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Next Section： Using multi-threading, Python crawlers are sped up 10 times
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Python concurrent programming practice
Chapter1 Chapter One
1-1 Introduction to Python Concurrent Programming
1-2 How to choose multi-threading, multi-process and multi-coroutine
1-3 The main culprit behind Python’s slowness is the global interpreter lock GIL
1-4 Using multi-threading, Python crawlers are sped up 10 times
1-5 Python implements producer-consumer crawler
1-6 Python thread safety issues and solutions
1-7 Python's easy-to-use thread pool ThreadPoolExecutor
1-8 Python uses thread pools to achieve acceleration in web services
1-9 Use the multiprocessing module to speed up program execution
1-10 Python uses multi-process pool to accelerate program running in Flask service
1-11 Python asynchronous IO implements concurrent crawler
1-12 Using semaphores to control crawler concurrency in asynchronous IO
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![Front-end project-Shangyou [HTML/CSS/JS technology comprehensive practice]](https://img.php.cn/upload/course/000/000/068/63bfcb2974f91471.jpg)
![Zero-based front-end course [Vue advanced learning and practical application]](https://img.php.cn/upload/course/000/000/068/63bbd15353895588.jpg)
![WEB front-end tutorial [HTML5+CSS3+JS]](https://img.php.cn/upload/course/000/000/068/63ba60c8da520536.jpg)
![Apipost practical application [api, interface, automated testing, mock]](https://img.php.cn/upload/course/000/000/068/63b3c967a20f4861.jpg)
