shubham sharma’s Post

🚨 Most Developers Don't Realize This in Spring Boot... Everything works fine in the beginning. But as your project grows: ⚠ APIs slow down ⚠ Code becomes messy ⚠ Debugging becomes painful Here are some mistakes I’ve seen (and personally faced): ❌ Writing business logic inside controllers ❌ Ignoring database performance (no indexing, no pagination) ❌ Poor layering structure ❌ No proper logging or exception handling What actually helped me improve: ✅ Clean architecture (Controller → Service → Repository) ✅ Constructor-based dependency injection ✅ Query optimization + pagination ✅ Using Elasticsearch for fast search ✅ Writing scalable and maintainable APIs 💡 Biggest lesson: Backend development is not just about writing APIs — it's about designing systems that scale. Have you faced any of these issues in real projects?.. #SpringBoot #JavaDeveloper #BackendDevelopment #Microservices #SoftwareEngineering #CleanCode #Java #TechCareers #DevelopersLife #CodingJourney #Elasticsearch #PostgreSQL #API #SystemDesign #LearningInPublic #LinkedInTech

I'm building a Multi-Tenant SaaS platform in Java from scratch — Spring Boot, React, PostgreSQL, AWS. Before I opened IntelliJ I wrote a full BRD: 26 functional requirements, an RBAC matrix across 4 roles, subscription plan limits enforced server-side, risk register. The kind of doc most solo projects never have. Then a TDD. Full database schema with DDL, 25 API contracts, JWT security design, AWS deployment plan. The whole thing on paper before a single class file existed. Today was the first real code day. Spring Boot 3.5.1 up in 20 minutes. PostgreSQL running in Docker. Seven Flyway migrations — 6 tables, 10 indexes — applied cleanly. Six JPA entities validated by Hibernate against the actual schema. App starts. No errors. The part I keep thinking about: designing tenant isolation before touching code. Every table gets a tenant_id. Every query filters by it. That's not something you bolt on later. You either get it right at the schema level or you spend a week undoing it. Next up: Spring Security 6 + JWT auth layer. #Java #SpringBoot #PostgreSQL #Docker #SaaS #BuildInPublic #FullStack

🚨 Real Problem I Solved: Fixing a Slow System Using Microservices (Java + Spring Boot) Recently, I worked on a system where users were facing serious performance issues. 👉 Dashboard APIs were taking 8–12 seconds 👉 Frequent timeouts during peak traffic 👉 CPU usage was constantly high At first glance, it looked like a database issue… But the real problem was deeper. 💥 Root Cause The application was a monolith (Spring Boot) where: Every API request was doing too much work Even a simple dashboard load was triggering heavy report generation logic No separation between fast reads and heavy background processing 👉 So when traffic increased, the system choked. 🛠️ What I Did (Microservices Solution) I redesigned the flow using a microservices-based approach: ✔️ Separated services based on responsibility Dashboard Service (fast, read-heavy APIs) Report Service (CPU-intensive processing) ✔️ Introduced async processing using Kafka Instead of generating reports during API calls Requests were pushed to a queue and processed in background ✔️ Added Redis caching Frequently accessed data served instantly ✔️ Applied API Gateway + Rate Limiting Prevented system overload ⚙️ New Flow Before ❌ API → Generate Report → Return Response (slow + blocking) After ✅ API → Fetch cached/precomputed data → Return instantly Background → Kafka → Report Service → Store results 📈 Results 🚀 Response time improved from 10s → <500ms 🚀 System handled 5x more traffic 🚀 Zero timeouts during peak usage 🧠 Key Takeaway Microservices are not about splitting code. They are about: 👉 Designing for scalability 👉 Separating workloads (read vs heavy compute) 👉 Using async processing effectively 💼 Why This Matters If you're building: High-traffic web apps Data-heavy dashboards Scalable backend systems These patterns make a huge difference. I work on building scalable Java full-stack systems using: 👉 Spring Boot 👉 Microservices 👉 Kafka / Async Processing 👉 Redis / Caching 👉 React (for frontend) If you're facing performance or scaling issues in your application, let’s connect 🤝 #Java #SpringBoot #Microservices #Kafka #Redis #FullStackDeveloper #FreelanceDeveloper #SystemDesign #BackendDevelopment

