A Combiner is a mini-reduce process which operates only on data generated by a Mapper. When Mapper emits the data, combiner receives it as input and sends the output to a reducer.
In Hadoop, a combiner is a feature that allows the intermediate output of the map tasks to be combined or reduced before being sent over the network to the reduce tasks. The primary purpose of a combiner is to reduce the amount of data that needs to be transferred between the map and reduce tasks, thereby improving the overall efficiency of the MapReduce job.
The combiner is similar to a reducer but operates on the output of the map tasks before it is shuffled and sent to the reducers. It helps in reducing the volume of data transferred over the network, which can significantly improve the performance of a MapReduce job by minimizing the amount of data that needs to be processed by the reducers.
However, it’s important to note that the use of a combiner is not guaranteed, and it depends on factors such as the Hadoop framework’s decision and the specific configuration of the job. The combiner function must be both associative and commutative, as it may be applied multiple times and in any order during the MapReduce job execution.