Vaishnavi Kon’s Post

🚨 𝗠𝗼𝘀𝘁 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀𝘀𝘂𝗲𝘀 𝗮𝗿𝗲 𝗡𝗢𝗧 𝗰𝗮𝘂𝘀𝗲𝗱 𝗯𝘆 𝗰𝗼𝗱𝗲. They come from the database layer. I’ve seen APIs blamed for being “slow”… 𝗕𝘂𝘁 𝘄𝗵𝗲𝗻 𝘄𝗲 𝘁𝗿𝗮𝗰𝗲𝗱 𝗶𝘁 𝗱𝗼𝘄𝗻, 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗶𝘀𝘀𝘂𝗲 𝘄𝗮𝘀: 👉 Poor query design 👉 Missing indexes 👉 Too many DB calls per request 🌟 In microservices, this gets worse: Each service = its own DB interaction 🧠 𝗪𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻: ▪️ Keep queries simple and optimized (avoid N+1 problems) ▪️ Add proper indexing based on real query patterns ▪️ Cache frequently accessed data (not everything) ▪️ Avoid unnecessary DB calls in service chains 📒 One small query inefficiency × multiple services = major latency 🤖 Most developers optimize code… Few optimize data access. That’s where the real performance gains are. ✒️ What’s the biggest DB-related issue you’ve faced in production? #Java #Microservices #Database #Performance #BackendEngineering

Most system design diagrams look clean… Until you try building them in real life. A user request seems simple: Click → Load → Response. But behind the scenes? It’s a completely different story. A single request can travel through: → Load Balancer → API Gateway → Multiple services (Auth, Product, Order, Payment) → Separate databases → Message queues for async processing And every step introduces: ⚠ Latency ⚠ Failure points ⚠ Data consistency challenges That’s when I realized: 👉 System design isn’t about drawing boxes — it’s about handling what happens between them. So I started breaking it down: ✔ When to use sync vs async communication ✔ Where caching (Redis) actually makes a difference ✔ How message brokers (Kafka) improve reliability ✔ Why each service should own its data The deeper I go, the more I understand: 👉 Scalable systems are built on trade-offs, not perfection. Curious — What’s the hardest part of system design for you? #SystemDesign #Microservices #BackendDevelopment #SoftwareEngineering #ScalableSystems #DistributedSystems #Java #SpringBoot #Kafka #Redis #CloudComputing

GraphQL N+1 is easy to solve inside a single service. Distributed N+1 across microservices is NOT. Until today. In this demo, I show how to eliminate the network overhead of distributed data fetching without writing a single line of manual DataLoader logic. The Setup: • Same query, same microservices. • Batching OFF: 100 remote calls → 814ms • Batching ON: 1 call → 165ms (~80% faster) The Magic: It’s 100% Annotation-Driven and Declarative. No manual resolvers. No duplicated logic. No complex boilerplate. I solved this at the platform level using a custom instrumentation that intercepts the GraphQL AST, collects keys, and executes batched remote calls through a dynamic registry. This is part of Spring Middleware — a registry-driven platform layer for Spring Boot microservices that I’ve been conceptualizing since 2017 and have finally brought to life. 🌐 Platform: https://lnkd.in/eDTPHnWY I’d love to hear your thoughts on this approach! cc: Josh Long,Tanmai Gopal,GraphQL Java,GraphQL Foundation #SpringBoot #GraphQL #Microservices #Java21 #SoftwareArchitecture #DistributedSystems #Performance

⚡ Why Most APIs Fail in Production (Not in Development) In development, everything works. Clean data. Few users. No pressure. Then production happens. And suddenly: APIs slow down Requests fail Users complain I’ve seen this happen more than once. Here’s what I learned 👇 • The issue is rarely the API logic it’s usually the database queries • Not handling edge cases leads to unexpected failures • Lack of caching increases load on backend services • Poor logging makes debugging 10x harder • Systems behave very differently under real traffic In one case, a simple API started timing out under load. After optimizing queries and adding caching, response time dropped significantly. 💡 Lesson: Don’t just test if your API works. Test if it survives real usage. 💬 What’s one issue you faced only after going to production? #BackendDevelopment #APIs #SystemDesign #SoftwareEngineering #Java #Microservices #Cloud

🧠 4. “Value Bomb Thread” (Save-worthy Content) If you're building scalable backend systems, read this 👇 Here are 5 lessons from my 10+ years as a Technical Lead: Don’t start with microservices Start with a modular monolith Database design matters more than API design Bad DB = slow system forever Caching is a superpower Use Redis early Logging > Debugging Invest in proper logs Performance is decided at architecture level Not after deployment Most developers learn these too late. Which one do you agree with the most?

