Hi Chandan, Inlined below.
On Sat, Oct 19, 2013 at 3:31 AM, Chandan Biswas <[email protected]> wrote: > Please correct me if I am wrong. I want to understand more how crunch > create map reduce jobs as pointed out by Micah in earlier mail. > Suppose I am doing some steps of operation as follows: > I have a PTable<K,T> table. > PGroupedTable<K,T> grpedTable1=table.groupByKey(); > Now I am applying CombineFn on grpedTable1 and getting table2 > PTable<K,T> table2=grpedTable1.parallelDo(..,CombineFn<K,T>,..); > PGrpoupedTable<K,T> grpedTable2=table2.groupByKey(); > PTable<K,U> table3=grpedTable2.parallelDo(..,DoFn,...); > > So, which type of grpedTable2 or grpdTable1 will be used for reducers? My > understanding is type of grpedTable2 will be used for reducers and type of > grpedTable1 will be used for shuffle/sorting at map side. Otherwise, there > will be no way send the Iterable data to reducers. > If that is the case, then the point of not changing the type by CombineFn > doesn't hold. Otherwise, not changing the type by CombineFn makes complete > sense. > In this example, there would be two MapReduce jobs kicked off. The first one would read in table, and then use a Combiner (based on the CombineFn) before the reducer (i.e. before the groupByKey), and then the same CombineFn within the reducer, to create table2. Going from table2 would be another MapReduce job that would do nothing in the mapper, and execute the supplied DoFn in the reducer. > It will be awesome to have such functionality like Spark as Josh pointed > out to overcome it in Crunch. Just to be clear, adding the "Aggregatable" functionality in Crunch won't actually add anything that isn't possible right now -- instead, it will just wrap current functionality into a more readable unit (at least that's how I see it). - Gabriel > Thanks, > Chandan > > > > On Fri, Oct 18, 2013 at 7:34 PM, Josh Wills <[email protected]> wrote: > >> I'm certainly not opposed to having something like this. Spark makes this >> distinction via Accumulable vs. Accumulator: >> >> >> http://spark.incubator.apache.org/docs/0.8.0/api/core/index.html#org.apache.spark.Accumulable >> >> http://spark.incubator.apache.org/docs/0.8.0/api/core/index.html#org.apache.spark.Accumulator >> >> Maybe we want something like "Aggregatable<R, T>" to go along with our >> Aggregator<T> (which could extend Aggregatable<T, T>)? >> >> >> >> On Fri, Oct 18, 2013 at 1:36 PM, Gabriel Reid <[email protected] >> >wrote: >> >> > This use case (map/combine <K,V> to <K,U>) seems to come up >> > repeatedly. The solution (map <K,V> to <K, Collection<V>> and then >> > combine) works but is also pretty unintuitive. >> > >> > Any thoughts on adding a util in Crunch to do this? It would basically >> > just need to be a static util method that takes a MapFn<<K,V><K,U>> >> > and a CombineFn<K,U> and would take care of the singleton collection >> > mapping stuff internally. On the one hand I'm thinking that this could >> > be pretty useful, but I'm not sure if it would make things more >> > intuitive or possibly have the reverse effect. >> > >> > Any opinions? I'm up for putting it together if people think it's worth >> it. >> > >> > - Gabriel >> > >> > >> > On Fri, Oct 18, 2013 at 4:14 PM, Micah Whitacre <[email protected]> >> wrote: >> > > 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> >> > >>> > > > >> > >>> > > >> > >>> > >> > >>> >> > >> >> > >> >> > >> >> >> >> -- >> Director of Data Science >> Cloudera <http://www.cloudera.com> >> Twitter: @josh_wills <http://twitter.com/josh_wills> >>
