The Hi/Lo Algorithm in Hibernate: Optimizing Database Identifier Generation

Avoid these 5 Common Performance Pitfalls in Spring Data JPA: Practical Solutions and Tips

In the world of JVM backend development, Spring Data JPA is an indispensable tool. It provides a convenient and powerful way to manage and manipulate data. However, like any powerful tool, there are pitfalls that can affect performance. In this article, we will explore five common performance pitfalls in Spring Data JPA and present solutions to avoid them.

1. Index Definition with Spring Data JPA

One commonly overlooked aspect when working with Spring Data JPA is the proper definition of indexes. A well-defined index can significantly speed up data access, while a missing or incorrectly defined index can lead to performance issues. In this article , you will learn how to define an index with Spring Data JPA and the considerations you should keep in mind.

Marcus Held

Efficient Data Queries with Spring Data JPA Projections

In the world of JVM Backend Development, efficient data processing is a crucial concern. One of the main strengths of Spring Data JPA lies in its ability to simplify and optimize interaction with the persistence layer. A particularly powerful feature provided by Spring Data JPA is the use of projections. They offer an excellent way to make data queries more efficient and improve system performance.

What are Spring Data JPA Projections?

Spring Data JPA projections allow you to create custom Partial Views of your entity classes. This means that instead of retrieving a complete entity, you can only retrieve selected fields, significantly reducing the size of the returned data and thereby improving the overall performance.

Marcus Held

The Hi/Lo Algorithm in Hibernate: Optimizing Database Identifier Generation

In the world of JVM persistence, Hibernate has a firm place. As one of the most popular frameworks for data persistence, it offers a multitude of possibilities to make developers’ lives easier. One of these possibilities is the Hi/Lo algorithm, a database identifier generation strategy that allows reducing the number of database calls when new entities are persisted.

The Challenge of Identifier Generation

Before we dive into the Hi/Lo algorithm, let’s briefly look at the challenge of identifier generation in a database. Every entity in a database needs a unique identifier (often referred to as ID) to distinguish it from other entities. With each insertion of a new entity into the database, the sequence on the database is usually queried to get the next ID for numerical IDs. However, this can lead to a high number of database calls, especially when many new entities are being persisted.

Marcus Held

Avoiding Multiple Data Fetches Using the First-Level-Cache in Spring Data JPA

If you’re dealing with backend development on the JVM, you’ll surely come across the Java Persistence API (JPA). A well-known implementation framework for it is Hibernate. In this article, we’ll show you how you can optimize the performance of your application in Spring Data JPA with caching by preventing the same resources from being fetched multiple times.

What is the First-Level-Cache?

Spring Data JPA uses Hibernate as the default ORM (Object-Relational Mapping), which provides an inbuilt First-Level-Cache. Each session has its own cache, which we refer to as the First-Level-Cache. Every time you retrieve an object (or more precisely, an entity instance) from the database, it is first stored in the First-Level-Cache. When retrieving the same object again, it is fetched directly from the cache and not from the database. This can lead to data repetition and impair performance.

Marcus Held

Hibernate ORM: Eager or Lazy Loading? When to Use Each

Hibernate is a well-established Object-Relational Mapping (ORM) framework that is widely used in Java development. This framework helps developers optimize the code for database operations such as fetching data. Choosing the right fetching strategy - Eager Loading or Lazy Loading - is crucial for your application’s performance. In this article, we will examine these two strategies and discuss when and how you should use them in your Spring Data project.

Marcus Held