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From acmur...@apache.org
Subject svn commit: r1082677 [11/38] - in /hadoop/mapreduce/branches/MR-279: ./ assembly/ ivy/ mr-client/ mr-client/hadoop-mapreduce-client-app/ mr-client/hadoop-mapreduce-client-app/src/ mr-client/hadoop-mapreduce-client-app/src/main/ mr-client/hadoop-mapredu...
Date Thu, 17 Mar 2011 20:21:54 GMT
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+<html>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<body>
+
+<p>Utilities for managing dependent jobs.</p>
+
+</body>
+</html>

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@@ -0,0 +1,105 @@
+<HTML>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<BODY>
+
+<p>Given a set of sorted datasets keyed with the same class and yielding equal
+partitions, it is possible to effect a join of those datasets prior to the map.
+This could save costs in re-partitioning, sorting, shuffling, and writing out
+data required in the general case.</p>
+
+<h3><a name="Interface"></a>Interface</h3>
+
+<p>The attached code offers the following interface to users of these
+classes.</p>
+
+<table>
+<tr><th>property</th><th>required</th><th>value</th></tr>
+<tr><td>mapred.join.expr</td><td>yes</td>
+    <td>Join expression to effect over input data</td></tr>
+<tr><td>mapred.join.keycomparator</td><td>no</td>
+    <td><tt>WritableComparator</tt> class to use for comparing keys</td></tr>
+<tr><td>mapred.join.define.&lt;ident&gt;</td><td>no</td>
+    <td>Class mapped to identifier in join expression</td></tr>
+</table>
+
+<p>The join expression understands the following grammar:</p>
+
+<pre>func ::= &lt;ident&gt;([&lt;func&gt;,]*&lt;func&gt;)
+func ::= tbl(&lt;class&gt;,"&lt;path&gt;");
+
+</pre>
+
+<p>Operations included in this patch are partitioned into one of two types:
+join operations emitting tuples and "multi-filter" operations emitting a
+single value from (but not necessarily included in) a set of input values.
+For a given key, each operation will consider the cross product of all
+values for all sources at that node.</p>
+
+<p>Identifiers supported by default:</p>
+
+<table>
+<tr><th>identifier</th><th>type</th><th>description</th></tr>
+<tr><td>inner</td><td>Join</td><td>Full inner join</td></tr>
+<tr><td>outer</td><td>Join</td><td>Full outer join</td></tr>
+<tr><td>override</td><td>MultiFilter</td>
+    <td>For a given key, prefer values from the rightmost source</td></tr>
+</table>
+
+<p>A user of this class must set the <tt>InputFormat</tt> for the job to
+<tt>CompositeInputFormat</tt> and define a join expression accepted by the
+preceding grammar. For example, both of the following are acceptable:</p>
+
+<pre>inner(tbl(org.apache.hadoop.mapred.SequenceFileInputFormat.class,
+          "hdfs://host:8020/foo/bar"),
+      tbl(org.apache.hadoop.mapred.SequenceFileInputFormat.class,
+          "hdfs://host:8020/foo/baz"))
+
+outer(override(tbl(org.apache.hadoop.mapred.SequenceFileInputFormat.class,
+                   "hdfs://host:8020/foo/bar"),
+               tbl(org.apache.hadoop.mapred.SequenceFileInputFormat.class,
+                   "hdfs://host:8020/foo/baz")),
+      tbl(org.apache.hadoop.mapred/SequenceFileInputFormat.class,
+          "hdfs://host:8020/foo/rab"))
+</pre>
+
+<p><tt>CompositeInputFormat</tt> includes a handful of convenience methods to
+aid construction of these verbose statements.</p>
+
+<p>As in the second example, joins may be nested. Users may provide a
+comparator class in the <tt>mapred.join.keycomparator</tt> property to specify
+the ordering of their keys, or accept the default comparator as returned by
+<tt>WritableComparator.get(keyclass)</tt>.</p>
+
+<p>Users can specify their own join operations, typically by overriding
+<tt>JoinRecordReader</tt> or <tt>MultiFilterRecordReader</tt> and mapping that
+class to an identifier in the join expression using the
+<tt>mapred.join.define.<em>ident</em></tt> property, where <em>ident</em> is
+the identifier appearing in the join expression. Users may elect to emit- or
+modify- values passing through their join operation. Consulting the existing
+operations for guidance is recommended. Adding arguments is considerably more
+complex (and only partially supported), as one must also add a <tt>Node</tt>
+type to the parse tree. One is probably better off extending
+<tt>RecordReader</tt> in most cases.</p>
+
+<a href="http://issues.apache.org/jira/browse/HADOOP-2085">JIRA</a>
+
+</BODY>
+
+</HTML>

