Python 3.x software with a Java-style futures implementation for efficient concurrency and asynchronous programming.
The futures3 module is a great addition to Python, providing Java-style futures implementation. This module allows you to perform multiple tasks over several URLs in parallel, making downloading URLs much faster.
To get started with this module, you need to import the necessary libraries: functools, urllib.request, and futures. Next, you can create a list of URLs to download. Once you have your URL list, you can define a function to load each URL and set a timeout for each request.
With the ThreadPoolExecutor() function from futures, you can create a thread pool that will execute a set of futures based on the URL list. In this example, we set the maximum number of threads to 50, so that we can perform multiple requests in parallel.
Once the thread pool is created, you can run the futures using the run_to_futures() method. This method takes a list of partial functions as arguments, each of which contains the URL to download and the specified timeout for that request.
After all the futures have been run, you can iterate through the results and check for any exceptions that may have occurred. If an exception has been raised, it will be printed out. Otherwise, the result will be printed along with the URL and the size of the downloaded page.
All in all, futures3 is an excellent module for Python developers who need to perform many tasks concurrently. It is easy to use, and its integration with Python makes it a valuable addition to any developer's toolkit.
To get started with this module, you need to import the necessary libraries: functools, urllib.request, and futures. Next, you can create a list of URLs to download. Once you have your URL list, you can define a function to load each URL and set a timeout for each request.
With the ThreadPoolExecutor() function from futures, you can create a thread pool that will execute a set of futures based on the URL list. In this example, we set the maximum number of threads to 50, so that we can perform multiple requests in parallel.
Once the thread pool is created, you can run the futures using the run_to_futures() method. This method takes a list of partial functions as arguments, each of which contains the URL to download and the specified timeout for that request.
After all the futures have been run, you can iterate through the results and check for any exceptions that may have occurred. If an exception has been raised, it will be printed out. Otherwise, the result will be printed along with the URL and the size of the downloaded page.
All in all, futures3 is an excellent module for Python developers who need to perform many tasks concurrently. It is easy to use, and its integration with Python makes it a valuable addition to any developer's toolkit.
What's New
Version 1.0: N/A