Actually what I am trying to find to top n% of the whole data. This n could be very large if my data is large.
Assuming I have uniform rows of equal size and if the total data size is 10 GB, using the above mentioned approach, if I have to take top 10% of the whole data set, I need 10% of 10GB which could be rows worth of 1 GB (roughly) in my mappers. I think that would not be possible given my input splits are of 64/128/512 MB (based on my block size) or am I making wrong assumptions. I can increase the inputsplit size, but is there a better way to find top n%. My whole actual problem is to give ranks to some values and then find out the top 10 ranks. I think this context can give more idea about the problem ? Regards Praveenesh On Sat, Feb 2, 2013 at 11:53 AM, Eugene Kirpichov <ekirpic...@gmail.com> wrote: > Hi, > > Can you tell more about: > * How big is N > * How big is the input dataset > * How many mappers you have > * Do input splits correlate with the sorting criterion for top N? > > Depending on the answers, very different strategies will be optimal. > > > > On Fri, Feb 1, 2013 at 9:05 PM, praveenesh kumar <praveen...@gmail.com>wrote: > >> I am looking for a better solution for this. >> >> 1 way to do this would be to find top N values from each mappers and >> then find out the top N out of them in 1 reducer. I am afraid that >> this won't work effectively if my N is larger than number of values in >> my inputsplit (or mapper input). >> >> Otherway is to just sort all of them in 1 reducer and then do the cat of >> top-N. >> >> Wondering if there is any better approach to do this ? >> >> Regards >> Praveenesh >> > > > > -- > Eugene Kirpichov > http://www.linkedin.com/in/eugenekirpichov > http://jkff.info/software/timeplotters - my performance visualization tools
