Multithreading and Multiprocessing in Python

In this unit, we will delve into the concepts of multithreading and multiprocessing in Python. These are advanced concepts that allow us to run multiple threads or processes simultaneously, thereby improving the efficiency and performance of our Python programs.

Understanding the Concept of Threading in Python

In Python, threading allows for the execution of multiple threads (smaller units of a process) concurrently. This is particularly useful when you're working on tasks that are I/O bound, such as making requests to a web server or reading and writing to files.

Creating and Managing Threads

Python's threading module provides a Thread class to create and manage threads. You can create a thread by instantiating the Thread class and passing the function you want to run in the thread to the target argument. You can start the thread with the start() method and wait for it to finish with the join() method.

Synchronization Between Threads

When multiple threads are modifying a shared resource, you may encounter race conditions, where the output depends on the sequence of execution. Python's threading module provides several synchronization primitives, including Lock, RLock, Semaphore, and Condition, to help avoid these issues.

Understanding the Concept of Multiprocessing in Python

Multiprocessing, on the other hand, involves running multiple processes concurrently. This is beneficial when you're working on tasks that are CPU bound, such as mathematical computations. Each process in Python runs in its own memory space, so they don't share global variables.

Creating and Managing Processes

Python's multiprocessing module provides a Process class to create and manage processes. The usage is similar to the Thread class. You instantiate the Process class, passing the function you want to run in the process to the target argument, and then start the process with the start() method.

Synchronization Between Processes

Just like with threads, you may need to synchronize processes when they're modifying a shared resource. The multiprocessing module provides several synchronization primitives, including Lock, RLock, Semaphore, and Condition, as well as Queue and Pipe for inter-process communication.

Use Cases of Multithreading and Multiprocessing

Multithreading is best used for I/O-bound tasks, such as web scraping or interacting with the file system, where the program spends a lot of time waiting for input and output operations. Multiprocessing is best used for CPU-bound tasks, such as computations, where the program spends a lot of time using the CPU.

Difference Between Multithreading and Multiprocessing

The main difference between multithreading and multiprocessing lies in the way they use system resources. Threads of a process share the same memory space, which makes sharing data between threads efficient but can lead to conflicts. Processes, on the other hand, have separate memory spaces, which makes sharing data more complex but avoids conflicts.

Choosing Between Multithreading and Multiprocessing

The choice between multithreading and multiprocessing depends on the nature of the task. For I/O-bound tasks, multithreading is usually a better choice, as it allows the program to continue running while waiting for I/O operations. For CPU-bound tasks, multiprocessing is usually a better choice, as it allows the program to leverage multiple CPUs and avoid the Global Interpreter Lock (GIL) in Python.