🚀 Spring Boot Learning Journey – Phase 2 After building my first Spring Boot project, I wanted to go beyond CRUD and explore how real backend systems actually work. This phase was all about adding real-world capabilities to backend applications. What I explored: • Logging with Logback → SLF4J • Code quality & analysis using SonarQube, SonarLint, SonarCloud • External API integration (Weather API ) • MongoDB advanced queries using MongoTemplate, Criteria & Query • Sending Emails using Spring Boot • Scheduling tasks using Cron Jobs Demos: Sending emails using Spring Boot ✉️ What changed in this phase: • Learned how to monitor and improve code quality • Understood how backend systems interact with external services • Explored background processing and scheduling Challenges I faced: • Understanding and configuring logging properly • Setting up Sonar tools and fixing code quality issues • Handling API integration errors and edge cases • Writing efficient MongoDB queries • Managing scheduled tasks and debugging timing issues 🚀 What’s next: • Redis (caching) • Kafka (event-driven architecture) • Microservices architecture • Spring Boot + React integration Grateful for the guidance and content from Vipul Tyagi 🙌 Slowly moving from learning concepts → building scalable backend systems ⚡ #springboot #java #backenddevelopment #mongodb #kafka #redis #microservices #learninginpublic

🧠 Most APIs are slow for the wrong reasons. It’s usually not the framework. Not Java. Not Spring Boot. It’s the design. After working on backend systems, I’ve seen the same patterns over and over 👇 ⚖️ Common mistakes: ❌ Too many database calls per request ❌ Blocking operations everywhere ❌ No caching strategy ❌ Over-fetching data (returning more than needed) 🔹 What actually improves performance: ✔ Reduce DB calls (batching, proper queries) ✔ Use async processing when possible ✔ Add caching where it makes sense ✔ Return only what the client needs 🚨 The mistake: Trying to “optimize” with tools before fixing the fundamentals. 💡 Rule of thumb: Good backend performance starts with good design — not with more infrastructure. Because: A simple, well-designed API will outperform a complex one every time. What’s the biggest performance issue you’ve seen in APIs? #Backend #Java #SpringBoot #API #Performance #SoftwareEngineering #AWS

I finally wrote a blog last week. I’ve been playing around with Spring Boot for a while, but MongoDB was always one of those things that felt like - “I kinda get it… but not really.” So I decided to build something simple. Started with the setup, struggled a bit with configurations, and worked through basic CRUD and that’s when it finally clicked. What I liked? No rigid schema. Just build and move fast. I’ve put this whole learning into a short, practical blog. If you’re getting started or just need a quick refresher, feel free to check it out - would love your feedback :) 🔗 https://lnkd.in/gSxMCjGT #SpringBoot #MongoDB #Java #Tech #LearningInPublic

I’ve been diving deep into Spring Boot and PostgreSQL while building VelaRoute, a logistics tracking system. Today provided a classic lesson in why environment configuration matters as much as the code itself. The Challenge: I hit a persistent java: JDK isn't specified error in IntelliJ. Despite the settings looking correct, the build kept failing. The Pivot: 1. Cloud vs. Local: I realized my project was sitting in an iCloud-synced folder. Cloud syncing can "lock" files, interfering with the Java compiler. Moving the project to a strictly local directory was step one. 📂 2. Clean Slate: I used Spring Initializr to generate a fresh, standardized structure (Java 21 + Maven), ensuring all dependencies for JPA and Postgres were perfectly aligned from the start. 🏗️ 3. Container Connection: Successfully linked the app to a PostgreSQL instance running in Docker, resolving the DataSource configuration. The Result: The green "Started" logs are finally scrolling! Now that the "plumbing" is set up, I’m moving on to building out the Package entities and repositories. #Java #SpringBoot #BackendDevelopment #Docker #PostgreSQL #LearningInPublic #VelaRoute