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+<html>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<body>
+
+Classes for performing various counting and aggregations.
+<p />
+<h2><a name="Aggregate"></a>Aggregate framework </h2>
+<p />
+Generally speaking, in order to implement an application using Map/Reduce
+model, the developer needs to implement Map and Reduce functions (and possibly
+Combine function). However, for a lot of applications related to counting and
+statistics computing, these functions have very similar
+characteristics. This provides a package implementing 
+those patterns. In particular, the package provides a generic mapper class,
+a reducer class and a combiner class, and a set of built-in value aggregators.
+It also provides a generic utility class, ValueAggregatorJob, that offers a static function that 
+creates map/reduce jobs:
+<blockquote>
+<pre>
+public static JobConf createValueAggregatorJob(String args&#91;]) throws IOException;
+</pre>
+</blockquote>
+To call this function, the user needs to pass in arguments specifying the input directories, the output directory, 
+the number of reducers, the input data format (textinputformat or sequencefileinputformat), and a file specifying user plugin class(es) to load by the mapper. 
+A user plugin class is responsible for specifying what 
+aggregators to use and what values are for which aggregators. 
+A plugin class must implement the following interface:
+<blockquote>
+<pre>
+ public interface ValueAggregatorDescriptor { 
+     public ArrayList&#60;Entry&#62; generateKeyValPairs(Object key, Object value); 
+     public void configure(JobConfjob); 
+} 
+</pre>
+</blockquote>
+Function generateKeyValPairs will generate aggregation key/value pairs for the
+input key/value pair. Each aggregation key encodes two pieces of information: the aggregation type and aggregation ID.
+The value is the value to be aggregated onto the aggregation ID according to the aggregation type. Here 
+is a simple example user plugin class for counting the words in the input texts:
+<blockquote>
+<pre>
+public class WordCountAggregatorDescriptor extends ValueAggregatorBaseDescriptor { 
+    public ArrayList&#60;Entry&#62; generateKeyValPairs(Object key, Object val) {
+        String words &#91;] &#61; val.toString().split(&#34; &#124;\t&#34;);
+        ArrayList&#60;Entry&#62; retv &#61; new ArrayList&#60;Entry&#62;();
+        for (int i &#61; 0; i &#60; words.length; i++) {
+            retv.add(generateEntry(LONG&#95;VALUE&#95;SUM, words&#91;i], ONE))
+        }
+        return retv;
+    }
+    public void configure(JobConf job) {}
+} 
+</pre>
+</blockquote>
+In the above code, LONG_VALUE_SUM is a string denoting the aggregation type LongValueSum, which sums over long values.
+ONE denotes a string "1". Function generateEntry(LONG_VALUE_SUM, words[i], ONE) will inperpret the first argument as an aggregation type, the second as an aggregation ID, and the the third argumnent as the value to be aggregated. The output will look like: "LongValueSum:xxxx", where XXXX is the string value of words[i]. The value will be "1". The mapper will call generateKeyValPairs(Object key, Object val)  for each input key/value pair to generate the desired aggregation id/value pairs. 
+The down stream combiner/reducer will interpret these pairs as adding one to the aggregator XXXX.
+<p />
+Class ValueAggregatorBaseDescriptor is a base class that user plugin classes can extend. Here is the XML fragment specifying the user plugin class:
+<blockquote>
+<pre>
+&#60;property&#62;
+    &#60;name&#62;aggregator.descriptor.num&#60;/name&#62;
+    &#60;value&#62;1&#60;/value&#62;
+&#60;/property&#62;
+&#60;property&#62;
+   &#60;name&#62;aggregator.descriptor.0&#60;/name&#62;
+   &#60;value&#62;UserDefined,org.apache.hadoop.mapred.lib.aggregate.examples.WordCountAggregatorDescriptor&#60;/value&#62;
+&#60;/property&#62; 
+</pre>
+</blockquote>
+Class ValueAggregatorBaseDescriptor itself provides a default implementation for  generateKeyValPairs:
+<blockquote>
+<pre>
+public ArrayList&#60;Entry&#62; generateKeyValPairs(Object key, Object val) {
+   ArrayList&#60;Entry&#62; retv &#61; new ArrayList&#60;Entry&#62;();     
+   String countType &#61; LONG&#95;VALUE&#95;SUM;
+   String id &#61; &#34;record&#95;count&#34;;
+   retv.add(generateEntry(countType, id, ONE));
+   return retv;
+}
+</pre>
+</blockquote>
+Thus, if no user plugin class is specified, the default behavior of the map/reduce job is to count the number of records (lines) in the imput files.
+<p />
+During runtime, the mapper will invoke the generateKeyValPairs function for each input key/value pair, and emit the generated 
+key/value pairs:
+<blockquote>
+<pre>
+public void map(WritableComparable key, Writable value,
+            OutputCollector output, Reporter reporter) throws IOException {
+   Iterator iter &#61; this.aggregatorDescriptorList.iterator();
+   while (iter.hasNext()) {
+       ValueAggregatorDescriptor ad &#61; (ValueAggregatorDescriptor) iter.next();
+       Iterator&#60;Entry&#62; ens &#61; ad.generateKeyValPairs(key, value).iterator();
+       while (ens.hasNext()) {
+           Entry en &#61; ens.next();
+           output.collect((WritableComparable)en.getKey(), (Writable)en.getValue());
+       }
+   }
+}
+</pre>
+</blockquote>
+The reducer will create an aggregator object for each key/value list pair, and perform the appropriate aggregation.
+At the end, it will emit the aggregator's results:
+<blockquote>
+<pre>
+public void reduce(WritableComparable key, Iterator values,
+            OutputCollector output, Reporter reporter) throws IOException {
+   String keyStr &#61; key.toString();
+   int pos &#61; keyStr.indexOf(ValueAggregatorDescriptor.TYPE&#95;SEPARATOR);
+   String type &#61; keyStr.substring(0,pos);
+   keyStr &#61; keyStr.substring(pos+ValueAggregatorDescriptor.TYPE&#95;SEPARATOR.length());       
+   ValueAggregator aggregator &#61; 
+       ValueAggregatorBaseDescriptor.generateValueAggregator(type);
+   while (values.hasNext()) {
+       aggregator.addNextValue(values.next());
+   }         
+   String val &#61; aggregator.getReport();
+   key &#61; new Text(keyStr);
+   output.collect(key, new Text(val)); 
+}
+</pre>
+</blockquote>
+In order to be able to use combiner, all the aggregation type be aggregators must be associative and communitive.
+The following are the types supported: <ul>
+<li> LongValueSum: sum over long values 
+</li> <li> DoubleValueSum: sum over float/double values 
+</li> <li> uniqValueCount: count the number of distinct values 
+</li> <li> ValueHistogram: compute the histogram of values compute the minimum, maximum, media,average, standard deviation of numeric values
+</li></ul> 
+<p />
+<h2><a name="Create_and_run"></a> Create and run an application </h2>
+<p />
+To create an application, the user needs to do the following things:
+<p />
+1. Implement a user plugin:
+<blockquote>
+<pre>
+import org.apache.hadoop.mapred.lib.aggregate.ValueAggregatorBaseDescriptor;
+import org.apache.hadoop.mapred.JobConf;
+
+public class WordCountAggregatorDescriptor extends ValueAggregatorBaseDescriptor {
+   public void map(WritableComparable key, Writable value,
+            OutputCollector output, Reporter reporter) throws IOException {
+   }
+   public void configure(JobConf job) {
+    
+   } 
+}
+</pre>
+</blockquote>
+
+2. Create an xml file specifying the user plugin.
+<p />
+3. Compile your java class and create a jar file, say wc.jar.
+
+<p />
+Finally, run the job:
+<blockquote>
+<pre>
+        hadoop jar wc.jar org.apache.hadoop.mapred.lib.aggregate..ValueAggregatorJob indirs outdir numofreducers textinputformat|sequencefileinputformat spec_file
+</pre>
+</blockquote>
+<p />
+
+
+</body>
+</html>

