CODEWITHSUNDEEP
hadoopmapreduce
de1337ed
de1337ed

Reputation: 3325

"Map" and "Reduce" functions in Hadoop's MapReduce

I've been looking at this word count example by hadoop: http://hadoop.apache.org/docs/r1.0.4/mapred_tutorial.html#Source+Code

And I'm a little confused about the Map function. In the map function shown, it takes in a "key" of type LongWritable, but this parameter is never used in the body of the Map function. What does the application programmer expect Hadoop to pass in for this key? Why does a map function require a key if it simply parses values from a line of text or something. Can someone give me an example where both a key and a value is required for input? I only see map as V1 -> (K2, V2).

Another question: In the real implementation of hadoop, are their multiple reduction steps? If so, how does hadoop apply the same reduction function multiple times if the function is (K2, V2) -> (K3, V3)? If another reduction is performed, it needs to take in type (K3, V3)...

Thank you!

Upvotes: 1

Views: 4805

Answers (1)

Chris Gerken
Chris Gerken

Reputation: 16390

There's a key there because the map() method is always passed a key and a value (and a context). It's up to you as to whether you actually use the key and/or value. In this case, the key represents a line number from the file being read. The word count logic doesn't need that. The map() method just uses the value, which in the case of a text file is a line of the file.

As to your second question (which really should be its own stack overflow question), you may have any number of map/reduce jobs in a hadoop workflow. Some of those jobs will read as input pre-existing files and others will read the output of other jobs. Each job will have one or more mappers and a single reducer.

Upvotes: 2

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