The common approach for background tasks in Django typically involves using Redis and Celery. However, it's important to remember that defaults are habits, not strict requirements. In a recent Django API project, a different solution was implemented by using Postgres as the task queue, utilizing a concurrency primitive known as SELECT FOR UPDATE SKIP LOCKED—something many developers overlook. This approach features: - A single table for the queue - Atomic job claims by workers - Built-in retries, scheduling, and concurrency control As a result, the docker-compose setup was simplified from four services to just two. Is this method suitable for every project? Not necessarily. However, for many Django applications focused on I/O-bound background tasks, it proves to be more than adequate. The entire journey was documented, detailing the reasons, methods, and scenarios where this approach may not be ideal. Read more here: https://lnkd.in/dyhwBQaU

Leveling Up My TypeScript: Intermediate Concepts for Next.js & PostgreSQL! Hey #TypeScript community! 👋 Just completed a deep dive into crucial intermediate TypeScript concepts. This learning is key for building robust, type-safe applications, especially for my journey with Next.js and PostgreSQL. Here's a quick recap of what I've been mastering: 🔹 Powerful Utility Types: Partial: Making types optional for flexible updates (think API PATCH requests!) Pick<T, K> / Omit<T, K>: Precisely selecting or excluding properties to tailor types (perfect for API requests/responses) Record<K, T>: Defining dynamic key-value objects for configs or lookup tables 🔹 Smart Type Narrowing Techniques: Using typeof, instanceof, in operator, and truthiness checks to tell TypeScript the exact type of a variable at runtime. This eliminates errors and allows for smarter conditional logic. 🔹 Discriminated Unions: A game-changer for handling complex state and actions (like Redux reducers). By using a common 'discriminant' property (e.g., type: "LOADING" | "SUCCESS"), TypeScript intelligently narrows down the specific type, making state management super robust and error-free. These tools are essential for cleaner code, fewer bugs, and efficient development in any modern stack, including Next.js with PostgreSQL. 💬 What's your go-to TypeScript feature that boosts your productivity? Let's connect and share insights! 👇 #TypeScript #NextJS #PostgreSQL #WebDevelopment #Programming #DeveloperLife #TechLearning

🚀 I built a production-grade Workflow Execution Engine from scratch — a mini GitHub Actions clone. Built to understand what no tutorial ever explains. A real, end-to-end distributed system taking a codebase from webhooks to isolated Docker execution, streaming the results live to the browser. Here is exactly how it works under the hood: 1️⃣ You run `git push` on any linked repository. 2️⃣ GitHub fires an HMAC-secured webhook to my Node.js engine. 3️⃣ A background job is queued in Redis (via Bull) to prevent server blocking. 4️⃣ A separate Worker process picks up the job, clones the repo, reads the `.pipeline.json` config, and executes each pipeline step inside a fresh, ephemeral Docker container. 5️⃣ Every standard output log ([stdout] and [stderr]) streams live to the React dashboard using a WebSocket connected to a Redis Pub/Sub channel. 6️⃣ Run history, statistics, and pipeline metrics are persisted in PostgreSQL. 7️⃣ On pipeline failure, an automated Slack Block Kit notification fires with a direct link to the failed logs. ⚙️ The Tech Stack: • Backend: Node.js, Express, WebSockets, Bull Queue • Infrastructure: Docker, Redis, PostgreSQL (orchestrated via Docker Compose) • Frontend: React 18, Vite, Tailwind CSS, Recharts • Security: JWT Auth + RBAC, HMAC Webhook verification This project taught me more about distributed systems than any course ever could. I had to solve real engineering problems like: 👉 How do you safely stream live terminal output across 4 network hops without blocking the main event-loop? 👉 How do you handle crash recovery if the worker dies mid-execution? (Built recoverStuckJobs() to re-queue stuck runs). 👉 How do you support testing code in any language? (Pipelines dynamically pull specific Docker images like python:3.11-alpine or node:20-alpine per step). If you are a developer who has ever wondered how GitHub Actions or Jenkins actually works under the hood — try building one. It will completely change how you view CI/CD. This is FlowForge. Check out the GitHub repository link in the comments below! 👇 #SystemDesign #NodeJS #Docker #Redis #PostgreSQL #WebSockets #BackendDevelopment #SoftwareEngineering #DevOps #CI_CD #BuildInPublic #OpenSource #React