How to Convert AsyncIterable to asyncio Task in Python

Learn how to seamlessly convert `AsyncIterable` to `asyncio Task` and run them in parallel using Python's asynchronous capabilities.
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How to Convert AsyncIterable to asyncio Task in Python

Asynchronous programming in Python allows developers to write more efficient code that can handle multiple operations simultaneously. A common scenario you may encounter is needing to run multiple instances of an AsyncIterable in parallel using asyncio. This guide will walk you through converting an AsyncIterable into an asyncio Task and effectively running these tasks in parallel.

Understanding the Problem

How to convert an AsyncIterable into an asyncio task that iterates until exhaustion.

How to run multiple of these tasks in parallel, ensuring that results are matched 1-1 with the original AsyncIterable.

The Solution

To solve this problem, we can create a new coroutine that consumes the async iterable() and collects its items into a list. Below is a snippet of code that demonstrates this process.

Step 1: Create the Collect Coroutine

First, we need to define the collect coroutine that will handle the async iteration and capture items.

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

Step 2: Set Up Parallel Tasks

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

At this point, we'll have three lists of results, but there's a slight hitch: without an await statement in our generator, the iterables are running sequentially.

Step 3: Introduce Concurrency

Here’s the updated code with adjustments:

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

Output Explanation

Running the above code would yield results like this:

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

All three instances of iterable() produce the same output concurrently, confirming that each was processed in parallel.

Conclusion

In this guide, we've discussed how to convert an AsyncIterable to an asyncio Task and run them in parallel using Python. By creating a coroutine to collect items from the iterable and adding await calls to yield control back to the event loop, we achieved true concurrency. This method allows your asynchronous code to perform optimally while maintaining clarity.

Now you can leverage these techniques in your own asynchronous Python applications!