Concurrency and parallelism are two techniques that let you run several programs simultaneously. Python has multiple options for handling tasks concurrently and in parallel, which can be confusing.
Explore the tools and libraries available for properly implementing concurrency and parallelism in Python, and how they differ.
Understanding Concurrency and Parallelism
Concurrency and parallelism refer to two fundamental principles of task execution in computing. Each has its distinct characteristics.
The Importance of Concurrency and Parallelism
The need for concurrency and parallelism in computing cannot be overstated. Here’s why these techniques matter:
Concurrency in Python
You can achieve concurrency in Python using threading and asynchronous programming with the asyncio library.
Threading in Python
Threading is a Python concurrency mechanism that allows you to create and manage tasks within a single process. Threads are suitable for certain types of tasks, particularly those that are I/O-bound and can benefit from concurrent execution.
Python’sthreadingmoduleprovides a high-level interface for creating and managing threads. While the GIL (Global Interpreter Lock) limits threads in terms of true parallelism, they can still achieve concurrency by interleaving tasks efficiently.
The code below shows an example implementation of concurrency using threads. It uses the Python request library to send an HTTP request, a common I/O blocking task. It also uses thetime module to calculate execution time.
Running this program, you should see how much faster the threaded requests are than the sequential requests. Although the difference is just a fraction of a second, you get a clear sense of the performance improvement when using threads for I/O-bound tasks.
Asynchronous Programming With Asyncio
asyncioprovides an event loop that manages asynchronous tasks called coroutines. Coroutines are functions that you may pause and resume, making them ideal for I/O-bound tasks. The library is particularly useful for scenarios where tasks involve waiting for external resources, such as network requests.
You can modify the previous request-sending example to work withasyncio:
Using the code, you may download web pages concurrently usingasyncioand take advantage of asynchronous I/O operations. This can be more efficient than threading for I/O-bound tasks.
Parallelism in Python
you’re able to implement parallelism usingPython’smultiprocessingmodule, which allows you to take full advantage of multicore processors.
Multiprocessing in Python
Python’smultiprocessingmodule provides a way to achieve parallelism by creating separate processes, each with its own Python interpreter and memory space. This effectively bypasses the Global Interpreter Lock (GIL), making it suitable for CPU-bound tasks.
In this example,multiprocessingspawns multiple processes, allowing thedownload_urlfunction to run in parallel.
When to Use Concurrency or Parallelism
The choice between concurrency and parallelism depends on the nature of your tasks and the available hardware resources.
You can use concurrency when dealing with I/O-bound tasks, such asreading and writing to filesor making network requests, and when memory constraints are a concern.
Use multiprocessing when you have CPU-bound tasks that can benefit from true parallelism and when you have robust isolation between tasks, where one task’s failure should not impact others.
Take Advantage of Concurrency and Parallelism
Parallelism and concurrency are effective ways of improving the responsiveness and performance of your Python code. It’s important to understand the differences between these concepts and select the most effective strategy.
Python offers the tools and modules you need to make your code more effective through concurrency or parallelism, regardless of whether you’re working with CPU-bound or I/O-bound processes.
