GraphQL vs REST: Choosing the Right API for Your Needs

Ever tried to change the database of an application and ended up having to change even your business rules? That's exactly the pain Onion Architecture was designed to fix. Onion Architecture is all about putting your business logic at the core, surrounded by layers that depend inward, never outward. Each outer layer can depend on the one inside it, but the core is completely independent. ✅ The layers typically look like this - Core (Domain): Entities, value objects, and business rules - Application: Use cases and services that orchestrate domain logic - Infrastructure: Databases, frameworks, external APIs, UI, etc. The golden rule: the core never knows about the outside world. Databases, frameworks, and APIs depend on the domain, not the other way around. ✅ Why it matters: - Keeps business logic framework-agnostic - Makes testing simpler and faster - Encourages clean, maintainable architecture - Allows easy swapping of infrastructure (e.g., from REST to GraphQL, SQL to NoSQL) ✅ Use Onion Architecture when: - You want long-term maintainability and flexibility - Your app has complex business rules or multiple data sources - You're tired of framework lock-in and messy dependencies The takeaway: Protect your core. Everything else is just an outer layer.

Clean Architecture in Real Systems Clean Architecture isn’t theory, it’s what keeps your system alive two years later. In Piqla, every service followed a clean, layered structure: Core logic isolated from frameworks (Framework Agnostic Code) Repositories abstracted from DB specifics (PostgreSQL, MongoDB). Event publishers decoupled from transport layers (RabbitMQ). This allowed us to: Replace Express with Fastify without touching business logic. Add audit logging via RMS (Reporting Management Service) without breaking existing flows. Lesson: Architecture is not about rules, it’s about making tomorrow’s change cheap. #RideHailing #Scalability #DistributedSystems #CloudArchitecture #MobilityTech

The System Design Master Template is your go-to blueprint for acing interviews and building scalable systems. At the heart of many architectures is the API Gateway, which serves as the single entry point to all backend services. It handles routing, authentication, rate limiting, and request forwarding—making it a key layer for security and traffic management. To ensure fast and reliable content delivery to users around the world, systems often rely on a Content Delivery Network (CDN). A CDN caches static assets like images, videos, and stylesheets across globally distributed servers. This brings content closer to the end user, improving load times and reducing latency significantly. As systems scale, databases need to be partitioned to handle the growing load. Horizontal partitioning, also known as sharding, splits rows of a table across multiple servers, helping distribute data and improve performance. On the other hand, vertical partitioning breaks tables down by columns, which is useful when different queries need access to different subsets of data. In distributed architectures, communication between services is often handled by distributed messaging systems like Apache Kafka or RabbitMQ. These systems allow asynchronous message passing, decouple components, and support high-throughput data pipelines—making them ideal for event-driven systems. For handling files at scale, distributed file systems come into play. These systems span multiple servers but offer a unified way to store and retrieve files. They ensure redundancy, scalability, and consistent access, critical for large-scale applications and data platforms. Lastly, when users need to search for text across large datasets, full-text search is the solution. It uses an inverted index—a structure that maps each word to the documents where it appears—enabling fast and relevant search results. This is how systems like Elasticsearch and Solr power search functionality in apps and websites.

System Design — Step-by-Step Guide (Cheat Sheet) Designing a scalable system isn’t just about drawing boxes and arrows — it’s about asking the right questions and balancing trade-offs. Here’s a simple 12-step roadmap 🧭 ⸻ 🧩 1️⃣ Understand the Requirements ✔ Functional & non-functional requirements ✔ Constraints & scope (users, scale, features) ⸻ ⚙️ 2️⃣ Define Use Cases & APIs List core features and define high-level API endpoints Example: POST /uploadPhoto GET /feed POST /like ⸻ 🧮 3️⃣ Estimate Scale Users/day, requests/sec, data growth — helps in choosing DBs & planning capacity ⸻ 🗺️ 4️⃣ High-Level Architecture 💡 Components: Client → API Gateway → App Server → DB → Cache → Queue → CDN ⸻ 🧱 5️⃣ Choose Databases & Storage • SQL for structured data • NoSQL for scalability • Object storage (e.g., S3) for images/files ⸻ ⚡ 6️⃣ Add Caching Use Redis / Memcached / CDN to speed up responses and reduce DB load ⸻ 📨 7️⃣ Message Queues Kafka / RabbitMQ for async tasks like notifications, logs, image processing ⸻ 📈 8️⃣ Scalability Horizontal scaling, load balancers, sharding, auto-scaling groups ⸻ 🔁 9️⃣ Reliability & Fault Tolerance Replication, retries, failover, monitoring, fallback mechanisms ⸻ 🔒 🔟 Security HTTPS, OAuth/JWT, encryption, rate limiting ⸻ 📊 11️⃣ Logging & Monitoring ELK Stack, Prometheus, Grafana — visibility is everything ⸻ 🧠 12️⃣ Trade-offs Every choice has one — 🟢 Consistency vs Availability 🟢 SQL vs NoSQL 🟢 Monolith vs Microservices ⸻ 💬 Pro Tip: Don’t jump into tech choices too early — understand the problem before you design the solution. #SystemDesign #JavaDevelopers #Microservices #TechCheatSheet #CodingInterview

