Enhancing Performance and Scalability: Migrating Data Processing to Databricks

By Teodora Mitrovic, Full Stack Engineer at Rockdata

At Rockdata we are working on data driven web applications. These applications need to be able to process substantial volumes of data. Traditional web applications are not always suited for these kinds of data requests. When the scale of data exceeds the capacity of traditional web hosting environments, memory and computation issues can disrupt the application's performance and hinder user experience. In this article, we'll explore a real-world scenario where a part of data processing, initially managed by a web application, was migrated to a Databricks cluster to overcome memory overflow challenges, unlock enhanced performance, and improve scalability.

In this article, we'll explore a real-world scenario where a part of data processing, initially managed by a web application, was migrated to a Databricks cluster to overcome memory overflow challenges.

The Challenge: Unforeseen Data Volume

We have built an application that allows users to do complex scenario planning based on vast datasets. The platform provides users with data analytics and reporting capabilities. The platform was initially designed to process large datasets, and it performed well within the limits set by the resource configuration. However, as the platform evolved, and the true scale of data volumes became clear, new challenges were revealed.

The root cause of the issues lays in the application's architecture. Traditional web hosting environments typically have limited memory and computing resources. When dealing with exceptionally large datasets, the limitations manifested in memory overflow, causing performance bottlenecks and delays in data processing, and a significant impact on processing time, ultimately compromising the overall user experience.

The Solution: Migration to Databricks

Recognizing the need for a more robust solution, the development team made the strategic decision to migrate a significant part of the data processing workload to a Databricks cluster. Databricks, a cloud-based platform, is renowned for its powerful data processing capabilities, including Apache Spark, which excels at handling massive datasets and complex algorithms. This migration enabled the application to harness the distributed computing power of Databricks, thereby addressing memory overflow and processing time challenges.

Why Databricks?

We’ve chosen Databricks because it offers parallel processing, is easily scalable, it supports our Python codebase, and we have already had experience with it on previous projects.

Steps in the Migration Process

• Setting Up Databricks: A Databricks workspace was provisioned, clusters were configured, and necessary libraries and dependencies were installed. This laid the foundation for the application's transition to Databricks.
• Algorithm Transformation: The algorithm responsible for processing large volumes of data had to be refactored into a script so that it can be referenced from Databrick and executed.
• Code Deployment Pipelines Setup: To facilitate a smooth transition, code deployment pipelines were set up. This allowed for automated deployment processes, streamlining the migration to Databricks.
• Testing and Cluster Fine Tuning: Rigorous testing was conducted to verify that the application could handle the same data load on Databricks without memory overflow issues. The development team also engaged in cluster fine-tuning to optimize configuration for optimal performance.

Benefits of Migrating to Databricks

The migration to Databricks yielded several noteworthy benefits:

• Scalability: Databricks allowed the application to scale resources dynamically, accommodating the sheer volume of data efficiently. The distributed computing capabilities of Databricks effectively addressed memory overflow concerns.
• Enhanced Performance: Response times of the application improved significantly, thanks to parallel processing and optimized algorithms. Users now experienced faster, more reliable performance.
• Cost Efficiency: Databricks' pay-as-you-go pricing model enabled cost savings. Resources could be allocated only when needed, reducing overall infrastructure costs.

Downsides of Using Databricks

While the migration to Databricks brought significant improvements in performance, scalability, and overall user experience, its essential to acknowledge certain downsides:

• Complexity in Learning Curve: Databricks, with its full set of features, may present a steeper learning curve for developers not familiar with the platform. This could lead to an initial slowdown in development and implementation.
• Cost Considerations: While Databricks offers a pay-as-you-go pricing model, it's crucial to carefully manage resource allocation to control costs. The flexibility of scaling resources dynamically may result in increased expenses if not monitored closely.
• Maintenance Overhead: Managing and maintaining Databricks clusters, especially as the application scales, introduces additional overhead. Continuous monitoring, updates, and optimizations are necessary to ensure the platform operates at peak efficiency.

Conclusion

The migration of a web application's data processing workload to Databricks exemplifies the importance of choosing the right infrastructure for high-load applications, particularly when dealing with extensive datasets and resource-intensive algorithms. When dealing with big data volumes, transitioning to platforms like Databricks can be a game-changer, mitigating memory overflow issues and paving the way for enhanced performance, scalability, and a seamless user experience.

Are you struggling with similar challenges, or would you like to know more about us? Feel free to reach out at rockdata.nl