combiners

I'm making a Hadoop application in Java with the MapReduce framework. I use only Text keys and values for both input and output. I use a combiner to do an extra step of computations before reducing to the final output. But I have the problem that the keys do not go to the same reducer. I create and add the key/value pair like this in the combiner: public static class Step4Combiner extends Reducer<Text,Text,Text,Text> { private static Text key0 = new Text(); private static Text key1 = new Text(); public void reduce(Text key, Iterable<Text> values, Context context) throws IOException,

I'm getting confused after reading below article on Hadoop- Definitive guide 4th edition(page-204) Before it writes to disk, the thread first divides the data into partitions corresponding to the reducers that they will ultimately be sent to. Within each partition, the background thread performs an in-memory sort by key, and if there is a combiner function, it is run on the output of the sort. Running the combiner function makes for a more compact map output, so there is less data to write to local disk and to transfer to the reducer. Here is my doubt: 1) Who will execute first combiner or

A Combiner runs after the Mapper and before the Reducer, it will receive as input all data emitted by the Mapper instances on a given node. It then emits output to the Reducers. So the records of the combiner input should less than the maps ouputs. 12/08/29 13:38:49 INFO mapred.JobClient: Map-Reduce Framework 12/08/29 13:38:49 INFO mapred.JobClient: Reduce input groups=8649 12/08/29 13:38:49 INFO mapred.JobClient: Map output materialized bytes=306210 12/08/29 13:38:49 INFO mapred.JobClient: Combine output records=859412 12/08/29 13:38:49 INFO mapred.JobClient: Map input records=457272 12/08/29

In many MapReduce programs, I see a reducer being used as a combiner as well. I know this is because of the specific nature of those programs. But I am wondering if they can be different. Binary Nerd Yes, a combiner can be different to the Reducer, although your Combiner will still be implementing the Reducer interface. Combiners can only be used in specific cases which are going to be job dependent. The Combiner will operate like a Reducer, but only on the subset of the Key/Values output from each Mapper. One constraint that your Combiner will have, unlike a Reducer, is that the input/output

This question already has an answer here: Java8 stream.reduce() with 3 parameters - getting transparency 2 answers I am using a simple reduce method with three arguments viz. identity, accumulator and combiner. Here is my code... Integer ageSumComb = persons .stream() .reduce(0, (sum, p) -> { System.out.println("Accumulator: Sum= "+ sum + " Person= " + p); return sum += p.age; }, (sum1, sum2) -> { System.out.format("Combiner: Sum1= " + sum1 + " Sum2= "+ sum2); return sum1 + sum2; But what is happening is the Combiner is not getting executed. I am not getting the reason behind this. Here is my

As per definition "The Combiner may be called 0, 1, or many times on each key between the mapper and reducer." I want to know that on what basis mapreduce framework decides how many times cobiner will be launched. Simply the number of spills to disk. Sorting happens after the MapOutputBuffer filled up, at the same time the combining will take place. You can tune the number of spills to disk with the parameters io.sort.mb , io.sort.spill.percent , io.sort.record.percent - those are also explained in the documentation (books and online resources). Example for specific numbers of combiner runs: 0