Resolving Memory Issues When Not Awaiting Asyncio Tasks in Python

Learn how to effectively manage asyncio tasks without causing memory leaks. We'll discuss best practices for handling long-running functions in Python 3.7 without waiting immediately.
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Resolving Memory Issues When Not Awaiting Asyncio Tasks in Python

Handling asynchronous code in Python can sometimes be tricky, especially when you're working with long-running tasks. A common issue arises when you create asyncio tasks without awaiting them, which can lead to memory leaks and unexpected behavior in your applications. In this guide, we'll explore a common scenario where this problem occurs and provide a robust solution to help manage your asyncio tasks effectively.

The Problem

Imagine you have a slow function that you don't want to block the execution of your program by awaiting it immediately. Instead, you create a task for it using the following approach:

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

Within your slow_function_wrapper, you await slow_function(), but you do not await this initial call directly. While this may seem efficient, it can inadvertently lead to a memory leak. Here’s the breakdown of the potential pitfalls:

Memory Leak: The task references are kept even after the task finishes, leading to increased memory usage over time.

Lack of Control: You might lose track of task completion and the values returned by these tasks.

The Solution

Step-by-Step Implementation

Create the Async Task: Start by creating your asynchronous function as usual, making sure to create a task without awaiting it right away.

Use a Callback: Add a callback to the task which executes once the task is complete. This callback will be responsible for handling any results or exceptions without needing to store references unnecessarily.

Here's how you can implement this approach:

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

Explanation of the Code

Result Pinger: The result_pinger coroutine keeps checking for the completion of slow_function and prints out the result once it is available. This keeps your asyncio loop busy and avoids it from stopping prematurely.

Conclusion