Concurrency bugs in Node.js: Why we need Postgres

Excited to share a behind-the-scenes look at my latest backend project: a high-performance RESTful API built with the Drogon framework in C++ and powered by PostgreSQL! 🚀 In this screenshot, you'll see the ProductsController in action—handling a POST request to create new products. It establishes a database connection, inserts structured data (including ID, title, description, image URL, price, and quantity), and responds with a 200 OK status. On the right, I'm testing it end-to-end with an API client like Postman, sending JSON payloads and verifying the results. Why C++ for APIs? Drogon's asynchronous design and built-in ORM make it incredibly efficient for handling high loads, while Postgres ensures robust, scalable data management. This combo is ideal for modern web apps where speed and reliability are non-negotiable. What do you think—have you experimented with C++ frameworks like Drogon for backend services, or do you stick to more common choices like Node.js or Go? Share your experiences in the comments, or let's connect if you're into performance-oriented development! #Cpp #DrogonFramework #PostgreSQL #RESTfulAPI #BackendDevelopment #WebDevelopment #SoftwareEngineering #API #OpenSource #TechCommunity [Poll: What's your go-to language for building RESTful APIs? C++ Node.js/JavaScript Go Python Other (comment below!)]

🚀 Spring Boot + GraphQL: Modern API Development Made Easy REST APIs have served us well for years but as applications grow more complex, the need for flexible data fetching becomes hard to ignore. That’s where GraphQL comes in 💡 Instead of sending multiple REST requests, GraphQL lets clients ask for exactly what they need no more, no less. And when combined with Spring Boot, it becomes a developer’s dream for building modern, efficient APIs ⚙️ 🧩 Schema-First Approach In GraphQL, everything starts with a schema the single source of truth for your API. You define types, queries, and mutations in a schema file (schema.graphqls), and Spring Boot automatically wires it up through resolvers. This means your API design drives your code not the other way around. 🧠 Resolvers — The Brains of GraphQL Resolvers handle what happens behind each query or mutation. For example: 💠 A QueryResolver fetches data from your service layer 💠 A MutationResolver updates or inserts records 💠 You can even nest resolvers to fetch related entities efficiently No need for multiple endpoints a single /graphql endpoint can serve all your queries. ⚙️ Why Spring Boot + GraphQL Rocks ✅ Strongly typed schema for consistent APIs ✅ One endpoint for all queries ✅ Perfect for microservices and frontend-heavy apps ✅ Easy integration with Spring Data JPA and Security In short GraphQL with Spring Boot gives you REST simplicity with query precision. It’s the modern way to design APIs that scale with your users, not against them. 💬 Have you tried building APIs with GraphQL yet? Did you prefer the schema-first or code-first approach? Share your experience below 👇 #SpringBoot #GraphQL #BackendDevelopment #Java #Microservices #SoftwareEngineering

Just shipped a robust project that demonstrates foundational backend expertise! 🛠️ I've successfully built a complete Task Manager RESTful API using Spring Boot and Spring Data JPA. Project Highlights: Task Manager API 📝 In the attached video, I walk through the API's functionality and testing. I focused on building a clean architecture with: Full CRUD: Implemented and tested all core HTTP methods (GET, POST, PUT, DELETE). Data Filtering: Added logic to filter tasks by their completion status directly via query parameters. Layered Design: Maintained strict separation between Controller, Service, and Repository layers for clean, maintainable code. Technology: Java, Spring Boot, Spring Data JPA, and Postman for endpoint verification. I've included a short video demo below showing the key endpoints in action using Postman, proving the API's integrity and responsiveness. My next focus? Turning this API into a polished, production-ready application by building an attractive and user-friendly frontend with React! Stay tuned for the full-stack deployment. Take a look! 👇 💡 What are your favorite styling frameworks to use with React? Share your tips below! #SpringBoot #Java #RESTAPI #BackendDevelopment #SoftwareEngineering #API #Postman #React #Frontend #FullStack

