You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat: the mapper applies a regular expression over input values and emits key- values pairs with the…

Question

Options

• AThere is no difference in output between the two settings.
• BWith zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of
• CWith zero reducers, all instances of matching patterns are gathered together in one file on HDFS.
• DWith zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one reducer,

• A
  17% (10)
• B
  5% (3)
• C
  8% (5)
• D
  69% (41)

Explanation

• It is legal to set the number of reduce-tasks to zero if no reduction is desired. In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by setOutputPath(Path). The framework does not sort the map-outputs before writing them out to the * Often, you may want to process input data using a map function only. To do this, simply set mapreduce.job.reduces to zero. The MapReduce framework will not create any reducer tasks. Rather, the outputs of the mapper tasks will be the final output of the job. In this phase the reduce(WritableComparable, Iterator, OutputCollector, Reporter) method is called for each <key, (list of values)> pair in the grouped inputs. The output of the reduce task is typically written to the FileSystem via OutputCollector.collect(WritableComparable, Writable). Applications can use the Reporter to report progress, set application-level status messages and update Counters, or just indicate that they are alive. The output of the Reducer is not sorted.

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