REST vs GraphQL: Choosing the Right API Approach

Stop defaulting to Node.js for every new microservice without testing the alternatives. 🛑 Don't get me wrong, Node is incredibly reliable. But after heavily experimenting with Bun in my recent TypeScript backend architectures, the developer experience and sheer speed are hard to ignore. Here is why my perspective on JavaScript runtimes is shifting in 2026: 1. Out-of-the-box TypeScript: No more wrestling with ts-node, nodemon, or complex build steps just to get a basic Express server running in development. Bun executes .ts files natively. 2. The Installation Speed: Running bun install versus npm install feels like upgrading from dial-up to fiber optic. In a CI/CD pipeline, those saved seconds per build add up to massive cost and time savings. 3. The All-in-One Toolkit: Having the runtime, package manager, and test runner bundled into a single incredibly fast binary reduces toolchain fatigue. While I still rely heavily on the robust Node ecosystem for legacy enterprise systems, Bun is quickly becoming a serious contender for new, lightweight microservices where performance is non-negotiable. Backend engineers: Are you still strictly starting new projects with npm init, or has your team started migrating towards faster runtimes? Let me know below. 👇 #BackendDevelopment #TypeScript #Bun #NodeJS #SoftwareEngineering #Microservices #WebDev

Most devs retry failed API calls immediately. That's the wrong move. I was going through the Webpack docs on code splitting, and sometimes I kept seeing this error in our production and doc also mentioning about - "Loading chunk failed" Error. That got me thinking - in a Module Federation setup, when a remote module fails to load, when do we retry it? And how? That question led me down a rabbit hole, and I discovered Exponential Backoff. The idea is simple: → 1st retry: wait 1s → 2nd retry: wait 2s → 3rd retry: wait 4s → 4th retry: wait 8s Each wait doubles. You give the server room to breathe. But there's a catch -- if everyone retries on the exact same schedule, we get a thundering herd: thousands of clients all retrying at second 4, second 8... and crashing the server again. The fix? Add jitter. A small random offset breaks the synchronization. const delay = Math.min(1000 * 2 ** attempt + Math.random() * 1000, 30000); This is used by AWS, Google Cloud, Stripe — practically every resilient distributed system in production. Read full blog with code snippets: https://lnkd.in/dvi4s8yE I didn't know about this before. But from now on, I'll be using Exponential Backoff every time I write retry logic — whether it's API calls, remote module loading in Module Federation, or WebSocket reconnections. Definitely worth adding to production code. 🚀 Drop a 👍 if you've been burned by missing retry logic in production. #frontend #javascript #webperf #webpack #modulefederation #react #webdevelopment

𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗧𝘆𝗽𝗲𝗦𝗰𝗿𝗶𝗽𝘁 & 𝗡𝗼𝗱𝗲.𝗷𝘀 𝗳𝗼𝗿 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗕𝗮𝗰𝗸𝗲𝗻𝗱𝘀: 𝗔 𝗦𝗵𝗶𝗲𝗹𝗱 & 𝗦𝘄𝗼𝗿𝗱 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 Building a robust Enterprise Backend Ecosystem requires more than just code; it requires a structural foundation that ensures reliability at scale. At the core of this architecture, TypeScript acts as a protective shield through Type Safety, enforcing consistency from the initial logic down to the most complex business rules. Integrating TypeScript into this ecosystem significantly enhances Architecture & Tooling, especially when working with modern frameworks like NestJS or Express. This synergy allows for Enhanced Collaboration across teams, where IDEs provide immediate feedback via Rich Autocomplete and Error Checking, ensuring that everyone is working with clear, well-defined contracts. A key technical advantage highlighted in this workflow is the use of Shared DTOs & Interfaces. This ensures Schema Synchronization and enables Type-Safe Queries when interacting with databases like PostgreSQL or MongoDB. By sharing these definitions across the stack, changes in API contracts—whether REST or GraphQL—are detected instantly, bridging the gap between frontend and backend. Ultimately, this approach builds Production Confidence. By focusing on Pre-deployment Error Prevention and rigorous API Contract Verification, we move away from the "nightmare" of runtime errors. The result is a system that is not only functional but resilient, scalable, and built for the demands of modern enterprise environments. #TypeScript #Nodejs #BackendDevelopment #Architecture #EnterpriseSoftware #NestJS #DevOps

Today I learned about Multi-Stage Builds in Docker — and honestly, this is one of the coolest ways to reduce image size and keep #Dockerfiles clean. Instead of building and running everything in a single image, we can use multiple stages: • Stage 1 → Build the application • Stage 2 → Use a lightweight image to run it • Copy only the required build output Example idea: • Use node image to install dependencies & build • Use nginx:alpine to serve only the final build • Copy /app/build from builder stage • Final image becomes smaller, faster, and more secure Why Multi-Stage Builds are useful: • Smaller Docker image size • No dev dependencies in production • Better security • Cleaner Dockerfile • Faster deployments This is the pattern I explored today: Stage 1 → Build Stage 2 → Runtime (nginx) Copy only what is needed #Docker #DevOps #Containers #SoftwareEngineering #LearningInPublic #Backend #NodeJS #Nginx