Is REST Still King of the API Hill? For years, REST has dominated, and for good reason, don't get me wrong. Mature frameworks make it fast and easy to deliver: FastAPI/Django REST (Python), Spring Boot/JAX-RS (Java), Express/NestJS (JS/TS). In my Java work, the pattern repeats: there are multiple endpoints per resource (GET/POST/PUT/DELETE), the client calls one or more endpoints to get all the data it needs, the server returns a JSON. Simple… but is it efficient? Here’s my experience: you often over-fetch (get more fields than you really require) or under-fetch (need multiple round trips hitting different endpoints). Data rarely lives in one resource... sometimes frustrating for the front-end developers, have you ever heard of it? So, is there a better fit? GraphQL could be. The client asks exactly for the fields it needs—across nested relations—in a single query, guided by a typed schema. That can cut chatter and improve performance and UX. But it’s not a rule moving from REST to GraphQL. It’s more about business-fit: 📌 Choose REST when you have clear resource boundaries, want to leverage HTTP semantics (caching, ETags, status codes), CDN/edge caching, and simple public APIs. 📌 Choose GraphQL when clients need tailored payloads, you’re aggregating data from many downstream microservices, or you want strong contracts with rapid iteration. 📌 Trade-offs: GraphQL adds query-cost control, caching complexity, N+1 risk, and a steeper learning curve; REST can sprawl endpoints and versioning. Quick check: Do your clients frequently stitch multiple REST calls or trim large payloads? If yes, a GraphQL gateway may pay off. Otherwise, keep it simple with REST. This is not a recipe; you must check the business first. Where are you leaning today—REST, GraphQL, or a hybrid (REST for public/infra, GraphQL as a BFF gateway)? What’s worked—or burned—you in production? Share in the comments. #SoftwareArchitecture #API #REST #GraphQL #Java #SpringBoot #SoftwareDevelopment #Microservices

Recently, I had the chance to test Junie, the new AI coding agent integrated into IntelliJ IDEA. I gave it a spec for an MVP project — a full-stack Java / Spring Boot / Angular / MySQL application. The results after just 6 hours of work were honestly impressive. Here are some of my key takeaways 1. It can write about 95% of correct code, though occasionally adds some “creative” elements — an expert review is still a must. 2. Handles frontend/backend synchronization surprisingly well. 3. Performance and accuracy slightly drop as the project grows (which makes sense as complexity increases). 4. Works best when the spec is fed in smaller chunks. One big spec for a full MVP doesn’t go smoothly. 5. Legacy code adaptation isn’t its strong suit (yet). 6. The code structure sometimes needs guidance — for example, Junie initially skipped the service layer in the Spring Boot setup. Overall, I’m impressed with what can be achieved in just a few hours. And, to be honest, it reminded me how much I miss Java programming — something we don’t often get to do in Data Engineering! GitHub repo: https://lnkd.in/d594edJs #AI #IntelliJIDEA #Junie #Java #SpringBoot #ReactJS #SoftwareEngineering #MVP #ArtificialIntelligence #DataEngineering #Productivity #AIinDevelopment

API slow? But query looks fine… Backend devs confused? Wait, let me tell you about the N+1 Problem — the silent performance killer😅 Let’s understand it 👇 You want to fetch all users from a board, and for each user — their tasks. ✅ Step 1: SELECT * FROM users WHERE board = X → 1 query ❌ Step 2: For each user, SELECT * FROM tasks WHERE user = ? → N queries Total = 1 (for users) + N (for tasks per user) That’s your N+1 queries — simple logic, exponential latency. Why it happens: Because ORMs like Django, Hibernate, or Sequelize use lazy loading — fetching related data one record at a time. How to fix: Django: .select_related() or .prefetch_related() SQLAlchemy: joinedload() or subqueryload() General fix: Batch your queries or use joins instead of fetching per record. Think of it like this: Instead of asking your friend 10 times for each student’s score, ask once — “Give me all students with their scores.” Result: Performance boost. Fewer DB hits. Happier backend dev. 😎 Drop a 👍 if you found this useful & follow for more real-world system design breakdowns. #SystemDesign #Database #ORM #Backend #Engineering #SDE #Performance #DevLife

