Category : concurrency

I’m seeing some behaviour with Python 3.8 and the concurrent.futures.ThreadPoolExecutor method which I’m not sure is working as designed. What my script does is requests multiple RTSP streams from an RTSP server and then uses OpenCV to save the first frame as a jpg. Here is my code: from cv2 import VideoCapture, imwrite import concurrent.futures ..

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I have a dataframe that consists of all the links that I will use requests to scrape the data. I am using concurrent.futures.ThreadPoolExecutor to loop through all the links from the dataframe and scrape it. When the scrapings are done, I want to store all the data back into the dataframe. How can I assure ..

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Lets say there is four core cpu processor and I am running all four process. To each process, I assigned a queue. But how do i run all queue concurrently. class ParentProcess(multiprocessing.Process): def __init__(self, queue): multiprocessing.Process.__init__(self) self.queue = queue def run(self): while not self.queue.empty(): data = self.queue.get() print(data) def main(): numProcs = 4 queueList = ..

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I’ve noticed that concurrent.futures.ThreadPoolExecutor works somewhat well on Darwin OS in cleanly exiting the application when interrupted by KeyboardInterrupt. However, this is not the case for Windows 10, where the process hangs for minutes without any indication of a thrown exception. Here’s an example code: import concurrent.futures import urllib.request URLS = [‘http://www.foxnews.com/’, ‘http://www.cnn.com/’, ‘http://europe.wsj.com/’, ‘http://www.bbc.co.uk/’, ..

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