We have been helping one of our clients moves their massive collectionof audio and video media to S3 over the last few weeks. After most of the files were in place, we saw that our usage reports on for one of the buckets was reporting much higher usage than expected. We ran some CSV usage report dumps to try to get a better idea of what was going on, but found ourselves wanting more details. For example:
- Who are the biggest consumers of our media? (IP Addresses)
- What are the most frequently downloaded files?
- Are there any patterns suggesting that we are having our content scraped by bots or malicious users?
- How do the top N users compare to the average user in resource consumption.
Enter: Bucket Logging
One of S3’s many useful features includes Server Access Logging. The basic idea is that you go to the bucket you’d like to log, enable bucket logging, and tell S3 where to dump the logs. You then end up with a bunch of log keys that are in a format that resembles something you’d get from Apache or Nginx. We ran some quick and dirty scripts against a few day’s worth of data, but quickly found ourselves wanting to be able to form more specific queries on the fly without having to maintain a bunch of utility scripts. We also needed to prepare for the scenario where we need to automatically block users that were consuming disproportionately large amounts of bandwidth.
Tamarin screeches its way into existence
The answer for us ended up being to write an S3 access log parser with pyparsing, dumping the results into a Django model. We did the necessary leg work to get the parser working, and tossed this up on GitHub as Tamarin. Complete documentation may be found here.
Tamarin contains no real analytical tools itself, it is just a parser, two Django models, and a log puller (retrieves S3 log keys and tosses them at the parser). Our analytical needs are going to be different than the next person’s, and we like to keep apps like this as focused as possible. We very well may release apps in the future that leverage Tamarin for things like the automated blocking of bandwidth hogs we mentioned, or apps that plot out pretty graphs. However, these are best left up to other apps so Tamarin can be light, simple, and easy to tweak as needed.
Going back to our customer with higher-than-expected bandwidth usage, we ended up finding that aside from a few bots from Nigeria and Canada, usage patterns were pretty normal. The media that was uploaded into that bucket was never tracked for bandwidth usage on the old setup, so the high numbers were actually legitimate. With this in mind, we were able to go back to our client and present concrete evidence that they simply had a lot more traffic than previously imagined.
Where to go from here
If anyone ends up using Tamarin, please do leave a comment for me with any interesting queries you’ve built. We can toss some of them up on the documentation site for other people to draw inspiration from.
GitHub Project: https://github.com/duointeractive/tamarin