我正在学习如何在executor.map()和executor.submit()中使用并发。ensure threads are cleaned up promptly # Start the load operations and mark each future with its URL
future_to_url = {executor.submit
当通过异步run_in_executor启动同步功能,然后取消它时,我看到了一个不想要的行为。ThreadPoolExecutor (以及ProcessPoolExecutor)上下文管理器将在退出时调用executor.shutdown,并等待所有挂起的工作完成(在其线程上使用join() )run_in_executor的实现应该考虑到这一点吗?或者这是应该处理的(就像在线程上调用executor.shutdown一样)?datetime.now()} I am in a thread')
s
我使用Spark并行处理一些执行数据提取并返回熊猫数据的现有代码。我想把这些熊猫的数据转换成一个或多个火花数据。下面是一个简化的代码示例: # Lots of existing code that returns a large pandas dataframe return pd.DataFrame({'x': s, 'y': [1, 2, 3],
关于Python run_in_executor的代码import concurrent.futures
# FileRun in the default loop's executor: None, blocking_iopool:
with concurrent.futures.ProcessPoolExecutor() a
因此,concurrent.futures.Executor.shutdown()将向执行器发出信号,表示在执行当前未决的期货时,它应该释放它正在使用的任何资源。但是,当我使用executor的when ()方法时,它不是立即关闭线程,而是在完成整个执行之后调用shutting ()。a sleep of 100 secs, as the list will always have the urls that were not executed. # Start the