Disappear till DECEMBER 2025 and.. Clear all System Design Rounds Here is HOW you can do it in 5 steps. 𝗦𝘁𝗲𝗽 𝟭: 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Networking Basics (HTTP, TCP/IP, Load Balancing) - API Design (REST vs GraphQL, Rate Limiting, Authentication) - Database Basics (SQL vs NoSQL, Indexing, Partitioning) - Caching Concepts (Strategies, In-Memory Caching, CDN) 𝗦𝘁𝗲𝗽 𝟮: 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 - Vertical vs Horizontal Scaling - Load Balancing Techniques - Database Replication & Sharding - Asynchronous Processing & Messaging Queues 𝗦𝘁𝗲𝗽 𝟯: 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 - Monolithic vs Microservices Architecture - Event-Driven vs Request-Response Model - CQRS and Event Sourcing - Fault Tolerance & High Availability 𝗦𝘁𝗲𝗽 𝟰: 𝗗𝗮𝘁𝗮 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - CAP Theorem & Consistency Models - Storage Systems (Relational, NoSQL, Distributed) - Data Partitioning & Replication - Indexing & Query Optimization 𝗦𝘁𝗲𝗽 𝟱: 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 - Design and document real-world systems - Explain trade-offs in your design decisions - Practice mock system design interviews - Get feedback and refine your approach ➤ 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗬𝗼𝘂𝘁𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗳𝗼𝗹𝗹𝗼𝘄: 1. Rajat Gajbhiye: https://lnkd.in/d2U-pprx 2. Shrayansh Jain: https://lnkd.in/dUABhnbT 3. Hussein Nasser: https://lnkd.in/dubTqr22 4. Gaurav Sen: https://lnkd.in/dAPhQyBp 5. Arpit Bhayani: https://lnkd.in/du8RyS7R 𝗞𝗲𝗲𝗽𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗶𝗻 𝗺𝗶𝗻𝗱, 𝗜’𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗶𝗻 𝗗𝗲𝗽𝘁𝗵 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗚𝘂𝗶𝗱𝗲, 𝟰𝟬𝟬𝟬+ 𝗽𝗲𝗼𝗽𝗹𝗲 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘂𝘀𝗶𝗻𝗴 𝗶𝘁. 𝗢𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝟰𝟬% 𝗼𝗳𝗳 𝗳𝗼𝗿 𝗮 𝗹𝗶𝗺𝗶𝘁𝗲𝗱 𝘁𝗶𝗺𝗲! 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dte69Z5N Use code 𝗦𝗗𝗘𝟰𝟬. Stay Hungry, Stay FoolisH!

System Design Roadmap If you’re diving deeper into System Design & Architecture, here’s a curated and re-organized collection of must-know concepts, building blocks, patterns, and High-Level Design (HLD) problems — all in one place. 🔑 Core Concepts Scalability → https://lnkd.in/gpge_z76 Latency vs Throughput → https://lnkd.in/g_amhAtN CAP Theorem → https://lnkd.in/g3hmVamx Strong vs Eventual Consistency → https://lnkd.in/gJ-uXQXZ Fault Tolerance → https://lnkd.in/dVJ6n3wA Batch vs Stream Processing → https://lnkd.in/g4_MzM4s API Design → https://lnkd.in/ghYzrr8q ACID Transactions → https://lnkd.in/gMe2JqaF Rate Limiting → https://lnkd.in/gWsTDR3m Distributed Tracing → https://lnkd.in/d6r5RdXG Synchronous vs Asynchronous → https://lnkd.in/gC3F2nvr 🧩 Building Blocks Database → https://lnkd.in/gti8gjpz Caching → https://lnkd.in/gC9piQbJ Distributed Caching → https://lnkd.in/g7WKydNg Load Balancing → https://lnkd.in/gQaa8sXK Horizontal vs Vertical Scaling → https://lnkd.in/gAH2e9du Database Scaling → https://lnkd.in/gAXpSyWQ Database Sharding → https://lnkd.in/gMqqc6x9 Database Indexes → https://lnkd.in/gCeshYVt Data Replication → https://lnkd.in/gVAJxTpS Data Redundancy → https://lnkd.in/gNN7TF7n SQL vs NoSQL → https://lnkd.in/g3WC_yxn Proxy Server → https://lnkd.in/gi8KnKS6 WebSocket → https://lnkd.in/g76Gv2KQ API Gateway → https://lnkd.in/gnsJGJaM Message Queues → https://lnkd.in/gTzY6uk8 🏗 Architectural Patterns Event-Driven → https://lnkd.in/dp8CPvey Microservices → https://lnkd.in/gFXUrz_T Serverless → https://lnkd.in/gQNAXKkb Client–Server → https://lnkd.in/dAARQYzq 📝 High-Level Design (HLD) Problems Unique ID Generator → https://lnkd.in/dudvTKPN Bitly → https://lnkd.in/df5wThJq Search Autocomplete → https://lnkd.in/dyHjWFUb Which of these areas do you find most challenging?

