Python Parallelism: Essential Guide to Speeding up Your Python Code in Minutes

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Essential guide to multiprocessing with Python. Executing tasks sequentially might not be a good idea. If the input to the second task isn’t an output of the first task, you’re wasting both time and CPU. As you probably know, Python’s Global Interpreter Lock (GIL) mechanism allows only one thread to execute Python bytecode at once. It’s a severe limitation you can avoid by changing the Python interpreter or implementing process-based parallelism techniques. Today you’ll learn how to execute tasks in parallel with Python with the concurrent.futures library. You’ll understand the concept with a hands-on example – fetching data from multiple API endpoints. The article […]

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