aioS3: Efficient File Handling in Asyncio with aiobotocore

When working with large files in an asynchronous environment, such as AWS S3 with Python, efficiency and memory management become crucial.

The aiobotocore library offers a way to read large files in chunks, which is essential for not overloading your application’s memory.

However, when you need to handle these files with operations that expect a file-like object—like pickle.load() or json.load()—things get a bit trickier.

Reading Files in Chunks with aiobotocore

To read a complete file from S3 using aiobotocore, you typically set up a loop to read the file in parts.

Here’s what that looks like:

resp = yield from s3.get_object(Bucket='mybucket', Key='k')
stream = resp['Body']
    while True:
      chunk = yield from
      if len(chunk) > 0:

This method is effective for managing memory usage, as it prevents your application from loading the entire file into memory all at once.

But what if your application needs a file-like object?

The Challenge with File-like Operations

Certain operations in Python, such as pickle.load() or json.load(), expect a file-like object and will attempt to read the entire file at once.

This approach can be inefficient, especially when dealing with large files or when only a part of the file is needed, as it loads the full file into memory.

For example, pickle reads objects piece by piece; you don’t need to load the full file into memory to work with it.

However, the standard chunk-reading approach doesn’t provide a file-like object that these operations require.

aioS3 to the Rescue

Enter aioS3. This tool extends the functionality of aiobotocore by offering a stream() method that provides a file-like interface with a customizable chunk size.

import json
from aios3.file import stream

        bucket = "mybucket", 
        key = "my/key/1.json",

This means you can efficiently read large files from S3 in chunks, just like before, but now in a way that’s compatible with operations that need a file-like object.

With aioS3, you can enjoy the best of both worlds: efficient memory management and the ability to use file-like operations on your S3-hosted files without loading the entire file into memory.

It’s an essential tool for any asynchronous Python application working with large files in AWS S3.