AsyncIO-1

Understanding processes, threads, multithreading, and multiprocessing.

To better understand how concurrency works in Python, we first need to grasp the basics of threads and processes. We'll then explore how to use them for multithreading and multiprocessing to achieve concurrency. Let's start with some definitions around processes and threads.

Process:

• A process is a program that runs in its own memory space.
• Multiple processes can run on one machine, either in parallel or by switching.
• The operating system decides when and how to switch between processes.

Thread:

• Threads are smaller units of work that share the memory of a process.
• A process has at least one thread, called the main thread, and can create more threads.
• Threads can run concurrently on a multi-core CPU or by time slicing.

Multithreaded applications are a common way to achieve concurrency in many programming languages. However, there are some challenges when using concurrency with threads in Python.

Multithreading is only useful for I/O-bound work because of limitations caused by the Global Interpreter Lock (GIL). Fortunately, multithreading is not the only method to achieve concurrency. We can also create multiple processes to work concurrently, a technique known as multiprocessing.

In multiprocessing, a parent process creates one or more child processes that it manages. It can distribute work to these child processes. Python provides the multiprocessing module to handle this, with an API similar to the threading module.

1. First, we create a process with a target function.
2. Then, we call its start method to execute it.
3. Finally, we use its join method to wait for it to complete.

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