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--- courses:mapreduce-tutorial:step-26 [2012/01/28 15:29]
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+++ courses:mapreduce-tutorial:step-26 [2012/01/31 14:41] (current)
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@@ Line 1: / Line 1: @@
-====== MapReduce Tutorial : Counters and job configuration ======
+====== MapReduce Tutorial : Compression and job configuration ======
-===== Counters =====
-As in the Perl API, a mapper or a reducer can increment various counters by using ''context.getCounter("Group", "Name").increment(value)'':
+===== Compression =====
+The output files can be compressed using
 <code java>
-public void map(Text key, Text value, Context context) throws IOException, InterruptedException {
+  FileOutputFormat.setCompressOutput(job, true);
-  ...
-  context.getCounter("Group", "Name").increment(value);
-  ...
-}
 </code>
-The ''getCounter'' method returns a [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Counter.html|Counter]] object, so if a counter is incremented frequently, the ''getCounter'' method can be called only once:
+The default compression format is ''deflate'' -- raw Zlib compression. Several other compression formats can be selected:
 <code java>
-public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
+import org.apache.hadoop.io.compress.*;
   ...
-  Counter values = context.getCounter("Reducer", "Number of values");
+  FileOutputFormat.setOutputCompressorClass(GzipCodec.class);   //.gz
-  for (IntWritable value : values) {
+  FileOutputFormat.setOutputCompressorClass(BZip2Codec.class);  //.bz2
-    ...
-    values.increment(1);
-  }
-}
 </code>
+Of course, any of these formats is decompressed transparently when the file is being read.
 ===== Job configuration =====
 The job properties can be set:
-  * on the command line -- the [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/util/ToolRunner.html|ToolRunner]] parses options in format ''-Dname=value''
+  * on the command line -- the [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/util/ToolRunner.html|ToolRunner]] parses options in format ''-Dname=value''. See the [[.:step-24#running-the-job|syntax of the hadoop script]].
-  * using the [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html|Job]] object.
+  * using the [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/mapreduce/Job.html|Job]]''.getConfiguration()'' a [[http://hadoop.apache.org/common/docs/r1.0.0/api/org/apache/hadoop/conf/Configuration.html|Configuration]] object is retrieved. It provides following methods:
-See the [[.:step-9#a-brief-list-of-hadoop-options|brief list of Hadoop properties]].
+    * ''String get(String name)'' -- get the value of the ''name'' property, ''null'' if it does not exist.
+    * ''String get(String name, String defaultValue)'' -- get the value of the ''name'' property
+    * ''getBoolean'', ''getClass'', ''getFile'', ''getFloat'', ''getInt'', ''getLong'', ''getStrings'' -- return a typed value of the ''name'' property (i.e., number, file name, class name, ...).
+    * ''set(String name, String value)'' -- set the value of the ''name'' property to ''value''.
+    * ''setBoolean'', ''setClass'', ''setFile'', ''setFloat'', ''setInt'', ''setLong'', ''setStrings'' -- set the typed value of the ''name'' property (i.e., number, file name, class name, ...).
+  * in a mapper or a reducer, the ''context'' object also provides the ''getConfiguration()'' method, so the job properties can be accessed in the mappers and reducers too.
+Apart from already mentioned [[.:step-9#a-brief-list-of-hadoop-options|brief list of Hadoop properties]], there is one important Java-specific property:
+  * **''mapred.child.java.opts''** with default value **''-Xmx200m''**. This property sets the Java options used for every task attempt (mapper, reducer, combiner, partitioner). The default value ''-Xmx200m'' specifies the maximum size of memory allocation pool. If your mappers and reducers need 1GB memory, use ''-Xmx1024m''. Other Java options can be found in ''man java''.
+----
-There is one
+<html>
+<table style="width:100%">
+<tr>
+<td style="text-align:left; width: 33%; "></html>[[step-25|Step 25]]: Reducers, combiners and partitioners.<html></td>
+<td style="text-align:center; width: 33%; "></html>[[.|Overview]]<html></td>
+<td style="text-align:right; width: 33%; "></html>[[step-27|Step 27]]: Running multiple Hadoop jobs in one source file.<html></td>
+</tr>
+</table>
+</html>

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