Map Reduce in Hadoop Last Updated : 04 Aug, 2025 Comments Improve Suggest changes Like Article Like Report MapReduce is the processing engine of Hadoop. While HDFS is responsible for storing massive amounts of data, MapReduce handles the actual computation and analysis. It provides a simple yet powerful programming model that allows developers to process large datasets in a distributed and parallel manner. It is a two-phase data processing model in Hadoop:Map Phase: Breaks the data into smaller chunks, processes them, and generates intermediate (key, value) pairs.Reduce Phase : Aggregates and merges the intermediate results to produce the final output.Map and Reduce interfacesHow MapReduce Works – Step by StepExample Input File: sample.txtHello I am GeeksforGeeks How can I help you How can I assist you Are you an engineer Are you looking for coding Are you looking for interview questions what are you doing these days what are your strengths When stored in HDFS, this file is divided into input splits (e.g., first.txt, second.txt, etc.). Each split is assigned to a Mapper for processing.Step 1: Input Splitting & Record ReaderThe input file is divided into splits.Each split is converted into (key, value) pairs using a RecordReader.In TextInputFormat (default), the key is the byte offset of the line and the value is the line itself.Example:(0, "Hello I am GeeksforGeeks") (26, "How can I help you") File Formats in HadoopThe way a RecordReader converts text into (key, value) pairs depends on the input file format. Hadoop provides several built‑in formats:TextInputFormat (Default) : Each line becomes (byte offset, line).KeyValueTextInputFormat : Splits each line into (key, value) pairs using a delimiter.SequenceFileInputFormat : Stores data in a binary (key, value) format (efficient for data exchange between MapReduce jobs).SequenceFileAsTextInputFormat : Reads binary sequence files but presents keys and values as text.By default, Hadoop uses TextInputFormat.Step 2: Map PhaseEach Mapper processes its assigned (key, value) pair and generates intermediate data.For (0, "Hello I am GeeksforGeeks"):(Hello, 1) (I, 1) (am, 1) (GeeksforGeeks, 1) For (26, "How can I help you"):(How, 1) (can, 1) (I, 1) (help, 1) (you, 1) All Mappers run in parallel, one per input split.Step 3: Shuffling and SortingThe Mapper outputs are not final. Before reducing:Shuffling groups all values of identical keys:(How, [1,1]) (Are, [1,1,1]) (I, [1,1]) Sorting arranges keys so that they can be processed sequentially.This prepares data for the reducer.Step 4: Reduce PhaseThe Reducer aggregates values for each key:(How, [1,1]) → (How, 2) (Are, [1,1,1]) → (Are, 3) (I, [1,1]) → (I, 2) Final output (saved in result.output):Hello - 1 I - 2 am - 1 GeeksforGeeks - 1 How - 2 Are - 3 are - 2 you - 3 what - 2 ... MapReduce FundamentalsNumber of Mappers = Number of Input SplitsReducer Output Files : By default, one reducer produces one output file. Multiple reducers → multiple output files.Intermediate Data : Mapper output is not final; it needs shuffling, sorting, and reducing.Fault Tolerance : If a TaskTracker fails, the JobTracker reassigns tasks to another node with a copy of the data.Advantages of MapReduce in HadoopScalable : Efficiently processes petabytes of data across thousands of nodes.Parallel : Executes tasks simultaneously on distributed data blocks.Fault-Tolerant : Recovers automatically by reassigning failed tasks.Simple : Developers only write Map and Reduce logic; Hadoop manages execution.Related ArticlesHadoop Distributed File System (HDFS)Different Modes of Hadoop OperationHadoop Schedulers and its types Hadoop Ecosystem Components Comment More infoAdvertise with us V vivek5252 Follow Improve Article Tags : C# Similar Reads IntroductionC# TutorialC# (pronounced "C-sharp") is a modern, versatile, object-oriented programming language developed by Microsoft in 2000 that runs on the .NET Framework. 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