Explain phases of MapReduce.

 Phases of MapReduce

The MapReduce program is executed in three main phases: mapping, shuffling and reducing. There is also an optional phase known as the combiner phase.

Mapping Phase

• This is the first phase of the program. There are two steps in this phase: splitting and mapping. A dataset is split into equal units called chunks (input splits) in the splitting step. Hadoop consists of a RecordReader that uses TextInputFormat to transform input splits into key-valuepairs.

• The key-value pairs are then used as inputs in the mapping step. This is the only data format that a mapper can read or understand. The mapping step contains a coding logic that is applied to these data blocks. In this step, the mapper processes the key-value pairs and produces an output of the same form (key-value pairs).

Shuffling phase

• This is the second phase that takes place after the completion of the Mapping phase. It consists of two main steps: sorting and merging. In the sorting step, the key-value pairs are sorted using the keys. Merging ensures that key-value pairs are combined.

• The shuffling phase facilitates the removal of duplicate values and the grouping of values. Different values with similar keys are grouped. The output of this phase will be keys and values, just like in the Mapping phase.

Reducer phase

In the reducer phase, the output of the shuffling phase is used as the input. The reducer processes this input further to reduce the intermediate values into smaller values. It provides a summary of the entire dataset. The output from this phase is stored in the HDFS.

Combiner phase

This is an optional phase that's used for optimizing the MapReduce process. It's used for reducing the pap outputs at the node level. In this phase, duplicate outputs from the map outputs can be combined into a single output. The combiner phase increases speed in the Shuffling phase by improving the performance of Jobs.