API Optimization Mistakes: Why Measurement Comes First

You split the monolith. You put queues between the services. You're sending JSON over the wire. And yet — one change in Service A still forces a recompile in Service B. You didn't decouple anything. You just made your method calls slower and harder to debug. That's a **Distributed Monolith**. The root cause is almost always the same: nobody asked **who owns the contract**. The answer isn't obvious, because it's different for Commands vs. Events: ✅ A **Command** ("Ship this order") is owned by the *receiver* — they define what they need ✅ An **Event** ("Order was shipped") is owned by the *publisher* — they define what happened ✅ Mixing them up — or dumping both into a shared `MyCompany.Contracts` NuGet — is where the coupling sneaks back in I walk through the distinction, the common-library trap, and a practical "Owner-Specific Contract Package" pattern that keeps boundaries clean without the copy-paste nightmare. Read more here: https://lnkd.in/eEnsru5X #DistributedSystems #Microservices #SoftwareArchitecture #DotNet #SystemDesign

At scale, performance issues rarely come from “slow code” — they come from misplaced responsibility. Common patterns: - APIs doing async-heavy work synchronously - Databases acting as both source of truth and cache - Frontends over-fetching due to poor contract design What actually helps: - Push non-critical work to queues - Be intentional about data access patterns (read vs write paths) - Design APIs around usage, not structure Most scalability problems are architectural, not technical.

When building Rails APIs, returning raw ActiveRecord objects can expose unnecessary fields and create messy responses. ActiveModel::Serializer helps structure API responses cleanly by defining exactly which attributes should be returned. This keeps controllers simple and maintains a clear separation between database models and API output. A small architectural decision like using serializers can make your APIs much easier to maintain and scale.

Stop building "just" UIs. Understand the Backend Architecture first! 🏗️ Body: Most people think a "Search" button is just a simple query. But as an engineer, I know the real magic happens in those 200ms between the click and the result. ⚡ In my latest video, I’ve broken down the "Under the Hood" journey of a search request: 🔹 The API Handshake: How the client communicates with the server. 🔹 Server Logic: Processing the query and handling business logic. 🔹 Database Indexing: Why we don't just "scan" data, but use indexing for lightning-fast

🚀 How to Improve API Performance (5 Practical Techniques) Building APIs is easy. Building fast, scalable APIs is where real engineering begins. Here are 5 powerful techniques every backend developer should know 👇 1️⃣ Pagination Instead of returning thousands of records in one go, return data in small chunks. ✅ Reduces response size ✅ Improves load time ✅ Better user experience Example: GET /products?page=1&limit=20 2️⃣ Async Logging Synchronous logging can slow down your API. Use asynchronous logging with buffers so logs are handled in the background. ✔ Higher throughput ✔ Lower latency 3️⃣ Caching Avoid hitting the database for every request. Use tools like Redis or Memcached. Flow: Request → Cache → Database (only on cache miss) ⚡ Faster responses ⚡ Reduced database load 4️⃣ Payload Compression Large responses increase network time. Use compression like: • Gzip • Brotli ✔ Smaller payload size ✔ Faster data transfer 5️⃣ Connection Pooling Creating a DB connection per request is expensive. Use connection pooling to reuse existing connections. ✔ Lower latency ✔ Better scalability ✔ Efficient resource usage 💡 Pro Tip Combine: 👉 Caching + Pagination + Connection Pooling This can improve API performance 10x or more in real-world systems. If you're working in backend development, system design, or scalable architecture, mastering these techniques is essential. #API #BackendDevelopment #SystemDesign #SoftwareEngineering #WebDevelopment #Microservices #ScalableSystems #Programming

