Fixing Broken Rate Limiters in Distributed Systems

⚡ Signals & reactivity — Rethink state management; Zone.js-based change detection is deprecated in favour of signal() and computed(). 🖥️ Standalone components — NgModules are no longer the default; migrate to standalone + bootstrapApplication(). ⚠️ Removed APIs — ComponentFactoryResolver, old router guards, and legacy TestBed APIs are gone. Run ng update early. 🔀 New control flow — Replace *ngIf / *ngFor with @if / @for. CLI schematics automate most of it. 📦 Library compatibility — Verify all third-party deps support v21 before upgrading; this is the most common blocker. 🛠️ Node.js & TypeScript — Angular 21 requires Node 20+ and TypeScript 5.x. Update your CI pipeline accordingly.

I spent 6+ hours debugging a production issue… and the scary part? It wasn’t a bug in my code. 😅 My Node.js API suddenly became slow under load. 📉 What I observed: → Response time jumped from 200ms → 3s → CPU usage was completely normal 🤯 → Logs showed… nothing At first, I assumed the usual suspects: → Database bottleneck → Network latency → Inefficient queries But none of them were the problem. 👉 The real issue: **Connection Pool Exhaustion** I wasn’t releasing DB connections properly. Under load: → All connections got occupied → Incoming requests were stuck waiting → System looked “slow”… not “broken” That’s what made it tricky. 💡 What I fixed: → Ensured every connection is released after use → Added monitoring on connection pool limits → Implemented timeouts + retry strategy 💭 What this taught me: Not every issue throws errors. Not every failure crashes your system. Sometimes your system is: 👉 Alive 👉 Healthy-looking 👉 But silently waiting And that’s even more dangerous. 🚨 New rule I follow: Before blaming code, always check: → Connection pools → Memory usage → Event loop delays Because performance bugs don’t shout… they whisper. Have you ever debugged something that *looked fine* but wasn’t? 👀 #backend #nodejs #systemdesign #webdevelopment #performance #mern #debugging

You think your code is async… but your API is still slow. That usually means one thing: Something is blocking the event loop. const result = await fetchData(); process(result); The API call is async. But "process(result)" might not be. If it’s CPU-heavy, it blocks other requests from executing. So everything looks correct in code… but performance drops under load. Async helps with I/O. It doesn’t protect you from CPU work. #NodeJS #AsyncProgramming #EventLoop #BackendEngineering #PerformanceOptimization

Type safety often breaks at the API boundary. Frontend and backend define their own types, leading to duplication, mismatches, and runtime errors⚠️ tRPC takes a different approach by enabling end-to-end type safety using TypeScript inference🧠, without requiring schema definitions or code generation. https://lnkd.in/g-fgqxaf Types are shared directly between client and server, reducing complexity and improving development speed⚡ The result is a simpler, more reliable way to build APIs where type consistency is guaranteed. #TypeScript #API #SoftwareArchitecture #WebDevelopment #Tech

Rate limiting is often overlooked until systems start slowing down. Controlling request flow helps protect APIs, maintain performance, and prevent misuse. Small limits can make a big difference in production stability. #BackendEngineering #APIDesign #SystemDesign #NodeJS

🚀 Built and Deployed a High-Performance Load Testing Platform Over the past few days, I worked on building a system to simulate high-concurrency API traffic and analyze performance in real time. 💡 Tech Stack: Go (high-performance load engine using goroutines) Node.js (orchestrator & API layer) React (interactive dashboard) ⚙️ What it does: Simulates thousands of concurrent requests Supports both fixed-request and RPS-based load testing Measures latency (P50, P95, P99), throughput, and error rates Displays results in a clean UI dashboard 🚀 Key Engineering Highlights: Implemented worker pool pattern in Go for efficient concurrency Used token-bucket rate limiting for accurate RPS control Optimized HTTP client for connection reuse Built lock-free metrics collection using atomic operations Solved real-world deployment issues (Linux permissions, binary execution, environment mismatch) 📊 Successfully tested: 1000+ requests 50 concurrent users ~450+ req/sec throughput 🔧 Live Demo: 👉 https://lnkd.in/g-bJS7_H 💻 GitHub: 👉 https://lnkd.in/gyJYqGGC This project helped me dive deeper into system design, performance tuning, and real-world deployment challenges. Would love to hear feedback or suggestions to improve it further! #golang #nodejs #react #backend #systemdesign #performance #loadtesting #softwareengineering #opentowork