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--- hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/lib/db/package.html (added)
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@@ -0,0 +1,44 @@
+<html>
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<body>
+<h2>org.apache.hadoop.mapred.lib.db Package</h2>
+<p>
+This package contains a library to read records from a database as an 
+input to a mapreduce job, and write the output records to the database.   
+</p>
+<p>
+The Database to access can be configured using the static methods in the 
+DBConfiguration class. Jobs reading input from a database should use 
+DBInputFormat#setInput() to set the configuration. And jobs writing 
+its output to the database should use DBOutputFormat#setOutput().
+</p>
+<p> 
+Tuples from/to the database are converted to/from Java objects using 
+DBWritable methods. Typically, for each table in the db, a class extending
+DBWritable is defined, which holds the fields of the tuple. The fields 
+of a record are read from the database using DBWritable#readFields(ResultSet),
+and written to the database using DBWritable#write(PreparedStatament 
+statement). 
+</p>
+<p>
+An example program using both DBInputFormat and DBOutputFormat can be found 
+at src/examples/org/apache/hadoop/examples/DBCountPageview.java.
+</p>
+</body>
+</html>
\ No newline at end of file

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@@ -0,0 +1,25 @@
+<html>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<body>
+
+<p>Library of generally useful mappers, reducers, and partitioners.</p>
+
+</body>
+</html>

