A few weeks ago, we celebrated the launch of the new PUMA.com, the culmination of a nearly two-year effort here at Viget. The whole site is driven by a CMS written in Rails, and I’m very proud of the technological platform we’ve developed. I want to focus on one piece of that platform, Redis, and how it makes the site both rock solid and screaming fast.

Fragment Caching

The app was initially created to serve category marketing sites like Running and Football. When we set out to overhaul it to serve the main PUMA site, we knew performance was of paramount importance. We made extensive use of fragment caching throughout the site, using Redis as our cache store. Some claim that Redis is not as well suited for this purpose as Memcached, but it held up well in our pre-launch testing and continues to perform well in production.

We used Redis as our cache store for two reasons. First, we were already using it for other purposes, so reusing it kept the technology stack simpler. But more importantly, Redis' wildcard key matching makes cache expiration a snap. It’s well known that cache expiration is one of two hard things in computer science, but using wildcard key searching, it’s dirt simple to pull back all keys that begin with “views” and contain the word “articles” and expire them everytime an article is changed. Memcached has no such ability.

API Caching

The PUMA site leverages third-party APIs to pull in product availability, retail store information, and marketing campaigns, among other things. External APIs are good for only two things: being slow and returning unexpected results. In a defensive masterstroke, we developed CacheBar to keep our responses speedy and stable.

CacheBar sits between HTTParty and the web. When it receives a successful response, it stores it in Redis in two places: as a normal string value with an expiration set on a per-API basis (usually between an hour and a day) and in a hash of all that API’s responses. When the primary key expires, we attempt to fetch the data from the API. Successful responses are again stored in both locations, but if the response is unsuccessful, we pull the saved response from the hash and set it as the value for the primary key with a five-minute expiration. This way, we avoid the backup that happens as a result of too many slow responses.

More information is available on the CacheBar GitHub page.

Data Structures

The PUMA app uses Redis' hashes, lists, and sets (sorted and unsorted) as well as normal string values. Having all these data structures at our disposal has proven incredibly useful, not to mention damn fun to use.

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Redis has far exceeded my expectations in both usefulness and performance. Add it to your stack, and you’ll be amazed at the ways it can make your app faster and more robust.

If you’re in North Carolina’s Triangle region and you’d like to hear more about the PUMA project, come out to tomorrow night’s Refresh the Triangle meeting, where I’ll be talking about this stuff alongside several other team members.