Ujith B’s Post

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

Monolith vs Microservices — simple explanation 👇 Monolith: • Single codebase • Easy to start • Hard to scale Microservices: • Multiple services • Scalable • Better fault isolation Most companies start with monolith and move to microservices as they grow. Choose based on need, not trend. #Microservices #SystemDesign #Java #Backend #Architecture

⚙️ Microservices Are Not Always the Best Choice… Everyone talks about microservices like it’s the ultimate solution. But I learned: 👉 Microservices add complexity 👉 Debugging becomes harder 👉 Deployment needs proper setup 💡 Sometimes, a well-structured monolith is better. Right architecture depends on problem — not trend. What do you prefer: Monolith or Microservices? 👇 #Microservices #Java #BackendDevelopment #SystemDesign

💡 One thing I recently understood better: Microservices communication While working on backend services using Spring Boot, I explored how different services interact in a microservices architecture. 🔗 Key takeaways: • REST APIs play a crucial role in communication between services • Proper error handling and validation are critical for reliability • API performance directly impacts overall system efficiency It’s interesting to see how small design decisions can affect scalability at a larger level. Still exploring and learning every day 🚀 #Microservices #SpringBoot #Java #BackendDevelopment #RESTAPI #LearningJourney

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

Hi everyone, I recently went through some concepts on Microservices Architecture and found them really useful for understanding how modern scalable systems are built. Sharing a quick PDF that covers key ideas like service decomposition, scalability, and communication between services. Hope this helps anyone looking to get started or revise the basics! hashtag #Microservices #BackendDevelopment #Java #SpringBoot #Learning #SoftwareEngineering

Hi everyone, I recently went through some concepts on Microservices Architecture and found them really useful for understanding how modern scalable systems are built. Sharing a quick PDF that covers key ideas like service decomposition, scalability, and communication between services. Hope this helps anyone looking to get started or revise the basics! #Microservices #BackendDevelopment #Java #SpringBoot #Learning #SoftwareEngineering

Topic: Distributed Tracing When a request flows through multiple services, how do you track it? In microservices, a single request may pass through: • API Gateway • Multiple backend services • Databases • External APIs When something slows down or fails, it’s hard to pinpoint where. That’s where distributed tracing helps. It allows you to: • Track a request across services • Identify bottlenecks • Debug latency issues • Understand system behavior end-to-end Tools like Jaeger, Zipkin, and OpenTelemetry make this possible. Because in distributed systems, visibility is everything. How do you trace requests across your services? #Microservices #DistributedTracing #SystemDesign #Java #BackendDevelopment

👋 Hello Connections, Recently, while working on a microservices feature, everything looked perfectly fine… until one downstream API started failing. At first, I handled it using Fallback. 👉 If the service fails → return a default response. It worked… but something felt off. All failures looked the same. Whether it was a not found, server error, or timeout — the response didn’t change. Debugging became harder, and the real issue stayed hidden. That’s when I explored FallbackFactory. With it, I could access the actual exception and handle failures more intelligently: Return meaningful responses based on the error Log the exact issue Improve overall system visibility 💡 What I learned Handling failure is not just about giving a backup response — it’s about understanding what failed and why. Fallback → quick and simple FallbackFactory → controlled and insightful ⚙️ This approach, combined with Spring Boot and Resilience4j, helps build more resilient microservices. ✨ More such learnings coming soon — stay tuned! #Microservices #Java #SpringBoot #BackendDevelopment #SoftwareEngineering

Java plays a crucial role in microservices architecture, enabling the development of modular, scalable, and resilient applications. This approach #Java #Microservices #SoftwareArchitecture #CloudNative #DevOps Nimbus Teck INC #ScalableApps