Hadoop Combiner – Learn How MapReduce Combiner Works?   Recently updated !


1. Objective

Hadoop Combiner is also known as “Mini-Reducer” that summarizes the Mapper output record with the same Key before passing to the Reducer. In this tutorial on Combiner in Hadoop MapReduce, we are going to answer what is a MapReduce combiner, MapReduce program with and without combiner, advantages of Hadoop combiner and disadvantages of the combiner in Hadoop.

Introduction to Apache Hadoop Combiner.

2. What is Hadoop Combiner?

We have already seen earlier what is mapper and what is reducer in Hadoop MapReduce. Let us now see what is combiner in Hadoop MapReduce?

On a large dataset when we run MapReduce job, so large chunks of intermediate data is generated by the Mapper and this intermediate data is passed on the Reducer for further processing, which leads to enormous network congestion. MapReduce framework provides a function known as Combiner that plays a key role in reducing network congestion.

The combiner in MapReduce is also known as ‘Mini-reducer’. The primary job of Combiner is to process the output data from the Mapper, before passing it to Reducer. It runs after the mapper and before the Reducer and its use is optional.

3. How does MapReduce Combiner work?

Let us now see the working of the Hadoop combiner in MapReduce and how things change when combiner is used as compared to when combiner is not used in MapReduce?

3.1. MapReduce program without Combiner 

Example of MapReduce program without Combiner.

In the above diagram, no combiner is used. Input is split into two mappers and 9 keys are generated from the mappers. Now we have (9 key/value) intermediate data, the further mapper will send directly this data to reducer and while sending data to the reducer, it consumes some network bandwidth (bandwidth means time taken to transfer data between 2 machines). It will take more time to transfer data to reducer if the size of data is big.

Now in between mapper and reducer if we use a combiner, then combiner shuffles intermediate data (9 key/value) before sending it to the reducer and generates 4 key/value pair as an output.

3.2. MapReduce program with Combiner in between Mapper and Reducer

 Example of MapReduce program with Combiner in between Mapper and Reducer

Reducer now needs to process only 4 key/value pair data which is generated from 2 combiners. Thus reducer gets executed only 4 times to produce final output, which increases the overall performance.

4. Advantages of Combiner in MapReduce

As we have discussed what is Hadoop MapReduce Combiner in detail, now we will discuss some advantages of Mapreduce Combiner.

  • Combiner reduces the time taken for data transfer between mapper and reducer.
  • It decreases the amount of data that needed to be processed by the reducer.
  • The Combiner improves the overall performance of the reducer.

5.  Disadvantages of Combiner in MapReduce

There are also some disadvantages of hadoop Combiner. Let’s discuss them one by one-

  • MapReduce jobs cannot depend on the Hadoop combiner execution because there is no guarantee in its execution.
  • In the local filesystem, the key-value pairs are stored in the Hadoop and run the combiner later which will cause expensive disk IO.

6. Conclusion

In conclusion, we can say that Combiner plays a key role in reducing network congestion. It improves the overall performance of the reducer by summarizing the output of Mapper.

I hope this post has helped you to understand the role of Combiner in Hadoop. If you have any query related to Hadoop Combiner, so, please drop me a comment below.

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