Or maybe Digg's model is flawed. I don't know if it is or not, but from everything I'd read it was far from optimal. I'd love to see more about it though. Now, relationship graph traversal is an issue for normalized relational systems, but in these cases, things could be split pretty from articles versus recommendations.

One big problem I see in these comparisons is when a NoSQL person claims that their box is processing 5000 req/sec, what does that mean? Are they denormalizing this so much that it's equivalent to 500-1000 req/sec on a RDBMS?

Another thing: when Digg was starting their type of site was very novel. There wasn't much out there that approached the scale and growth they experienced. I'm sure that StackOverflow has been designed with scaling in mind.

I'm not that familiar with Digg (it's the same as Reddit though, right?), but two major things occur to me:

1) I'm much less likely to vote a answer/question up/down on SO than I am at Reddit. On SO, if I'm not asking or answering, I'm rarely causing any writes to the data store. On Reddit, I vote on most of what I look at. I could see this having a huge impact.

2) Obviously Reddit can do some caching, but I think SO can cache much larger pieces of data. As far as I know, everyone who goes to the main page of SO sees the exact same list of questions. On Reddit, each subreddit's top items can be cached, but they are mixed differently for each user.

I'm not familiar with the Digg data model, but the SO data model is not particularly complicated, and any nested interactions between, if there even is such a thing, I'm sure is done in non real-time batch processing.

Facebook, on the other hand, is incredibly complex because of all the interactions between users, not to mention the data is stored if I recall correctly, geographically disparately throughout the world. I don't have a link, but the shit that happens behind the scenes when you logon to your facebook account is wild.