Threading Basics And The Global Interpreter Lock (Gil)

This section introduces threading in Python, explaining its role in concurrency along with the significance of the Global Interpreter Lock (GIL).

What Is Threading?

Threading allows concurrent operation in Python, where multiple threads can run in the same process space, sharing memory but limited by the Global Interpreter Lock (GIL).

What Is The Gil?

The Global Interpreter Lock (GIL) is a mutex in Python that permits only one thread to execute bytecode at a time, affecting concurrency and parallelism.

Key Points

This section discusses key concepts of concurrency and parallelism in Python, including threading, the Global Interpreter Lock (GIL), and the use of multiprocessing.

Using The Threading Module For Concurrent Execution

This section covers essential techniques for using Python's threading module to manage concurrent execution of tasks.

Basic Thread Example

This section introduces the basic concepts of threading in Python using a simple example to demonstrate concurrent execution of tasks.

Daemon Threads

Daemon threads run in the background and terminate when the main program exits, offering an efficient way to handle tasks without blocking program completion.

Thread Safety Warning

This section emphasizes the importance of thread safety in Python's concurrent programming due to shared data, advising the use of synchronization primitives to prevent race conditions.

Multiprocessing: When And How To Use It

This section discusses when and how to utilize multiprocessing in Python for CPU-bound tasks, explaining its benefits and drawbacks compared to threading.

Using The Multiprocessing Module

This section covers the multiprocessing module in Python, detailing its usage, benefits, and when to employ it for optimal performance.

Pros And Cons

This section discusses the advantages and disadvantages of using concurrency and parallelism in Python, emphasizing the benefits and limitations of threading and multiprocessing.

Concurrent.futures: High-Level Thread And Process Pools

The `concurrent.futures` module in Python simplifies the management of threads and processes through high-level abstractions known as Executor classes.

Threadpoolexecutor

ThreadPoolExecutor is a high-level API in Python that facilitates concurrent execution of I/O-bound tasks, providing an efficient way to manage multiple threads.

Processpoolexecutor

The ProcessPoolExecutor simplifies concurrent programming in Python by allowing developers to easily utilize multiple CPU cores for CPU-bound tasks.

Benefits

This section outlines the advantages of using concurrency and parallelism in Python, focusing on their impact on performance and ease of use.

Thread Synchronization Primitives: Locks, Events, Conditions

This section covers synchronization primitives in Python, including Locks, Events, and Conditions, essential for ensuring safe access to shared resources in concurrent programming.

Lock: Mutual Exclusion

This section covers the concept of mutual exclusion in Python programming, specifically using locks to prevent simultaneous access to shared resources by multiple threads.

Rlock: Reentrant Lock

RLock (Reentrant Lock) allows the same thread to acquire a lock multiple times safely, ensuring better management of thread synchronization in Python.

Event: Thread Communication

This section covers how to use events for communication between threads in Python, providing a mechanism for one thread to signal another.

Condition: Complex Coordination

This section covers the concept of complex coordination in threads using condition variables for fine-grained synchronization.

When To Use What?

This section discusses when to use threading and multiprocessing in Python, outlining the types of tasks best suited for each approach.

Conclusion

The conclusion summarizes Python's concurrency and parallelism capabilities, emphasizing the suitability of different tools for distinct tasks.