Upgrade to Python 3.14 for Faster Database Performance with UUIDv7

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

I built the fastest Python logging framework. 446K ops/sec. 2.7x faster than stdlib. 20% faster than Microsoft's picologging, which is written in C. It's a one-line migration:  import logging → from logxide import logging Same getLogger(). Same format strings. Flask, Django, FastAPI all work. Sentry and OTLP are built in. Zero config. Wrote up the production guide with copy-paste examples. ⬇️ See comment #Python #Rust #OpenSource

I’ve published my first technical article: a walkthrough of the SOLID principles—with Python examples. It started as “I’ve heard these letters everywhere—what do they actually mean in code?” Turning that into something concrete helped me more than skimming another diagram. In the post I break things down into bite-sized pieces, including: • Single Responsibility: One job per module—easier to reason about and change. • Open/Closed: Extend behavior without rewriting existing code. • Liskov Substitution: Subtypes that don’t break expectations. • Interface Segregation: Small, focused contracts instead of fat interfaces. • Dependency Inversion: Depend on abstractions, not concrete details. Beyond the theory, each section includes short Python snippets so the ideas map to something you can run and tweak—not just memorize. The full post is here: https://lnkd.in/gFXSE4d9 #SoftwareEngineering #SOLID #Python #CleanCode #OOP #DesignPatterns

The moment you add a new condition to an if/else chain in your pipeline, you’ve modified existing code! That’s exactly the smell OCP is meant to catch. 🔍 Open for extension, closed for modification means adding new behavior by writing new code, not patching old one! In my latest article, I break down what OCP looks like in Python data projects: strategy patterns, abstract base classes, and the tradeoffs of when the extra structure is actually worth it. https://lnkd.in/eE9yicvC This is Part 2 of my SOLID series. What’s the most painful “just add another elif” you’ve ever had to maintain? 😅 #Python #SoftwareEngineering #SOLID #DataScience #CleanCode

Day 2 of my Python Full Stack journey. ✅ Today I covered the very first building block of Python: → Variables → Data Types (int, float, str, bool) → f-strings to print dynamic output Looks simple. But this is the foundation everything else is built on. Here's what I actually typed today: name = "Punith" # str age = 24. # int score = 9.5 # float is_dev = True # bool print("Hi, I'm {Punith}, age {24}") and many. One thing that clicked today: Python figures out the data type automatically. No need to declare it like in Java or C. This is called dynamic typing — and it makes Python so much cleaner to write. 45 minutes. Committed to GitHub. Showing up again tomorrow. If you're learning to code right now — what was the first concept that actually made sense to you? #PythonFullStack #Day2 #BuildingInPublic #100DaysOfCode #Bangalore

What if you could forecast any CSV without opening a single Python file? Getting a quick forecast usually means writing a full Python script first. Even for a simple dataset, you often need to load the data, configure a model, and run the code. TimeCopilot removes that setup by allowing you to forecast any public CSV directly from the terminal. Just run timecopilot forecast with a URL to the dataset and it handles the rest. You can also specify the LLM to use or ask a business question in plain English from the same command. After running it, you get: • A forecast generated automatically • The best model selected for your data • A plain-English answer to your question #TimeSeries #Forecasting #Python #CLI

𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗹𝗲𝘀𝘀𝗼𝗻𝘀, 𝘂𝗻𝗳𝗶𝗹𝘁𝗲𝗿𝗲𝗱 I added logging to my Python app and thought I was done. Two lessons later — I wasn't even started. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟭 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴 𝗳𝗶𝗹𝗲 𝗶𝘀 𝗮 𝘁𝗶𝗰𝗸𝗶𝗻𝗴 𝗯𝗼𝗺𝗯 No size limit. No rotation. No cleanup. It just grows. Forever. Dev - fine. Production - it silently eats your disk until your app is down at 2AM. One swap in Python's standard library fixes this. Max size. Backup count. Auto-rotates. Auto-deletes oldest. That's it. 𝗟𝗲𝘀𝘀𝗼𝗻 𝟮 : 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴𝘀 𝗮𝗿𝗲 𝘂𝗻𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 🔍 Human-readable text is perfect for dev. In production with thousands of requests ⁉️ Useless. Switch to JSON logs. Every line becomes a structured event : timestamp, level, request_id, user_id. Want all errors from one specific request? One query. Done. No regex. No grepping through walls of text. The difference between logging and logging for production is bigger than I thought. That gap - that's what this series is about #Python #Backend #LearnInPublic #SoftwareEngineering

Coming from Go, Python's type system felt loose to me at first. No compile step. No enforcement. Just... hints. But the more I dig in, the more I appreciate tools like `NewType` and `Literal`. In Go, the compiler stops you from mixing up types that share the same underlying type. Python can do something similar — not at runtime, but statically with Mypy. ```python from typing import NewType, Literal UserID = NewType("UserID", int) ProductID = NewType("ProductID", int) # Mypy catches this — both are ints, but they're not the same type process_order(ProductID(1), UserID(2)) # ❌ LogLevel = Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] # Mypy catches this too set_log_level("WRONG") # ❌ ``` The discipline is on you to run the linter. But once you do, you get a surprisingly Go-like experience — catching swapped IDs and invalid strings before they ever hit production. Python's type system isn't weak. It's just opt-in. #Python #Go #TypeSafety #BackendEngineering #SoftwareDevelopment

I’ve been using Jupyter notebooks for years, but they tend to get messy once they stop being "temporary". I recently tried marimo, and it feels like a different approach: • notebooks as plain Python files • dependency-based execution (no more weird states) • much cleaner to keep in git What I like most is that it sits somewhere between a notebook and a small app. I also show a real example: using it to recover deleted S3 files. 👉 https://lnkd.in/d_YdRCbd

Machine Learning Time Series Data using tsaug #machinelearning #datascience #timeseriesdata #tsaug tsaug is a Python package for time series augmentation. It offers a set of augmentation methods for time series, as well as a simple API to connect multiple augmenters into a pipeline. https://lnkd.in/gURVDkPv