1. What Mapper does?
Ans: Maps are the individual tasks that transform i
nput records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.
2. What is the InputSplit in map reduce software?
Ans: An InputSplit is a logical representation of a unit (A chunk) of input work for a map task; e.g., a filename and a byte range within that file to process or a row set in a text file.
3.Explain the shuffle?
Ans: Input to the Reducer is the sorted output of the mappers. In this phase the framework fetches the relevant partition of the output of all the mappers, via HTTP.
4.What is MapReduce ?
Ans: Map reduce is an algorithm or concept to process Huge amount of data in a faster way. As per its name you can divide it Map and Reduce.
The main MapReduce job usually splits the input data-set into independent chunks. (Big data sets in the multiple small datasets)
MapTask: will process these chunks in a completely parallel manner (One node can process one or more chunks).
The framework sorts the outputs of the maps.
Reduce Task : And the above output will be the input for the reducetasks, produces the final result.
Your business logic would be written in the MappedTask and ReducedTask. Typically both the input and the output of the job are stored in a file-system (Not database). The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.
5.It can be possible that a Job has 0 reducers?
Ans: It is legal to set the number of reduce-tasks to zero if no reduction is desired.