python - Split S3 file into smaller files of 1000 lines

Question

I have a text file on S3 with around 300 million lines. I'm looking to split this file into smaller files of 1,000 lines each (with the last file containing the remainder), which I'd then like to put into another folder or bucket on S3.

So far, I've been running this on my local drive using the linux command:

split -l 1000 file

which splits the original file into smaller files of 1,000 lines. However, with a larger file like this, it seems inefficient to download and then re-upload from my local drive back up to S3.

What would be the most efficient way to split this S3 file, ideally using Python (in a Lambda function) or using other S3 commands? Is it faster to just run this on my local drive?

Answer 1

Anything that you do will have to download the file, split it, and re-upload it. The only question is where, and whether local disk is involved.

John Rotenstein gave you an example using local disk on an EC2 instance. This has the benefit of running in the AWS datacenters, so it gets a high-speed connection, but has the limitations that (1) you need disk space to store the original file and its pieces, and (2) you need an EC2 instance where you can do this.

One small optimization is to avoid the local copy of the big file, by using a hyphen as the destination of the s3 cp: this will send the output to standard out, and you can then pipe it into split (here I'm also using a hyphen to tell split to read from standard input):

aws s3 cp s3://my-bucket/big-file.txt - | split -l 1000 - output.
aws s3 cp output.* s3://dest-bucket/

Again, this requires an EC2 instance to run it on, and the storage space for the output files. There is, however, a flag to split that will let you run a shell command for each file in the split:

aws s3 cp s3://src-bucket/src-file - | split -b 1000 --filter 'aws s3 cp - s3://dst-bucket/result.$FILE' -

So now you've eliminated the issue of local storage, but are left with the issue of where to run it. My recommendation would be AWS Batch, which can spin up an EC2 instance for just the time needed to perform the command.

You can, of course, write a Python script to do this on Lambda, and that would have the benefit of being triggered automatically when the source file has been uploaded to S3. I'm not that familiar with the Python SDK (boto), but it appears that get_object will return the original file's body as a stream of bytes, which you can then iterate over as lines, accumulating however many lines you want into each output file.