Handling Failure in Microservices with FallbackFactory

Monolith is not a scary monster in a Geek story It's a scary monster in the world of software development When you have a service that does a lot of different things and you can't scale it because it will send duplicate info downstream, what do you do? You got to smash that monolith into microservices One of the projects I’m proud of was helping convert a monolithic service into 7 microservices using Java Spring Boot, Docker, Kubernetes, and AWS SQS The result: 🩷 About 1 million messages processed per month 🩷85% less debugging time 🩷46% better resolution of missing-data escalations. That’s the kind of backend work I care about: 🔙 Not architecture for its own sake 🔙 But architecture that reduces pain #microservices #java #springboot #distributedsystems

Most Spring Boot microservices tutorials lie to you. They show: CRUD APIs Clean controllers Happy path flows They don’t show: What happens when 3 services fail together Why your DB melts under load How one retry can take down your system If your microservice only works on localhost, it’s not a microservice. Over the next 30 days, I’ll show how to build Spring Boot microservices that actually survive production. Follow along if you’re tired of theory. #microservices #springboot #backend #java #softwareengineering

Most backend performance issues are not caused by code. They're caused by architecture decisions. Recently, I worked on a system where we were facing performance bottlenecks and scalability limitations. Instead of just optimizing queries or adding more resources, we focused on a few key changes: Breaking down tightly coupled services into smaller microservices Improving database access patterns and reducing unnecessary queries Introducing asynchronous processing for heavy operations Identifying and removing bottlenecks between services The result was better performance, improved scalability, and a much more resilient system. One thing I’ve learned over the years working with Java, Spring Boot, and microservices is that scaling is less about code, and more about how your system is designed. #Java #Backend #SoftwareEngineering #DevOps #Production #Perfomance

Migrating microservices isn’t just a rewrite problem. It’s a consistency problem. Moving from Java to TypeScript (or any stack change) sounds straightforward: Rewrite services. Deploy. Done. But in reality, you are running a system in transition: - some services are old - some are new - data flows between both - transactions span across them That’s where things get tricky: - different runtimes - different transaction models - different failure modes And suddenly: - updates get lost - messages get duplicated - system state drifts This article walks through a real migration scenario — and it highlights how complex distributed systems become during transitions. Link to article in the comments. What was the hardest part of a microservices migration in your system: code, or consistency?

Everyone is rushing to microservices. Few stop to ask whether they actually need them. A modular monolith gives you clear boundaries between domains, enforced at the module level, without the operational overhead of distributed systems. No service mesh. No inter-service latency. No distributed tracing headaches on day one. In Spring Boot, this is surprisingly practical: - Each module gets its own package structure, its own internal API, and its own persistence boundary. - Module-to-module communication goes through well-defined interfaces or Spring events, never direct entity access. - You enforce encapsulation with ArchUnit tests or Java module system (JPMS) if you want hard boundaries. - When a module genuinely needs to become its own service later, the extraction path is clean because the boundaries already exist. The real discipline is not in choosing microservices or monolith. It is in designing proper boundaries regardless of deployment topology. Most teams that jump to microservices too early end up with a distributed monolith with all the complexity of both worlds, the benefits of neither. Start modular. Extract when the data proves you should. Not before. #SpringBoot #SoftwareArchitecture #BackendDevelopment #ModularMonolith #Java

Microservices aren’t always the solution. Sometimes… they’re the problem. 🚨 I’ve seen simple applications split into multiple services way too early. What started as: → Easy to build → Easy to debug Turned into: → Network calls everywhere → Failures you can’t trace → Debugging across services at 2 AM All for no real reason. Here’s the truth 👇 Microservices solve scaling problems. But most teams don’t have scaling problems yet. What actually works: ✔ Start with a clean monolith ✔ Split only when there’s real pressure (scale, teams, domains) ✔ Don’t add complexity you don’t need A distributed system doesn’t just distribute load… It distributes problems. When did you decide to move to microservices? 👇 #Java #SpringBoot #Microservices #SystemDesign #BackendDevelopment #SoftwareEngineering

👉 Microservices with Spring Boot have become a key architecture choice for building scalable backend systems. From my experience working on enterprise applications, a few things consistently stand out: • Defining proper service boundaries is more important than the framework itself • Spring Boot simplifies development, but good design drives success • Each service should own its data to avoid tight coupling • Observability (logs, tracing, monitoring) is critical in distributed systems • CI/CD pipelines (like Jenkins) play a major role in reliable deployments Microservices are not just about splitting applications—they’re about building systems that are independent, scalable, and easier to maintain when done right. Curious how others are handling data consistency and service communication in their systems. #SpringBoot #Microservices #Backend #Java #SystemDesign

💻 Backend developers will understand this one 👇 Why did the microservice break up with the monolith? 👉 Because it needed space… and independent scaling 😅 --- In tech (and life), architecture matters. ✔️ Loose Coupling — Less dependency, more freedom ✔️ Scalability — Grow without breaking ✔️ Resilience — Fail, but recover stronger ✔️ Independence — Deploy without fear --- Sometimes, it’s not about breaking apart… It’s about building something better. #Microservices #Java #SpringBoot #SystemDesign #BackendDeveloper #TechHu

Most APIs function correctly, but very few are designed well Swipe to understand what good REST API design actually involves Early on, I approached APIs as simple CRUD implementations define endpoints, connect services, and move on Over time, it became clear that building scalable systems requires more than that This breakdown highlights key aspects that often get overlooked • Applying REST principles beyond basic implementation • Choosing the right HTTP methods based on intent • Structuring resources in a clear and consistent way • Using status codes and headers effectively • Considering authentication, caching, and rate limiting from the start The shift from writing endpoints to designing systems changes how backend development is approached What aspects of API design have been the most challenging in your experience #BackendDevelopment #Java #SpringBoot #RESTAPI #SoftwareEngineering #SystemDesign #JavaDeveloper

Everyone wants microservices. Few are ready for them. Monolith: • Simple to build • Easy to debug • Faster to deploy early on Microservices: • Independent scaling • Better fault isolation • More flexibility Here’s the truth: Microservices don’t remove complexity. They distribute it. Start simple. Scale when it hurts. #Java #Microservices #SystemDesign #Backend