Thinking about the technical issues at first glance you could say the restriction is just the way the java generics are written for the CombineFn class but if you think about what is actually happening it would be awkward to support changing types in the CombineFn especially when it is paired with a GroupByKey. As I showed in the example the CombineFn essentially bookends the GBK operation by performing processing on the types before and after the sorting. The GBK's types describe the output of the map phase and input to the reduce. If the CombineFn changed the types then the output wouldn't match the types describe by the GBK. I'm guessing this could lead to a number of problems trying to compute the types and plan for the job.
On Fri, Oct 18, 2013 at 8:55 AM, Micah Whitacre <[email protected]> wrote: > I'm not sure I follow how there is extra effort involved. Are you talking > development effort or processing effort? From a development effort in both > cases you need to write code that translates T to U and combines the > values. The difference is whether that logic exists inside of a single > DoFn or is split into a MapFn + CombineFn. So the development effort > should be the same. > > > On Fri, Oct 18, 2013 at 8:11 AM, Chandan Biswas <[email protected]>wrote: > >> yeah.. i see what you are talking about. But it will take extra effort to >> convert to U type. So, is there any specific reason the way CombineFn >> created initially that CombineFn will not allow other return type. Was >> there any constraints (design / complexity) to restrict to this behavior? >> Thanks, >> >> >> On Thu, Oct 17, 2013 at 8:47 PM, Micah Whitacre <[email protected]> wrote: >> >> > Chandan, >> > So let's apply your situation to the types and conversion that is >> > proposed and break it down where logic will be applied. Say we have a >> > PCollection that is like the following: >> > >> > Mapper 1: >> > <id1, "Hello"> >> > <id2, "World"> >> > <id1, "I like turtles"> >> > >> > Mapper 2 >> > <id2, "Goodbye"> >> > >> > This will be represented by the PTable<String, Comment>. We then apply >> a >> > MapFn to transform it into PTable<String, Book> and we'd get the >> following >> > in our PCollection: >> > >> > Mapper 1 >> > <id1, <"Hello", 1>> >> > <id2, <"World", 1>> >> > <id1, <"I like turtles", 1>> >> > >> > Mapper 2 >> > <id2, <"Goodbye", 1>> >> > >> > Then if we were to use the GBK + CombineFn, the output of the map phase >> > would be.. >> > >> > Mapper 1 >> > <id2, <"World", 1>> >> > <id1, <"I like turtles", 2>> >> > >> > Mapper 2 >> > <id2, <"Goodbye", 1>> >> > >> > Notice Mapper 1 would only be emitting 2 items instead of 3 and >> therefore >> > less data is sent over the wire and has to be sorted. Also in the >> reducer >> > after the GBK is completed the CombineFn would finish its work and you'd >> > get the following: >> > >> > Reducer 1 >> > <id2, <"Goodbye", 2>> >> > <id1, <"I like turtles", 2>> >> > >> > The only case where this would not improve performance is if you never >> emit >> > data for the same key from the same mapper or your mapper doesn't reduce >> > the size of the data. >> > >> > >> > On Thu, Oct 17, 2013 at 8:18 PM, Chandan Biswas <[email protected] >> > >wrote: >> > >> > > I have PTable<String,Comment>. and getting after reduce PTable<String, >> > > Book> >> > > >> > > T--> Comment{ String comment, String author}, U--> Book{String id, >> String >> > > lengthiestComment, int noOfComments} >> > > >> > > But wanted to some aggregations in the map side based on some logic >> > instead >> > > of all aggregations at reduce side. >> > > Yes in worst case, data flow over the n/w will remain same, but >> sorting >> > > will be improved. >> > > >> > > Thanks, >> > > Chandan >> > > >> > > >> > > On Thu, Oct 17, 2013 at 6:46 PM, Josh Wills <[email protected]> >> wrote: >> > > >> > > > On Thu, Oct 17, 2013 at 4:41 PM, Chandan Biswas < >> [email protected] >> > > > >wrote: >> > > > >> > > > > Yeah, I agree with Micah that it will not eliminate the reduce >> phase >> > > > > entirely. But the dummy object of U suggested by Josh (or >> converting >> > > to U >> > > > > type in map for every record) will not improve performance >> because >> > > same >> > > > > amounts of records will be sorted and aggregated in the reduce >> phase. >> > > > >> > > > >> > > > I don't think that's true-- the records of type U will be combined >> on >> > the >> > > > map-side, which would reduce the amount of data that is pushed over >> the >> > > > network and improve performance. >> > > > >> > > > Can you give any additional details about what T and U are in this >> > > > scenario? :) >> > > > >> > > > >> > > > >> > > > > But >> > > > > my point is, can we improve it by applying a combiner where the >> > > combineFn >> > > > > provides output as different type. If we have same type, we can >> use >> > the >> > > > > combiner to do some aggregation in map side which improves >> > performance. >> > > > > But, can we have some mechanism by which the same advantage can be >> > > > achieved >> > > > > when combineFn emits different type. I think, emitting same type >> by >> > > > > CombineFn has restricted its use. Can we have new CombineFn that >> > allows >> > > > us >> > > > > to output different type not only same type as input? >> > > > > >> > > > > >> > > > > On Thu, Oct 17, 2013 at 5:05 PM, Josh Wills <[email protected]> >> > > wrote: >> > > > > >> > > > > > Yeah, my experience in these kinds of situations is that you >> need >> > to >> > > > come >> > > > > > up with a "dummy" or singleton version of U for the case where >> > there >> > > is >> > > > > > only a single T and do that conversion on the map side of the >> job, >> > > > before >> > > > > > the combiner runs. I think Chao had an issue like this awhile >> ago, >> > > > where >> > > > > he >> > > > > > had a PTable<String, Double> and wanted to write a combiner that >> > > would >> > > > > > return a PTable<String, Collection<Double>>. The solution was to >> > > > convert >> > > > > > the map-side object to a PTable<String, Collection<Double>>, >> where >> > > the >> > > > > > value on the map-side was a singleton list containing just that >> > > double >> > > > > > value. Does that sort of trick work here? >> > > > > > >> > > > > > >> > > > > > On Thu, Oct 17, 2013 at 2:57 PM, Micah Whitacre < >> [email protected]> >> > > > > wrote: >> > > > > > >> > > > > > > Ok so the feature you are trying to achieve is the proactive >> > > > > combination >> > > > > > of >> > > > > > > data before performing the GBK like the javadoc describes. >> > > > Essentially >> > > > > > in >> > > > > > > that situation the CombineFn is being used as a Combiner[1] to >> > > > combine >> > > > > > the >> > > > > > > data local to that mapper before doing the GBK and then >> further >> > > > > combining >> > > > > > > the data in the reduce operation. It will not necessarily >> > > eliminate >> > > > > the >> > > > > > > need for all processing in the reduce. >> > > > > > > >> > > > > > > If you want to use this functionality you will need to do the >> > > > > following: >> > > > > > > >> > > > > > > PTable<S, T> map to PTable<S, U> >> > > > > > > PTable<S, U> gbk to PGT<S, U> >> > > > > > > PGT<S, U> combine PTable<S, U> >> > > > > > > >> > > > > > > This will take advantage of any optimization provided by the >> > > > CombineFn. >> > > > > > > >> > > > > > > [1] - http://wiki.apache.org/hadoop/HadoopMapReduce >> > > > > > > >> > > > > > > >> > > > > > > >> > > > > > > On Thu, Oct 17, 2013 at 4:30 PM, Chandan Biswas < >> > > > [email protected] >> > > > > > > >wrote: >> > > > > > > >> > > > > > > > Hello Micah, >> > > > > > > > Yes we are using MapFn now. That aggregation and >> computation is >> > > > being >> > > > > > > done >> > > > > > > > in reduce phase. As CombineFn after GBK runs