I thought Node.js could handle high traffic easily… until this happened. I built an API. Flow was simple: 1. Receive request 2. Process data 3. Send response Everything worked fine in testing. ✅ Fast responses. No issues. --- But in production? The server started freezing. - Requests got delayed - Some APIs never responded - CPU usage went high No crashes. Still unusable. --- 💡 THE INVESTIGATION: I checked async code. Everything looked fine. Then I found it… 👉 A heavy synchronous operation. --- ⚠️ THE MISTAKE: const result = heavyComputation(); // blocking 👉 This blocked the event loop. Which meant: - Other requests had to wait - Server handled requests one by one - Performance dropped drastically --- ⚙️ THE FIX: Moved heavy work outside main thread. const result = await runInWorkerThread(); Also: - Used async processing - Optimized heavy logic --- ⚡ THE IMPACT: - Server became responsive again - Requests handled smoothly - Performance improved significantly --- 📌 THE REAL LESSON: Node.js is single-threaded. 👉 If you block the event loop… You block everything. --- 🧠 WHAT I LEARNED: - Avoid CPU-heavy synchronous code - Use worker threads for heavy tasks - Always think about event loop impact --- 👇 Have you ever accidentally blocked the event loop? #nodejs #backend #performance #eventloop #programmin

Last weekend, I built something I’m really excited about: Nextpressjs A zero-dependency Node.js HTTP framework built from scratch with a focus on performance, scalable architecture, clean, minimal design But I didn’t stop at just building it. I benchmarked it against popular frameworks: Benchmark Results: (Autocannon — 100 connections, pipelining 10) Nextpress → 121,843 req/s Raw Node HTTP → 134,406 req/s Hono → 100,077 req/s Koa → 83,731 req/s Fastify → 81,491 req/s Express 5 → 69,843 req/s That’s ~75% faster than Express And ~90% of raw Node.js performance Average latency: ⏱️ 7.7 ms (Nextpress) vs 13.8 ms (Express) Open-sourced for developers who care about performance and clean architecture. Nextpress Official: https://lnkd.in/gzVAwy49 Github Repo: https://lnkd.in/gkPwfRTt npm: https://lnkd.in/gc5iyq7y #opensource #npm #nodejs

🚨 Circular Dependencies in Node.js — A Silent Outage Risk Ever seen a module return {} or undefined without any errors? One common reason is a circular dependency: A → B → A Node.js loads modules partially, which can lead to incomplete objects and incorrect data flow — something that can even cause production outages. 💡 The fix? Dependency Injection Instead of modules importing each other: Inject dependencies from outside Control initialization order Keep modules loosely coupled ✅ Result: No circular dependency More reliable systems Easier debugging 👉 Key takeaway: Circular dependency isn’t just a code issue — it can impact system reliability. Dependency Injection helps prevent it. #NodeJS #Backend #SystemDesign #CleanCode #Debugging

There is a memory leak in Blazor most developers don't notice until production. It creates ghost components. Invisible instances that live in your server's memory long after the user navigated away; still executing code on every state change. Here is how it happens: → You create a scoped state service with a C# event → You inject it into a component and subscribe in OnInitialized → The user navigates away; the component disappears from the UI But the service still holds a delegate reference to it. The GC sees that reference and cannot collect the component. The UI removes the component. Memory doesn't. Every time the event fires, every ghost executes StateHasChanged. Navigate enough times and you have dozens of invisible components burning CPU in the background. The fix is one interface and one method: → Implement IDisposable → Unsubscribe in Dispose → Reference severed. GC can now collect. →The rule that explains all of this: Blazor's EventCallback system handles cleanup automatically because it lives inside the component tree. The moment you use a standard .NET Action or EventHandler from a long-lived service, you own the cleanup entirely. A += without a matching -= is a memory leak. It should be a hard block on any PR. 🚫 How many components in your current codebase are missing that Dispose? #Blazor #DotNet #BlazorServer #CleanCode #SoftwareEngineering