Addressing JPA Pagination Issues with Large Datasets in Spring Boot

Learn how to solve JPA pagination performance issues when handling large datasets within a Spring Boot application.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: JPA pagination query becomes slower with every subsequent call

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the JPA Pagination Problem in Spring Boot

Working with large datasets in Spring Boot can sometimes lead to performance bottlenecks, especially when using pagination with JPA (Java Persistence API). In this guide, we dive into a specific issue where JPA pagination queries become increasingly slower with each subsequent call, particularly when dealing with a large table of over 1 million records. We will unpack the problem and explore effective solutions that can improve performance.

The Problem Scenario

Consider a scenario in a Spring Boot application where there's a database table named vehicle containing over 1 million records. The table includes an indexed field called type, and the application needs to fetch all records grouped by type. The data retrieval for each type occurs in batches of 1000 records.

The implementation involves the findByType method in the VehicleRepository, which retrieves vehicle records based on the provided type and pagination parameters. The structure of the code looks something like this:

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

For different vehicle types, the performance of this retrieval varies significantly:

For type A (0 vehicles): completion time is less than 100ms, which is acceptable.

For type B (100,000 vehicles): the first fetch takes about 200ms, but with increasing OFFSET values, the response time increases drastically (up to 6000-7000ms for OFFSET 90,000).

Types C, D, and E (0 vehicles): inexplicably take 3000-4000ms each, despite having no records to retrieve.

The Underlying Causes

Slow Pagination with High OFFSET: The significant delay noted while fetching type B with a high OFFSET indicates a potential performance hit due to how JPA manages pagination with large datasets. The slowdowns are often attributed to the way the database processes queries with large OFFSET values, as it must traverse many records to return the desired result set.

Entity Retention in Memory: Profiling results indicated that even after each iteration, the collection of vehicle records kept increasing rather than being cleared. This suggests that objects were retained in memory, leading to increased memory consumption and garbage collection delays.

Solution Approaches

To tackle the performance issue with JPA pagination queries, we propose two solutions that can help manage memory use and improve overall speed.

1. Use Multiple Transactional Contexts

Instead of processing multiple types of vehicles within a single transaction, break up each type into its own transactional context. JPA's EntityManager is designed to handle individual units of work, and keeping it alive for extended periods while processing large batches can lead to performance degradation.

Implementation Steps:

Modify your service method to call findByType within separate transaction boundaries, as shown below:

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

2. Clear the EntityManager

Implementation Example:

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

Conclusion

Performance issues with JPA pagination in Spring Boot applications arise primarily due to the handling of large datasets and the inherent context management of the EntityManager. By encapsulating each batch processing within its own transaction or clearing the EntityManager after processing, you can significantly improve your application's responsiveness and memory efficiency.

With the right adjustments, your application can handle pagination more effectiv