Multithreading in Python
In this video, we’ll dive deep into the concept of Multithreading — from theory to practical implementation.
Here’s what we’ll cover 👇
▶️ What are Threads?
We’ll understand what threads are, their advantages, and in which scenarios using threads is the best approach.
▶️ Multithreading vs Multiprocessing:
We’ll explore the difference between processes and threads, looking at execution, memory sharing, and performance from both perspectives.
▶️ Practical Implementation:
We’ll implement real examples of both multithreading and multiprocessing, to clearly understand the differences.
▶️ Ways to Implement Multithreading:
We’ll cover two important approaches to implement multithreading in Python:
Class-level multithreading
Function-level multithreading
Both will be explained step-by-step.
▶️ Thread Creation Syntax:
We’ll explore the syntax for creating threads using Python’s built-in threading tools.
▶️ Thread Scheduling & Execution:
We’ll understand how the scheduler decides which thread to execute and how multiple threads run concurrently.
▶️ join() Method:
We’ll see how to use the join() method to synchronize threads and join them with the main thread.
▶️ GIL (Global Interpreter Lock):
We’ll discuss what the GIL is and its actual impact on multithreading in Python.
▶️ GIL, Memory & Execution Comparison:
Lastly, we’ll compare the memory model of threads vs processes and understand how GIL affects execution in real scenarios.
By the end of this video, you’ll have a complete understanding of multithreading, multiprocessing, GIL, and how they affect performance in Python — explained with practical coding examples.
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