Introduction Retry handles transient failures. Skip handles permanent ones — bad data rows, constraint violations, malformed records that will never succeed no matter how many times you retry. Skip logic lets your job continue processing good records while recording bad ones for human review. This article covers: Configuring skip for specific exception types Custom SkipPolicy for fine-grained control Dead-letter table pattern for tracking skipped items Stopping a job intentionally vs failing it Handling abandoned executions Designing jobs that restart safely after any failure Basic Skip Configuration return new StepBuilder("importOrdersStep", jobRepository) .

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.

Large language models know a lot — but not about your data. RAG (Retrieval-Augmented Generation) solves this: find the relevant context from your documents, inject it into the prompt, and let the model answer grounded in your data. This article builds a complete RAG API with Spring AI 2.0. What You’ll Build A Q&A API over your product documentation: User: "What's the return policy for electronics?" → Search vector store for relevant docs → Inject matching paragraphs into prompt → Claude/GPT answers based on your actual docs Without RAG: the LLM guesses or hallucinate your policy.

Every bean in the Spring container has a scope (how many instances exist and for how long) and a lifecycle (what happens when it’s created and destroyed). Understanding these prevents subtle bugs and lets you optimize resource usage. Bean Scopes Overview Scope Instances Available in singleton One per ApplicationContext All apps prototype New instance every time All apps request One per HTTP request Web apps session One per HTTP session Web apps application One per ServletContext Web apps Singleton (Default) By default, every Spring bean is a singleton — one instance per ApplicationContext.

Article 2 showed how chunk processing works conceptually. This article sets up the real project you’ll build on for the rest of the series — complete Maven configuration, MySQL metadata tables, multiple ways to trigger jobs, and correct use of JobParameters. Everything in this article is production-ready from the start. Project Dependencies Add spring-boot-starter-batch to your Maven project. That one starter brings in everything you need. <?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.

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.

A running application is not enough — you need to know if it’s healthy, how it’s performing, and what it’s doing. Spring Boot Actuator exposes that information through HTTP endpoints and metrics. Setup <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-actuator</artifactId> </dependency> By default, only /actuator/health and /actuator/info are exposed over HTTP. Everything else is available via JMX. Enable what you need: management: endpoints: web: exposure: include: health,info,metrics,prometheus,conditions,beans,env,loggers,threaddump,heapdump base-path: /actuator endpoint: health: show-details: when-authorized # or 'always' (dev), 'never' (public) show-components: when-authorized metrics: enabled: true server: port: 8081 # expose actuator on a separate port (not public-facing) Health Endpoint GET /actuator/health — used by Kubernetes liveness/readiness probes and load balancers:

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.