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@@ -0,0 +1,230 @@
+<html>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<body>
+
+<p>A software framework for easily writing applications which process vast 
+amounts of data (multi-terabyte data-sets) parallelly on large clusters 
+(thousands of nodes) built of commodity hardware in a reliable, fault-tolerant 
+manner.</p>
+
+<p>A Map-Reduce <i>job</i> usually splits the input data-set into independent 
+chunks which processed by <i>map</i> tasks in completely parallel manner, 
+followed by <i>reduce</i> tasks which aggregating their output. Typically both 
+the input and the output of the job are stored in a 
+{@link org.apache.hadoop.fs.FileSystem}. The framework takes care of monitoring 
+tasks and re-executing failed ones. Since, usually, the compute nodes and the 
+storage nodes are the same i.e. Hadoop's Map-Reduce framework and Distributed 
+FileSystem are running on the same set of nodes, tasks are effectively scheduled 
+on the nodes where data is already present, resulting in very high aggregate 
+bandwidth across the cluster.</p>
+
+<p>The Map-Reduce framework operates exclusively on <tt>&lt;key, value&gt;</tt> 
+pairs i.e. the input to the job is viewed as a set of <tt>&lt;key, value&gt;</tt>
+pairs and the output as another, possibly different, set of 
+<tt>&lt;key, value&gt;</tt> pairs. The <tt>key</tt>s and <tt>value</tt>s have to 
+be serializable as {@link org.apache.hadoop.io.Writable}s and additionally the
+<tt>key</tt>s have to be {@link org.apache.hadoop.io.WritableComparable}s in 
+order to facilitate grouping by the framework.</p>
+
+<p>Data flow:</p>
+<pre>
+                                (input)
+                                <tt>&lt;k1, v1&gt;</tt>
+       
+                                   |
+                                   V
+       
+                                  <b>map</b>
+       
+                                   |
+                                   V
+
+                                <tt>&lt;k2, v2&gt;</tt>
+       
+                                   |
+                                   V
+       
+                                <b>combine</b>
+       
+                                   |
+                                   V
+       
+                                <tt>&lt;k2, v2&gt;</tt>
+       
+                                   |
+                                   V
+       
+                                 <b>reduce</b>
+       
+                                   |
+                                   V
+       
+                                <tt>&lt;k3, v3&gt;</tt>
+                                (output)
+</pre>
+
+<p>Applications typically implement 
+{@link org.apache.hadoop.mapred.Mapper#map(Object, Object, OutputCollector, Reporter)} 
+and
+{@link org.apache.hadoop.mapred.Reducer#reduce(Object, Iterator, OutputCollector, Reporter)} 
+methods.  The application-writer also specifies various facets of the job such
+as input and output locations, the <tt>Partitioner</tt>, <tt>InputFormat</tt> 
+&amp; <tt>OutputFormat</tt> implementations to be used etc. as 
+a {@link org.apache.hadoop.mapred.JobConf}. The client program, 
+{@link org.apache.hadoop.mapred.JobClient}, then submits the job to the framework 
+and optionally monitors it.</p>
+
+<p>The framework spawns one map task per 
+{@link org.apache.hadoop.mapred.InputSplit} generated by the 
+{@link org.apache.hadoop.mapred.InputFormat} of the job and calls 
+{@link org.apache.hadoop.mapred.Mapper#map(Object, Object, OutputCollector, Reporter)} 
+with each &lt;key, value&gt; pair read by the 
+{@link org.apache.hadoop.mapred.RecordReader} from the <tt>InputSplit</tt> for 
+the task. The intermediate outputs of the maps are then grouped by <tt>key</tt>s
+and optionally aggregated by <i>combiner</i>. The key space of intermediate 
+outputs are paritioned by the {@link org.apache.hadoop.mapred.Partitioner}, where 
+the number of partitions is exactly the number of reduce tasks for the job.</p>
+
+<p>The reduce tasks fetch the sorted intermediate outputs of the maps, via http, 
+merge the &lt;key, value&gt; pairs and call 
