Gagik Kyurkchyan’s Post

We didn’t have a performance issue. We had an API identity crisis. Everything looked fine on the surface. - Requests were fast. - Errors were low. - Dashboards were green. But users kept complaining. “Why is this so slow?” “Why does it load unnecessary data?” “Why does it break randomly?” We scaled servers. Optimized queries. Added caching. Nothing worked. Until we realized: We weren’t dealing with a performance problem. We were dealing with the wrong API strategy. Here’s what was actually happening: → Frontend needed flexibility → we forced rigid REST → Data relationships were complex → we avoided GraphQL → Multiple services talking → no proper internal API contracts → External integrations growing → zero partner structure So every fix… Was just a patch on a broken foundation. That’s when it clicked: APIs are not just endpoints. They define how your entire system thinks. Choose wrong, and you’ll spend months optimizing the wrong layer. Choose right, and half your problems never show up. Most developers debug code. Few debug their architecture. That’s the real difference. Comment API and I'll send you a complete API reference guide. Follow Arun Dubey for more. #APIs #BackendDevelopment #Java #SystemDesign #WebDevelopment #SoftwareEngineering #Programming #TechLearning

We didn’t have a performance issue. We had an API identity crisis. Everything looked fine on the surface. - Requests were fast. - Errors were low. - Dashboards were green. But users kept complaining. “Why is this so slow?” “Why does it load unnecessary data?” “Why does it break randomly?” We scaled servers. Optimized queries. Added caching. Nothing worked. Until we realized: We weren’t dealing with a performance problem. We were dealing with the wrong API strategy. Here’s what was actually happening: → Frontend needed flexibility → we forced rigid REST → Data relationships were complex → we avoided GraphQL → Multiple services talking → no proper internal API contracts → External integrations growing → zero partner structure So every fix… Was just a patch on a broken foundation. That’s when it clicked: APIs are not just endpoints. They define how your entire system thinks. Choose wrong, and you’ll spend months optimizing the wrong layer. Choose right, and half your problems never show up. Most developers debug code. Few debug their architecture. That’s the real difference. Comment API and I'll send you a complete API reference guide. Follow Nishika Verma for more. #APIs #BackendDevelopment #Java #SystemDesign #WebDevelopment #SoftwareEngineering #Programming #TechLearning

We didn’t have a performance issue. We had an API identity crisis. Everything looked fine on the surface. - Requests were fast. - Errors were low. - Dashboards were green. But users kept complaining. “Why is this so slow?” “Why does it load unnecessary data?” “Why does it break randomly?” We scaled servers. Optimized queries. Added caching. Nothing worked. Until we realized: We weren’t dealing with a performance problem. We were dealing with the wrong API strategy. Here’s what was actually happening: → Frontend needed flexibility → we forced rigid REST → Data relationships were complex → we avoided GraphQL → Multiple services talking → no proper internal API contracts → External integrations growing → zero partner structure So every fix… Was just a patch on a broken foundation. That’s when it clicked: APIs are not just endpoints. They define how your entire system thinks. Choose wrong, and you’ll spend months optimizing the wrong layer. Choose right, and half your problems never show up. Most developers debug code. Few debug their architecture. That’s the real difference. Comment API and I'll send you a complete API reference guide. Follow Antony Johith Joles R for more. #APIs #BackendDevelopment #Java #SystemDesign #WebDevelopment #SoftwareEngineering #Programming #TechLearning

🚀 **Understanding API Status Codes – A Must for Every Developer** When working with APIs, responses are not just data — they tell a story through **status codes**. 📊 API status codes are grouped into **5 main categories:** 🔹 **1xx – Informational** Request received, continuing process 🔹 **2xx – Success** ✔️ 200 OK – Request successful ✔️ 201 Created – Resource created ✔️ 204 No Content – Success, no response body 🔹 **3xx – Redirection** ➡️ 301 Moved Permanently ➡️ 302 Found ➡️ 304 Not Modified 🔹 **4xx – Client Errors** ❌ 400 Bad Request – Invalid input ❌ 401 Unauthorized – Authentication required ❌ 403 Forbidden – Access denied ❌ 404 Not Found – Resource missing ❌ 429 Too Many Requests – Rate limit exceeded 🔹 **5xx – Server Errors** 🔥 500 Internal Server Error 🔥 502 Bad Gateway 🔥 503 Service Unavailable 🔥 504 Gateway Timeout 💡 **Quick Tip:** 1xx → Info → 2xx → Success → 3xx → Redirect → 4xx → Client Error → 5xx → Server Error --- 🎯 Mastering these codes helps you: ✔️ Debug faster ✔️ Build better APIs ✔️ Improve user experience 💬 Which status code do you encounter the most in your projects? #API #BackendDevelopment #WebDevelopment #HTTP #Coding #SoftwareEngineering #TechLearning #Developers

