Running Functions in Parallel with asyncio in Python

Learn how to enhance your Python scripts by running I/O bound tasks in parallel using `asyncio` and `gather`. This guide breaks down the process step-by-step for clarity.
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Unlocking the Power of asyncio: Running Functions in Parallel

Many Python developers grapple with the challenge of improving the efficiency of I/O operations. If you've ever found yourself waiting for seemingly endless data reads or file writes, you're not alone! In this post, we'll explore how to leverage Python's asyncio library to run multiple functions in parallel while keeping the logical flow of your program intact.

The Problem

Here’s the original script outline:

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

In this case, both read_db and to_parquet are executed one after the other. As the size of your dataset grows, so does the waiting time.

The Solution: Using asyncio for Concurrency

To solve this problem, we can modify our approach by using asyncio to allow read_db and to_parquet to run concurrently. However, we still want to ensure that each ID's reading and saving operations complete in the correct order.

Step 1: Wrapping Functions with async_wrap

First, we need to create a utility function that allows our synchronous functions to be utilized in an async manner:

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Step 2: Defining the Main Asynchronous Function

Next, let’s define the core asynchronous version of the main function:

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

Explanation of Key Parts

async_wrap: This decorator allows synchronous functions to be called in an asynchronous context.

Sequential Operations: Even though we're performing I/O operations concurrently, the sequential flow of each ID is maintained by waiting for the completion of one before moving to the next.

Conclusion: The Results You Can Expect

After updating your code with the above pattern, you should notice a more efficient operation, with logs that reveal concurrent processing:

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

By following these steps, you can significantly improve the performance of I/O operations in your Python scripts using asyncio.

Now, stop waiting and start optimizing your scripts today!