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MAPREDUCE-6583. Clarify confusing sentence in MapReduce tutorial document. Contributed by Kai Sasaki. 

(cherry picked from commit 7995a6ea4d)
(cherry picked from commit 8607cb6074)
This commit is contained in:
Akira Ajisaka 2015-12-21 00:16:14 +09:00
parent 6f30919336
commit 5f34cbff5c
2 changed files with 6 additions and 3 deletions
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3
hadoop-mapreduce-project/CHANGES.txt
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@ -385,6 +385,9 @@ Release 2.7.3 - UNRELEASED
MAPREDUCE-5883. "Total megabyte-seconds" in job counters is slightly MAPREDUCE-5883. "Total megabyte-seconds" in job counters is slightly
misleading (Nathan Roberts via jlowe) misleading (Nathan Roberts via jlowe)
MAPREDUCE-6583. Clarify confusing sentence in MapReduce tutorial document.
(Kai Sasaki via aajisaka)
Release 2.7.2 - UNRELEASED Release 2.7.2 - UNRELEASED
INCOMPATIBLE CHANGES INCOMPATIBLE CHANGES

6
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,
} }
``` ```
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). 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).
Thus the output of the job is: Thus the output of the job is:
@ -348,7 +348,7 @@ Maps are the individual tasks that transform input records into intermediate rec
The Hadoop MapReduce framework spawns one map task for each `InputSplit` generated by the `InputFormat` for the job. The Hadoop MapReduce framework spawns one map task for each `InputSplit` generated by the `InputFormat` for the job.
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. 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.
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). 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).
@ -848,7 +848,7 @@ In the following sections we discuss how to submit a debug script with a job. Th
##### How to distribute the script file: ##### How to distribute the script file:
The user needs to use [DistributedCache](#DistributedCache) to *distribute* and *symlink* thescript file. The user needs to use [DistributedCache](#DistributedCache) to *distribute* and *symlink* to the script file.
##### How to submit the script: ##### How to submit the script: