Python Threads Slow? The Hidden Reason | GIL Explained in Tamil

In this video, we take a deep dive into one of the most important and often misunderstood concepts in Python — the Global Interpreter Lock (GIL). If you have already watched the previous video on multithreading, this is the perfect continuation because here we connect the dots and uncover what actually happens behind the scenes when multiple threads run in Python. Many learners assume that creating multiple threads automatically means parallel execution, but Python behaves differently due to the presence of the GIL, and this video is designed to give you a clear, intuitive understanding of why that happens. We begin by understanding what the GIL really is and why it exists in Python. Instead of treating it as a limitation alone, we explore the design decision behind it and how it simplifies memory management inside the Python interpreter. From there, we move into how the GIL directly affects multithreading. Even if your system has multiple CPU cores, Python threads do not truly run in parallel for CPU-bound tasks because the GIL ensures that only one thread executes Python bytecode at a time. This is a critical concept for anyone aiming to write efficient and scalable Python programs, especially when working on performance-heavy applications. The video then breaks down how thread switching works and why it might give the illusion of parallelism. You will understand how context switching allows multiple threads to make progress, even though they are not executing simultaneously on multiple cores. This explanation is especially useful if you’ve ever wondered why your multithreaded CPU-bound code doesn’t speed up as expected. One of the most important takeaways from this video is understanding when multithreading is actually useful in Python. We clearly explain why multithreading shines in I/O-bound scenarios such as network requests, file handling, or database operations. In such cases, while one thread is waiting for an external operation to complete, another thre