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Faster Python: Concurrency in async/await and threading
- Concurrency in Python can be achieved using async/await and threading to make programs execute multiple tasks concurrently.
- Async/await and threading libraries in Python enable the appearance of concurrency, although CPU processing is still limited by the GIL in multithreading.
- Async/await involves cooperative concurrency where coroutines yield control to each other, while threading uses the operating system scheduler to manage processes.
- Python's asyncio package and async/await keywords simplify asynchronous programming by using coroutines to pause and resume functions.
- By leveraging asyncio, tasks can be completed faster by allowing other processes to run concurrently while waiting for I/O operations.
- In contrast, threading can be used for true parallelism on multiple CPU cores, but it requires careful handling of race conditions to prevent errors.
- Concurrency is beneficial for speeding up programs, especially in I/O-bound tasks, where async/await can optimize waiting time during operations like database queries.
- For CPU-bound tasks, Python's GIL limits true parallel processing, but techniques like multiprocessing and parallel programming can be used for better performance.
- Debugging concurrent code in PyCharm can be simplified using breakpoints, real-time variable views, and console integration to track execution flow across different threads.
- Overall, mastering concurrency in Python with async/await and threading, along with proper debugging techniques, can improve program efficiency and performance.
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