Learn Python Concurrency and Parallel Programming

🚀 Implementing Protobuf Serialization in Python (System Design) This Python example demonstrates how to serialize and deserialize data using Protocol Buffers. First, define the message structure in a `.proto` file. Then, use the `protoc` compiler to generate Python classes from the `.proto` file. These classes provide methods for encoding and decoding data, enabling efficient communication between services. Protobuf ensures data integrity and reduces network overhead. #SystemDesign #Architecture #Scalability #DistributedSystems #professional #career #development

Every programming language trains a different kind of taste into the developer. Rust = precision Python = readability When AI writes the syntax, that taste will matter even more. Special thanks to Alexandr Kurilin for his insights on this. Link to full blog in comment

🚀 Exciting Update: Python 3.14's No-GIL Release! 🚀 Python 3.14 introduces a game-changing feature — the removal of the Global Interpreter Lock (GIL). This means improved performance for multi-threaded applications, allowing true parallel execution of threads that were previously limited. 🔍 Benefits: - Enhanced multitasking capabilities - Simplified code for concurrent execution - Reduced need for multiprocessing in CPU-bound tasks Will this No-GIL release replace the multiprocessing module? 👀 According to me multiprocessing remains valuable, this update paves the way for more effective threading, making parallel programming even more accessible and efficient. Check out the attached performance comparison image to see the real-world impact on multi-threading! 💻 #Python #Programming #Multithreading #SoftwareDevelopment

🚀 Deep Copying in Python using `deepcopy()` (Oop Concepts) For creating completely independent copies of objects in Python, the `copy.deepcopy()` function is used. It recursively copies all the attributes of the object and the objects they reference, creating a new object with completely independent data. This is essential when dealing with complex objects that contain mutable attributes, as it prevents unintended side effects when modifying the cloned object. Deep copying ensures data isolation and prevents modification of the original object. #oopconcepts #programming #coding #tech #learning #professional #career #development

When choosing a programming language, simplicity matters. For those new to programming or less experienced, Python offers an accessible entry point. While other options exist, Python's ease of use and readily available resources make it a practical choice for many. For optimal results with Python, consider utilizing async_arithmetic. #Python #Programming #Coding #TechTips

💡 Understanding Python’s Global Interpreter Lock (GIL) Ever wondered why Python threads don’t always run in parallel? I recently explored this concept and created a short PDF guide “PYTHON-GIL” that breaks down what the Global Interpreter Lock is, why it exists, and how it affects multithreading. 🔍 Inside the PDF: [*] What the GIL actually does [*] Why Python uses it (and how it simplifies memory management) [*] How it impacts CPU-bound vs I/O-bound tasks [*] Ways to bypass it using multiprocessing or C extensions If you’ve ever been confused about why your multi-threaded Python code isn’t speeding up, this guide is for you. #Python #Programming #Multithreading #Developers #GIL #Learning #PythonTips

Forget scrolling start scripting. The world runs on code and Python is the language everyone’s speaking. You don’t need magic to create, just a few lines of Python. Make that move. Start coding today '#python programming #codinglife #codinglife

Data transfer between Siemens LOGO and a PC using Modbus TCP and Python code. The Python code reads the input states of the Siemens LOGO and controls its outputs. Link to my project on GitHub: https://lnkd.in/dJt2_2wG #SimensLogo #Modbus #Python

Here's a Python closure puzzle that catches even experienced developers! This code creates lambda functions in a loop, but the output might surprise you. The issue involves how Python handles variable binding in closures - a subtle behavior that can lead to unexpected bugs. What do you think gets printed when we call these functions? Understanding closure semantics is crucial for functional programming patterns.