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Beginners Guide To Understanding MapReduce-Career in Big Data Hadoop
MapReduce can be interpreted as a programming model which is well suited for the application of processing huge data. MapReduce program which are written in various languages live Java, Ruby, Python, and C++ can be run on Hadoop.
MapReduce supports performing analysis operations for relatively large volumes of data which is by utilizing the presence of multiple machines present in the cluster. This is due to the fact that which is due to the fact that the programs here in MapReduce are parallel in nature,
The Functioning Process In MapReduce-
The functioning process in MapReduce is executed in four phases. These include
Let’s get to know in detail about each of these four phases
The input given to a MapReduce gets splits into multiple pieces of fixed size which are called as input splits. An Input split can be interpreted as a chunk of the input which is consumed by a single map
Mapping can be considered as the first phase of execution of map-reduce program. In mapping phase, the output values are attained by passing the data in each split to a mapping function. In our example, counting the number of occurrences of each would from input splits would be the job of mapping phase and prepare a list in the form of <word, frequency>
The output which is generated by the Mapping phase gets consumed in this Shuffling phase. In this phase the operation is to consolidate the relevant records from Mapping phase output.
The output generation from the Shuffling phase gets aggregated in this phase. By combining the values that are generated from the Shuffling phase it generates a single output value. In short, this phase summarizes the complete dataset.
Similarly there are several complex steps that are involved in the architecture of MapReduce. Get to build real-time expertise in working on the architecture of MapReduce in Hadoop by being a part of our Orien IT institutes advanced Hadoop Training In Hyderabad program.