What Is Python concurrent.futures? (with examples)
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As a Python learner, I've faced several challenges, but so far, one of the most difficult topics to understand has been concurrency. In the beginning, it can be incredibly confusing, especially if you're a beginner. The aim of this blog post is to simplify concurrency by breaking it down with a couple of examples and an analogy to help you understand this challenging concept. So, let's get started.
Why does Concurrency Matter?
When writing Python programs, you might find yourself needing to execute multiple tasks simultaneously or in parallel. This is where concurrency comes in. Concurrency allows your program to run multiple tasks at the same time, which can significantly improve performance and efficiency, particularly when handling time-consuming tasks.
The Magic of Python concurrent.futures
Python's concurrent.futures
module simplifies concurrent programming by providing a high-level interface for asynchronously executing callable (functions/methods). ThreadPoolExecutor and ProcessPoolExecutor are two popular classes within this module that enable you to easily execute tasks concurrently, using threads or processes, respectively.
When deciding between ThreadPoolExecutor and ProcessPoolExecutor, consider the following analogy - ThreadPoolExecutor is like having multiple chefs in a shared kitchen, while ProcessPoolExecutor is like having multiple chefs, each with their own kitchen.
ThreadPoolExecutor is Continue reading