+{@link org.apache.hadoop.mapred.Reducer#reduce(Object, Iterator, OutputCollector, Reporter)} 
+for each &lt;key, list of values&gt; pair. The output of the reduce tasks' is 
+stored on the <tt>FileSystem</tt> by the 
+{@link org.apache.hadoop.mapred.RecordWriter} provided by the
+{@link org.apache.hadoop.mapred.OutputFormat} of the job.</p>
+
+<p>Map-Reduce application to perform a distributed <i>grep</i>:</p>
+<pre><tt>
+public class Grep extends Configured implements Tool {
+
+  // <i>map: Search for the pattern specified by 'grep.mapper.regex' &amp;</i>
+  //      <i>'grep.mapper.regex.group'</i>
+
+  class GrepMapper&lt;K, Text&gt; 
+  extends MapReduceBase  implements Mapper&lt;K, Text, Text, LongWritable&gt; {
+
+    private Pattern pattern;
+    private int group;
+
+    public void configure(JobConf job) {
+      pattern = Pattern.compile(job.get("grep.mapper.regex"));
+      group = job.getInt("grep.mapper.regex.group", 0);
+    }
+
+    public void map(K key, Text value,
+                    OutputCollector&lt;Text, LongWritable&gt; output,
+                    Reporter reporter)
+    throws IOException {
+      String text = value.toString();
+      Matcher matcher = pattern.matcher(text);
+      while (matcher.find()) {
+        output.collect(new Text(matcher.group(group)), new LongWritable(1));
+      }
+    }
+  }
+
+  // <i>reduce: Count the number of occurrences of the pattern</i>
+
+  class GrepReducer&lt;K&gt; extends MapReduceBase
+  implements Reducer&lt;K, LongWritable, K, LongWritable&gt; {
+
+    public void reduce(K key, Iterator&lt;LongWritable&gt; values,
+                       OutputCollector&lt;K, LongWritable&gt; output,
+                       Reporter reporter)
+    throws IOException {
+
+      // sum all values for this key
+      long sum = 0;
+      while (values.hasNext()) {
+        sum += values.next().get();
+      }
+
+      // output sum
+      output.collect(key, new LongWritable(sum));
+    }
+  }
+  
+  public int run(String[] args) throws Exception {
+    if (args.length &lt; 3) {
+      System.out.println("Grep &lt;inDir&gt; &lt;outDir&gt; &lt;regex&gt; [&lt;group&gt;]");
+      ToolRunner.printGenericCommandUsage(System.out);
+      return -1;
+    }
+
+    JobConf grepJob = new JobConf(getConf(), Grep.class);
+    
+    grepJob.setJobName("grep");
+
+    FileInputFormat.setInputPaths(grepJob, new Path(args[0]));
+    FileOutputFormat.setOutputPath(grepJob, args[1]);
+
+    grepJob.setMapperClass(GrepMapper.class);
+    grepJob.setCombinerClass(GrepReducer.class);
+    grepJob.setReducerClass(GrepReducer.class);
+
+    grepJob.set("mapreduce.mapper.regex", args[2]);
+    if (args.length == 4)
+      grepJob.set("mapreduce.mapper.regexmapper..group", args[3]);
+
+    grepJob.setOutputFormat(SequenceFileOutputFormat.class);
+    grepJob.setOutputKeyClass(Text.class);
+    grepJob.setOutputValueClass(LongWritable.class);
+
+    JobClient.runJob(grepJob);
+
+    return 0;
+  }
+
+  public static void main(String[] args) throws Exception {
+    int res = ToolRunner.run(new Configuration(), new Grep(), args);
+    System.exit(res);
+  }
+
+}
+</tt></pre>
+
+<p>Notice how the data-flow of the above grep job is very similar to doing the
+same via the unix pipeline:</p>
+
+<pre>
+cat input/*   |   grep   |   sort    |   uniq -c   &gt;   out
+</pre>
+
+<pre>
+      input   |    map   |  shuffle  |   reduce    &gt;   out
+</pre>
+
+<p>Hadoop Map-Reduce applications need not be written in 
+Java<small><sup>TM</sup></small> only. 
+<a href="../streaming/package-summary.html">Hadoop Streaming</a> is a utility
+which allows users to create and run jobs with any executables (e.g. shell 
+utilities) as the mapper and/or the reducer. 
+<a href="pipes/package-summary.html">Hadoop Pipes</a> is a 
+<a href="http://www.swig.org/">SWIG</a>-compatible <em>C++ API</em> to implement
+Map-Reduce applications (non JNI<small><sup>TM</sup></small> based).</p>
+
+<p>See <a href="http://labs.google.com/papers/mapreduce.html">Google's original 
+Map/Reduce paper</a> for background information.</p>
+
+<p><i>Java and JNI are trademarks or registered trademarks of 
+Sun Microsystems, Inc. in the United States and other countries.</i></p>
+
+</body>
+</html>

