Distributed processing in MapReduce may be summarized in three phases: a map phase, a shuffle phase, and a reduce phase. These phases can be overlapped to some degree to improve efficiency. The map step applies the map function to data local to the processor.

MapReduce

This system is built by Google based on Java where the actual data from the HDFS store gets processed significantly. MapReduce helps to make the big data processing job into a simpler task by breaking it into smaller tasks. This is also responsible for analyzing huge datasets in parallel before minimizing it to find the outcome. Within the Hadoop ecosystem, Hadoop MapReduce acts as a framework based on YARN architecture. Moreover, YARN based Hadoop architecture supports distributed parallel processing of big data sets. And MapReduce offers the framework for easy to write applications on thousands of nodes. It also considers the fault and failure management to minimize risk.

Worker nodes

These nodes include most of the virtual machines (VM) within a Hadoop cluster. They execute the job of data storage and running computations across clusters. Moreover, each worker node here runs the DataNode and TaskTracker services within it. They are useful to get the instructions from the master nodes for further processing.

Client nodes

The client nodes are responsible for data loading into the cluster. These nodes first submit MapReduce jobs defining how data needs to be processed. Later, they fetch the results once the processing completes.