JavaScript vs Rust: Surprising Performance Differences in JSON Parsing

𝐉𝐚𝐯𝐚𝐒𝐜𝐫𝐢𝐩𝐭 𝐍𝐚𝐦𝐢𝐧𝐠 𝐂𝐨𝐧𝐯𝐞𝐧𝐭𝐢𝐨𝐧𝐬 𝐍𝐨 𝐎𝐧𝐞 𝐅𝐨𝐥𝐥𝐨𝐰𝐬 (𝐁𝐮𝐭 𝐒𝐡𝐨𝐮𝐥𝐝) Messy variable names make JS code hard to read, even if it works. In my latest blog, I cover: - 💡 Start and end variable names with letters — not _ or $ - 💡 Snake_case isn’t dead — sometimes it’s the cleanest choice - 💡 Reserved words are more than a restriction; they’re a legacy from 1950s memory limits Tips inspired by 𝐃𝐨𝐮𝐠𝐥𝐚𝐬 𝐂𝐫𝐨𝐜𝐤𝐟𝐨𝐫𝐝 Read it here 👉 https://lnkd.in/d_j6X-8J

Stop Worshiping Benchmarks: Smarter JS Runtime Picks Yesterday I was four espresso shots deep, squinting at yet another Spreadsheet-From-Hell comparing Node, Deno, and Bun. Columns everywhere: "hello-world rps", "RAM on a Raspberry Pi", someone even measured lines of output when you mistype a flag. That was the moment I realised — we’re obsessing over the wrong stats. So let’s talk about a better way to choose a JavaScript runtime in 2025 without the cargo-cult benchmarking ritual. Think of this as a runtime buyers-guide for your brain. Instead of arguing about whose "hello world" runs in fewer nanoseconds, we’ll judge runtimes the way we judge code reviews: Does it run fast when it matters? Performance (speed, memory, cold starts, WASM chops) The Runtime Line-Up (In Plain English) Node.js – the OG, still powering 97 % of Fortune 100, recently learned new tricks (built-in test runner, experimental permission flags, better WASM support). Think battle-tested pickup truck that now has CarPlay. Performance: More Than ab -n 1000 Node.js surpr https://lnkd.in/g9s5VWtk

Most of the time, when someone asks how the JS Engine executes code, we simply say: “JavaScript is interpreted line by line.” Then we start explaining Call Stack, Execution Context, and Memory Heap — and that’s true… But do you know what’s really happening behind the scenes? 👀 Modern JS engines like V8 (Chrome), SpiderMonkey (Firefox), Chakra (Edge), and JavaScriptCore (Safari) are doing insane hidden optimizations to make JS run almost as fast as compiled C++! ⚡ 🧩 1️⃣ Modern Function Execution Structure (Activation Record / Call Frame) 🚀 2️⃣ Hidden Optimizations You (Probably) Didn’t Know About: 🔥 Inline Caching (IC) 🧱 Hidden Classes ⚡ Function Inlining (Very Advanced) 🕶️ Lazy Parsing 💨 Escape Analysis 🔁 Deoptimization 🏗️ JIT Compilation Pipeline (V8) All happening while your app is running! 🚀 ⚡ In Short: JavaScript isn’t just “interpreted.” It’s interpreted + optimized + compiled + deoptimized — all dynamically, in milliseconds. Next time someone asks “How does the JS engine execute code?”, don’t stop at “Call Stack” and “Execution Context.” Say this 👇 “Modern JS engines like V8 use JIT compilation, inline caching, hidden classes, escape analysis, and deoptimization to execute JavaScript at near-native speed 💪.” #JavaScript #V8Engine #WebPerformance #Frontend #NodeJS #Coding #HappyCoding #WebDevelopment

From JS to WASM — Just One Step? It’s no secret that WebAssembly is much faster than JavaScript. For example, summing 2 billion elements takes 15.5 seconds in JS, but only 1.4 seconds in WASM. Impressive — but that’s synthetic benchmarking. Let’s move closer to real-world product development. Say we need to invert the colors of a 25 MB image. I tried four approaches: 1. JS in the main thread — 32.4 ms 2. JS in a Web Worker — 78.69 ms 3. WASM in the main thread — 282 ms → optimized to 30.9 ms 4. Rust + WASM in the main thread — 47.8 ms After 4 hours of writing what seemed like the simplest possible code for image inversion, the result was… disappointing. The first WASM implementation was 8.7× slower than plain JS. Plus, both WASM and Web Workers add resource overhead — and that’s not even counting manual memory management, which is a total nightmare. After another hour of tinkering, I realized that the functions, conditions, and loops I wrote in WASM for readability were significantly slowing everything down. Also, the more parentheses (hello Lisp!) — the slower the execution. Obvious? Maybe. Painful? Definitely. After flattening the assembly into one giant unreadable block, performance increased 9.12×. I finally beat JS — by 1.5 ms! The V8 engine is so optimized that, in some cases, an interpreted language beats a carefully written binary. Crazy, right? Next, I tried Rust → WASM. I expected the binary to be far superior to my hand-written WASM, but surprisingly (or not), my WASM code was 55% faster. 🫡 Yes, humans can write faster assembly than compilers — but honestly, it's better to let the machine handle that. It will generate optimized binary from your high-level code in seconds, saving you days of work and a few grey hairs 💀 P.S. I’ve never gone this deep before. The process was insanely fun — I highly recommend trying something like this at least once!