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@@ -0,0 +1,2 @@
+/hadoop/core/branches/branch-0.19/mapred/src/java/org/apache/hadoop/mapred/pipes/Submitter.java:713112
+/hadoop/core/trunk/src/mapred/org/apache/hadoop/mapred/pipes/Submitter.java:776175-785643

Propchange: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/pipes/UpwardProtocol.java
------------------------------------------------------------------------------
--- svn:mergeinfo (added)
+++ svn:mergeinfo Thu Mar 17 20:21:13 2011
@@ -0,0 +1,2 @@
+/hadoop/core/branches/branch-0.19/mapred/src/java/org/apache/hadoop/mapred/pipes/UpwardProtocol.java:713112
+/hadoop/core/trunk/src/mapred/org/apache/hadoop/mapred/pipes/UpwardProtocol.java:776175-785643

Added: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/pipes/package.html
URL: http://svn.apache.org/viewvc/hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/pipes/package.html?rev=1082677&view=auto
==============================================================================
--- hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/pipes/package.html (added)
+++ hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/pipes/package.html Thu Mar 17 20:21:13 2011
@@ -0,0 +1,127 @@
+<html>
+
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<body>
+
+Hadoop Pipes allows C++ code to use Hadoop DFS and map/reduce. The
+primary approach is to split the C++ code into a separate process that
+does the application specific code. In many ways, the approach will be
+similar to Hadoop streaming, but using Writable serialization to
+convert the types into bytes that are sent to the process via a
+socket.
+
+<p>
+
+The class org.apache.hadoop.mapred.pipes.Submitter has a public static
+method to submit a job as a JobConf and a main method that takes an
+application and optional configuration file, input directories, and
+output directory. The cli for the main looks like:
+
+<pre>
+bin/hadoop pipes \
+  [-input <i>inputDir</i>] \
+  [-output <i>outputDir</i>] \
+  [-jar <i>applicationJarFile</i>] \
+  [-inputformat <i>class</i>] \
+  [-map <i>class</i>] \
+  [-partitioner <i>class</i>] \
+  [-reduce <i>class</i>] \
+  [-writer <i>class</i>] \
+  [-program <i>program url</i>] \ 
+  [-conf <i>configuration file</i>] \
+  [-D <i>property=value</i>] \
+  [-fs <i>local|namenode:port</i>] \
+  [-jt <i>local|jobtracker:port</i>] \
+  [-files <i>comma separated list of files</i>] \ 
+  [-libjars <i>comma separated list of jars</i>] \
+  [-archives <i>comma separated list of archives</i>] 
+</pre>
+
+
+<p>
+
+The application programs link against a thin C++ wrapper library that
+handles the communication with the rest of the Hadoop system.  The C++
+interface is "swigable" so that interfaces can be generated for python
+and other scripting languages. All of the C++ functions and classes
+are in the HadoopPipes namespace. The job may consist of any
+combination of Java and C++ RecordReaders, Mappers, Paritioner,
+Combiner, Reducer, and RecordWriter.
+
+<p>
+
+Hadoop Pipes has a generic Java class for handling the mapper and
+reducer (PipesMapRunner and PipesReducer). They fork off the
+application program and communicate with it over a socket. The
+communication is handled by the C++ wrapper library and the
+PipesMapRunner and PipesReducer.
+
+<p>
+
+The application program passes in a factory object that can create
+the various objects needed by the framework to the runTask
+function. The framework creates the Mapper or Reducer as
+appropriate and calls the map or reduce method to invoke the
+application's code. The JobConf is available to the application.
+
+<p>
+
+The Mapper and Reducer objects get all of their inputs, outputs, and
+context via context objects. The advantage of using the context
+objects is that their interface can be extended with additional
+methods without breaking clients. Although this interface is different
+from the current Java interface, the plan is to migrate the Java
+interface in this direction.
+
+<p>
+
+Although the Java implementation is typed, the C++ interfaces of keys
+and values is just a byte buffer. Since STL strings provide precisely
+the right functionality and are standard, they will be used. The
+decision to not use stronger types was to simplify the interface.
+
+<p>
+
+The application can also define combiner functions. The combiner will
+be run locally by the framework in the application process to avoid
+the round trip to the Java process and back. Because the compare
+function is not available in C++, the combiner will use memcmp to
+sort the inputs to the combiner. This is not as general as the Java
+equivalent, which uses the user's comparator, but should cover the
+majority of the use cases. As the map function outputs key/value
+pairs, they will be buffered. When the buffer is full, it will be
+sorted and passed to the combiner. The output of the combiner will be
+sent to the Java process.
+
+<p>
+
+The application can also set a partition function to control which key
+is given to a particular reduce. If a partition function is not
+defined, the Java one will be used. The partition function will be
+called by the C++ framework before the key/value pair is sent back to
+Java.
+
+<p>
+
+The application programs can also register counters with a group and a name 
+and also increment the counters and get the counter values. Word-count 
+example illustrating pipes usage with counters is available at 
+<a href="https://svn.apache.org/repos/asf/hadoop/core/trunk/src/examples/pipes/impl/wordcount-simple.cc">wordcount-simple.cc</a>
+</body>
+</html>

