95 lines
2.8 KiB
Python
95 lines
2.8 KiB
Python
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"""
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扩展性系统性能
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- 垂直扩展 - 增加单节点处理能力
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- 水平扩展 - 将单节点变成多节点（读写分离/分布式集群）
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并发编程 - 加速程序执行 / 改善用户体验
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耗时间的任务都尽可能独立的执行，不要阻塞代码的其他部分
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- 多线程
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1. 创建Thread对象指定target和args属性并通过start方法启动线程
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2. 继承Thread类并重写run方法来定义线程执行的任务
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3. 创建线程池对象ThreadPoolExecutor并通过submit来提交要执行的任务
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第3种方式可以通过Future对象的result方法在将来获得线程的执行结果
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也可以通过done方法判定线程是否执行结束
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- 多进程
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- 异步I/O
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"""
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import glob
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import os
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import time
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from concurrent.futures import ThreadPoolExecutor
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from threading import Thread
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from PIL import Image
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# class ThumbnailThread(Thread):
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# def __init__(self, infile):
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# self.infile = infile
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# super().__init__()
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# def run(self):
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# file, ext = os.path.splitext(self.infile)
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# filename = file[file.rfind('/') + 1:]
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# for size in (32, 64, 128):
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# outfile = f'thumbnails/{filename}_{size}_{size}.png'
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# image = Image.open(self.infile)
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# image.thumbnail((size, size))
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# image.save(outfile, format='PNG')
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def gen_thumbnail(infile):
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file, ext = os.path.splitext(infile)
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filename = file[file.rfind('/') + 1:]
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for size in (32, 64, 128):
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outfile = f'thumbnails/{filename}_{size}_{size}.png'
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image = Image.open(infile)
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image.thumbnail((size, size))
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image.save(outfile, format='PNG')
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# def main():
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# start = time.time()
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# threads = []
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# for infile in glob.glob('images/*'):
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# # t = Thread(target=gen_thumbnail, args=(infile, ))
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# t = ThumbnailThread(infile)
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# t.start()
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# threads.append(t)
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# for t in threads:
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# t.join()
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# end = time.time()
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# print(f'耗时: {end - start}秒')
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def main():
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pool = ThreadPoolExecutor(max_workers=30)
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futures = []
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start = time.time()
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for infile in glob.glob('images/*'):
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# submit方法是非阻塞式的方法
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# 即便工作线程数已经用完，submit方法也会接受提交的任务
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future = pool.submit(gen_thumbnail, infile)
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futures.append(future)
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for future in futures:
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# result方法是一个阻塞式的方法 如果线程还没有结束
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# 暂时取不到线程的执行结果 代码就会在此处阻塞
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future.result()
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end = time.time()
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print(f'耗时: {end - start}秒')
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# shutdown也是非阻塞式的方法 但是如果已经提交的任务还没有执行完
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# 线程池是不会停止工作的 shutdown之后再提交任务就不会执行而且会产生异常
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pool.shutdown()
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if __name__ == '__main__':
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main()
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