Hi Austin, I am not sure whether you had this kind of mistake or not but in any case, I would like to state: that you might be trying to read whole input values,(corresponding key values) to reducer function from beginning to end(which is the output value of mapper) while merging them into one output.
If you can send reducer code, you may get more useful replies. On Tue, Mar 5, 2013 at 1:00 PM, Mahesh Balija <balijamahesh....@gmail.com>wrote: > The reason why the reducer is fast upto 66% is be because of the Sorting > and Shuffling phase of the reduce and when the actual task is NOT yet > started. > > The reduce side is divided into 3 phases of 33~% each -> shuffle (fetch > data), sort and finally user-code (reduce). That is why your reduce might > be faster upto 66%. In order to speed up your program you may either have > to have more number of reducers or make your reducer code as optimized as > possible. > > Best, > Mahesh Balija, > Calsoft Labs. > > > On Tue, Mar 5, 2013 at 1:27 AM, Austin Chungath <austi...@gmail.com>wrote: > >> Hi all, >> >> I have 1 reducer and I have around 600 thousand unique keys coming to it. >> The total data is only around 30 mb. >> My logic doesn't allow me to have more than 1 reducer. >> It's taking too long to complete, around 2 hours. (till 66% it's fast >> then it slows down/ I don't really think it has started doing anything till >> 66% but then why does it show like that?). >> Are there any job execution parameters that can help improve reducer >> performace? >> Any suggestions to improve things when we have to live with just one >> reducer? >> >> thanks, >> Austin >> > >
