Where a mapper or reducer runs?
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First, the number of mappers and reducers for your cluster is configurable. You can have 1 reducer and 1 mapper or m reducers and n mappers.
A reducer is one that runs aggregation on the results the n mappers provided, that is grouped and sorted by keys mappers generated.A reducer can, in theory, run on any available worker node in a cluster: a single reducer draws its input from every mapper used in the program, sorts the data, and exercises some aggregate function to produce a result. There's no particular advantage to dedicating one node to do this work. In fact, in a cluster that's running multiple concurrent MapReduce jobs, it would probably appear as a bottleneck before too long.
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the MapReduce Framework supports Mapper and Reducer parallelism. Compare this to Multithreading.
Assuming the input is a large collection of simple text documents, and the following MyMapper and MyReducer (written in Hadoop) will be applied to these text documents: public static class MyMapper extends Mapper<Object, Text, IntWritable, Text> { private Text word = new Text(); public void map(Object key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { String currentWord = itr.nextToken(); word.set(currentWord); context.write(new IntWritable(currentWord.length()), word); } ...
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