Spring Boot | Spring Batch

Spring Batch Journey Part-2: Implementing Partitioning for High-Performance Data Import

Spring batch journey

Introduction:

In Part-1 of my Spring Batch journey, I shared how to effectively import a product catalog of 12,500 records from a CSV file into a database. We explored custom implementations of the Reader, Processor, and Writer components, focusing on optimizing the performance for a straightforward data import job.

In this article, Part-2, we will dive deeper into the Spring Batch by introducing the concept of Partitioning. Partitioning is a powerful technique that allows you to split a large batch job into smaller, parallelized tasks, enabling your application to handle even larger datasets with greater efficiency.

Why Partitioning?

When dealing with large-scale data, a single-threaded batch job might not suffice in terms of performance and scalability. Partitioning allows us to divide the workload into multiple smaller chunks that can be processed concurrently, leveraging the full potential of your system’s resources. This approach is especially beneficial when working with a massive product catalog or other large datasets in an e-commerce environment.