Punit Bhardwaj’s Post

The code worked flawlessly for 7 years… until one innocent await brought everything down. 😅 We recently hit a strange production issue. A module that had been stable for years suddenly started producing inconsistent results — but only in production, under high load. Local? Fine. Staging? Perfect. Production? Absolute chaos. Logs started showing errors! I Panicked. The only change I had made: adding an await to call a new asynchronous function. After a rollback and some deep digging, I found the culprit... A variable named queryParams wasn’t declared inside any function — meaning it lived in global scope. So when one request paused on await, another concurrent request came in and modified the same object. When the first one resumed, it unknowingly worked with the mutilated data, to run a sql query, which started throwing errors. A true race condition, hiding in plain sight for 7 years — only revealed by a single async call. We replicated the behavior by bombarding the staging environment with concurrent requests, confirmed the theory, and fixed it by simply scoping the variable locally. Lesson learned: Even in single-threaded Node.js, async code can create concurrency issues if shared state isn’t handled carefully. Sometimes, one misplaced variable is all it takes to cause production chaos. The bug wasn’t new — it was just waiting 7 years for the right await to wake it up. 😅 #nodejs #backend #javascript #developer #expressjs

🚀 Unleash Performance: A Deep Dive into Django's Asynchronous ORM Queries The 5-Second Wait That Changed Everything I watched my Django API return data in 5 seconds. Five Entire Seconds. The culprit? Sequential database queries waiting for each other like customers in a slow checkout line. Then I discovered Django's async ORM, and those 5 seconds became 500 milliseconds—a 10x performance boost. Today, I'll show you exactly when to use synchronous vs asynchronous Django, how async/await transforms database operations, and which approach fits your project. By the end, you'll make confident architectural decisions that scale. Imagine a restaurant with one chef who: Takes order #1 → Cooks it → Serves it Takes order #2 → Cooks it → Serves it Takes order #3 → Cooks it → Serves it This is synchronous programming. Each task blocks the next one. Customers wait in line. # Synchronous Django \(Traditional\) def get\_dashboard\_data\(request\): user = User.objects.get\(id=1\) # Wait 50ms orders = Order.objects.filter\(user=user\) # Wait 100ms products = Pr https://lnkd.in/ghSEfDGQ

When you read a file, stream a video, or send data over the network in Node.js — have you ever wondered how raw binary data gets handled so smoothly? 🧐 Let’s talk about the unsung hero behind it all — Buffer in Node.js! ⚙️ Imagine this 👇 When your computer processes files (text, images, videos), it works with 0s and 1s — pure binary. Now, JavaScript was originally built to handle text data, not raw binaries. So… how does Node.js deal with binary data? That’s where Buffer comes in — a temporary storage area for binary data. 💡 Think of it as a “holding zone” for raw bytes when Node.js talks to the file system or network. Why is Buffer so important? JavaScript strings are UTF-16 encoded — not ideal for binary data. Node.js frequently handles files, images, or network packets — all of which are binary. Buffer bridges that gap efficiently! Key Features: Fixed size (once created, size can’t change) Stores raw binary data (not UTF-8 text) Memory is allocated outside the V8 heap Global class — no need to import Super useful in: File System (fs) Networking (TCP, UDP, WebSockets) Streams (working with data chunks) Buffer vs V8 Memory 🧠 Normally, JavaScript stores data like objects and strings in V8-managed memory, which has limits (~1.4GB) and relies on garbage collection. Buffers, on the other hand, use memory directly from the OS, outside the V8 engine. This means: No garbage collection overhead Can handle larger files (3GB+) Much faster binary performance In short — Buffers = raw power for raw data. ⚡ Next time you read a file or send a network request in Node.js, remember — Buffer is silently doing the heavy lifting behind the scenes 💪 Follow me for more such content https://lnkd.in/dFzTW9wt #backend #webdevelopment #node.js #Javascript #fullstack #Mern

fast-json-format: Format JSON Without Data Loss fast-json-format is a JavaScript library that pretty-prints JSON strings without parsing them, which means you can format JSON containing BigInt literals without losing precision. The library handles these scenarios: Preserves BigInt values like 12345678901234567890n that would break JSON.parse Keeps decimal formatting intact (1.2300 stays 1.2300) Tolerates malformed JSON instead of throwing errors Zero dependencies, single file implementation Check it out if you're working with APIs that return large integers or need to format JSON-like strings from logs where syntax might be imperfect. 👉 Blog Post 👉 GitHub Repo 👉 Live Demo https://lnkd.in/gtgfqtNk

fast-json-format: Format JSON Without Data Loss fast-json-format is a JavaScript library that pretty-prints JSON strings without parsing them, which means you can format JSON containing BigInt literals without losing precision. The library handles these scenarios: Preserves BigInt values like 12345678901234567890n that would break JSON.parse Keeps decimal formatting intact (1.2300 stays 1.2300) Tolerates malformed JSON instead of throwing errors Zero dependencies, single file implementation Check it out if you're working with APIs that return large integers or need to format JSON-like strings from logs where syntax might be imperfect. 👉 Blog Post 👉 GitHub Repo 👉 Live Demo https://lnkd.in/gtgfqtNk