We’ve all been there debugging a perfectly fine API response... only to realize your data is failing validation because of a few invisible spaces. These trailing spaces can silently break string comparisons, UI bindings, and even backend validations. So, I wrote a simple recursive utility in JavaScript to clean them deeply. 🧹 𝗪𝗼𝗿𝗸𝘀 𝗳𝗼𝗿: Nested objects Arrays Deeply nested strings 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿𝘀 𝗹𝗼𝘃𝗲 𝗮𝘀𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁: Recursion Object traversal Data normalization or deep cloning This question can easily appear as: “Write a function to remove spaces from all string values in a nested object.” #JavaScript #WebDevelopment #CodingTips #Frontend

To write DSA code manually, you must know: ✔ Loops ✔ If/else ✔ Arrays ✔ Strings ✔ Objects (hashmaps) ✔ Pointers ✔ Recursion ✔ Big-O ✔ Manual versions of common built-ins ✔ Basic algorithms (sorting/searching) That’s it. You DO NOT need: - frameworks - React - DOM - async - OOP - advanced JS tricks

🔍 A destructuring pattern in JS/TS that most devs forget exists You can destructure and index an array inside an object in a single step. Example snippet is given below. What’s happening here? • `coverImage:` grabs the `coverImage` property • `[coverImage]` immediately pulls out the first element of that array • same for `file` This avoids repetitive lookups like: req.files.coverImage[0] req.files.file[0] and keeps the intent brutally clear: “From this object, give me the first items of these two arrays.” This pattern works anywhere you have an object of arrays — API responses, form data, grouped results, etc. It’s one of those small language features that removes noise and makes your code read like it’s doing exactly what you meant. #JavaScript #TypeScript #CleanCode #DeveloperTips #WebDevelopment  #CodingBestPractices #NodeJS #BackendDevelopment #TechTips

Today’s focus: Loops in JavaScript : Loops allow us to execute a block of code multiple times — making our programs efficient and reducing repetition. Here are some examples I practiced today // For loop for (let i = 0; i < cars.length; i++) {   text += cars[i] + "<br>"; } // While loop while (i < 10) {  text += "The number is " + i;  i++; } Use for loops when you know the number of iterations. Use while loops when looping depends on a condition. Always make sure your loop has a stopping condition to avoid infinite loops Every day, I’m understanding how small logic blocks combine to form powerful programs. Excited for Day 6 tomorrow! #JavaScript #Loops #CodingJourney #WebDevelopment #100DaysOfCode #FrontendDevelopment #LearnToCode

🚀 I used to think JavaScript was just “interpreted”… Until I discovered how much magic happens before a single line runs. When you write something simple like let sum = 10 + 5, the JS engine doesn’t just read it; it compiles it. Yes, JavaScript is compiled before execution (just-in-time). ⚙️ Here’s what actually happens behind the scenes: 1️⃣ Tokenization – your code is broken into keywords, operators, and identifiers. 2️⃣ Parsing – those tokens form an Abstract Syntax Tree (AST) that maps out the structure of your program. 3️⃣ Interpretation – the AST is turned into bytecode. 4️⃣ JIT Compilation – engines like V8’s TurboFan optimize bytecode into fast machine code. 5️⃣ Garbage Collection – memory is automatically cleaned up when no longer needed. All of this happens in milliseconds ⚡ Every single time your JS runs. I broke down each step in detail in my new Medium article 👇 👉 https://lnkd.in/dM7yNH6f #JavaScript #WebDevelopment #Programming #NodeJS #Frontend #V8 #SoftwareEngineering

⚙️ JavaScript is Awesome, But Rust or Go is the Next Step , This transition wasn't just a syntax change; it was a fundamental shift in how I think about performance and safety. Go has always been my go-to for developer speed and high productivity. Its simple structure, fantastic compilation times, and the elegant Goroutine model make launching microservices and scaling distributed systems smooth and straightforward. Go allows you to concentrate purely on the business logic, trusting the efficient Garbage Collector (GC) to manage memory. It truly is the language of "Get Things Done", and I value its practical nature. But then there's Rust. The initial stages of deep-diving into Rust revealed a level of rigor and scrutiny I hadn't encountered before. The Borrow Checker presents challenges at every turn, but that very early challenge is Rust's ultimate strength. This strict scrutiny gives you an unparalleled guarantee: The time you spend satisfying the compiler is time you save debugging runtime errors or chasing elusive data races in production. The Core Trade-Offs I Now Recognize If your priority is flexibility, rapid iteration, and simple horizontal scaling (like most standard API services), Go remains the superior choice. You trade a fraction of peak speed for the best possible team productivity. However, if your project demands maximum possible performance, precise memory control, and absolute certainty of stability (such as core engine components, or systems running on limited resources), Rust is the tool you need. It forces you to adopt a deeply engineered mindset, resulting in code that is inherently safer and faster at execution. Ultimately, both languages are essential. I now have two powerful tools: one optimized for speed, and one optimized for absolute correctness. #JavaScript #TypeScript #Rust #Go #WebDevelopment #Frontend #React #FullStack