Most engineers obsess over features, UI, or "performance" But here's something nobody talks about, and it's the thing that actually saves you when things go wrong: Centralized logging.... When you're building a serious application. Especially something with multiple services, workers, queues, WebSockets, API layers, etc...there will be errors. That is a guarantee. And here's what happens if you're NOT centralizing your logs: - You only notice errors when users complain - You lose context about where something failed - Each env logs differently - You spend hours digging through random console outputs At scale, this becomes chaos. The Fix: I just implemented this for Gextron system, and it's a huge improvement. - One global initialization - One unified logger across every module - Structured messages (timestamp, context, metadata) - Errors captured with stack traces - Works the same in dev, staging, and prod As your product grows: - More users = more edge cases - More features = more moving parts - More traffic = more concurrency issues - More services = bigger blast radius when something fails If you don't have a centralized logging in place early, the pain compounds fast. Once you do: - Debugging becomes 10x faster - You catch silent failures immediately - You can trace issues across services This is one of the real "grown-up" engineering steps most indie dev skip. Don't skip it.

Hi, my name is Anshuman. I live in India and I’m currently pursuing my Master’s degree in Computer Applications (MCA) — I’m in my final year (3rd semester). Along with my studies, I’ve been working as a MERN Stack Developer for the past year. I would love to gain the following three things from this community: 1. 💬 Experiences and insights from developers. 2. ⚠️ Guidance on what mistakes to avoid while growing as a developer. 3. 🚀 Anything that can help me become a better developer than I was yesterday. For fun, I like to do these 3 things: 1.  🎮 Gaming. 2.  🎧 Listening to music . 3. 💻 Building small side projects to practice production-level coding.

🧩 JSON (JavaScript Object Notation) As JavaScript developers, we love JSON [IMG 1]. It's extremely simple to understand. But JSON is nothing more than just text representing data. That's it...just text. When you parse it, the parser decides what structure it becomes in memory. ⚙️ After Parsing (in memory) When parsed in JS: - JSON objects become plain objects, which then are then implemented much like hashmaps/dictionaries. As I continue to increase the performance of Gextron dot com, I see myself using less and less JSON objects. Here are some weaknesses of JSON and where tuples have an upper advantage: ⚡️ HIGH Frequency Ingest JSON pain: - Every message has to be prased from text into object - Parsing is CPU intensive, especially a lot of strings with a lot of characters - Field names repeat in every record, so wasted bandwidth Imagine doing this – "strike": 505, "delta": 0.95, "gamma": 0.03" – for 4200+ contracts for a single ticker. Tuple win: - Define schema: [strike, delta, gamma] - Then ingest an array of fixed width tuples - No per-field string parsing - Much smaller payload → less network, less garbage collection stress This matters even more when firing a WebSocket provider. 🥵 Hot Path Cache Read JSON pain: - Pull a blob from Redis/DB - JSON.parse() to filter or enrich data - Parse does a full walk of the entire structure, even if you only need 2 fields - Large heap allocation = more GC pauses Tuple wins: - Slice out columns you need without materializing the rest - Index into a field without parsing the whole record - Return to the client as-is or lightly transform This matters when I'm building a full option chain that needs to be served =< 100ms 🚌 Bulk Transport JSON pain: - Repetitive keys in every row - JSON.stringify is very expensive the bigger the object Tuple wins: - Tuples are naturally tight - Sending a matrix of numbers/strings without field names - Compresses extremely well - Faster to serialize

As I’m building Gextron, one of the most expensive operations is generating a full option chain. I actually wrote about the bottleneck here: 👉 How Node.js Turns API Bottlenecks Into Instant Data Moving this work to Node workers helped a lot — but there was still another hidden cost: how data was stored. So I completely changed the model of how Redis is used inside Gextron. 🧩 Before — Redis as a Key-Value JSON Cache I was just storing option-chain data as raw JSON strings. [IMG 1] Redis stored this as plain UTF-8 text, which came with heavy costs: - 🔸 Lots of heap allocation - 🔸 CPU-heavy serialization (JSON.stringify / JSON.parse) - 🔸 Huge Redis memory usage (3 – 5 MB per chain) - 🔸 Slow iteration and high GC pressure ⚡ Now — Redis as a Binary Data Store (Columnar Cache) Now the data is serialized into typed arrays (Float32Array, Uint32Array) and packed into one binary block, then Brotli-compressed. [IMG 3] Redis now stores a compact numeric binary snapshot, not text. This change alone brought massive wins: - ⚙️ Pure numbers, no strings - 💾 ~10× smaller payloads (≈ 300 KB vs 3 MB) - 🚀 Fast transfers - ⚡ Decompression 2–4 ms vs 20–40 ms for JSON.parse() 🧠 Lets breakdown a simple analogy: 📚 Bookshelf - JSON: Thousands of loose pages (repeated titles, redundant text) - Columnar: One clean binder with tabbed sections 🧮 CPU Access - JSON: Skims ever page to find a number - Columnar: Reads one tight stack of numbers 📦 Compression - JSON: Random text - Columnar: Predictable patterns (tiny) 🧰 The Takeaway By switching from JSON blobs to binary columnar storage, Redis went from being a text cache to an in-memory analytics engine. These are the same principles behind systems like Parquet and ClickHouse

Hi everyone, I’m a Senior Software Engineer with over 10 years of experience in the IT industry. I’m currently working with a U.S.-based client, focusing on building scalable and high-performance web applications. I’m excited to be here and look forward to connecting and learning with you all.