{"id":2099,"date":"2017-04-19T14:32:06","date_gmt":"2017-04-19T14:32:06","guid":{"rendered":"http:\/\/data-flair.training\/blogs\/?p=2099"},"modified":"2018-11-21T11:16:58","modified_gmt":"2018-11-21T05:46:58","slug":"hadoop-mapper-in-mapreduce","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/hadoop-mapper-in-mapreduce\/","title":{"rendered":"Hadoop Mapper – 4 Steps Learning to MapReduce Mapper"},"content":{"rendered":"

1. Hadoop Mapper Tutorial – Objective<\/h2>\n

Mapper<\/strong> task is the first phase of processing that processes each input record (from RecordReader) and generates an intermediate key-value pair. Hadoop<\/strong> <\/a>Mapper store intermediate-output on the local disk.\u00a0In this Hadoop mapper tutorial, we will try to answer what is a MapReduce <\/a><\/strong>Mapper how to generate key-value pair<\/strong><\/a> in Hadoop, what is InputSplit and RecordReader in Hadoop, how mapper works in Hadoop.<\/p>\n

We will also discuss the number of mapper in Hadoop MapReduce for running any program and how to calculate the number of Hadoop mappers required for a given data. After this tutorial you can refer our tutorial on MapReduce Reducer<\/strong> <\/a>to gain complete insights on both mapper and reducer in Hadoop. It is really fun to understand mapper and reducer in Hadoop.<\/p>\n

We will also discuss the number of mapper in Hadoop MapReduce for running any program and how to calculate the number of mappers required for a given data.<\/p>\n

\"Hadoop<\/a>

Hadoop Mapper – 4 Steps Learning to MapReduce Mapper<\/p><\/div>\n

2. What is Hadoop Mapper?<\/h2>\n

Hadoop Mapper<\/strong> task processes each input record and it generates a new <key, value> pairs. The <key, value> pairs can be completely different from the input pair. In mapper task, the output is the full collection of all these <key, value> pairs. Before writing the output for each mapper task, partitioning<\/strong><\/a> of output take place on the basis of the key and then sorting<\/strong><\/a> is done. This partitioning specifies that all the values for each key are grouped together.
\nMapReduce frame generates one map task for each InputSplit (we will discuss it below.) generated by the InputFormat<\/strong><\/a> for the job.<\/p>\n

Mapper only understands <key, value> pairs of data, so before passing data to the mapper, data should be first converted into <key, value> pairs.
\nRead: What is MapReduce?<\/a><\/strong><\/p>\n

3. How is key value pair generated in Hadoop?<\/h2>\n

Let us now discuss the key-value pair generation in Hadoop.<\/p>\n