Sequelize Transactions in JavaScript

🔗 How URL Shorteners Work Behind the Scenes? Ever wondered what happens when you click a short link like bit.ly/abc123? Let’s break it down 👇 🧠 Step 1: Creating the Short URL When a user submits a long URL: ➡️ The backend generates a unique key ➡️ Stores mapping in database: short_code → original_url abc123 → https://lnkd.in/gsbnW_N4 → How is short_code generated? • Base62 encoding (a-z, A-Z, 0-9) • Hashing (MD5/SHA + trimming) • Auto-increment ID → encoded 🗄️ Step 2: Database Mapping A typical table: Table: "url_mapping" id (PK) short_code (unique) original_url created_at expiry (optional) click_count 🚀 Step 3: When User Clicks Short URL User hits: https://short.ly/abc123 Backend flow: 1. Extract abc123 2. Query database: SELECT original_url FROM url_mapping WHERE short_code = 'abc123'; 3. Return HTTP redirect HTTP 301/302 → https://lnkd.in/gsbnW_N4 📊 Step 4: Analytics & Usage Tracking This is where URL shorteners become powerful 👇 Every time a short URL is clicked: ➡️ Increment click_count ➡️ Capture metadata: •📍 Location (Geo IP) •📱 Device / Browser •⏱ Timestamp •🌐 Referrer (where user came from) ➡️ Store or stream this data for analysis 💡 This helps answer: • Which links are most popular? • Where are users coming from? • What time do users engage the most? 💡 URL shorteners look simple… But they are a great example of system design, scalability, AND real-time analytics systems. #SystemDesign #Backend #Java #SpringBoot #DistributedSystems #Analytics #Coding

hi connections Day 27 of 30: Deep Cleaning Data with LeetCode 2705 🚀 Today’s challenge, Compact Object, is a powerful lesson in data sanitization. In real-world applications, especially when working with the MERN stack, we often receive "noisy" data from APIs or forms containing null, 0, or undefined values that can break our UI or clutter our database. The Problem The goal is to recursively traverse an object or array and remove all falsy values (like false, 0, "", null, undefined, and NaN), no matter how deeply they are nested. The Strategy: Recursive Sanitization Since the data can be multi-layered, a simple loop isn't enough. I used a recursive approach: Base Case: If the input isn't an object or is null, return it immediately. For Arrays: Create a new array, recursively "compact" each item, and only push it if it evaluates to truthy. For Objects: Create a new object and only assign keys whose recursively compacted values are truthy. Why It’s a Game Changer This utility is incredibly practical for: ✅ Payload Optimization: Reducing the size of JSON sent over the network. ✅ State Management: Cleaning up React form data before submission. ✅ Database Integrity: Ensuring only valid, meaningful data is saved to MongoDB. Mastering this recursive logic ensures that your data remains clean, predictable, and efficient. We are officially in the final 3-day countdown! The end is in sight. 💻✨ #JavaScript #LeetCode

Most data analysts on my team spent more time writing SQL than actually analysing data. So I built a fix — without touching our existing Superset setup. It's called a Text-to-SQL Sidecar: a standalone FastAPI microservice that sits alongside Apache Superset and turns plain English into validated, safe SQL. You ask: "which products had the highest return rate last quarter?" It generates, validates, and executes the SQL — then hands the results back. A few things I was deliberate about: → AST-level SQL validation (not string matching — trivially bypassable) → Per-database table allowlists so the LLM can only touch what it's supposed to → Schema caching so we're not hammering the DB on every request → LLM-agnostic design — swap the endpoint URL, change the model → Reasoning traces returned alongside SQL so analysts can actually trust the output Superset never needs to know it exists. It just receives SQL. I wrote up the full implementation — architecture, code walkthrough, and the design decisions that make it production-ready. Link in the comments 👇 #DataEngineering #AI #SQL #FastAPI #ApacheSuperset #LLM #Python

