S3 | Download Multiple Files As Zip Python Repack

For handling larger datasets without filling up memory, you can use . By using the smart-open library or iter_chunks , you can pipe data from S3 directly into a ZIP archive.

Use a library like stream-zip on PyPI to start returning compressed data before all files are even retrieved.

Some advanced implementations use smart-open[s3] to write chunks directly to an output stream, which is the most memory-efficient way to handle gigabyte-sized files . Method 3: Parallel Downloads (Performance Focused) s3 download multiple files as zip python

import boto3 import zipfile import io def create_s3_zip(bucket_name, file_keys): s3 = boto3.client('s3') zip_buffer = io.BytesIO() with zipfile.ZipFile(zip_buffer, 'w', zipfile.ZIP_DEFLATED) as zipf: for key in file_keys: # Download object into memory response = s3.get_object(Bucket=bucket_name, Key=key) content = response['Body'].read() # Add file to ZIP archive zipf.writestr(key.split('/')[-1], content) zip_buffer.seek(0) return zip_buffer # Ready for download or upload Use code with caution.

To download multiple files from Amazon S3 as a ZIP file using Python, you must use a compute resource (like EC2 or AWS Lambda) to perform the compression, as S3 is a storage-only service. The most efficient method for small to medium tasks is an , which avoids writing to local disk by using Python's io.BytesIO and zipfile modules. Method 1: In-Memory ZIP Creation (Best for Lambda) For handling larger datasets without filling up memory,

Constrained by the available RAM; very large files may trigger "out of memory" errors. Method 2: Streaming Large Files (Memory Efficient)

Ideal for AWS Lambda functions where disk space is limited. The most efficient method for small to medium

This approach reads each S3 object into a memory buffer, zips them, and can either return the ZIP to a user or upload it back to another S3 bucket.