I'm trying to automate my data backup process and need to upload local files to an S3 bucket programmatically. I’ve installed boto3 and configured my AWS credentials, but I'm confused between using upload_file(), put_object(), and upload_fileobj(). Which method is recommended for large files, and how do I handle metadata or set the Access Control List (ACL) to "public-read" during the upload process?
3 answers
For most standard use cases, upload_file() is the preferred method. It is a high-level managed strategy that automatically handles "Multipart Uploads" for large files, meaning it splits the file into chunks and uploads them in parallel to improve speed and reliability.
When should I use upload_fileobj() instead of the standard upload_file()? I'm working with a web framework where the file is held in memory as a file-like object rather than being saved to the local disk first.
For very large files (e.g., several GBs), I highly recommend using the boto3.s3.transfer.TransferConfig to customize the threshold and concurrency. It allows you to fine-tune how many threads Boto3 uses, which can drastically speed up uploads on high-bandwidth connections.
I agree with Brenda. Setting a multipart_threshold ensures that Boto3 starts parallelizing the upload as soon as the file exceeds a certain size (like 100MB). This is a game-changer for data engineers moving massive log files or database dumps into S3.
David, upload_fileobj() is exactly what you need for that scenario. It’s designed to handle any object that has a .read() method (like an io.BytesIO object or a file stream from a Flask/Django request). It still benefits from the same managed transfer logic as upload_file(), so you get the benefits of multipart uploads without needing to write the data to a temporary file on your server's storage first.