🔐 Backend Validation Libraries & Frameworks — Which One Should You Choose? In backend systems, data validation is not optional — it’s the gatekeeper for security, consistency, and reliability. From sanitizing inputs to enforcing business rules, the right validation library/framework can save you from vulnerabilities and bad data. Here are some popular backend validation tools and their best use-cases: ✅ Joi (Node.js / Hapi ecosystem) Schema-based, rich validation rules. Great for APIs & complex data structures. ✅ Zod (Node.js, TypeScript-friendly) Type-safe schemas, validation + parsing in one. Works great for both frontend & backend. ✅ Express-Validator (Node.js + Express) Middleware-based, built for REST APIs. Perfect for validating request bodies, params, headers. ✅ class-validator (TypeScript / NestJS) Decorator-based, integrates deeply with NestJS. Type-safe class-level validation. ✅ Cerberus (Python) Lightweight, schema-based validation. Ideal for JSON & API inputs. ✅ Marshmallow (Python) Serialization + validation in one. Perfect for Python APIs (Flask, FastAPI). ✅ Hibernate Validator (Java / Spring Boot) Based on Bean Validation (JSR 380). Excellent for enterprise-level apps with strict models. 💡 Takeaway: Use Joi / Zod for Node.js backends. Use class-validator if you’re in NestJS + TypeScript. In Python, go for Marshmallow or Cerberus. In Java, Hibernate Validator is the industry standard. 👉 Which validation library do you prefer for backend projects? #Backend #NodeJS #SpringBoot #NestJS #FastAPI #Flask #Python #Java #TypeScript #Validation #DataValidation #WebSecurity #API #Middleware #SoftwareEngineering #Developers #FullStack #CleanCode #BestPractices #BackendDevelopment #Scalability #Enterprise #TechTrends #OpenSource

🚀 API vs REST API — What’s the Real Difference? Many developers (including me once 😅) confuse the two terms — but they’re not the same thing. Let’s understand it clearly 👇 🔹 What is an API? API (Application Programming Interface) is a set of rules that allows two software systems to communicate with each other. Think of it as a bridge that lets one program “talk” to another — sharing data or triggering actions. 🧩 Examples: Java API → lets you use built-in Java classes. Database API → connects applications to databases. OS API → allows apps to use system resources. ➡️ APIs are a broad concept — they don’t always rely on the web or HTTP. _________________________________________________________________________________ 🔹 What is a REST API? REST (Representational State Transfer) is a specific type of API that follows REST architectural principles and works over HTTP. It’s mainly used for web communication between frontend and backend systems. ✅ Key REST Rules: Uses standard HTTP methods — GET, POST, PUT, DELETE Stateless — every request is independent Client-server separated Responses can be cached Data is usually exchanged in JSON format 🧩 Example: When your app fetches data like GET https://lnkd.in/gHMyuwWY — that’s a REST API call. _________________________________________________________________________________ 💡 In short: 🗣️ All REST APIs are APIs, but not all APIs are REST APIs. Understanding this difference helps when building scalable backend systems with Spring Boot, Node.js, or any modern web framework. #API #RESTAPI #WebDevelopment #Backend #SpringBoot #NodeJS #Java #Coding #Developers #Learning #TechKnowledge

🚀 Day 82 #skillupwithgfg & #nationskillup Node.js CRUD Operations Using Mongoose CRUD operations — Create, Read, Update, and Delete — are the backbone of any database-driven application. In Node.js, Mongoose simplifies MongoDB operations by providing an elegant schema-based solution to model data. 1. Create – save() Method The save() method is used to add new documents to a MongoDB collection. It creates an instance of a model and stores it in the database. Example: const user = new User({ name: "Tarun", age: 22 }); user.save(); This inserts a new record in the “users” collection. 2. Read – find() and findById() Methods find() retrieves multiple documents based on a query. If no condition is passed, it fetches all records. User.find({ age: 22 }); findById() fetches a single document using its unique _id. User.findById("652df12a9b..."); 3. Update – updateOne() Method The updateOne() method modifies the first matching document. You can specify conditions and update fields. User.updateOne({ name: "Tarun" }, { $set: { age: 23 } }); It returns an acknowledgment of the update result, not the updated document itself. 4. Delete – deleteOne() Method The deleteOne() method removes the first document that matches the condition. User.deleteOne({ name: "Tarun" }); If the condition matches, the document is permanently deleted from the collection. ✍️ 𝗧𝗮𝗿𝘂𝗻 GeeksforGeeks #NodeJS  #JavaScript #WebDevelopment #Coding #CRUD #FullStack #Developers 📖 More Learning: Full Stack Web Development – Skill Up [https://lnkd.in/dVxtmCEf]

SQL Developer Extension for VSCode has just been updated. That's a ton of new features! 🚀 One of the coolest? Post-Execution Debugging — now you can step back through your JavaScript code like a forensic analyst using a graphical debugger interface 🔍 In my latest post, I walk through: • What it is (and why it’s awesome) • How to set it up in VSCode • When it can save you hours of head-scratching Link to the post: https://lnkd.in/ef-aWGPY