into map side, >> > then >> > > > > those >> > > > > > > most >> > > > > > > > computations can be done in map side which are now running >> in >> > > > reduce >> > > > > > > phase. >> > > > > > > > Some smaller aggregations and computations can be done on >> > reduce >> > > > > phase. >> > > > > > > > My point was to do some aggregation (and create a new >> object) >> > in >> > > > map >> > > > > > > phase >> > > > > > > > instead of in reduce phase. >> > > > > > > > >> > > > > > > > Thanks, >> > > > > > > > Chandan >> > > > > > > > >> > > > > > > > >> > > > > > > > On Thu, Oct 17, 2013 at 3:48 PM, Micah Whitacre < >> > > [email protected]> >> > > > > > > wrote: >> > > > > > > > >> > > > > > > > > Chandan, >> > > > > > > > > I think what you are wanting will just be a simple >> MapFn >> > > > instead >> > > > > > of >> > > > > > > a >> > > > > > > > > CombineFn. The doc of the CombineFn[1] sounds like what >> you >> > > want >> > > > > > with >> > > > > > > > the >> > > > > > > > > statement "A special >> > > > > > > > > DoFn< >> > > > > > > >> > http://crunch.apache.org/apidocs/0.7.0/org/apache/crunch/DoFn.html >> > > > >> > > > > > > > > implementation >> > > > > > > > > that converts an >> > > > > > > > > Iterable< >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> http://download.oracle.com/javase/6/docs/api/java/lang/Iterable.html?is-external=true >> > > > > > > > > > >> > > > > > > > > of >> > > > > > > > > values into a single value" but it is expecting the value >> to >> > be >> > > > of >> > > > > > the >> > > > > > > > same >> > > > > > > > > time. Since you are wanting to combine the values into a >> > > > different >> > > > > > > form >> > > > > > > > it >> > > > > > > > > should be fairly trivial to write a MapFn that converts >> the >> > > > > > Iterable<T> >> > > > > > > > -> >> > > > > > > > > U. >> > > > > > > > > >> > > > > > > > > [1] - >> > > > > > > > > >> > > > > > > >> > > > > >> > > >> http://crunch.apache.org/apidocs/0.7.0/org/apache/crunch/CombineFn.html >> > > > > > > > > >> > > > > > > > > >> > > > > > > > > On Thu, Oct 17, 2013 at 3:30 PM, Chandan Biswas < >> > > > > > [email protected] >> > > > > > > > > >wrote: >> > > > > > > > > >> > > > > > > > > > I was trying to refactoring some stuffs and trying to >> use >> > > > > > combineFn. >> > > > > > > > > > But when I went into deeper, found that I can't do it as >> > > Crunch >> > > > > > > doesn't >> > > > > > > > > > allow it the functionality I needed. For example, I >> have a >> > > > > > > > > > PGroupedTable<S,T>. I wanted to apply CombineFn<S,T> on >> it >> > > and >> > > > > > wanted >> > > > > > > > to >> > > > > > > > > > get PCollection<S,U> instead of T. Right now, CombineFn >> > > allows >> > > > > only >> > > > > > > > same >> > > > > > > > > > type as return value. The use case of this need is that >> > there >> > > > > will >> > > > > > be >> > > > > > > > > some >> > > > > > > > > > time saving in sorting. It's natural that when >> aggregating >> > > some >> > > > > > > objects >> > > > > > > > > at >> > > > > > > > > > map side can create a new different type object. >> > > > > > > > > > >> > > > > > > > > > Any thought on it? Am I missing any thing? If this can >> be >> > > > written >> > > > > > in >> > > > > > > > > > different way using existing way please let me know. >> > > > > > > > > > >> > > > > > > > > > Thanks >> > > > > > > > > > Chandan >> > > > > > > > > > >> > > > > > > > > >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > > >> > > > > > >> > > > > > -- >> > > > > > Director of Data Science >> > > > > > Cloudera <http://www.cloudera.com> >> > > > > > Twitter: @josh_wills <http://twitter.com/josh_wills> >> > > > > > >> > > > > >> > > > >> > > > >> > > > >> > > > -- >> > > > Director of Data Science >> > > > Cloudera <http://www.cloudera.com> >> > > > Twitter: @josh_wills <http://twitter.com/josh_wills> >> > > > >> > > >> > >> > >