Week 2 Recap — 7 Concepts That Actually Matter in Real-World Systems Two weeks in. 7 concepts. And every single one solves a real production problem 👇 Let’s break it down: 🔹 1. Backend internals most devs misunderstand @Transactional is a proxy — not magic Internal method calls bypass it. Private methods don’t trigger it. That “random” data inconsistency bug? This is often why. Angular Change Detection (OnPush) Default strategy checks everything on every interaction. Switch to OnPush + immutability + async pipe → ~94% fewer checks. 👉 This is the difference between “it works” and “it scales.” 🔹 2. Data & security fundamentals at scale Database Indexing Without index → full table scan (millions of rows) With index → milliseconds Same query. Completely different system behavior. JWT Reality Check JWT ≠ encryption It’s just Base64 encoded → anyone can read it Use httpOnly cookies, short expiry, refresh tokens And never put sensitive data inside 👉 Most performance issues and auth bugs come from ignoring these basics. 🔹 3. Distributed systems patterns that save you in production Node.js Streams Loading a 2GB file into memory = server crash Streams process chunk by chunk (~64KB) Bonus: built-in backpressure handling SAGA Pattern You can’t rollback across microservices So you design compensating actions instead Every service knows how to undo itself 👉 Distributed systems don’t fail if — they fail how. These patterns handle that. 🔹 4. Architecture that simplifies everything API Gateway One entry point for all clients Centralized auth, logging, rate limiting Aggregates multiple calls into one 👉 Cleaner clients. Safer backend. More control. 📊 What this looks like in the real world: 8s → 12ms query time ~94% fewer unnecessary UI checks ~64KB RAM for huge file processing 0 DB lookups for JWT validation 1 client call instead of many 14 days. 14 posts. 7 concepts. No theory. Just things that break (or save) real systems. Which one changed how you think about building systems? 👇 #BackendDevelopment #SoftwareDeveloper #Programming #Coding #DevCommunity #Tech #TechLearning #LearnToCode

Building a bridge between different software systems shouldn't feel like deciphering a secret code. In today's integrated world, understanding the "language" of APIs and Web Services is no longer just for backend engineers—it’s a foundational skill for anyone in the tech ecosystem. Whether you are building a custom application or connecting enterprise platforms, the mechanics of how data moves are what make modern innovation possible. Here is a quick breakdown of the essentials for mastering the flow of data: 🌐 The Core Concept Think of Web Services as a universal translator. They allow applications to share data over the internet, regardless of whether one is written in Java and the other in Python. Request Payload: What you send to the system. Response Payload: What the system sends back to you. ⚖️ SOAP vs. REST: Choosing Your Path Understanding the protocol is key to choosing the right tool for the job. SOAP (Simple Object Access Protocol): The "Rule Follower." It uses strictly XML and relies on a WSDL (Web Services Description Language) as a formal contract. REST (Representational State Transfer): The "Flexible Architect." It’s an architectural style that supports JSON, XML, and HTML. It uses standard HTTP verbs (GET, POST, etc.) and is the industry standard for lightweight web communication. 🚦 Decoding the Status Codes Ever wonder what the system is trying to tell you? These status codes are your roadmap: ✅ 200/201: You’re all set! Success or resource created. 🚫 401: Unauthorized. Time to check your credentials. 🔍 404: Resource not found. Does that URI exist? ⚠️ 500: Internal Server Error. Something went wrong on the other end. 📖 The Jargon Cheat Sheet WSDL: The XML "manual" for SOAP services. JSON: The lightweight, human-readable format that keeps REST fast. URI: The specific "path" that identifies exactly where your resource lives. The Bottom Line: APIs aren't just about code; they are about connectivity. Mastering these fundamentals allows you to build more scalable, interoperable, and efficient systems. Which do you find yourself working with more often lately—the strict structure of SOAP or the flexibility of REST? Let's discuss in the comments! #APIDevelopment #WebServices #SoftwareEngineering #RESTAPI #TechTips

🚫 Not just another CRUD API. ✅ A backend system built to scale. I’ve been working on a Modular Monolith + Clean Architecture based system in .NET — and now the API is almost complete 🚀 But I didn’t stop at basic functionality. 🔐 I’ve now integrated JWT Authentication to make the system secure and production-ready. At the beginning, this approach felt complex… More layers, more structure, more thinking 🤯 But now it’s clear — this is what makes a system survive real-world growth. 💡 What this system focuses on: ✔ Clean separation of concerns (Domain, Application, Infrastructure) ✔ Scalable modular structure ✔ Business logic independent from frameworks ✔ Secure authentication with JWT ⚙️ Current progress: ✔ Modular Monolith architecture in place ✔ CRUD operations implemented using CQRS ✔ Dapper for efficient data access ✔ JWT Authentication added 🔐 🚀 What’s next: 🔥 Role-based authorization 🔥 Feature expansion (real-world use cases) 🔥 Performance optimization 🔥 Moving towards production-ready deployment One thing I’m learning every day: Building software is easy — building maintainable and scalable systems is the real challenge. Still learning. Still building. 💡 🔗 GitHub: https://lnkd.in/gY-eU5cE #dotnet #aspnetcore #jwt #authentication #cleanarchitecture #cqrs #dapper #backenddevelopment #softwareengineering #csharp #api

