Hadoop what is a combiner

Once the Map task is done the data partitions have to be sent to the reducers on different nodes working on specific partitions.

For example — Suppose you have sales data of several items and you are trying to find the maximum sales number per item.

For Item1 if following key,value pair are the output of Map-1 and Map If you are using a combiner in MapReduce job and the reducer class itself is used as the combiner class then combiner will be called for each map output.

This is the last phase of MapReduce where the Record Writer writes every key-value pair from the Reducer phase and sends the output as text. Following is the expected output. Save the above program as WordCount. The compilation and execution of the program is given below. You can download the jar from mvnrepository. Wait for a while till the file gets executed. After execution, the output contains a number of input splits, Map tasks, and Reducer tasks.

This file is generated by HDFS. MapReduce - Combiners Advertisements. Lets copy WordCountWithCombiner. Compile the above two programs and build a jar file and run the mapreduce job as shown in below screen shot:. In the below screen shot, we can verify the results of wordcount mapreduce job with fixed combiner issue.

Thus the Combiner in mapreduce can be used safely for aggregation functions like summation but need to be careful in other cases. Your email address will not be published. About WordPress. View AMP version. Configuration; import org. Path; import org. IntWritable; import org. Text; import org. Job; import org. Mapper; import org. Reducer; import org.

FileInputFormat; import org. Configuration ;. The Combiner is used to solve this problem by minimizing the data that got shuffled between Map and Reduce. In this article, we are going to cover Combiner in Map-Reduce covering all the below aspects.

What is a combiner? How combiner works Advantage of combiners Disadvantage of combiner What is a combiner? Combiner always works in between Mapper and Reducer. The output produced by the Mapper is the intermediate output in terms of key-value pairs which is massive in size. If we directly feed this huge output to the Reducer, then that will result in increasing the Network Congestion.

So to minimize this Network congestion we have to put combiner in between Mapper and Reducer. These combiners are also known as semi-reducer.