On Sunday 03 January 2010 11:30:29 Nathan Marz wrote:
I did some analysis on the performance of Hadoop-based workflows. Some of
the results are counter-intuitive so I thought the community at large would
Would love to hear any feedback or comments you have.
Just thinking out loud: runtime generally is not equal to the "hours of
data". In your processing model it's your next iteration that will have this
amount of data. By equating them, you're assuming an equilibrium case. But
if you're in an equilibrium, what would increasing the capacity mean? - I
think it'd mean that you process your so-far accumulated data faster, so your
next iteration will have less data to process and so on until you get to a new
equilibrium (but how fast will you get there?). Lesson learned: increasing
capacity shifts equilibrium. It's kind of like a reaction time of your
system... Sometimes, I imagine, as long as you're keeping up with the arrival
rate you don't care if it takes a week's full or a day's. In fact there may
be some constraints on the minimum size of the input.
Another comment: you're assuming that processing time is linear in the input
size. Of course it depends on the processing you're doing, but even if YOUR
processing is linear, Hadoop needs to sort keys, and that is at best
O(n*log(n)), so there is inherent non-linearity present.
Similarly about the number of nodes: doubling your nodes will not double your
service rate for a variety of reasons.