I built Talk2DB ⚓ — A Security-First SQL Agent. The Problem: Giving LLMs direct access to a database is powerful but dangerous. A single "hallucination" or malicious prompt could drop an entire table. The Solution: I developed a full-stack interface (Next.js 16.2 + FastAPI) using LangChain to bridge the gap between natural language and relational data. Technical Highlights: 🛡️ Logic Guardrails: A custom security layer that intercepts and blocks destructive SQL commands (DROP/DELETE) in real-time. 🧠 Agentic Intelligence: The system analyzes schema metadata to map vague intent (e.g., "Who is our MVP?") into precise SQL aggregations. 🎨 Modern UX: A reactive, dark-mode interface with Markdown support for professional business insights. Check out the code and the technical breakdown on GitHub: https://lnkd.in/eX4vH5_r #AI #SoftwareEngineering #NextJS #FastAPI #DataSecurity #GenAI #SQL

We cut peak-time dashboard resource usage by ~50% without adding new servers. Here’s the breakdown. 🚀 As traffic grew, one of our internal dashboards started slowing down exactly when usage was highest. Response times increased, database load spiked, and unnecessary queries were consuming resources. The issue wasn’t infrastructure. It was application-level inefficiency. The Challenge The dashboard was making repeated database hits while rendering data-heavy views. Classic symptoms: • Slow response times during peak hours • Increased DB utilization • Higher CPU/memory pressure on the app layer After profiling the flow, the root cause was clear: 👉 N+1 query patterns + repeated data fetching logic What I Changed 1️⃣ Consolidated Data Fetching Used Django ORM features like: • select_related() for ForeignKey joins • prefetch_related() for reverse/M2M relationships This ensured related data was fetched in batches instead of per record. 2️⃣ Reduced Repeated Query Execution • Removed queryset evaluations inside loops • Cached reusable datasets during request lifecycle • Avoided duplicate ORM calls across helper methods 3️⃣ Shifted Transformations to Python Once the required data was fetched efficiently, grouping/filtering/manipulation was done in-memory rather than repeatedly querying the DB. 4️⃣ Leaner Payloads Used .values() / targeted field selection where full model objects were unnecessary. The Impact ⚡ • ~50% reduction in resource usage during peak load • Significant drop in DB hits • Faster dashboard response times • Better stability under concurrent traffic 🚀 3 Lessons for Scaling Django Backends Query count matters more than query elegance One clean query repeated 500 times is still expensive. Fetch once, process many Databases should retrieve data. Business logic can often run in memory. Profile peak traffic scenarios Many bottlenecks only appear under real concurrency. Performance wins don’t always come from bigger infra. Sometimes they come from better data flow design. #Django #Python #BackendEngineering #PerformanceOptimization #Scalability #SoftwareEngineering

Your ORM is LIES and your database DIES…… Prisma. Sequelize. Type ORM [Object-Relational Mapping]. Great DX. Terrible SQL. Here's why 👇 You write this: await db.user.findMany({ include: { posts: true } }) Clean, right? Under the hood, your ORM fires: → 1 query to fetch all users → 1 query per user to fetch their posts 50 users = 51 queries. 500 users = 501 queries. This is the N+1 problem and ORMs generate it silently, constantly. More ORM traps that wreck performance: → Lazy loading pulling entire relations you never use → No query batching by default → Generated SQL with unnecessary subqueries and redundant joins → Zero awareness of your index structure The scary part? Your ORM abstracts the SQL, so you never see the damage. Most devs only find this during a production incident. By then, the query has run millions of times. Raw SQL isn't always the answer. But understanding what your ORM actually generates,https://lnkd.in/dYGfeSmt is non-negotiable. Dharmops shows you the real query behind your code and tells you exactly what's wrong. No guessing. No log diving at midnight. → Diagnose your queries free="https://lnkd.in/dYGfeSmt" Are you using an ORM in prod? Which one? 👇 #ORM #Prisma #DatabaseOptimization #BackendDevelopment #NodeJS #QueryPerformance #Dharmops #SoftwareEngineering #DevTools #TechFounders

