Optimize Big Data Processing with MapReduce Combiner

In the MapReduce programming model, a combiner is a function that is applied to the intermediate key-value pairs generated by the map tasks before they are sorted and grouped by key. The combiner is optional and is not a required part of a MapReduce program, but it can also be used to reduce the amount of data that needs to be transferred over the network and processed by the reduce tasks, which can improve the performance of the MapReduce program.

Combiner, also referred to as a “Mini-Reducer,” summarises the output record from the Mapper with the same Key before sending it on to the Reducer.

When a MapReduce job is made to run on a large dataset. Large amounts of intermediate data are thus produced by the Mapper. The framework then sends the Reducer this intermediate data for additional processing.

The result is severe network congestion. Combiner, a feature of the Hadoop architecture, is essential for decreasing network congestion.

A combiner is similar to a reduce function, but it is applied to the intermediate key-value pairs generated by the map tasks, rather than the final output key-value pairs generated by the reduce tasks. The combiner is applied to each group of intermediate key-value pairs for a single key, and it generates a single output key-value pair for that key.

The combiner is applied after the map tasks and before the reduce tasks, and it can be thought of as a “mini-reduce” that is applied to the intermediate key-value pairs generated by the map tasks. By applying the combiner, the MapReduce program can then reduce the amount of data which needs to be transferred over the network and processed by the reduce tasks, which can improve the performance of the program.

Overall, the combiner is a useful optimization that can improve the performance of a MapReduce program by reducing the amount of data that needs to be transferred over the network and processed by the reduce tasks. It is optional and is not required for all MapReduce programs, but it can be useful in cases where it is possible to apply a reduce-like function to the intermediate key-value pairs generated by the map tasks.