async-concurrency

To handle I/O-bound and CPU-bound tasks concurrently in Python, use `asyncio` and `threading` for non-blocking I/O operations, and `multiprocessing` for CPU-bound parallel execution. This approach improves application responsiveness by efficiently distributing workload.

The best way to fetch multiple URLs in parallel using `asyncio` is utilizing the `aiohttp` library for non-blocking HTTP requests. This pattern executes multiple web scraper API calls concurrently, significantly reducing total fetch time compared to sequential requests.

Use `multiprocessing` instead of `threading` for Python performance when processing large datasets or CPU-bound tasks. `multiprocessing` achieves true parallelism by bypassing the GIL, whereas `threading` is better suited for I/O-bound concurrency like database queries.

Yes, Python `asyncio` supports semaphores and timeouts for concurrent operations. These patterns limit the number of simultaneous concurrent tasks and prevent indefinite blocking, ensuring robust concurrent application development when handling asynchronous operations.

Yes, you can process large datasets in parallel with Python `multiprocessing`. It facilitates CPU-bound parallelism by distributing data chunks across multiple processes, maximizing multi-core processor utilization and speeding up computation-heavy dataset operations.

A Python web scraper hangs during concurrent API calls if it uses blocking operations instead of non-blocking `asyncio` tasks. Implementing timeouts and semaphores prevents indefinite waiting on network responses, ensuring robust concurrent execution.