Скачать или смотреть How to Run Tasks Concurrently Using ThreadPoolExecutor in Python

Learn how to effectively use `ThreadPoolExecutor` for concurrent task execution in Python. We'll guide you through a common issue and its solution step-by-step.
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Unlocking the Power of Concurrency with ThreadPoolExecutor

In today's fast-paced world, efficiently processing multiple tasks simultaneously is critical in programming. If you've ever faced challenges running tasks concurrently in Python, especially with threading, you're not alone. Many developers encounter snags when trying to execute multiple operations at once.

In this guide, we'll dive into using the ThreadPoolExecutor from Python's concurrent.futures module. We'll explore a common problem and walk step-by-step through its solution. Let's get started!

The Problem: Ineffective Concurrent Execution

Imagine you have a list of tasks, and you want to run them concurrently. This means that instead of waiting for one task to complete before starting the next, you want them to run at the same time.

A reader faced the challenge of running multiple tasks from a list but found that the output was inconsistent—tasks weren't running concurrently. Below is the code example that illustrates this issue:

[[See Video to Reveal this Text or Code Snippet]]

In the above code, the output repeatedly showed the same task being executed, much to the developer's frustration. They were hoping for a mix of outputs like this:

[[See Video to Reveal this Text or Code Snippet]]

The Solution: Correctly Submitting Tasks

Adjusting the Submission Method

The key to achieving concurrent execution lies in how tasks are submitted to the ThreadPoolExecutor. Instead of passing the value argument directly to the function within the submit method, we can provide the function and its arguments separately.

Here's the Fixed Code:

Replace this line:

[[See Video to Reveal this Text or Code Snippet]]

with:

[[See Video to Reveal this Text or Code Snippet]]

Why This Works

By changing the submission line:

Old Method: executor.submit(parse_value(value=get_values)) calls the function immediately and submits its return value (which is None) to the executor. Thus, the executor doesn't actually get the intended task but simply a value with no threading involved.

New Method: executor.submit(parse_value, get_values) tells the executor to queue the function parse_value to run with get_values as its argument. This allows the executor to manage the timing and threading for each task independently.

Final Code Example

The following is the updated and fully functional version of the initial code:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Using ThreadPoolExecutor effectively can simplify concurrent task execution in Python. By making a small adjustment in how you submit your tasks, you can achieve the desired outcome of running multiple operations at the same time without dependencies.

Now, you're better equipped to leverage the power of multithreading in your applications, allowing for enhanced performance and responsiveness.

Happy coding!