Choosing the wrong data structure can make your code 100x slower. Here is how to pick the right one! Every data structure has a specific use case. Using the wrong one is like using a hammer to cut wood. Array ✅ Fast random access by index (O(1)) ❌ Fixed size, slow insertions/deletions Use case: When you know the size and need fast lookups Queue (FIFO) ✅ First In, First Out operations Use case: Task scheduling, breadth-first search, handling requests Stack (LIFO) ✅ Last In, First Out operations Use case: Undo/redo, function calls, depth-first search, expression evaluation Linked List ✅ Fast insertions/deletions (O(1) at head) ❌ Slow search (O(n)) Use case: When you need frequent insertions/deletions, implementing queues/stacks Tree ✅ Hierarchical data, fast search in balanced trees (O(log n)) Use case: File systems, databases, decision trees, BST for sorted data Graph ✅ Represents relationships between entities Use case: Social networks, maps/routing, recommendation systems Matrix ✅ 2D data representation Use case: Image processing, game boards, mathematical computations Max Heap ✅ Fast access to maximum element (O(1)) Use case: Priority queues, finding top K elements, median streaming Trie ✅ Fast prefix searches (O(m) where m is string length) Use case: Autocomplete, spell checkers, IP routing HashMap ✅ Fast key-value lookups (O(1) average) Use case: Caching, counting occurrences, fast lookups HashSet ✅ Fast membership checks, no duplicates (O(1) average) Use case: Removing duplicates, checking existence Pro tip: The best data structure is not always the most complex one. Sometimes a simple array is all you need. Which data structure do you find yourself using the most? Share below! #DataStructures #Programming #Java #BackendDevelopment #Algorithms #SoftwareDevelopment

Don't you find it annoying when you need to represent an object oriented model in JSON schema, and you end up duplicating the same properties everywhere and you're constrained by a tree structure when your data is really a graph? I've been working on a spec to support OO in a JSON schema, which supports inheritance, abstract types, has-one and has-many relationships, while supporting hierarchical relationships, like normal JSON Schema, and the same permissive types and constraints for those types. Sounds interesting? You can contribute! it's open source.

Stop Googling "JSON Formatter" and hoping they aren't logging your data. Most online dev tools are bloated, ad-ridden, or worst of all, send your sensitive inputs to a backend server. I got tired of it, so I built DevLoft: a collection of 19 essential utilities built purely with Vanilla JS. No React. No Webpack. No Node modules. Just index.html, style.css, and a bunch of scripts. Why did I build it this way? - Zero Latency: It loads faster than a framework can even initialize. - True Privacy: Since there’s no backend, it is physically impossible for your data to leave your machine. - Low Barrier to Entry: Want to add a tool? You don't need to learn a framework. Just write a function and open a PR. The Toolkit includes: Data Science: Z-Score outliers, Haversine distance, and Log Parsers. Security: PII Redaction and XSS Sanitizers. AI/LLM: Recursive text chunkers (RAG prep) and Token cost estimators. Classic Dev: Regex testers, SQL Schema generators, and Text diffs. This is an open-source "sandbox" for all of us. If you’ve ever written a quick script to solve a repetitive task, don't let it die in your Gists,add it to DevLoft and let the community use it. Explore the tools (link in comments) and feel free to contribute on GitHub I’m looking for contributors to help optimize the CSS and add more niche data-science utilities. What’s the one script you’re currently running locally that should be a UI tool? #OpenSource #VanillaJS #BuildInPublic #DataScience #WebDev #SoftwareEngineering

🚀 From raw data… to meaningful insights At first, my system only stored IDs: machine_id = 1 category_id = 1 Technically correct — but not useful. So I improved the backend by implementing relational queries (JOIN) using Sequelize. Now the system returns: • Machine name instead of machine_id • Category name instead of category_id • Structured, readable data for analysis This small change makes a big difference. Because in real systems, data is not just stored — it needs to be understood. Still improving the system step by step 🚀 #BackendDevelopment #NodeJS #DatabaseDesign #Manufacturing #CareerGrowth