Maybe this version is easier to use: plist.mapValues((v) => (if (v >0) 1 else 0, 1)).reduceByKey((x, y) => (x._1 + y._1, x._2 + y._2))
It has similar behavior with combineByKey(), will by faster than groupByKey() version. On Thu, Oct 9, 2014 at 9:28 PM, HARIPRIYA AYYALASOMAYAJULA <aharipriy...@gmail.com> wrote: > Sean, > > Thank you. It works. But I am still confused about the function. Can you > kindly throw some light on it? > I was going through the example mentioned in > https://www.safaribooksonline.com/library/view/learning-spark/9781449359034/ch04.html > > Is there any better source through which I can learn more about these > functions? It would be helpful if I can get a chance to look at more > examples. > Also, I assume using combineByKey helps us solve it parallel than using > simple functions provided by scala as mentioned by Yana. Am I correct? > > On Thu, Oct 9, 2014 at 12:30 PM, Sean Owen <so...@cloudera.com> wrote: >> >> Oh duh, sorry. The initialization should of course be (v) => (if (v > >> 0) 1 else 0, 1) >> This gives the answer you are looking for. I don't see what Part2 is >> supposed to do differently. >> >> On Thu, Oct 9, 2014 at 6:14 PM, HARIPRIYA AYYALASOMAYAJULA >> <aharipriy...@gmail.com> wrote: >> > Hello Sean, >> > >> > Thank you, but changing from v to 1 doesn't help me either. >> > >> > I am trying to count the number of non-zero values using the first >> > accumulator. >> > val newlist = List (("LAX",6), ("LAX",0), ("LAX",7), ("SFO",0), >> > ("SFO",0), >> > ("SFO",9)) >> > >> > val plist = sc.parallelize(newlist) >> > >> > val part1 = plist.combineByKey( >> > (v) => (1, 1), >> > (acc: (Int, Int), v) => ( if(v > 0) acc._1 + 1 else acc._1, acc._2 + >> > 1), >> > (acc1: (Int, Int), acc2: (Int, Int)) => (acc1._1 + acc2._1, acc1._2 + >> > acc2._2) >> > ) >> > >> > val Part2 = part1.map{ case (key, value) => (key, >> > (value._1,value._2)) } >> > >> > This should give me the result >> > (LAX,(2,3)) >> > (SFO,(1,3)) >> > >> > >> > >> > On Thu, Oct 9, 2014 at 11:48 AM, Sean Owen <so...@cloudera.com> wrote: >> >> >> >> You have a typo in your code at "var acc:", and the map from opPart1 >> >> to opPart2 looks like a no-op, but those aren't the problem I think. >> >> It sounds like you intend the first element of each pair to be a count >> >> of nonzero values, but you initialize the first element of the pair to >> >> v, not 1, in v => (v,1). Try v => (1,1) >> >> >> >> >> >> On Thu, Oct 9, 2014 at 4:47 PM, HARIPRIYA AYYALASOMAYAJULA >> >> <aharipriy...@gmail.com> wrote: >> >> > >> >> > I am a beginner to Spark and finding it difficult to implement a very >> >> > simple >> >> > reduce operation. I read that is ideal to use combineByKey for >> >> > complex >> >> > reduce operations. >> >> > >> >> > My input: >> >> > >> >> > val input = sc.parallelize(List(("LAX",6), ("LAX",8), ("LAX",7), >> >> > ("SFO",0), >> >> > ("SFO",1), >> >> > ("SFO",9),("PHX",65),("PHX",88),("KX",7),("KX",6),("KX",1), >> >> > ("KX",9), >> >> > >> >> > >> >> > ("HOU",56),("HOU",5),("HOU",59),("HOU",0),("MA",563),("MA",545),("MA",5),("MA",0),("MA",0))) >> >> > >> >> > >> >> > val opPart1 = input.combineByKey( >> >> > (v) => (v, 1), >> >> > (var acc: (Int, Int), v) => ( if(v > 0) acc._1 + 1 else acc._1, >> >> > acc._2 + >> >> > 1), >> >> > (acc1: (Int, Int), acc2: (Int, Int)) => (acc1._1 + acc2._1, >> >> > acc1._2 + >> >> > acc2._2) >> >> > ) >> >> > >> >> > val opPart2 = opPart1.map{ case (key, value) => (key, >> >> > (value._1,value._2)) } >> >> > >> >> > opPart2.collectAsMap().map(println(_)) >> >> > >> >> > If the value is greater than 0, the first accumulator should be >> >> > incremented >> >> > by 1, else it remains the same. The second accumulator is a simple >> >> > counter >> >> > for each value. I am getting an incorrect output (garbage values >> >> > )for >> >> > the >> >> > first accumulator. Please help. >> >> > >> >> > The equivalent reduce operation in Hadoop MapReduce is : >> >> > >> >> > public static class PercentageCalcReducer extends >> >> > Reducer<Text,IntWritable,Text,FloatWritable> >> >> > >> >> > { >> >> > >> >> > private FloatWritable pdelay = new FloatWritable(); >> >> > >> >> > >> >> > public void reduce(Text key, Iterable<IntWritable> values,Context >> >> > context)throws IOException,InterruptedException >> >> > >> >> > { >> >> > >> >> > int acc2=0; >> >> > >> >> > float frac_delay, percentage_delay; >> >> > >> >> > int acc1=0; >> >> > >> >> > for(IntWritable val : values) >> >> > >> >> > { >> >> > >> >> > if(val.get() > 0) >> >> > >> >> > { >> >> > >> >> > acc1++; >> >> > >> >> > } >> >> > >> >> > acc2++; >> >> > >> >> > } >> >> > >> >> > >> >> > >> >> > frac_delay = (float)acc1/acc2; >> >> > >> >> > percentage_delay = frac_delay * 100 ; >> >> > >> >> > pdelay.set(percentage_delay); >> >> > >> >> > context.write(key,pdelay); >> >> > >> >> > } >> >> > >> >> > } >> >> > >> >> > >> >> > Please help. Thank you for your time. >> >> > >> >> > -- >> >> > >> >> > Regards, >> >> > >> >> > Haripriya Ayyalasomayajula >> >> > contact : 650-796-7112 >> > >> > >> > >> > >> > -- >> > Regards, >> > Haripriya Ayyalasomayajula >> > contact : 650-796-7112 > > > > > -- > Regards, > Haripriya Ayyalasomayajula > contact : 650-796-7112 --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
