How to Use JAR Files as Libraries in Databricks Clusters

Learn how to install JAR files as libraries in Databricks clusters using Azure with our comprehensive guide. Make your pipeline more efficient today!
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This video is based on the question https://stackoverflow.com/q/69212780/ asked by the user 'Subhash Ghai' ( https://stackoverflow.com/u/16875492/ ) and on the answer https://stackoverflow.com/a/69218614/ provided by the user 'Alex Ott' ( https://stackoverflow.com/u/18627/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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How to Use JAR Files as Libraries in Databricks Clusters: A Step-by-Step Guide

Setting up a Databricks cluster in your Azure Release pipeline can be challenging, particularly when it comes to installing libraries such as JAR files. As a data engineer or developer, you may find yourself in a situation similar to Subhash, who successfully set up most of the cluster but had some questions regarding library installation through initialization scripts. In this guide, we will address these problems and provide a simple, organized solution for incorporating JAR files as libraries in your Databricks cluster.

Overview of the Problem

Subhash has already taken steps to create a Databricks cluster definition using Azure CLI and downloaded a JAR file from a Maven repository into the pipeline agent folder. He has initiated the Databricks CLI and successfully copied the JAR file to the dbfs:/FileStore/jars/ directory.

However, he faced challenges with:

Ensuring that Python packages like pandas, azure-cosmos, and python-magic would be properly installed in the cluster.

Figuring out how to add the JAR file as a library in the cluster via an initialization script in an efficient manner.

Solution: Using Init Scripts for Library Installation

To tackle these issues, we'll focus on creating a proper initialization script that will handle both the installation of Python packages and the addition of JAR files as libraries. Below are the clear steps to follow:

Step 1: Modify the Init Script for Package Installation

Subhash's initial script was as follows:

[[See Video to Reveal this Text or Code Snippet]]

Improved Script

You can streamline the installation process by combining the package installations into a single command:

[[See Video to Reveal this Text or Code Snippet]]

Key Enhancements:

Multiple installations in one command: This method not only simplifies the script but also speeds up the installation process.

Remove error redirection: Avoid using 2>/dev/null to ensure that any errors during installation can be logged for debugging purposes.

Step 2: Add JAR Files to the Cluster

To ensure that the JAR files are accessible to your Databricks cluster, you need to copy them to the appropriate directory. Update your init script by adding the following line:

Copy JAR Files

[[See Video to Reveal this Text or Code Snippet]]

or, if you need to copy multiple JAR files at once:

[[See Video to Reveal this Text or Code Snippet]]

Why this Works:

Location: The /databricks/jars/ folder is where Databricks expects to find JAR files for cluster nodes. By copying your JAR files here as part of the initialization process, they will automatically be recognized and available during runtime.

Conclusion

By following these steps, you'll be able to effectively install JAR files and Python libraries automatically whenever your Databricks cluster starts up or is restarted. This approach not only saves time but also ensures consistency in the library environments of your clusters.

Whether you're setting up a new cluster or maintaining an existing one, an efficient initialization process is key. Implement these best practices for a smoother experience and to minimize potential errors in your Azure Release pipeline.

Now you can be more confident in your Databricks setup!

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