FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -MapReduce is a programming paradigm that enables massive scalability across hundreds or thousands of servers in a Hadoop cluster. As the processing component, MapReduce is the heart of Apache Hadoop. The term “MapReduce” refers to two separate and distinct tasks that Hadoop programs perform.

Detailed explanation-2: -MapReduce is a programming model used for efficient processing in parallel over large data-sets in a distributed manner. The data is first split and then combined to produce the final result. The libraries for MapReduce is written in so many programming languages with various different-different optimizations.

Detailed explanation-3: -The partitioner is responsible for processing the map output. Once MapReduce splits the data into chunks and assigns them to map tasks, the framework partitions the key-value data. This process takes place before the final mapper task output is produced. MapReduce partitions and sorts the output based on the key.

Detailed explanation-4: -The “MapReduce System” (also called “infrastructure” or “framework") orchestrates the processing by marshalling the distributed servers, running the various tasks in parallel, managing all communications and data transfers between the various parts of the system, and providing for redundancy and fault tolerance.