hi
I have a project on hadoop where I need to have hierarchal map functions.
Essentially, I have a map function which would take the input and apart from
emitting results(for reducer) it would also create new inputs.
I now need to have map threads working on these new inputs and they keep on
Thanks a lot guys..will go throught it all.
On Sun, Apr 17, 2011 at 3:33 AM, Ted Dunning tdunn...@maprtech.com wrote:
Sounds like this paper might help you:
Predicting Multiple Performance Metrics for Queries: Better Decisions
Enabled by Machine Learning by Ganapathi, Archana, Harumi Kuno,
Since general M/R jobs vary over a huge (Turing problem equivalent!) range of
behaviors, a more tractable problem might be to characterize the descriptive
parameters needed to answer the question: If the following problem P runs in
T0 amount of time on a certain benchmark platform B0, how long
ROC Convex Hull is an analysis technique for optimizing parameters for
given outputs.
For example, if a classification technique has tuning knobs, ROCCH
will find the settings that give a desired failure rate.
On Sun, Apr 17, 2011 at 12:07 PM, Matthew Foley ma...@yahoo-inc.com wrote:
Since
Turing completion isn't the central question here, really. The truth
is, map-reduce programs have considerably pressure to be written in a
scalable fashion which limits them to fairly simple behaviors that
result in pretty linear dependence of run-time on input size for a
given program.
The cool
Yup. I'm boring
On 2011-04-17, at 6:07 PM, Ted Dunning tdunn...@maprtech.com wrote:
Turing completion isn't the central question here, really. The truth
is, map-reduce programs have considerably pressure to be written in a
scalable fashion which limits them to fairly simple behaviors that
@mathew: initially i wanted to concentrate on generic class of
applications..wouldnt mind to stick on to one now..can i know something more
about the descriptive parameters?
@all: any results of anybody having done something similar?
On Mon, Apr 18, 2011 at 5:55 AM, James Seigel Tynt