Copied: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/ClientFactory.java (from r1082666, hadoop/mapreduce/branches/MR-279/src/java/org/apache/hadoop/mapreduce/ClientFactory.java)
URL: http://svn.apache.org/viewvc/hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/ClientFactory.java?p2=hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/ClientFactory.java&p1=hadoop/mapreduce/branches/MR-279/src/java/org/apache/hadoop/mapreduce/ClientFactory.java&r1=1082666&r2=1082677&rev=1082677&view=diff
==============================================================================
--- hadoop/mapreduce/branches/MR-279/src/java/org/apache/hadoop/mapreduce/ClientFactory.java (original)
+++ hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/ClientFactory.java Thu Mar 17 20:21:13 2011
@@ -25,8 +25,7 @@ import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.ipc.RPC;
-import org.apache.hadoop.mapred.JobTracker;
-import org.apache.hadoop.mapred.LocalJobRunner;
+import org.apache.hadoop.mapred.Master;
 import org.apache.hadoop.mapreduce.protocol.ClientProtocol;
 import org.apache.hadoop.net.NetUtils;
 import org.apache.hadoop.security.UserGroupInformation;
@@ -36,7 +35,8 @@ import org.apache.hadoop.security.UserGr
  *
  */
 public abstract class ClientFactory {
-
+  private static final Log LOG = LogFactory.getLog(ClientFactory.class);
+  
   @SuppressWarnings("unchecked")
   public static ClientProtocol create(Configuration conf) throws IOException {
     Class<ClientFactory> factory = (Class<ClientFactory>) conf.getClass(
@@ -45,20 +45,20 @@ public abstract class ClientFactory {
     try {
       return factory.newInstance().createClient(conf);
     } catch (Exception e) {
-      throw new IOException("could not create ClientProtocol", e);
+      throw new IOException("Could not create ClientProtocol", e);
     }
   }
 
   protected abstract ClientProtocol createClient(Configuration conf) 
-        throws IOException;
+  throws IOException;
 
   //the default factory handles the backward compatibility
   public static class DefaultClientFactory extends ClientFactory {
 
     @Override
     protected ClientProtocol createClient(Configuration conf)
-        throws IOException {
-      String tracker = conf.get("mapreduce.jobtracker.address");
+    throws IOException {
+      String tracker = conf.get(MRConfig.MASTER_ADDRESS);
       if ("local".equals(tracker)) {
         return createLocalClient(conf);
       } else {
@@ -67,21 +67,38 @@ public abstract class ClientFactory {
     }
   }
 
+  public static class NullDefaultClientFactory extends ClientFactory {
+
+    @Override
+    protected ClientProtocol createClient(Configuration conf)
+    throws IOException {
+      throw new IOException("Local Client factory not set. No Default");
+    }	  
+  }
+
   public ClientProtocol createLocalClient(Configuration conf) 
-        throws IOException {
+  throws IOException {
     conf.setInt("mapreduce.job.maps", 1);
-    return new LocalJobRunner(conf);
+    /* find the local job runner in config */	
+    @SuppressWarnings("unchecked")
+    Class<ClientFactory> localfactory = (Class<ClientFactory>) conf.getClass(
+        "mapreduce.local.clientfactory.class.name", NullDefaultClientFactory.class);
+    try {
+      return localfactory.newInstance().createClient(conf);
+    } catch(Exception e) {
+      throw new IOException("could not create local client", e);
+    }
   }
-  
+
   public ClientProtocol createJTClient(Configuration conf) throws IOException {
-    return createJTClient(JobTracker.getAddress(conf), conf);
+    return createJTClient(Master.getMasterAddress(conf), conf);
   }
 
   public ClientProtocol createJTClient(InetSocketAddress addr,
-        Configuration conf) throws IOException {
+      Configuration conf) throws IOException {
     return (ClientProtocol) RPC.getProxy(ClientProtocol.class,
         ClientProtocol.versionID, addr, 
-          UserGroupInformation.getCurrentUser(), conf,
-          NetUtils.getSocketFactory(conf, ClientProtocol.class));
-    }
+        UserGroupInformation.getCurrentUser(), conf,
+        NetUtils.getSocketFactory(conf, ClientProtocol.class));
+  }
 }

Propchange: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/ClientFactory.java
------------------------------------------------------------------------------
--- svn:mergeinfo (added)
+++ svn:mergeinfo Thu Mar 17 20:21:13 2011
@@ -0,0 +1,2 @@
+/hadoop/core/branches/branch-0.19/mapred/src/java/org/apache/hadoop/mapreduce/ClientFactory.java:713112
+/hadoop/core/trunk/src/mapred/org/apache/hadoop/mapreduce/ClientFactory.java:776175-785643

