Execute Python Functions Simultaneously Using Multithreading

Discover how to run multiple functions at the same time in Python and gather their results efficiently using multithreading!
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How to Execute Functions in Python at the Same Time and Get Values from Them

In Python, executing multiple functions sequentially can be time-consuming, especially when those functions involve waiting on external operations, such as web requests. If you find yourself needing to run several functions concurrently to improve the overall execution time, you’re in the right place! This guide will guide you through how to efficiently run multiple functions simultaneously using the multithreading capabilities of Python.

The Problem: Slow Sequential Execution

Imagine you have three functions designed to fetch different pieces of data. Executing these functions one after the other can take a significant amount of time if they involve network requests. In your scenario, functions session_ID, reCaptcha, and hCaptcha need to be executed simultaneously to obtain their results as quickly as possible.

Here's a snippet of the original code you're working with:

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

As you can see, each function is executed in turn, which can lead to unnecessary delays.

The Solution: Using Multithreading

To overcome this issue, you can utilize the ThreadPool from the multiprocessing library. This allows you to run each function in its own thread, enabling concurrent execution and faster overall response times.

Step-by-Step Implementation

Import the Required Libraries: You'll need to import the ThreadPool module.

Define Your Functions: This is where you define the functions you want to run concurrently.

Set Up the Thread Pool: Using a thread pool allows you to execute the functions in parallel.

Retrieve and Organize Results: Store the results in a dictionary for convenient access.

Here's a refined version of your original code using multithreading:

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

Explanation of the Code

Functions: Each function returns both its name (me) and a sample value.

ThreadPool: TP() creates a thread pool context in which the functions can run concurrently.

apply_async: This method schedules each function to be executed asynchronously.

Result Handling: Upon completion, results are retrieved and organized into a dictionary d, with function names as keys and their corresponding returned values as values.

Final Output

When you run this code, it will output a dictionary with results from all three functions:

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

Conclusion

Utilizing threading in Python can greatly enhance performance when dealing with I/O-bound tasks, such as network calls. By following the steps outlined in this post, you can make your functions execute simultaneously, improving the efficiency of your scripts and applications.

If you have any further questions or suggestions about using multithreading in Python, feel free to share in the comments below!