An Example Java Map Reduce Program - NYSE Yearly Analysis
High Level Map Reduce Execution in Brief: Firstly, a MapReduce job is split into the Mapping phase and the Reduce phase on a high level. The mapping part of a job is distributed to all nodes on a cluster which hold the blocks of the input files used in the job. Each node can then spawn multiple mappers, ie using all the cores of a Quad core CPU, to process the data residing on that computer. All of this occurs in parallel. The mapper class is mainly for data filtering and transformations. The Reducers run on different nodes and the number of reducers to be run are defined by the programmer in the job configuration. The selection of nodes to run reducer is done by the job tracker based on availability of resources. The process that a MapReduce job goes through to complete a job is as follows: The number of map and reduce functions are defined by the job you are running. Each map processes its input data, sorts it to group the keys and writes it to disk. The job define...