Prevent Kafka Connection Leaks with Python's with Block

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

How to build a sub-100ms live trading pipeline in Python (without writing C++). ⚡ If your tick-to-trade latency is over 500ms, your alpha is already decaying. Python is often called "too slow" for HFT, but the bottleneck is usually bad architecture, not the language. Here is the exact stack I use to keep execution ultra-fast: 1️⃣ Drop REST APIs: Use ZeroMQ for asynchronous, microsecond-level message passing between microservices. 2️⃣ Kill Disk I/O: Store the live order book and tick data entirely in Redis (in-memory) for zero-latency retrieval. 3️⃣ Stream, Don’t Poll: Use direct WebSocket integrations (XTS / Zerodha) instead of requesting data via REST. 4️⃣ Fast DataFrames: Swap Pandas for Polars on the hot path to crunch rolling spreads and time-series data instantly. Finding the strategy is math. Executing it before everyone else is pure engineering. 🛠️ Question for the developers: Are you team Pandas or team Polars for time-series data? Let me know below! 👇 #QuantDev #Python #SystemDesign #SoftwareArchitecture #HighFrequencyTrading #Redis #ZeroMQ #LowLatency #Polars #BackendEngineering

🚀 Built a Scalable Log Processing System using Python Recently, I worked on a backend-focused project where I designed a system to efficiently process large log files using multiprocessing and streaming techniques. 🔹 Key Highlights: Processed large log files without loading everything into memory Implemented batch-based streaming for better memory efficiency Used multiprocessing (Pool) for parallel execution Designed custom chunking logic for workload distribution Applied MapReduce-style aggregation for results Exported structured output in JSON format 🧠 Architecture: File → Batch → Chunk → Parallel Workers → Aggregation → JSON Output 💡 Key Learnings: Difference between multiprocessing and threading Importance of memory-efficient design Task vs process execution model Impact of data structures (append vs extend) 🔗 GitHub Repo: https://lnkd.in/g4Zc2CFf This project helped me understand how real-world backend systems handle large-scale data processing. #Python #BackendDevelopment #SystemDesign #Multiprocessing #Learning #Projects

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

🆕 My latest blog is out which covers AWS Lambda Powertools for Python. It's the library I add to every Lambda function before writing any business logic, and I wanted to put everything I've learned from using it across real projects into one comprehensive guide. The post covers the core observability stack: Logger, Tracer, and Metrics - along with utilities like idempotency, batch processing, event routing, and parameters. I included Terraform and SAM examples from two of my own projects to keep things practical. If you're building with Lambda and spending more time on observability boilerplate than business logic, this might save you some effort. Three decorators can replace a surprising amount of code. Check it out! https://lnkd.in/evB8U4np

🚀 Building Production-Ready APIs with Python Today I worked on setting up a backend service using FastAPI and Uvicorn, focusing on how modern Python APIs are structured and deployed. Some of the things I explored today: • Setting up a clean Python project environment using virtual environments • Running an ASGI server with Uvicorn for high-performance API execution • Building REST endpoints with FastAPI • Understanding how Python modules are structured for scalable applications • Debugging ASGI import paths and server configuration FastAPI is incredibly powerful for building modern backend services because it combines high performance, automatic documentation, and developer productivity. This is part of my ongoing journey of building cloud-ready backend systems and APIs with Python. Next steps: 🔹 Authentication systems 🔹 Database integration (PostgreSQL / SQLAlchemy) 🔹 Containerizing APIs with Docker Always learning. Always building. ⚡ #Python #FastAPI #BackendDevelopment #APIs #CloudComputing #SoftwareEngineering

A few months ago, I thought Python virtual environments, Docker, and Kubernetes were just different ways to “run code.” Then a small issue changed everything. I had a Kafka consumer working perfectly on my laptop. Clean logic, no errors. But when I moved it to another server… it failed. Missing libraries. Version conflicts. Classic “works on my machine” problem.😀 That’s when I truly understood the role of a Python virtual environment (venv). It helped me isolate dependencies — different projects, different package versions, no conflicts. But the problem wasn’t just Python packages… it was the environment itself. So I moved to Docker. Now, I wasn’t just shipping code — I was shipping the entire environment. Python version, libraries, configurations — everything packed into one image. And suddenly, the same Kafka consumer ran exactly the same everywhere. Problem solved? Not quite. What if the process crashes? What if I need 5 consumers running in parallel? What if one server goes down? That’s where Kubernetes came in. With Kubernetes Pods, my container wasn’t just running — it was being managed. Auto-restarts, scaling, load distribution… things I used to handle manually were now automated. That’s when it clicked: venv helps you develop Docker helps you deploy Kubernetes helps you scale and survive failures Today, I don’t see them as competing tools — they are layers of maturity in building reliable systems. Start simple. But build in a way that you’re ready to scale. #Python #Docker #Kubernetes #Kafka #DevOps #DataEngineering #SystemDesign

Built a Python backend system handling real-time data using Kafka. Biggest lesson: Scaling systems is about architecture, not just code. Key things that worked: • Event-driven design • Async processing • Efficient partitioning System design matters more than code at scale.

🚀 Today I went deeper into building backend APIs using Python, FastAPI, and Async SQLAlchemy. Instead of just learning theory, I implemented a mini backend system that includes: 🔹 REST API endpoints with FastAPI 🔹 Request & response validation using Pydantic 🔹 Async database integration with SQLAlchemy 🔹 SQLite async engine configuration 🔹 UUID-based primary keys for scalable data models 🔹 Automatic database table creation at startup 🔹 Error handling using HTTPException Example endpoints implemented: • GET /posts/{id} → Retrieve a post • POST /post → Create a new post Tech stack used today: Python 🐍 FastAPI ⚡ Pydantic 📦 Async SQLAlchemy 🗄️ SQLite (async driver) Key learning today: Modern Python backend development is moving toward asynchronous architectures, which significantly improve scalability and performance for real-world applications. Next steps in this project: ✔ File upload API ✔ Authentication system (JWT) ✔ Cloud deployment ✔ Production-grade database integration Building every day. Improving every day. #Python #FastAPI #BackendDevelopment #AsyncPython #SQLAlchemy #SoftwareEngineering #BuildInPublic

Building REST APIs makes us forget what actually happens at the TCP socket layer. In an HTTP request-response loop, sockets close quickly. But what happens when a client sends SUBSCRIBE and holds the connection hostage indefinitely? What happens when you have 10,000 idle subscribers, and a single PUBLISH triggers 10,000 asynchronous network writes? Welcome to Part 2 of my Redis internals journey: Building real-time Pub/Sub from scratch. 🔌🚀 The biggest takeaway? Managing stateful, long-lived connections natively in Python's asyncio is a completely different beast than building standard web backends. I wrote up a quick Engineering Case Study breaking down the core architectural challenges: 1️⃣ The "Long-Lived Connection" Problem (Handling idle sockets without burning OS threads) 2️⃣ The O(N) "Fan-Out" Bottleneck (And how I refactored to O(1) routing) 3️⃣ Managing Strict Protocol State Shifts Link to the deep dive below 👇 https://lnkd.in/dEpRhwnn Here you have the code 👇 https://lnkd.in/gEMR2KAx #Python #Redis #SystemDesign #SoftwareEngineering #Asyncio #PubSub