Every time a @Transactional method runs in your Spring Boot application, Spring grabs a JDBC connection from the pool. Immediately. Before your code even executes. Before it knows whether you'll actually touch the database. That connection sits there, reserved for you, doing absolutely nothing — while some other thread might be blocked waiting for a free connection. Your Hibernate second-level cache just returned the result from memory? Doesn't matter. Connection was already checked out. That's the equivalent of renting a car to walk across the street.

Spring Boot 4.1 fixes this with first-class support for lazy JDBC connections. One property. Massive performance win.

In this post we'll look at:

• The problem with eager connections — why Spring grabs a connection before you need one
• What LazyConnectionDataSourceProxy does — the mechanism behind lazy fetching
• Spring Boot 4.1's auto-configuration — enabling it with a single property
• Before vs after — what you had to do manually (and why nobody bothered)
• Real-world scenarios where this shines — cache hits, conditional logic, and read/write routing

Java records, introduced in Java 16 (JEP 395), finally let you delete Lombok. You know, that library you added to avoid writing 50+ lines of getters, then spent hours debugging @Data annotation conflicts and weird IDE plugin issues. One line. No plugins. No magic. Just the Java compiler doing what it should have done 25 years ago.

In this guide, you'll learn:

• The problem records solve — why we needed them
• Core syntax and features — compact constructors, immutability, and generated methods
• Real-world patterns — DTOs, value objects, projections, and configuration
• When records fit (and when they don't) — practical guidance for your codebase

Docker images for Spring Boot applications often start bloated—large downloads, slow CI/CD pipelines, and unnecessary attack surface. This post shows you how to dramatically reduce image size while improving security and build speed.

In this post we'll look at:

• Using JRE base images on Alpine Linux
• Multi-stage builds for clean, consistent images
• Layered JARs for blazing fast rebuilds
• Summary of improvements and trade-offs

In this article, we'll dive deep into implementing the Repository Pattern with Unit of Work in .NET Core applications. This architectural pattern helps abstract data access logic from business logic, making your application more maintainable, testable, and flexible.

Java 25 continues the recent Java trend: fewer flashy features, more small, sharp tools that make everyday code safer, faster, and easier to reason about.

In this post we’ll look at:

• simpler main and java.lang usage
• constructor “prologues” and inheritance
• ScopedValue vs ThreadLocal
• runtime & performance improvements
• _ as an unnamed parameter/pattern
• sealed types with safer switch
• **gatherers: custom intermediate stream operations **

In this article, we'll explore how to build a flexible and maintainable calculator engine using the Strategy and Factory design patterns in a Spring Boot application. This pattern is particularly useful when you need to apply different calculation rules based on different jurisdictions or conditions.

In this guide, we'll walk through setting up a robust testing environment for your React application using Vitest and React Testing Library. This combination provides a modern, fast, and efficient way to test your React components.

In Spring Framework, dependency injection (DI) is a fundamental concept that allows for the creation of loosely coupled components. Two common methods for DI are using the @Autowired annotation and constructor-based injection. This article explores when to use each method.

In Spring applications, proper transaction management is crucial for both data integrity and performance. This article explores how to optimize database operations using Spring's @Transactional annotation with a focus on read-only transactions and Hibernate optimizations.