Hadoop Combiner – Best Explanation to MapReduce Combiner

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1. Hadoop Combiner / MapReduce Combiner

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 MapReduce combiner we are going to answer what is a Hadoop combiner, MapReduce program with and without combiner, advantages of Hadoop combiner and disadvantages of the combiner in Hadoop.

Hadoop Combiner - Best Explanation to MapReduce Combiner

Hadoop Combiner – Best Explanation to MapReduce Combiner

2. What is Hadoop Combiner?

On a large dataset when we run MapReduce job, 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 Hadoop Combiner that plays a key role in reducing network congestion.

We have already seen earlier what is mapper and what is reducer in Hadoop MapReduce. Now we in the next step to learn Hadoop MapReduce Combiner.

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.

Read: Key-value Pairs in MapReduce

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 

MapReduce Combiner : MapReduce program without combiner

MapReduce Combiner : 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.

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

3.2. MapReduce program with Combiner in between Mapper and Reducer

MapReduce Combiner: MapReduce program with combiner

MapReduce Combiner: MapReduce program with combiner

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 MapReduce Combiner

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

  • Hadoop 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.

Read: Counters in MapReduce

5.  Disadvantages of Hadoop 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. Hadoop Combiner – Conclusion

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

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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No Responses

  1. Vijay kumar says:

    How to configure combiner in hadoop ?

  2. Nimish Jindal says:

    Hi, can you help me with following questions:
    1. Output of mapper is stored on local machine. Is Combiner also situated on the local machine? (If not then isn’t it that we are transferring same(or even more) amount of data in network because first we are transferring mapper output to Combiner and then to Partitioner/Reducer)?
    2. Why can’t we just combine on keys in the mapper code itself? (As i see, it takes same time complexity to compute and creates an output file with smaller size because keys are combined? )

  3. Manoj Kumar says:

    Dear Author,
    I’ve question as well as doubt on image you used in point 3.2 in this topic. I want to share an updated image on this for discussion and clarity of my confusion. Please let me know how can I share that with you OR send in a mail to me where I can reply and forward my question to you along with the image.
    Thanks & Regards

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