💡 Building High-Performance Financial Systems with C#: Lessons from the Field Working with C# in financial systems has taught me one thing: performance and reliability are not optional — they are core requirements. In environments where milliseconds matter and failures are not acceptable, architecture decisions make all the difference. 🧠 Key Principles I Apply Daily 🔹 Design for Resilience - Use messaging to decouple workflows - Implement idempotency to handle retries safely - Expect failures — and design for them 🔹 Optimize for Performance - Use tools like Dapper when full ORM is not needed - Apply caching strategies (e.g., Redis) - Minimize latency in critical flows 🔹 Keep Architecture Clean - Apply SOLID principles - Use Clean Architecture to isolate business rules - Keep infrastructure concerns separate 🔹 Leverage Cloud the Right Way - Scale horizontally, not vertically - Right-size resources to control costs - Use observability to understand real behavior 🚀 Real-World Insight In financial systems, it's common to combine: - Synchronous APIs (TCP/HTTP) for critical operations - Asynchronous messaging for processing and scalability The balance between both is what creates fast and reliable systems. 💬 Final Thought Technology choices matter — but how you combine architecture, performance, and reliability is what truly defines a great system. 💬 What’s the biggest challenge you face when building high-performance systems today? Latency, scalability, or architecture? Let’s discuss 👇

How to design a scalable API (practical guide) Many APIs work fine in the beginning… but start breaking as traffic grows. Scalability is not about fixing later — it’s about designing it right from the start. 🔑 1. Keep your API stateless Each request should contain all the required data. 👉 No dependency on previous requests 👉 Easy to scale horizontally 🧩 2. Use proper architecture Controllers → handle requests Services → business logic Repositories → data layer 👉 Keeps code clean and maintainable ⚡ 3. Add caching Cache frequent responses Reduce database load 👉 Improves performance instantly 🔐 4. Use rate limiting Prevent abuse and overload. 👉 Protect your system from too many requests 📦 5. Optimize database queries Avoid N+1 queries Use indexing Fetch only required data 🔄 6. Version your API Example: /api/v1/users /api/v2/users 👉 Helps in scaling without breaking existing clients 🎯 Final Thought A scalable API is not about writing more code… it’s about writing smarter architecture. What’s one mistake you made while building APIs? #API #BackendDevelopment #SystemDesign #Laravel #Programming

Byte-Sized Backend Architecture #3 Why Atomic Updates Don’t Scale In the previous post, we fixed overselling using atomic updates. Inventory can’t go negative anymore. Problem solved? Not really. The New Problem Imagine a flash sale: 500k+ purchase requests per second All hitting the same inventory row Every request is correct. But they all target the same place. What Actually Happens? Databases don’t allow concurrent writes to the same row. So: One request acquires the lock Others wait This leads to: • row-level locking • transaction queues • rising latency Contention Explosion More traffic → longer queues → slower system Eventually: • requests timeout • throughput drops • system becomes unstable Even though the logic is correct. Architecture View Millions of users → API servers → one database row This is called: Hot Row Contention Core Insight Atomic updates fix correctness. They do not fix scale. What This Means The system is: Correct But slow Which is still a failure in production. What Comes Next If the database can’t handle all requests directly, we need to control how requests reach it. #BackendEngineering #SystemDesign #Concurrency #DistributedSystems

What really happens when a request hits your backend? But in real backend systems (especially monolith and modular monolith architectures), the flow is much deeper. A typical request goes through multiple layers: * API Gateway → request enters the system * Filters → logging, rate limiting, request validation * Security layer → authentication & authorization * Controller → receives and maps the request * Service layer → applies business logic * Repository → interacts with the database * Response → flows back through the same path This flow looks clean on paper. But most bugs don’t happen in the obvious places. They usually appear: * between layers * in hidden assumptions * in inconsistent state handling * or when responsibilities are mixed It’s also very important for the middleware and request stack to be well-defined and clearly understood. That’s why structure matters. One important rule I follow: * controllers should stay thin They should only: * handle input/output * delegate logic to services No business logic, no hidden side effects. Because once controllers start doing too much, debugging becomes harder and responsibilities become unclear. Understanding the full request lifecycle changes how you design, debug, and reason about backend systems. It’s not just about writing code — it’s about understanding the flow. #ArchitectureDecisions #SystemDesign #SoftwareArchitecture #BackendDevelopment #SoftwareEngineering

While working on a production project, I experienced how messy and unstructured code can make debugging very difficult. Even small changes became time-consuming because there was no clear separation of logic. This is where architecture matters. Using an MVC structure brings clarity: Models → handle data and database logic Controllers → handle business logic Views → handle responses Separating responsibilities makes the codebase easier to understand, debug, and maintain. This experience reinforced how important clean architecture is in real-world backend systems.