Why Hooks Instead of Triggers—ORM (Sequelize) Hooks > Triggers: The Smarter, Cleaner & Safer Way to Build With Sequelize When you're using an ORM like Sequelize, database triggers might seem like a shortcut—but in reality, they create hidden complexity that hits you later. Here’s why hooks are the developer’s best friend and why triggers should be avoided when working with ORMs: 💙 Why Hooks Win Transparent & Maintainable: Hooks live in your codebase, making them easy to debug, track, and version-control. Predictable Behavior: They run exactly when your ORM executes, keeping your data flow clean and consistent. Smooth Transactions: Hooks work safely inside ORM-managed transactions without unexpected side effects. Cleaner Architecture: All business logic stays where it belongs—inside your application, not buried inside the DB. ❌ Why You Should Avoid Triggers Hidden Logic: Triggers run silently in the database, making issues harder to detect and debug. Unexpected Breakages: They can change data behind Sequelize’s back, causing errors and mismatched values. Extra Result Sets: Many triggers return internal messages or multiple result sets that Sequelize cannot handle. Transaction Conflicts: DB-level triggers often clash with ORM-level transactions, leading to inconsistent behavior. Hard to Maintain: Updating triggers means updating the database, not your code—which slows down development. 🔥 Bottom Line If you’re working with Sequelize, stick to Hooks. They give you clarity, control, and consistency—and they save you from painful debugging sessions later. Hooks keep your architecture clean. Triggers complicate it silently. #Sequelize #NodeJS #BackendDevelopment #WebDevelopment #SoftwareEngineering #CleanCode #DatabaseDesign #ORM #ProgrammingTips #TechLeadership #Developers #JavaScript #BestPractices

I used GraphQL for my project and I want to count some pros of why to use GraphQL over the REST API: - Good data: You get only what you want. It stops the problem of over-fetching (too much data) and under-fetching (not enough) that happens with REST API. It is better for performance. -One endpoint for mutations and queries. It works well when working with microservices. Forget about GET, POST, PUT, DELETE for CRUD operations in REST API. -Working with API is easier here: you control the query and get what you want with one request using nested fields. No need to make many requests like in REST API. -GraphQL and Apollo Client have great IDE for working with queries and mutations, and you can find more info and documentation. -I found error handling also easier than REST APIs, as you get a clear, detailed reason for the error. These are some benefits of GraphQL, but learning GraphQL is a bit harder as there are some concepts like schema, query, mutation types, resolvers, and similar concepts. #GraphQL #RESTAPI #APIDevelopment #WebDevelopment #Microservices #GraphiQL #ApolloClient #SoftwareEngineering #TechTips #APIManagement #Nodejs #NestJs #Typescript #Javascript

𝗨𝘀𝗶𝗻𝗴 .𝗼𝗻𝗹𝘆() 𝗮𝗻𝗱 .𝗱𝗲𝗳𝗲𝗿() 𝘁𝗼 𝗹𝗼𝗮𝗱 𝗳𝗲𝘄𝗲𝗿 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 In Django, you don’t always need every column from a model — but Django will happily load everything unless you tell it otherwise. Think of a typical IVR system:  • Recipient table has: id, phone_number, language, full_name, notes, last_call_json, metadata_json  • Contact table has: id, phone_number, status, ivr_flow_json, extra_data Now, imagine you're building an API that shows the next 500 recipients to call. Most teams do this: 𝘳𝘦𝘤𝘪𝘱𝘪𝘦𝘯𝘵𝘴 = 𝘙𝘦𝘤𝘪𝘱𝘪𝘦𝘯𝘵.𝘰𝘣𝘫𝘦𝘤𝘵𝘴.𝘢𝘭𝘭()[:500] This loads every column, including heavy fields like:  • notes (large text)  • last_call_json (JSON blob)  • metadata_json (sometimes 50–200 KB per row!) But the UI only needs:  • id  • phone_number  • language Instead, load only what you need: 𝘳𝘦𝘤𝘪𝘱𝘪𝘦𝘯𝘵𝘴 = 𝘙𝘦𝘤𝘪𝘱𝘪𝘦𝘯𝘵.𝘰𝘣𝘫𝘦𝘤𝘵𝘴.𝘰𝘯𝘭𝘺("𝘪𝘥", "𝘱𝘩𝘰𝘯𝘦_𝘯𝘶𝘮𝘣𝘦𝘳", "𝘭𝘢𝘯𝘨𝘶𝘢𝘨𝘦")[:500] Benefits:  • Fetches only selected columns  • Big reduction in DB → API payload  • Less RAM used inside Django  • Faster query execution Or, if you want almost everything except the heavy fields: 𝘳𝘦𝘤𝘪𝘱𝘪𝘦𝘯𝘵𝘴 = 𝘙𝘦𝘤𝘪𝘱𝘪𝘦𝘯𝘵.𝘰𝘣𝘫𝘦𝘤𝘵𝘴.𝘥𝘦𝘧𝘦𝘳("𝘭𝘢𝘴𝘵_𝘤𝘢𝘭𝘭_𝘫𝘴𝘰𝘯", "𝘮𝘦𝘵𝘢𝘥𝘢𝘵𝘢_𝘫𝘴𝘰𝘯") Same idea applies when listing contacts: 𝘤𝘰𝘯𝘵𝘢𝘤𝘵𝘴 = 𝘊𝘰𝘯𝘵𝘢𝘤𝘵.𝘰𝘣𝘫𝘦𝘤𝘵𝘴.𝘰𝘯𝘭𝘺("𝘪𝘥", "𝘱𝘩𝘰𝘯𝘦_𝘯𝘶𝘮𝘣𝘦𝘳", "𝘴𝘵𝘢𝘵𝘶𝘴") Real impact: In one of our IVR workloads, avoiding 2–3 JSON columns reduced API response time by 15–25% during peak 60L daily call scheduling — without touching servers or caching. If you haven't profiled your ORM queries yet… you might be moving way more data than you think. What’s the biggest query you've optimized in your codebase? #DjangoOptimization #PythonPerformance #BackendTips #WebScaling #Celery #IVR #DjangoORM