Everyone talks about scalability. Very few show how to structure a backend properly. Here’s a simple structure I use for production-ready systems: → routes/ Handles HTTP layer (FastAPI/Flask) → services/ Business logic (core system behavior) → repositories/ Database interactions (PostgreSQL queries) → models/ Data schemas (ORM / validation) → utils/ Shared helpers (logging, auth, etc.) Request flow: Client → Route → Service → Repository → Database → Response Why this works: ✔ Clean separation of concerns ✔ Easy to debug ✔ Easy to scale later ✔ No unnecessary complexity What I avoid early: ✘ Microservices ✘ Event-driven chaos ✘ Over-abstraction Rule: “Structure first. Scale later.” A clean monolith beats a messy distributed system. #Backend #SystemDesign #SoftwareEngineering #APIs #PostgreSQL #Mentee #follow #followformore

If you’ve ever built a distributed system, you’ve likely faced the nightmare of Dual-Write Consistency. It’s that moment when your database insert succeeds, but your event broker fails, leaving your system in a broken, "pending" state. The usual approach is to have the app server handle both: save to the DB and then ping a message broker like SNS or RabbitMQ. But if the network hiccups between those two steps, your data and your events are officially out of sync. I recently explored a much cleaner way to solve this using a built-in PostgreSQL feature: #NOTIFY and #LISTEN. Instead of the application trying to tell the world about a change, we let the database itself trigger the event internally. On the database side, you create a trigger function that captures new row data and converts it into a JSON payload. This function calls pg_notify, effectively turning your database into a real-time event orchestrator regardless of how the data was inserted. On the application side, your backend, I used Node.js with the pg library, keeps a persistent connection open and executes a LISTEN command. It fires a callback the instant the DB sends a notification, allowing for immediate, asynchronous processing. This approach guarantees consistency because the notification is baked into the database transaction itself. It also slashes user latency, as you can offload heavy tasks like analytics or credit updates to the background worker without making the user wait. For small to medium-scale projects, this is a massive win for architectural simplicity. You might not even need a heavy message broker like Kafka or RabbitMQ when Postgres can handle the orchestration for you with a much smaller infra footprint. Whether it’s triggering a delivery pipeline in E-commerce or real-time balance updates in FinTech, this pattern keeps your system robust. #PostgreSQL #SystemDesign #EventDrivenArchitecture #SoftwareEngineering #BackendDevelopment #DatabaseTips #RealTimeSystems #DevOps

Your microservices aren’t slow because of traffic… they’re slow because of THIS design flaw. Most teams scale infrastructure first. More pods. Bigger clusters. Higher costs. But the real bottleneck? Architecture. We had a typical flow: Client → API Gateway → Service A → Service B → Database Average response time: ~2 seconds Way too slow for real-time systems. So instead of scaling infra, we fixed the design. Here’s what actually made the difference: 🔹 Caching with Redis Cached hot data and reduced repeated DB calls → Faster reads, lower load 🔹 Reduced service hops Removed unnecessary chaining Merged tightly coupled logic → Less network latency 🔹 Query optimization Fixed N+1 issues Added proper indexing → Faster database response 🔹 Async processing Moved non-critical work to background jobs → Faster user-facing responses Final result: ~2s ➝ ~600ms 🚀 Here’s the real takeaway: Performance issues are rarely about code. They’re about design decisions. Scale architecture first. Infrastructure comes later. Thanks & Regards, Harshavardhan Sakhamuri 📞 314-690-7292 📧 harshasakhamuri.work@gmail.com #Microservices #SystemDesign #BackendDevelopment #Java #SpringBoot #DistributedSystems #PerformanceOptimization #SoftwareEngineering #TechArchitecture #Scalability #Caching #Redis #DevOps #CloudComputing #EngineeringLife

👉 “Your microservices are slow not because of traffic… but because of THIS design flaw.” Most teams scale infra before fixing architecture. We had a typical flow: Client → API Gateway → Service A → Service B → Database Response time: ~2 seconds Too slow for real-time systems After analysis, we made 4 changes: Introduced Redis Caching Cached hot data Reduced repeated DB calls Result: Faster reads Reduced Service Hops Removed unnecessary chaining Merged tightly coupled logic Result: Lower network latency Optimized Queries Fixed N+1 issues Added indexes Result: Faster DB response Enabled Async Processing Background jobs for non-critical tasks Result: Faster user response Final Results: 2s ➝ ~600ms Big Lesson: Performance issues are rarely in code. They’re in design. #Java #SpringBoot #Microservices #SystemDesign #BackendEngineering #SoftwareArchitecture #DistributedSystems #Scalability #PerformanceOptimization #LowLatency #Kafka

"Should I use microservices or a monolith?" Wrong question. Here's the right one: "How do I structure my code so it doesn't matter?" Ports and adapters architecture. This is how I built DiceHub. DH.Domain sits in the center -- pure business logic. It doesn't know about HTTP, PostgreSQL, or Firebase. It talks through interfaces (ports). The outside world connects through adapters: - DH.Adapter.Data -- PostgreSQL via EF Core - DH.Adapter.Authentication -- JWT + ASP.NET Identity - DH.Adapter.FileManager -- Supabase storage - DH.Adapter.Scheduling -- Quartz.NET jobs - DH.Adapter.PushNotifications -- Firebase Cloud Messaging - DH.Adapter.Email -- SMTP via Mailtrap Want to switch databases? Swap DH.Adapter.Data. I did -- MSSQL to PostgreSQL in 5 minutes. Want to change file storage? Swap DH.Adapter.FileManager. Core logic untouched. Want to test business logic? No database needed. Don't choose monolith vs microservices. Choose clean boundaries. Deploy however makes sense today. #SoftwareArchitecture #DotNet #CleanArchitecture #SaaS