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.

Running Spring Boot on Kubernetes is not just packaging the app in a container and deploying it. You need to configure health probes correctly, handle graceful shutdown so in-flight requests don’t get dropped, manage configuration without baking secrets into images, and make sure the JVM respects container memory limits. This guide covers the production-critical Kubernetes configuration for Spring Boot applications. Health Probes Kubernetes uses three probe types to manage pod lifecycle:

Testcontainers spins up real Docker containers for your tests — a real PostgreSQL database, a real Redis, a real Kafka broker. No more mocking JDBC connections or in-memory H2 databases that behave differently from production. Spring Boot 3.1 added @ServiceConnection, which removes the boilerplate of configuring connection URLs manually. This guide covers the right patterns for fast, reliable integration tests with Testcontainers. Why Testcontainers Over H2 Teams use H2 in-memory databases for testing because it’s fast.

Virtual Threads landed in Java 21 as a stable feature, and Spring Boot 3.2 added first-class support with a single property. The promise: write simple blocking code and get WebFlux-level throughput. The reality is mostly true — with some important exceptions. This article covers what Virtual Threads actually are, how to enable them in Spring Boot, real benchmark numbers, the three pitfalls that will silently destroy your performance, and a decision framework for when to use them (and when not to).

Every year, someone asks: “Should we use Spring Boot or Quarkus?” In 2026, both frameworks are mature, both run natively, and both work well on Kubernetes. The differences come down to developer experience, ecosystem size, and specific performance characteristics. This is an honest comparison with real numbers, not marketing claims. The Frameworks at a Glance Spring Boot 4 (November 2025): Built on Spring Framework 7. The de-facto standard for enterprise Java.

Microservices solve real problems: independent deployability, team autonomy, technology flexibility. They also create real problems: distributed transactions, network latency, operational complexity. Many teams split into microservices too early, before they understand their domain well enough to draw stable boundaries. Spring Modulith gives you module boundaries, enforced isolation, and event-driven decoupling inside a single deployable JAR. It’s the pragmatic middle ground. The Modular Monolith Problem It Solves A typical Spring Boot monolith looks like this after a year:

These are the questions that actually come up in Spring Boot interviews — at startups, scale-ups, and large enterprises. Each answer explains the concept clearly, with the level of depth an interviewer expects from a mid-level or senior developer. Questions are grouped by topic. For junior roles, focus on sections 1–3. For senior roles, everything here is fair game. Section 1: Core Fundamentals Q1. What is the difference between Spring and Spring Boot?