Copied: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/Cluster.java (from r1082666, hadoop/mapreduce/branches/MR-279/src/java/org/apache/hadoop/mapreduce/Cluster.java)
URL: http://svn.apache.org/viewvc/hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/Cluster.java?p2=hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/Cluster.java&p1=hadoop/mapreduce/branches/MR-279/src/java/org/apache/hadoop/mapreduce/Cluster.java&r1=1082666&r2=1082677&rev=1082677&view=diff
==============================================================================
--- hadoop/mapreduce/branches/MR-279/src/java/org/apache/hadoop/mapreduce/Cluster.java (original)
+++ hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/Cluster.java Thu Mar 17 20:21:13 2011
@@ -35,14 +35,10 @@ import org.apache.hadoop.io.Text;
 import org.apache.hadoop.ipc.RPC;
 import org.apache.hadoop.ipc.RemoteException;
 import org.apache.hadoop.mapred.JobConf;
-import org.apache.hadoop.mapred.JobTracker;
-import org.apache.hadoop.mapred.LocalJobRunner;
+import org.apache.hadoop.mapred.Master;
 import org.apache.hadoop.mapreduce.ClientFactory.DefaultClientFactory;
-import org.apache.hadoop.mapreduce.jobhistory.JobHistory;
 import org.apache.hadoop.mapreduce.protocol.ClientProtocol;
 import org.apache.hadoop.mapreduce.security.token.delegation.DelegationTokenIdentifier;
-import org.apache.hadoop.mapreduce.server.jobtracker.State;
-import org.apache.hadoop.mapreduce.server.jobtracker.TaskTracker;
 import org.apache.hadoop.mapreduce.util.ConfigUtil;
 import org.apache.hadoop.net.NetUtils;
 import org.apache.hadoop.security.AccessControlException;
@@ -96,9 +92,10 @@ public class Cluster {
    * Close the <code>Cluster</code>.
    */
   public synchronized void close() throws IOException {
-    if (!(client instanceof LocalJobRunner)) {
+    // FIXME
+    //if (!(client instanceof LocalJobRunner)) {
       RPC.stopProxy(client);
-    }
+    //}
   }
 
   private Job[] getJobs(JobStatus[] stats) throws IOException {
@@ -259,7 +256,7 @@ public class Cluster {
     if (jobHistoryDir == null) {
       jobHistoryDir = new Path(client.getJobHistoryDir());
     }
-    return JobHistory.getJobHistoryFile(jobHistoryDir, jobId).toString();
+    return (new Path(jobHistoryDir, jobId.toString())).toString();
   }
 
   /**
@@ -291,17 +288,6 @@ public class Cluster {
       throws IOException, InterruptedException {
     return client.getChildQueues(queueName);
   }
-
-  /**
-   * Get JobTracker's state
-   * 
-   * @return {@link State} of the JobTracker
-   * @throws IOException
-   * @throws InterruptedException
-   */
-  public State getJobTrackerState() throws IOException, InterruptedException {
-    return client.getJobTrackerState();
-  }
   
   /**
    * Get the tasktracker expiry interval for the cluster
@@ -322,7 +308,7 @@ public class Cluster {
       getDelegationToken(Text renewer) throws IOException, InterruptedException{
     Token<DelegationTokenIdentifier> result =
       client.getDelegationToken(renewer);
-    InetSocketAddress addr = JobTracker.getAddress(conf);
+    InetSocketAddress addr = Master.getMasterAddress(conf);
     StringBuilder service = new StringBuilder();
     service.append(NetUtils.normalizeHostName(addr.getAddress().
                                               getHostAddress()));

Propchange: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/Cluster.java
------------------------------------------------------------------------------
--- svn:mergeinfo (added)
+++ svn:mergeinfo Thu Mar 17 20:21:13 2011
@@ -0,0 +1,2 @@
+/hadoop/core/branches/branch-0.19/mapred/src/java/org/apache/hadoop/mapreduce/Cluster.java:713112
+/hadoop/core/trunk/src/mapred/org/apache/hadoop/mapreduce/Cluster.java:776175-785643

Propchange: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/ClusterMetrics.java
------------------------------------------------------------------------------
--- svn:mergeinfo (added)
+++ svn:mergeinfo Thu Mar 17 20:21:13 2011
@@ -0,0 +1,2 @@
+/hadoop/core/branches/branch-0.19/mapred/src/java/org/apache/hadoop/mapreduce/ClusterMetrics.java:713112
+/hadoop/core/trunk/src/mapred/org/apache/hadoop/mapreduce/ClusterMetrics.java:776175-785643

Propchange: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/Counter.java
------------------------------------------------------------------------------
--- svn:mergeinfo (added)
+++ svn:mergeinfo Thu Mar 17 20:21:13 2011
@@ -0,0 +1,2 @@
+/hadoop/core/branches/branch-0.19/mapred/src/java/org/apache/hadoop/mapreduce/Counter.java:713112
+/hadoop/core/trunk/src/mapred/org/apache/hadoop/mapreduce/Counter.java:776175-785643

Propchange: hadoop/mapreduce/branches/MR-279/mr-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/CounterGroup.java
------------------------------------------------------------------------------
--- svn:mergeinfo (added)
+++ svn:mergeinfo Thu Mar 17 20:21:13 2011
@@ -0,0 +1,2 @@
+/hadoop/core/branches/branch-0.19/mapred/src/java/org/apache/hadoop/mapreduce/CounterGroup.java:713112
+/hadoop/core/trunk/src/mapred/org/apache/hadoop/mapreduce/CounterGroup.java:776175-785643



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