This article discusses different approaches to concurrency in Python, highlighting the use of threads, subprocesses, and the asyncio library. It emphasizes that threads and subprocesses are beneficial for managing tasks that can run side by side, while asyncio is particularly advantageous for non-CPU-bound tasks, allowing for efficient handling of operations such as web scraping. It also points out the built-in async features available in Python 3.13 that enhance performance and readability for developers working on concurrent applications.
Python supports multiple methods for parallel execution including threads, subprocesses, and the async library, each suitable for different types of workloads like CPU-bound or network-bound tasks.
Using Python's built-in async features can optimize handling of non-CPU-bound tasks by allowing operations to yield control without blocking, making it ideal for scenarios like web scraping.
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