Shuffling is a process which is used to perform the sorting and transfer the map outputs to the reducer as input.

In Hadoop MapReduce, shuffling refers to the process of redistributing and exchanging data between the map tasks and the reduce tasks. It occurs after the map phase and before the reduce phase in a MapReduce job. During the map phase, each map task processes a portion of the input data and produces key-value pairs as output. The shuffling phase then takes these intermediate key-value pairs, groups them by key, and redistributes them across the reduce tasks based on the keys.

The shuffling process involves the following key steps:

Partitioning: The intermediate key-value pairs generated by the map tasks are partitioned based on the keys. Each partition corresponds to a specific reduce task. The number of partitions is usually equal to the number of reduce tasks.
Sorting: Within each partition, the key-value pairs are sorted based on the keys. Sorting is necessary to ensure that all values associated with a particular key are grouped together.
Data Transfer: The sorted and partitioned data is then transferred from the map tasks to the appropriate reduce tasks. This involves the movement of data over the network.
Merge: Within each reduce task, the received key-value pairs are merged and processed to produce the final output.

The shuffling phase is a crucial step in the MapReduce process, as it ensures that all values for a given key are available at the same reduce task. Efficient shuffling is essential for optimizing the overall performance of a MapReduce job, as it impacts the network and disk I/O, which can be significant bottlenecks in distributed computing environments.