Devops Monk

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:

Observability means you can answer “what is wrong and why” from your system’s outputs alone — without adding new instrumentation after an incident. It requires three types of data: metrics (what happened), traces (why it happened), and logs (the details). Spring Boot 4 ships a single OpenTelemetry starter that covers all three. This guide shows how to wire up the complete observability stack: Prometheus + Grafana for metrics, Grafana Tempo for distributed tracing, and Grafana Loki for logs.

JPA makes database access simple. It also makes it dangerously easy to write code that fires 100 SQL queries to load 10 records. The N+1 problem alone has caused more production performance incidents than almost any other JPA issue. This guide covers how to find and fix the five most common JPA performance problems: N+1 queries, LazyInitializationException, over-fetching, poor connection pool sizing, and Hibernate 6 breaking changes. Enable SQL Logging First Before optimizing anything, see exactly what queries are firing:

Building microservices requires more than splitting a monolith into services. You need service discovery, a gateway to route traffic, centralised configuration management, and resilience patterns to handle inevitable failures. Spring Cloud provides all of this as a cohesive stack. This guide builds a complete microservices architecture from scratch: Eureka for service discovery, Spring Cloud Gateway for routing, Spring Cloud Config Server for centralised config, and Resilience4j for circuit breakers and retry.

Zero-trust API security means every request is validated independently — no session state, no “trusted network” assumptions. A JWT bearer token is issued by an authorisation server, signed cryptographically, and validated on every API call. The API never calls back to the authorisation server during validation; it verifies the token’s signature locally. This guide covers the complete setup: dependencies, resource server configuration, token validation (both symmetric and asymmetric), extracting claims, role-based access control, method-level security, and the Spring Security 7 changes that break existing setups.