💥 “Logs tell you what happened. Metrics tell you when. Traces tell you why.” In a microservice world, a single user request can touch 10+ services — without tracing, debugging becomes pure guesswork. 😵💫 Today, we solve that with Distributed Tracing — connecting every service call into one beautiful, searchable timeline. 🎯 Today’s Goal: Implement Distributed Tracing across microservices using OpenTelemetry (OTel) with Jaeger or Tempo as the backend. ⚙️ Core Concepts Trace: A single request’s full journey across services. Span: A single operation (HTTP call, DB query, etc.) inside that trace. Context Propagation: Pass trace data between services via headers. Visualization: Tools like Jaeger or Grafana Tempo visualize trace timelines. 🧠 Analogy: Imagine your system as a relay race 🏃♂️🏃♀️🏃♂️ — each runner (microservice) passes a baton (trace context) to the next. The entire race (trace) tells you exactly who slowed down and where the error happened. 💡 Implementation Steps Add OpenTelemetry SDK or Spring Cloud Sleuth to services. Configure OTLP exporter to send spans to Jaeger or Tempo. Instrument HTTP calls, database queries, and messaging. Visualize end-to-end requests in Jaeger or Grafana. Correlate traces with logs using the same traceId. ⚠️ Watch Out: Avoid over-instrumentation — too many spans = performance hit. Use sampling (10–20%) in production. Protect trace data; don’t log user identifiers or secrets. 💬 Question for you: Have you ever visualized your full request flow with Jaeger or Tempo? What surprised you the most? #Microservices #DistributedTracing #OpenTelemetry #Jaeger #Grafana #Observability #SpringBoot #100DaysOfMicroservices

🚀 Future-Proof Your API Architecture with GraphQL Stitching & Federation As applications scale, so does the complexity of managing APIs. That’s where GraphQL Stitching and Federation come in — helping teams build high-performance, modular, and scalable systems. This new article breaks down: ✅ How stitching and federation work together ✅ Key performance benefits for modern systems ✅ Best practices to implement in 2025 ✅ Real-world use cases for teams and enterprises 📖 Read more: https://lnkd.in/gbmhszpq #GraphQL #APIArchitecture #WebDevelopment #TechLeadership #Innovation #SoftwareEngineering #LKTechAcademy #Performance #Developers

📋 WEEK 2 GO-FOCUSED ARCHITECTURE INSIGHTS: Cost-Efficient Patterns for Scalable Systems This week, we explored Go-optimized architectural patterns that enable applications to scale efficiently while maintaining cost control, performance excellence, and operational simplicity. Advanced Go Architecture Patterns Examined: 🔧 Go Microservices Architecture • Strategic Focus: Leveraging Go’s lightweight goroutines and small binaries for cost-efficient scaling. • Proven Result: 3-5x deployment speed, 30-50% cost reduction. 🗄️ Database Architecture • Strategic Focus: PostgreSQL optimisation with the pgx driver; Vector DB for AI workloads; efficient connection pooling. • Proven Result: 40-60% infrastructure savings, 3-5x query performance. 🔌 Go API Design • Strategic Focus: High-performance integration; OpenAI API caching achieving 50-70% cost reduction per API call. • Proven Result: 10-50x request handling capacity, 60-80% memory efficiency. 🐳 Containerization & Deployment • Strategic Focus: Docker optimisation for Go; GitLab CI/CD automation. • Proven Result: 5-minute deployments, 60% AWS cost reduction, 99.9% uptime. Go Architecture Principles That Drive Results 📈 • Early Go decisions compound significantly: Language choice, database integration, and API patterns determine long-term cost efficiency. • Cost optimisation at architecture level: Go’s efficiency enables handling more load with less hardware, translating directly to operational savings. • Developer productivity: Fast compilation cycles, excellent tooling, and straightforward deployment accelerate velocity. Strategic Go Insight: Architecture decisions leveraging Go’s advantages determine business scalability more than infrastructure resources. Well-designed Go systems scale efficiently with modest hardware while delivering exceptional performance and cost control. Next Week Preview: Security frameworks and advanced performance optimisation—exploring how Go’s security capabilities and systematic approaches enable enterprise-grade reliability while maintaining cost efficiency. The progression continues: Go foundations → advanced Go architecture → security & performance → business transformation through Go excellence. Which Go architectural pattern would deliver the highest impact for your current system scalability and cost optimisation requirements? #GoLang #AdvancedArchitecture #CostOptimisation #Week2Insights #GoMicroservices

Business development perspective: Week 2 perfectly demonstrates why companies choose Munimentum for Go architecture strategy. Instead of theoretical frameworks, we deliver Go-optimized solutions with proven ROI: 60% cost reduction + 3-5x faster deployments + 99.9% uptime + measurable AI cost savings. Every architectural decision focuses on business value, not just technical elegance. That’s sustainable competitive advantage through better Go expertise. See the full methodology from Alexis Morin and the team here! (Proud to be driving this strategy with Alex!) #GoArchitecture #ROI #BusinessValue #CompetitiveAdvantage