How to Run Two Functions in Parallel using Python

Learn the methods to execute two functions concurrently in Python, ensuring efficiency in your code, particularly for long-running tasks.
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How to Run Two Functions in Parallel using Python

Running multiple functions in parallel can significantly increase the efficiency of your programs, especially for resource-intensive tasks. If you have tasks that take several minutes to complete, handling them simultaneously can save you time. In this post, we'll explore how to run two functions in parallel using Python with threading.

The Problem: Running Functions Sequentially

As a Python user, you might have encountered the limitation of sequential execution. In the example below, we see how two functions, each executing a loop that takes time to process, run one after the other, not simultaneously:

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

Output

If you run the above code, you will see that the output from functionA completes before functionB even starts. This is due to the fact that both functions are executed one after the other, leading to wasted time.

The Solution: Using Threading for Parallel Execution

To run these functions in parallel, you can utilize Python's threading module. This allows you to create threads that can execute your functions simultaneously. However, you must be careful to pass your function's reference rather than executing it directly.

Here's How to Do It

Import the threading module:

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

Define your functions normally: (As you have already done)

Create threads:
Instead of calling the function directly in the threading target, you should pass the function name and its arguments separately.

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

Important Notes

Return Values: If you need to collect the return values from your threaded functions, consider using global variables, shared objects or threading's queue.Queue. Python’s standard threads do not return values as you might expect.

Wait for Completion: Using join() ensures that the main thread waits for the completion of the threads.

Example of Function Using Global Variables

Here's a modified example using global variables to collect results:

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

Final Thoughts

Using threads allows your Python program to perform tasks concurrently, effectively utilizing time and resources. While threading does have its limitations—such as the Global Interpreter Lock (GIL) affecting CPU-bound tasks—it's incredibly useful for I/O-bound applications like web scraping or file processing.

By understanding how to implement threading, you can take your Python skills to the next level and improve the performance of your applications. Happy coding!