Vamshi Polishetti’s Post

In less than a week, I learned the fundamentals of Python and used it to build a backend for stock analysis over the last few weeks. I started with a brute-force approach: • Sequential processing of ~50–100 stocks (Nifty 50 & Nifty 100)  • No caching—every request recomputed everything. Result: ~70–120 seconds per API call for ~50–100 stocks. Felt tired of waiting so long for each API call. 🤷♂️ Then I optimized the architecture: • Parallel processing for stock-level computations  • Redis caching (Upstash) to avoid redundant work Now: Response time down to ~1–2 seconds for ~250 stocks #Python #FastAPI #ReactJS #StockMarket

Background jobs look simple until you're actually running them in production. I recently wired up Python + Celery + Redis for a project. The core setup was pretty quick and easy and is well documented. The harder decisions are about task design and finding the right granularity so tasks are meaningful without becoming a bottleneck or a maintenance headache. Some of the questions that cropped up: - What actually happens when a task fails and nobody notices? - How do I know what's queued vs running vs stuck? - At what point does adding retry logic just paper over a worse problem? Still working through some of it. Anyone who's dealt with Celery at scale, I'm curious how you handled task monitoring specifically.

I was teaching a class on serverless backend. Lambda, DynamoDB, API Gateway. The code was clean. The logic was simple. Read from the database, update a value, return the result. We hit Test. "Object of type Decimal is not JSON serializable." Everything looked fine. The DynamoDB item was there. The number was a number. So why was Python refusing to serialize it? Here is what nobody tells you. DynamoDB does not return integers. It returns Decimal objects. Python's json.dumps cannot serialize Decimal. So it breaks. The fix is one word. int() 15 minutes debugging. 3 seconds to fix once you know. #AWS #DynamoDB #Lambda #Serverless #Python

Day 206 of 250 Days of Code Challenge. #CCBP #NxtWave #React_Hooks #useEffect #Rivison_Python #ReactJS #HTML #CSS #Javascript #Python #SQL  #NodeJS #ExpressJS #Components #MongoDB 🔹 Python Functions – The Building Blocks of Clean Code 🔹 Functions in Python help us write reusable, modular, and efficient code. They allow us to break down complex problems into smaller, manageable pieces. Whether it’s a simple def greet(): or a powerful function with parameters and return values, mastering functions is the first step toward writing professional, scalable applications. 💡 Tip: Always aim for functions that do one thing well – it makes debugging and collaboration much easier!

Sub-100ms APIs Serving 10K+ Requests/Day-Here's What That Actually Takes Spinning up a FastAPI endpoint takes 10 minutes. Making it production-ready takes a lot more. At my current role, I build and maintain REST APIs in Python (FastAPI) and Node.js that serve over 10,000 requests per day — with sub-100ms latency requirements. Here's what "production-ready" actually meant for us: Schema design before code. Every endpoint started with a PostgreSQL schema review. Badly normalized data shows up in latency later. Multithreading is not optional at scale. Single-threaded Python collapses under concurrent load. I built multithreaded data-processing pipelines that improved throughput by 30% under real-world concurrency. Observability from day one. Latency SLAs mean nothing if you can't measure them. Instrumentation and logging were part of the PR, not an afterthought. OOP principles keep it maintainable. Services that grow fast get messy fast. Clean object-oriented design was the only thing that kept the codebase sane as features stacked up. 10K requests/day is not massive by internet scale — but it taught me what production really means. What's the hardest production lesson you've learned? #BackendEngineering #FastAPI #PythonDevelopment #SoftwareEngineering #APIDesign

🚀 CRUD Operations with FastAPI & PostgreSQL Built a simple and powerful backend using FastAPI + PostgreSQL to perform: ✔️ Create ✔️ Read ✔️ Update ✔️ Delete ⚡ FastAPI for speed & validation 📊 PostgreSQL for reliable data storage 💡 CRUD is the backbone of every backend system—master it to build real-world applications. #FastAPI #Python #PostgreSQL #CRUD #BackendDevelopment

🚀 Improving API Performance with Caching – My Learning While working on backend APIs, I noticed that some endpoints were repeatedly fetching the same data, which affected performance. That’s when I started exploring caching. Here’s what I understood: 🔹 Caching helps store frequently used data temporarily 🔹 Reduces repeated database queries 🔹 Improves API response time significantly 💡 What I found useful: Instead of hitting the database every time, caching allows us to reuse data for a certain duration, making the system faster and more efficient. ⚠️ One important thing: Choosing what to cache and when to invalidate it is very important to avoid outdated data. This made me realize that performance optimization is not just about queries, but also about smart data handling. Still exploring more ways to build efficient backend systems 🚀 Have you used caching in your APIs? #Django #Python #BackendDevelopment #API #PerformanceOptimization #LearningInPublic

With most data engines, every Python UDF call serializes data across a process boundary. The engine runs in one process, Python in another, and every batch pays the serialization tax. As data grows, so does the number of batches, and the cost grows with it. A single function quickly gets expensive at volume. For an agent generating dozens of functions in a session, it compounds fast. We built Sail's engine in Rust. Python runs inside the engine process via PyO3. Arrow batches pass between Rust and Python with zero copies. Not an optimization layered on top of a traditional architecture. A different architecture entirely. See it for yourself. Get early access to LakeSail Cloud.

Django REST Framework Authentication Authentication is the backbone of secure APIs, yet it’s often misunderstood. In my latest deep-dive, I break down Basic, Session, and Token Authentication in Django REST Framework with real-world examples, edge case analysis, and production-ready insights. 🔑 Key takeaways: Authentication ≠ Permissions Why BasicAuth is risky in production without HTTPS The hidden CSRF pitfalls of SessionAuthentication How TokenAuthentication works, its limitations, and why tools like Knox or JWT are better for scaling Practical code snippets for login, logout, and securing views Read the full article here: https://lnkd.in/dmiTxTp4 #Django #RESTAPI #Authentication #Python #WebDevelopment #Security