同时执行多个线程

本文介绍了同时执行多个线程的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

当前代码为:

def export_data(file): <runs the db2 database command to export tables to file> def export_to_files(yaml): logger = logging.getLogger("export_to_files") thread1 = threading.Thread(target=export_data, args=[out_file1]) thread1.start() thread2 = threading.Thread(target=export_data, args=[out_file2]) thread2.start() thread1.join() thread2.join() def main(): export_to_files() if __name__ == "__main__": main()

我的理解是join()仅阻止调用线程.但是，我没有意识到thread1.join()甚至会阻止thread2的执行，实际上使代码只能运行1个线程，即thread1.

My understanding was that join() only blocks the calling thread. However, I did not realize that thread1.join() would even block thread2 from executing, essentially making the code to only run 1 thread i.e. thread1.

我如何同时执行两个线程，同时让主线程等待两个线程完成?

How can I execute both the threads concurrently, while have the main thread wait for both to complete?

我纠正了，这2个线程确实在运行，但是似乎只有1个线程实际上在某个时间点正在做"事情.

I stand corrected, the 2 threads do run, but it seems like only 1 thread is actually "doing" things at a point in time.

为了进一步详细说明，callable_method正在从数据库读取数据并写入文件.现在我可以看到有2个文件正在更新(每个线程都写入一个单独的文件)，但是其中一个文件已经有一段时间没有更新了，而另一个文件是最新的.

To elaborate further, the callable_method is reading data from the database and writing to a file. While I can now see 2 files being updated(each thread writes to a separate file), one of the files is not updated for quite some time now, while the other file is up-to-date as to current time.

没有没有连接对象.查询是从db2命令行界面运行的.

There is no connection object being used. The queries are run from the db2 command line interface.

推荐答案

您可以使用很大程度上未记录在案的 ThreadPool multiprocessing.pool中的类，以按照以下方式进行操作:

You could use the largely undocumented ThreadPool class in multiprocessing.pool to do something along these lines:

from multiprocessing.pool import ThreadPool import random import threading import time MAX_THREADS = 2 print_lock = threading.Lock() def export_data(fileName): # simulate writing to file runtime = random.randint(1, 10) while runtime: with print_lock: # prevent overlapped printing print('[{:2d}] Writing to {}...'.format(runtime, fileName)) time.sleep(1) runtime -= 1 def export_to_files(filenames): pool = ThreadPool(processes=MAX_THREADS) pool.map_async(export_data, filenames) pool.close() pool.join() # block until all threads exit def main(): export_to_files(['out_file1', 'out_file2', 'out_file3']) if __name__ == "__main__": main()

示例输出:

[ 9] Writing to out_file1... [ 6] Writing to out_file2... [ 5] Writing to out_file2... [ 8] Writing to out_file1... [ 4] Writing to out_file2... [ 7] Writing to out_file1... [ 3] Writing to out_file2... [ 6] Writing to out_file1... [ 2] Writing to out_file2... [ 5] Writing to out_file1... [ 1] Writing to out_file2... [ 4] Writing to out_file1... [ 8] Writing to out_file3... [ 3] Writing to out_file1... [ 7] Writing to out_file3... [ 2] Writing to out_file1... [ 6] Writing to out_file3... [ 1] Writing to out_file1... [ 5] Writing to out_file3... [ 4] Writing to out_file3... [ 3] Writing to out_file3... [ 2] Writing to out_file3... [ 1] Writing to out_file3...