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MAPREDUCE-6583. Clarify confusing sentence in MapReduce tutorial document. Contributed by Kai Sasaki.
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hadoop-mapreduce-project/CHANGES.txt

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@@ -692,6 +692,9 @@ Release 2.7.3 - UNRELEASED
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MAPREDUCE-6549. multibyte delimiters with LineRecordReader cause
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duplicate records (wilfreds via rkanter)
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MAPREDUCE-6583. Clarify confusing sentence in MapReduce tutorial document.
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(Kai Sasaki via aajisaka)
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Release 2.7.2 - UNRELEASED
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hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/site/markdown/MapReduceTutorial.md

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@@ -311,7 +311,7 @@ public void reduce(Text key, Iterable<IntWritable> values,
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```
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The `Reducer` implementation, via the `reduce` method just sums up the values, which are the occurence counts for each key (i.e. words in this example).
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The `Reducer` implementation, via the `reduce` method just sums up the values, which are the occurrence counts for each key (i.e. words in this example).
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Thus the output of the job is:
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The Hadoop MapReduce framework spawns one map task for each `InputSplit` generated by the `InputFormat` for the job.
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Overall, `Mapper` implementations are passed the `Job` for the job via the [Job.setMapperClass(Class)](../../api/org/apache/hadoop/mapreduce/Job.html) method. The framework then calls [map(WritableComparable, Writable, Context)](../../api/org/apache/hadoop/mapreduce/Mapper.html) for each key/value pair in the `InputSplit` for that task. Applications can then override the `cleanup(Context)` method to perform any required cleanup.
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Overall, mapper implementations are passed to the job via [Job.setMapperClass(Class)](../../api/org/apache/hadoop/mapreduce/Job.html) method. The framework then calls [map(WritableComparable, Writable, Context)](../../api/org/apache/hadoop/mapreduce/Mapper.html) for each key/value pair in the `InputSplit` for that task. Applications can then override the `cleanup(Context)` method to perform any required cleanup.
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Output pairs do not need to be of the same types as input pairs. A given input pair may map to zero or many output pairs. Output pairs are collected with calls to context.write(WritableComparable, Writable).
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##### How to distribute the script file:
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The user needs to use [DistributedCache](#DistributedCache) to *distribute* and *symlink* thescript file.
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The user needs to use [DistributedCache](#DistributedCache) to *distribute* and *symlink* to the script file.
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##### How to submit the script:
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