"What Markov chain generation does is give a nice mix between two different ways of generating load, combining the fuzzing of random request generation with the feel of replaying logs. I think of it as a happy medium."
Why do we need a medium? Replaying access logs at a high-velocity gets the job done.
Well, because there's no one right way to test is why.
Strictly repeating logs doesn't tell you what happens when your access patterns change. Markov chains give you an idea of how your site acts under variations on your current traffic.
It looks interesting, but does the added complexity have any tangible benefits over a benchmark app using a url-frequency table (a 0th-order markov chain)?
Great question. Maybe, maybe not. As I said, it just depends. Every test answers its own question.
Here's the thing about orders, they're like a sliding scale of similarity between source and generated text. Whether that scale is worth toggling for your data is up to you.
Companies that Pivotal has extensively worked with: Twitter, Square, and Google.
The first two seem, well, obvious. I can count many Pivots that ended up at Twitter over the last 2-3 years. Both companies rely on Ruby/Java and SF's talent pool is practically drained.
A Google acquisition, while far fetched, also makes sense given that Pivotal is stacked with agile/Java broskis. Hard to imagine what the Pivots would work on. Android? Google+?
I'm pretty disappointed in the direction Chill has gone. Social video is an incredibly interesting area. Chill, however, didn't iterate too much on the idea of Turntable for videos, and now they've pivoted to a Pinterest for videos.
