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--- courses:mapreduce-tutorial:step-3 [2012/01/24 19:14]
straka
+++ courses:mapreduce-tutorial:step-3 [2012/01/28 12:03]
majlis
@@ Line 1: / Line 1: @@
 ====== MapReduce Tutorial : Basic mapper ======
-The simplest MR job consists of a mapper only.  The input data is divided in several parts, every processed by an independent mapper, and the results are collected in one directory, one file per mapper.
+The simplest Hadoop job consists of a mapper only.  The input data is divided in several parts, every processed by an independent mapper, and the results are collected in one directory, one file per mapper.
+The Hadoop framework silently handles failures. If a mapper task fails, another is executed and the input of the failed attempt is discarded.
 ===== Example Perl mapper =====
-<code perl>
+<file perl>
 #!/usr/bin/perl
@@ Line 28: / Line 30: @@
 $runner->run();
-</code>
+</file>
 The values ''input_format'', ''output_format'' and ''output_compression'' could be left out, because they are all set to their default value.
-Resulting script can be executed using
+Resulting script can be executed locally in a single thread using
   perl script.pl run input_directory output_directory
 All files in input_directory are processes. The output_directory must not exist.
+----
+<html>
+<table style="width:100%">
+<tr>
+<td style="text-align:left; width: 33%; "></html>[[step-2|Step 2]]: Input and output format, testing data.<html></td>
+<td style="text-align:center; width: 33%; "></html>[[.|Overview]]<html></td>
+<td style="text-align:right; width: 33%; "></html>[[step-4|Step 4]]: Counters.<html></td>
+</tr>
+</table>
+</html>

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