The moment a project stopped feeling like a client project. During the project scalability phase the senior team made a call nobody expected. Scrap the current stack. We are rebuilding. LoopBack was holding us back old Node.js framework, limited structure, not built for where this product was going. Management saw it before it became a crisis. The decision came down: migrate to NestJS + TypeScript, introduce CQRS, move to PostgreSQL, containerize with Docker. As a team, we had maybe a week to wrap our heads around it. I remember thinking, this is either going to be a nightmare or the best thing that happened to this project. It was both. The migration was not clean. It never is. But every architectural choice had a clear reason behind it. CQRS because the read/write complexity was growing. Docker because deployment was inconsistent. TypeScript because the team was scaling and we needed guardrails. What changed for me was not the tech. It was watching leadership make a hard call, sacrifice short term velocity for long term stability, and then trusting the team to execute it. That's when I stopped counting hours. I was not just completing tickets anymore. I was building something that was meant to last. That feeling is rare. But once you have felt it, you know exactly what's missing when it's not there. #NodeJS #NestJS #TypeScript #PostgreSQL #Docker #CQRS #BackendDevelopment #SoftwareArchitecture

🚀 Reduced My Docker Image Size by More Than 50% — Here’s How Recently, I optimized one of my Node.js backend Docker images and the results were pretty solid. 📦 Before optimization: ~200MB ⚡ After optimization: ~90MB That’s more than 50% reduction — which directly improves build time, push/pull speed, and deployment efficiency. Here’s what made the difference: ✅ Used .dockerignore to exclude unnecessary files (huge impact) ✅ Installed only production dependencies with npm ci --omit=dev ✅ Improved Docker layer caching by copying package.json first ✅ Cleaned up unnecessary cache files ✅ Applied multi-stage build to remove build-time dependencies 💡 Key takeaway: Optimizing Docker images is not just about size — it’s about faster CI/CD pipelines, better scalability, and cleaner production environments. If you're working with Node.js and not optimizing your Docker images yet, you're leaving performance on the table. Next step for me: pushing this setup into a full CI/CD pipeline with automated builds and deployments. #Docker #DevOps #NodeJS #FullStackDevelopment #CI_CD #SoftwareEngineering

𝗬𝗼𝘂𝗿 𝗡𝗼𝗱𝗲.𝗷𝘀 𝗔𝗣𝗜 𝗵𝗮𝗻𝗱𝗹𝗲𝘀 𝟭𝟬𝗸 𝗿𝗲𝗾/𝘀 𝗶𝗻 𝗹𝗼𝗮𝗱 𝘁𝗲𝘀𝘁𝘀. 𝗖𝗿𝗮𝘀𝗵𝗲𝘀 𝗮𝘁 𝟮𝟬𝟬 𝗶𝗻 𝗽𝗿𝗼𝗱. The culprit is almost never a missing feature — it's unhandled errors silently rotting your process. Unhandled promise rejections swallow errors without a trace. One missing 𝘢𝘸𝘢𝘪𝘵 in a hot path can leave your app running in a broken state — no crash, no log, no clue. Why this works: 🔹 Fail fast — don't let the process limp in a broken state 🔹 Log before exit — full context for post-mortem 🔹 Let your process manager (𝗣𝗠𝟮, 𝗘𝗖𝗦 tasks, or container orchestration) restart clean — that's what they're for A dead process is recoverable. A zombie one silently corrupts everything around it. #NodeJS #JavaScript #BackendEngineering #AWS

Building a simple CRUD app is one thing, but designing a distributed, real-time code execution engine? That’s where the real engineering fun begins. 🚀 I recently built an Event-Driven Code Execution Engine—essentially the core architecture behind online judges like LeetCode. The goal wasn't just to run code, but to run it securely, efficiently, and at scale. Here is how the architecture flows: 🔹 Next.js App Router & Monaco Editor provide the IDE experience. 🔹 Express & Node.js API handles submissions and pushes them to an Apache Kafka topic, ensuring the API stays fast and responsive. 🔹 A Worker Service consumes the events and executes the code in highly isolated Docker containers. 🔹 To eliminate cold-start latency, I implemented Docker Container Pooling—warm containers are leased, used, and cleaned up, making execution near-instant. 🔹 PostgreSQL (via Drizzle ORM) stores the data, while Redis & Socket.IO fan out the final verdicts to the frontend in real-time. Using Kafka completely decoupled the request path from the heavy lifting of code execution, and implementing container pooling was a massive learning curve in optimizing system throughput. Check out the demo video below to see it in action! 👇 I’m currently planning to add multi-language support and richer execution metrics next. If you're interested in system design, DevOps, or backend architecture, I’d love to hear your thoughts on this setup! #Nextjs #Nodejs #SystemDesign #Kafka #Docker #DevOps #SoftwareEngineering #WebSockets #BackendDevelopment

Shipping with Docker rewrote my whole deployment experience I just shipped a custom eLibrary platform for NIESV, integrating secure member access control with a full-scale digital repository. Here's what changed my deployment game. Part of the requirement was setting up DSpace, an open-source digital repository platform built with Angular, Java, PostgreSQL, and Solr. I went the manual route first. Version conflicts. Dependency hell. Configuration bugs. You know the drill. Then I found their Docker deployment option. One compose file. Few commands. Done Everything spun up the frontend, backend, database, and search engine. Clean. No drama. That moment shifted something for me. If Docker could simplify a complex multi-service platform like DSpace this much, why wasn't I using it for my own projects? So I containerized my entire stack — Node.js proxy API, Next.js frontend, PostgreSQL. Pushed to GitHub. Pulled on the VPS. For the first time in my deployment story, going live felt smooth. Docker isn't just a DevOps tool. It's a developer superpower. If you're still deploying manually, I recommend trying containerization. 🐳 #Docker #WebDevelopment #NodeJS #NextJS #DSpace #SoftwareEngineering #Deployment #OpenSource

how to build good APIs like Stripe with nextjs and typescript. - tdd - route handler pattern - good error responses - rate limiting - idempotency keys https://lnkd.in/gPi3MaSY