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Returns an iterator over the future instances (possibly created by different executor instances) given by fs that yields futures as they complete (finished or cancelled futures). When the result of the task is needed, an application can use the future to block until the result is available. Concurrency in python can be efficiently handled using the concurrent.futures module

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The asynchronous execution can be be performed by threads using threadpoolexecutor or seperate processes using processpoolexecutor. When each task is started, a future instance is returned This works perfectly fine, but as i'm scraping multiple websites at the same time, i was using concurrent.futures.threadpoolexecutor at first to scrape with multiple threads.

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The concurrent.futures module in python allows you to manage asynchronous execution of callable objects

It provides two main types of executors When you create a new instance of the threadpoolexecutor class, python starts the executor Once completing working with the executor, you must explicitly call the shutdown() method to release the resource held by the executor. The concurrent.futures.executor.map function in python 3 allows you to easily pass multiple parameters to the function being executed concurrently

This enables you to parallelize tasks that require multiple inputs, such as calculations or file downloads. Achieving optimal performance through parallel execution is essential Python, a versatile programming language, provides several tools for concurrent execution To use a pool of workers, an application creates an instance of the appropriate executor class and then submits tasks for it to run