Optimizing API Performance with Pagination and Filtering

📚 What I Learned Today: REST API vs GraphQL Today I watched a great video by Coder's Gyan on REST vs GraphQL, and it really helped clarify when and why to use each one in real-world projects. 🔹 REST API Fixed endpoints Can lead to over-fetching or under-fetching of data Simple, widely adopted, and great for straightforward use cases 🔹 GraphQL Single endpoint with flexible queries Client requests exactly the data it needs Solves over-fetching & under-fetching issues Powerful, but adds complexity and isn’t always necessary 💡 Key takeaway: There’s no “one-size-fits-all.” Use REST for simple, stable APIs Use GraphQL when your app needs flexibility, complex data relationships, or optimized network usage Big thanks to Rakesh K sir for explaining this so clearly 🚀 #RESTAPI #GraphQL #BackendDevelopment #WebDevelopment #APIs #LearningJourney #CoderGyan #SoftwareEngineering

Small things that improved my Go APIs: • Using `json:"-"` to completely hide fields • `omitzero` to clean up empty values (Go 1.24+) • The `string` directive for client convenience Wrote up the patterns I keep reaching for: https://lnkd.in/gZTK8X6J #go #golang #api #backend #softwaredevelopment #learninpublic

Over the past few months, working with Go has been a steep and rewarding learning curve—especially around concurrency and backend performance. While exploring the Appwrite Go SDK, I built a data migration tool that moved 10,000 CSV records into Appwrite using a worker pool pattern. The result: 61 minutes → 90 seconds (~40× faster) From this experience, I broke down: - Why Appwrite works well for concurrent backend workloads - How the Go SDK fits naturally with worker pools - Benchmark results and architectural trade-offs - Practical lessons for production data migrations Full write-up: https://lnkd.in/ep2SwRU6 #Appwrite #GoLang #BackendEngineering #DevRel #Performance #BuildwithAppwrite

For people who’ve built systems like this, how do you decide when to stay with a BaaS like Supabase versus committing to a full backend framework? At what point does the added control justify the added complexity? Some time ago, I shared my progress building a marketplace meant to enable cross-border trade across Africa. The idea didn’t work out, but what remained was a well-optimized system: frontend-heavy, React-based, running serverless with Supabase handling auth, storage, and data. Now we’re regrouping and pivoting toward building an escrow service, and I’m rethinking the tech stack. Supabase worked well last time. It was flexible, fast to iterate with, and reduced a lot of operational overhead. Most things lived in one place, I didn’t have to juggle multiple codebases, and development felt fluid. But as I think about scalability and long-term ownership, I’m considering a different approach: NestJS for the backend, PostgreSQL with Prisma, background jobs, and using Supabase more selectively where it fits. On paper, this feels more customizable and future-proof. In practice, it also means more complexity, more context switching, and more energy spent maintaining backend infrastructure alongside a React frontend. I’m trying to separate real technical needs from the desire to “prove a point" on my previously shelved backend skills. I’d really appreciate hearing from anyone who’s navigated a similar tradeof

💥 THE DATA WAS RIGHT THERE IN THE DATABASE. BUT OUR USERS COULDN'T SEE IT! I checked the database: The registration records were clearly there. I knew the backend logic: If the record exists, the API must return the event details. 𝐈𝐭 𝐬𝐞𝐞𝐦𝐞𝐝 𝐢𝐦𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞. 🛑 I knew this wasn't a frontend issue. But I also couldn't explain why our backend was behaving this way. It felt like we were dealing with a "magical" bug—a phantom issue where the system was defying its own logic. 👻 I was 100% convinced that since the data existed, the backend *had* to be reading it. I spent hours trying to figure out why the system was silently failing. 𝐓𝐡𝐞𝐧 𝐜𝐚𝐦𝐞 𝐭𝐡𝐞 𝐭𝐮𝐫𝐧𝐢𝐧𝐠 𝐩𝐨𝐢𝐧𝐭. 💡 During a troubleshooting session, I glanced at my backend engineer’s screen. He ran the same query, but his result was 𝐞𝐦𝐩𝐭𝐲. I was looking at the 𝐌𝐚𝐬𝐭𝐞𝐫 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞. He was looking at the 𝐑𝐞𝐚𝐝 𝐑𝐞𝐩𝐥𝐢𝐜𝐚. That split-second realization solved the mystery: We were hitting a massive 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐠. The data I saw on the Master DB hadn’t synced to the Read Replica yet. --- 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐭𝐡𝐞 𝐜𝐨𝐫𝐞 𝐜𝐨𝐧𝐜𝐞𝐩𝐭 𝐈 𝐰𝐚𝐥𝐤𝐞𝐝 𝐚𝐰𝐚𝐲 𝐰𝐢𝐭𝐡: 🧠 𝟏. 𝐓𝐡𝐞 "𝐏𝐡𝐨𝐭𝐨𝐜𝐨𝐩𝐲" 𝐋𝐚𝐠 📠 Think of the Read Replica as a photocopy of the Master database. I was looking at the original document, but the app was serving users a blank page because the "copier" was running 3 hours behind. 𝟐. 𝐀𝐝𝐦𝐢𝐧 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 ≠ 𝐔𝐬𝐞𝐫 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 ⚡ Just because *you* can see the data (on the Master) doesn't mean the *app* can read it yet (from the Replica). Always verify the data source the user is actually hitting. 𝟑. 𝐀𝐬𝐤 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 🎯 Instead of asking "Is the data in the DB?", the better question during an incident is: "𝐇𝐚𝐬 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐫𝐞𝐚𝐜𝐡𝐞𝐝 𝐭𝐡𝐞 𝐑𝐞𝐚𝐝 𝐑𝐞𝐩𝐥𝐢𝐜𝐚?" It’s a small nuance that saves hours of debugging. #ProductManagement #TechnicalPM #Debugging #SystemArchitecture #LessonsLearned

