Publishing an event to Kafka after saving to the database looks simple. It has a subtle, dangerous flaw: if the Kafka publish fails after the DB commit, or the app crashes between the two, your event is lost and your data is inconsistent. The Transactional Outbox Pattern solves this by writing the event to the database in the same transaction as the business data, then publishing to Kafka separately. This guide covers the pattern, the implementation, and idempotent consumers.

Spring Boot applications running as GraalVM native images start in milliseconds, use a fraction of the memory, and fit in tiny containers. The tradeoff is a longer build time. In 2026, with Spring Boot 4 and GraalVM 24, native images are production-ready for most Spring applications. This guide covers everything: what Spring AOT does, how to build your first native image, how to fix the common issues, and how to add native builds to CI.

“Spring Boot is magic” is something you hear a lot. Add spring-boot-starter-data-jpa and suddenly you have a working DataSource, a JpaTransactionManager, and a LocalContainerEntityManagerFactoryBean — without writing a single @Bean method. Understanding how this actually works turns the magic into a tool you can control, debug, and extend. The Entry Point: @EnableAutoConfiguration @SpringBootApplication is a shorthand for three annotations: @Configuration @EnableAutoConfiguration // this is the one that matters here @ComponentScan public class MyApplication { public static void main(String[] args) { SpringApplication.

The OWASP Top 10 lists the most critical web application security risks. Spring Boot apps have their own common failure patterns: exposed Actuator endpoints, secrets in properties files, SQL built from string concatenation, and Spring Security misconfiguration. This guide covers the vulnerabilities that actually appear in Spring Boot applications and how to fix each one. 1. SQL Injection SQL injection remains one of the most critical vulnerabilities. It allows attackers to manipulate database queries.

Spring AI 1.0 GA shipped in May 2025. It brings the Spring programming model to AI development: a unified ChatClient API that works across Claude, OpenAI, Gemini, Ollama, and Azure OpenAI — switching AI providers is changing one dependency. This guide builds a complete RAG (Retrieval-Augmented Generation) application that answers questions about your documentation using any AI provider. What Is RAG? A large language model (LLM) knows everything in its training data but nothing about your specific documents, code, or business data.

Many teams are still running Spring Boot 2.7.x. Spring Boot 2.x reached end of life in November 2023, which means no more security patches. The jump to 4.0 is two generations, and the breaking changes are real — but they are also well-documented and mostly automatable. This guide walks through the migration in stages: 2.x → 3.0 first, then 3.x incremental updates, then 4.0. Each section lists what breaks and how to fix it.

Spring Boot 4.0 was released on November 20, 2025. It is built on Spring Framework 7 and represents the most significant shift in the Spring ecosystem since the Jakarta EE migration in Spring Boot 3. The headline change is full modularisation — the single spring-boot-autoconfigure JAR has been split into 70+ granular modules. But that is just the start. This guide covers every change that matters, what breaks on upgrade, and what is genuinely new and useful.

Spring Boot Actuator exposes production-ready operational endpoints — health checks, metrics, environment info, thread dumps — out of the box. Combined with Prometheus and Grafana, you get a full monitoring stack with minimal configuration. This guide covers everything from initial setup to Kubernetes health probes, custom metrics, and securing your management endpoints. Setup Dependencies <dependencies> <!-- Actuator --> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency> <!-- Micrometer Prometheus registry --> <dependency> <groupId>io.micrometer</groupId> <artifactId>micrometer-registry-prometheus</artifactId> <scope>runtime</scope> </dependency> </dependencies> Basic configuration # application.

Caching reduces database load and response latency. Spring Boot’s cache abstraction lets you add caching with annotations, then swap the implementation (Caffeine, Redis, multi-level) without changing your business code. This guide covers Caffeine for in-JVM caching, Redis for distributed caching, and a multi-level cache that combines both. Spring Cache Abstraction Spring’s cache abstraction uses three annotations: Annotation Behaviour @Cacheable Cache the return value. On subsequent calls, return from cache without executing the method.

There are three ways to containerise a Spring Boot application: a naive single-stage Dockerfile, a proper multi-stage Dockerfile with layered JARs, and Cloud Native Buildpacks. Each has different tradeoffs in build speed, image size, and maintenance overhead. This guide covers all three approaches, explains why layered JARs matter for CI/CD speed, and shows how to produce small, secure, production-ready images. The Problem with the Naive Dockerfile Most tutorials show this: