Final answer:
Combiners in MapReduce are used to aggregate intermediate map output on the same node, reducing data shuffle to reducers. They should be used when the reduction operation is commutative and associative, like sum or maximum, and not alter the final result. This optimization is especially beneficial for large datasets but isn't suitable for all operations.
Step-by-step explanation:
What Are Combiners in MapReduce?
In the MapReduce programming model, a combiner is an optional component that processes the output of the map tasks before it is sent to the reducers. The primary role of a combiner is to aggregate intermediate map output locally on the same node, to reduce the amount of data that must be shuffled across to the reducers and hence optimize the network and reduce the cost of data transfer.
When to Use a Combiner
A combiner should be used when the reduction operation is commutative and associative. This could be operations like sum, maximum, or minimum. Applying a combiner can greatly improve the performance of a MapReduce job, especially when dealing with large datasets. However, it's important to note that the use of a combiner is not suitable for every scenario. It should not change the output of the reduce operation, which means it should be used only when it does not affect the final result.
For example, if you're counting the occurrences of words in a set of documents (word count), a combiner will sum up the counts for each word within a document thereby reducing the data volume for the shuffle phase. In contrast, if the reduce function performs an operation that relies on the complete dataset, like concatenation of strings, using a combiner might alter the expected output.