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The Map implementation in Hadoop in a application

Mapper maps input key/value pairs to a set of intermediate key/value pairs.

Maps are the individual tasks that transform input records into intermediate records.

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Implementation

• Mapper implementations are passed to the job via Job.setMapperClass(Class) method.
• The framework then calls map(WritableComparable, Writable, Context) for each key/value pair in the InputSplit

Applications implements this map function and:

• collect Output pairs with calls to context.write(WritableComparable, Writable).
• can override the cleanup(Context) method to perform any required cleanup.
• can use the Counter to report its statistics.
• can control the grouping by specifying a Comparator via Job.setGroupingComparatorClass(Class))
• can specify a combiner, via Job.setCombinerClass(Class), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer.
• can control the compression via the Configuration.
• can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner.

The Mapper outputs are:

• sorted
• stored in a simple (key-len, key, value-len, value) format.
• passed to the Reducer.

Documentation / Reference

• https://hadoop.apache.org/docs/r2.9.0/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html