🚀 API Status Codes – Simplified for Developers Understanding API status codes is essential for building, testing, and debugging modern applications. Here’s a quick breakdown of what they actually mean: ✅ 2xx – Success Request was successfully processed • 200 – OK • 201 – Created • 204 – No Content 🔁 3xx – Redirection Further action needed to complete request • 301 – Moved Permanently • 302 – Found (Temporary Redirect) • 304 – Not Modified ⚠️ 4xx – Client Errors Issue from the client side (request problem) • 400 – Bad Request • 401 – Unauthorized • 403 – Forbidden • 404 – Not Found • 409 – Conflict • 422 – Unprocessable Entity • 429 – Too Many Requests 🔥 5xx – Server Errors Issue from the server side • 500 – Internal Server Error • 502 – Bad Gateway • 503 – Service Unavailable • 504 – Gateway Timeout 💡 Quick Debug Tip: • 2xx → All good • 3xx → Check redirects & caching • 4xx → Fix request/auth/inputs • 5xx → Check server logs Mastering these codes = faster debugging + better API design 💻 #API #WebDevelopment #Backend #Coding #Developers #SoftwareEngineering #TechTips

Most bugs I fixed… weren’t actually bugs. I used to spend hours debugging issues that looked like logic errors. Something breaks. The output is wrong. Users complain. It feels like a bug. But after digging deeper, the real problem was almost always somewhere else. 1. State updates happening in multiple places 2. API responses not normalized properly 3. Components reacting to stale or duplicated data 4. No clear ownership of where data should live The code wasn’t "wrong." The system was. In one case, fixing a "bug" didn’t require changing business logic at all. I just simplified the data flow and moved state closer to where it was used. The issue disappeared. That changed how I approach debugging. I stopped asking: "Where is the bug?" And started asking: "Why does this system allow this to happen?" The lesson? If your architecture is unclear, bugs will keep reappearing in different forms. Fixing them individually won’t scale. Good engineers fix bugs. Great engineers fix the conditions that create them. #softwareengineering #frontend #react

𝗠𝗼𝘀𝘁 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁 𝗱𝗲𝘃𝘀 𝗶𝗴𝗻𝗼𝗿𝗲 𝘁𝗵𝗶𝘀 𝘂𝗻𝘁𝗶𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗯𝗿𝗲𝗮𝗸𝘀. It's called Observability — and it has 3 pillars: 𝗟𝗼𝗴𝘀, 𝗠𝗲𝘁𝗿𝗶𝗰𝘀, 𝗧𝗿𝗮𝗰𝗲𝘀 Most devs only use logs. Here's what each one actually does 𝗟𝗼𝗴𝘀 — 𝘁𝗵𝗲 "𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱" You already use these. But in production with 10 microservices, raw logs are chaos. The fix? Structured logging + a correlation ID so you can filter logs for one user's request across all services. 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 — 𝘁𝗵𝗲 "𝗵𝗼𝘄 𝗶𝘀 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗲𝗲𝗹𝗶𝗻𝗴" Metrics answer questions like: * How many requests per second? * What's my DB connection pool usage? * Is memory climbing before a crash? 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗧𝗿𝗮𝗰𝗶𝗻𝗴 — 𝘁𝗵𝗲 "𝘄𝗵𝗲𝗿𝗲 𝗱𝗶𝗱 𝗶𝘁 𝗯𝗿𝗲𝗮𝗸" This is the one most devs skip — and regret later. When one user request touches 6 services, tracing gives you a visual timeline of every hop with exact latency. [API Gateway] 200ms   ├── [Auth Service] 30ms ✅   ├── [Order Service] 155ms ✅     │    └── [PostgreSQL] 140ms ⚠️ ← HERE   └── [Notification] 10ms ✅ 𝗧𝗵𝗲 𝗵𝗮𝗿𝗱 𝘁𝗿𝘂𝘁𝗵: In local dev, your app has 1 service and 0 users. In production, it has 10 services and 10,000 users. A practical roadmap: → Structured logging — implement today → Spring Actuator + Prometheus — configure this week → Distributed tracing with Zipkin — complete this month Production systems do not forgive gaps in visibility. Build observability in before you need it, not after. #SpringBoot #Java #BackendDevelopment #Microservices #SoftwareEngineering #Observability #DistributedSystems #SystemDesign #CloudNative #DevOps #SpringBootActuator #OpenTelemetry #Prometheus #Grafana #Zipkin #JavaDeveloper