Tornado: Best Choice for Async Python Development

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝗼𝘄 𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘂𝘀𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗵𝗿𝗲𝗮𝗱𝘀 ? The secret lies in how Python handles execution. I’ve put together a 12-slide deep dive into Python Concurrency, moving from absolute basics to the future of Python 3.13. What’s inside? ✅ Synchronous vs. Async: Why "𝘄𝗮𝗶𝘁𝗶𝗻𝗴" is the biggest bottleneck. ✅ The Event Loop: How 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 manages thousands of tasks on a single thread. ✅ The 𝗚𝗜𝗟 (𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗲𝗿 𝗟𝗼𝗰𝗸): Why traditional Python threading isn't always "parallel." ✅ The 𝗙𝘂𝘁𝘂𝗿𝗲 (𝗙𝗿𝗲𝗲-𝗧𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴): How Python 3.13+ finally enables true multi-core parallelism. 🟪 𝗧𝗵𝗲 "𝗞𝗶𝘁𝗰𝗵𝗲𝗻" 𝗔𝗻𝗮𝗹𝗼𝗴𝘆: Think of a single cook (Thread) multitasking between a gas stove (I/O) and a cutting board. That’s Async. Now imagine a kitchen with multiple cooks and multiple gas stoves. That’s Modern Free-Threading. Whether you're building 𝘄𝗲𝗯 𝘀𝗰𝗿𝗮𝗽𝗲𝗿𝘀 (𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱) or 𝗵𝗲𝗮𝘃𝘆 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 (𝗖𝗣𝗨-𝗯𝗼𝘂𝗻𝗱), choosing the right model is key to performance. Check out the slides below! #Python #Programming #SoftwareEngineering #Concurrency #AsyncIO #Multithreading #Python313 #TechLearning

Stateful UDFs just changed how Python scales. With @daft.cls, you can turn any Python class into a distributed operator that initialises once per worker and reuses state across every row. That means models, API clients, and database connections no longer get rebuilt on every call. The mental model stays simple: write normal Python classes, add a decorator, and Daft handles execution, scheduling, and parallelism. Find out more: https://lnkd.in/e79SePbN #PythonScaling #DaftCls #DistributedComputing #PythonClasses

One thing that significantly improved my Python code quality: Static analysis is not optional at scale. For a long time, I relied on code reviews to catch issues. Eventually, I realized something: 👉 Humans are bad at consistently spotting patterns. 👉 Tools are not. That’s where static analysis changed everything. Without running the code, these tools analyze your source and detect: bugs code smells complexity issues type inconsistencies All before production The combination that worked best for me: Ruff → fast linting and code quality Replaces multiple tools (flake8, isort, etc.) and runs extremely fast Mypy → type checking Uses type hints to catch bugs before runtime, bringing discipline to Python’s dynamic nature Radon → complexity analysis Measures cyclomatic complexity and highlights functions that are hard to maintain. #Python #StaticAnalysis #BackendEngineering #Django #CleanCode #SoftwareEngineering #DevOps

Ever tried posting nested data in DRF and hit the dreaded “writable nested fields not supported” error? In my latest article, I break down how to fix it cleanly — from overriding create() to adding transaction safety and optimizing with bulk_create(). You’ll learn: Why nested serializers fail on POST How .pop('items') saves your day The right way to separate read/write serializers Production-ready patterns for clean, scalable APIs Read the full breakdown here 👇 🔗 https://lnkd.in/d-3qARwR #Django #RESTFramework #Python #BackendDevelopment #APIs #SoftwareEngineering

If you’re still confused between lists vs tuples… this will fix it in 30 seconds 👇 Tuples in Python = simple, fast & unchangeable (immutable) ⚡ Here’s what you must know: ✅ Defined using () ✅ Can store multiple data types ✅ Ordered & indexed ✅ Faster than lists ❌ Cannot be modified after creation Key concepts covered: 🔹 Accessing elements & negative indexing 🔹 Single-element tuple (don’t forget the comma!) 🔹 Packing & unpacking 🔹 Common operations (len, count, index) 💡 Pro tip: Use tuples when your data should NOT change (like coordinates, configs, constants). Master basics like this → advanced Python becomes EASY 🚀 Save this for quick revision 💻 #Python #LearnPython #Coding #PythonBasics #Programming #DataStructures #CodeNewbie #PythonTips #Developer #TechSkills #100DaysOfCode

🧠 Python Concept: lambda functions Write quick functions in one line 😎 ❌ Traditional Way def square(x): return x * x print(square(5)) ❌ Problem 👉 Extra lines 👉 Not always needed ✅ Pythonic Way square = lambda x: x * x print(square(5)) 🧒 Simple Explanation Think of lambda like a mini function ⚡ ➡️ No name needed ➡️ One-line function ➡️ Quick & simple 💡 Why This Matters ✔ Less code ✔ Useful for short operations ✔ Works great with map(), filter() ✔ Cleaner for small tasks ⚡ Bonus Example nums = [1, 2, 3, 4] even = list(filter(lambda x: x % 2 == 0, nums)) print(even) 🐍 Small functions, big impact 🐍 Keep it simple & Pythonic #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode

Published pytures on crates.io — a small library that lets Rust Futures and Python Coroutines await each other. Came out of a real need: we have an algorithmic trading system in Rust and wanted to script it with async Python. Both sides are poll-based state machines under the hood, so adapting one to the other turned out to be a clean problem. The tricky part was getting the wakeup semantics right so nothing busy-polls unless necessary. The entire public API is two types. BSD-3-Clause. Github: https://lnkd.in/dFT5HBec crates.io: https://lnkd.in/dzzNAgRW #rust #python #async #opensource

🚀 #100DaysOfPython – Day 3: Lambda Functions 👉 Lambda = small anonymous function (one line) Example: add = lambda a, b: a + b print(add(2, 3)) # 5 Used commonly with: nums = [1, 2, 3, 4] squared = list(map(lambda x: x*x, nums)) ✨ Short and quick ✨ Useful for simple operations ⚠️ But here’s the catch: If your logic is more than one line → use a normal function. 🔍 My takeaway: Lambdas are great for simple transformations, not for complex logic. Read more: https://lnkd.in/eSSCUfmi #Python #Coding #100DaysOfCode #Developer

#Day10 Today I learned about stacks — a new data structure where you add and remove from the top only (Last In, First Out). In Python, a regular list works as a stack using .append() to push and .pop() to remove. My approach: Loop through each character If it's an opening bracket → push onto the stack If it's a closing bracket → check if the top of the stack is its matching opener using a dictionary If the stack is empty at the end → all brackets matched #DSA #Python #LeetCode #CodingJourney #Programming #SoftwareDevelopment #TechAfrica #ML #AI

Here’s a tiny Python change that pays off fast. Python tip: use `@dataclass(slots=True)` for high-volume models. It removes per-instance `__dict__`, which usually means lower memory usage and slightly faster attribute access. Great for DTOs, parser outputs, event payloads, and cache objects where shape is fixed. Mini rule: if the object schema is stable, add `slots=True` by default. #Python #Performance #CodeQuality #SoftwareEngineering