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How does the reducer function work in mapreduce?


Asked by Karsyn Bailey on Dec 07, 2021 FAQ



Reducer − The Reducer takes the grouped key-value paired data as input and runs a Reducer function on each one of them. Here, the data can be aggregated, filtered, and combined in a number of ways, and it requires a wide range of processing. Once the execution is over, it gives zero or more key-value pairs to the final step.
In fact,
The output of the Mapper is fed to the reducer as input. The reducer runs only after the Mapper is over. The reducer too takes input in key-value format, and the output of reducer is the final output. The map takes data in the form of pairs and returns a list of <key, value> pairs. The keys will not be unique in this case.
Next, Download Hadoop and Data Lakes now. At the crux of MapReduce are two functions: Map and Reduce. They are sequenced one after the other. The Map function takes input from the disk as <key,value> pairs, processes them, and produces another set of intermediate <key,value> pairs as output.
Also Know,
It communicates with the InputSplit in Hadoop MapReduce and converts the data into key-value pairs suitable for reading by the mapper. By default, it uses TextInputFormat for converting data into a key-value pair.
Besides,
The purpose of MapReduce in Hadoop is to Map each of the jobs and then it will reduce it to equivalent tasks for providing less overhead over the cluster network and to reduce the processing power. The MapReduce task is mainly divided into two phases Map Phase and Reduce Phase.