spark image into your local kind cluster:

kind load docker-image $IMAGE_NAME --name armada

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Development

Before submitting a pull request, please ensure that your code adheres to the project's coding standards and passes all tests.

Testing

To run the tests, use the following command:

mvn test

Linting

To check the code for linting issues, use the following command:

mvn spotless:check

To automatically apply linting fixes, use:

mvn spotless:apply

E2E

Make sure that the SparkPi job successfully runs on your Armada cluster before submitting a pull request.

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Running Example Workloads

SparkPi Example

The project includes a ready-to-use SparkPi job to test your setup:

./scripts/submitSparkPi.sh

This job leverages the same configuration parameters (ARMADA_MASTER, ARMADA_QUEUE, ARMADA_LOOKOUT_URL) as the scripts/config.sh script.