🚨 Day 1 – Backend Reality Check Most backend bugs are not code problems. There are data problems. When an API fails, we usually blame logic or implementation. But in production, the code often behaves exactly as written — what breaks is the data. Example: Your API expects: { "age": 25 } Production sends: { "age": "25" } Same field. Different type. Now validations fail, calculations behave unexpectedly, or bad data gets stored. Common causes: • Missing or null fields • Wrong data types • Unexpected enum values • Old schema data after updates The fix isn’t more code. It’s strict validation, clear API contracts, and strong data discipline. Code is predictable. Data is not. #BackendDevelopment #SoftwareEngineering #APIDesign #CleanCode #SaaS

Worked hands-on with Prisma ORM to design and manage relational databases and integrate them with a Node.js backend. ✔️ Designed Prisma models & ran migrations ✔️ Created and used Prisma Client ✔️ Implemented relations (1–1, 1–N, N–N) & enums ✔️ Built full CRUD operations ✔️ Worked with advanced queries, filtering & relational data Prisma’s type-safe approach makes backend development cleaner, faster, and more reliable—perfect for building scalable APIs. 📌 Building structured, production-ready backends. #Prisma #BackendDevelopment #NodeJS #TypeScript #Databases #APIs #FullStackDeveloper

Day 11 | Database Integration & Debugging (Prisma) 🛠️ Today was one of those days that truly tests your patience as a developer. I started integrating Prisma into our SaaS application to bring structure, scalability, and better data handling to the database layer. While doing so, I encountered multiple Prisma-related issues during setup and runtime. Some of the challenges included: • Prisma client initialization errors • Environment and configuration mismatches • Runtime failures during page rendering • Debugging stack traces across Next.js app routes • Understanding how Prisma behaves in a server-rendered environment Although progress felt slow at times, days like these are extremely valuable. They force you to understand the tooling deeply instead of just making it “work”. Debugging is not a setback — it’s part of building something reliable and production-ready. Back to fixing, learning, and improving the system. #Prisma #DatabaseDesign #Debugging #SaaS #BackendDevelopment #NextJS #BuildInPublic #SoftwareEngineering #DeveloperJourney 🚀

A small backend check that saved us from future bugs 🚨 While working on an order status update API, I realised something important: 👉 What if DELIVERED is updated twice? Without a check: - deliveredAt keeps changing - Reports & analytics break - Bugs stay hidden ✅ Simple fix Make the API idempotent. 💡 Lesson Backend isn’t just about APIs working — It’s about data integrity & safe updates. Always ask: “What happens if this API is called again?” Small checks. Big stability. 💯 #BackendDevelopment #NodeJS #APIDesign #SoftwareEngineering

Ever faced duplicate records or repeated actions in a backend without understanding why? While learning how to build reliable backend systems with limited resources, today I focused on an underrated concept: idempotent APIs. In many early-stage applications: • Clients retry requests when they don’t get a response • Network timeouts happen • Users click buttons multiple times If the backend is not idempotent, this can lead to: • Duplicate orders • Multiple payments • Inconsistent database state An idempotent API ensures that repeating the same request produces the same result, without causing side effects. This can be achieved without extra infrastructure: • Use unique request identifiers • Enforce constraints at the database level • Check if an operation already exists before creating it This greatly improves system reliability, especially for payment flows, form submissions, and background retries. Example: When creating an order, the client sends a unique request ID. If the same request is sent again, the backend detects it and returns the existing result instead of creating a new record. #backend #systemdesign #scalability #webdevelopment