hbase MapReduce程序样例入门

矫情吗；* 2022-09-18 13:47 1200阅读 0赞

## [hbase MapReduce程序样例入门][hbase MapReduce] ##

1、先看一个标准的hbase作为数据读取源和输出源的样例：

Configuration conf = HBaseConfiguration.create();
    Job job = new Job(conf, "job name ");
    job.setJarByClass(test.class);
    Scan scan = new Scan();
    TableMapReduceUtil.initTableMapperJob(inputTable, scan, mapper.class,
    		Writable.class, Writable.class, job);
    TableMapReduceUtil.initTableReducerJob(outputTable, reducer.class, job);
    job.waitForCompletion(true);

**首先创建配置信息和作业对象，设置作业的类。这些和正常的mapreduce一样，**

用如上代码：

数据输入源是hbase的inputTable表，执行mapper.class进行map过程，输出的key/value类型是ImmutableBytesWritable和Put类型，最后一个参数是作业对象。需要指出的是需要声明一个扫描读入对象scan，进行表扫描读取数据用，其中scan可以配置参数，这里为了例子简单不再详述。  
数据输出目标是hbase的outputTable表，输出执行的reduce过程是reducer.class类，操作的作业目标是job。与map比缺少输出类型的标注，因为他们不是必要的，看过源代码就知道mapreduce的TableRecordWriter中write(key,value)方法中，key值是没有用到的。value只能是Put或者Delete两种类型，write方法会自行判断并不用用户指明。

接下来就是mapper类：

继承的是hbase中提供的TableMapper类，其实这个类也是继承的MapReduce类。后边跟的两个泛型参数指定类型是mapper输出的数据类型，该类型必须继承自Writable类，例如可能用到的put和delete就可以。需要注意的是要和initTableMapperJob方法指定的数据类型一直。该过程会自动从指定hbase表内一行一行读取数据进行处理。  
  
然后reducer类：

public class countUniteRedcuer extends
    		TableReducer<KEYIN, VALUEIN, KEYOUT> {
    	public void reduce(Text key, Iterable<VALUEIN> values, Context context)
    			throws IOException, InterruptedException {
                    //reducer逻辑
    		context.write(null, put or delete);
    	}
    }

reducer继承的是TableReducer类。后边指定三个泛型参数，前两个必须对应map过程的输出key/value类型，第三个必须是put或者delete。write的时候可以把key写null，它是不必要的。这样reducer输出的数据会自动插入outputTable指定的表内。

2、有时候我们需要数据源是hdfs的文本，输出对象是hbase。这时候变化也很简单：

你会发现只需要像平常的mapreduce的作业声明过程一样，指定mapper的执行类和输出key/value类型，指定FileInputFormat.setInputPaths的数据源路径，输出声明不变。便完成了从hdfs文本读取数据输出到hbase的命令声明过程。 mapper和reducer如下：

Configuration conf = HBaseConfiguration.create();
    Job job = new Job(conf, "job name ");
    job.setJarByClass(test.class);
     
    job.setMapperClass(mapper.class);
    job.setMapOutputKeyClass(Text.class);
    job.setMapOutputValueClass(LongWritable.class);
    FileInputFormat.setInputPaths(job, path);
     
    TableMapReduceUtil.initTableReducerJob(tableName,
    				reducer.class, job);

public class mapper extends Mapper<LongWritable,Writable,Writable,Writable> {
    	public void map(LongWritable key, Text line, Context context) {
    		 //mapper逻辑
    		 context.write(k, one);
    	}
    }
    public class redcuer extends
    		TableReducer<KEYIN, VALUEIN, KEYOUT> {
    	public void reduce(Writable key, Iterable<Writable> values, Context context)
    			throws IOException, InterruptedException {
                    //reducer逻辑
    		context.write(null, put or delete);
    	}
    }

mapper还依旧继承原来的MapReduce类中的Mapper即可。同样注意这前后数据类型的key/value一直性。

3、最后就是从hbase中的表作为数据源读取，hdfs作为数据输出，简单的如下：

Configuration conf = HBaseConfiguration.create();
    Job job = new Job(conf, "job name ");
    job.setJarByClass(test.class);
    Scan scan = new Scan();
    TableMapReduceUtil.initTableMapperJob(inputTable, scan, mapper.class,
    		Writable.class, Writable.class, job);
    job.setOutputKeyClass(Writable.class);
    job.setOutputValueClass(Writable.class);
    FileOutputFormat.setOutputPath(job, Path);
    job.waitForCompletion(true);

mapper和reducer简单如下：

public class mapper extends
    		TableMapper<KEYOUT, VALUEOUT>{
     
    	public void map(Writable key, Writable value, Context context)
    			throws IOException, InterruptedException {
    		        //mapper逻辑
    			context.write(key, value);
    		}
     
    	}
    }
     
    public class reducer extends
    		Reducer<Writable,Writable,Writable,Writable>  {
     
    	public void reducer(Writable key, Writable value, Context context)
    			throws IOException, InterruptedException {
    		        //reducer逻辑
    			context.write(key, value);
    		}
    	}
    }

最后说一下TableMapper和TableReducer的本质 ，其实这俩类就是为了简化一下书写代码，因为传入的4个泛型参数里都会有固定的参数类型，所以是Mapper和Reducer的简化版本，本质他们没有任何区别。源码如下：

public abstract class TableMapper<KEYOUT, VALUEOUT>
    extends Mapper<ImmutableBytesWritable, Result, KEYOUT, VALUEOUT> {
    }
     
    public abstract class TableReducer<KEYIN, VALUEIN, KEYOUT>
    extends Reducer<KEYIN, VALUEIN, KEYOUT, Writable> {
    }

<table style="border:none; background-color:rgb(238,238,238); vertical-align:baseline; padding:0px; width:684px; border-collapse:collapse; margin:0px!important"> 
 <tbody style="background-color:transparent; border:0px none; vertical-align:baseline; margin:0px; padding:0px"> 
  <tr style="background-color:transparent; border:0px none; vertical-align:baseline; margin:0px; padding:0px"> 
   <td style="border-top-width:0px; border-right-width:0px; border-left-width:0px; border-style:none none solid; border-bottom-color:rgb(255,255,255); background-color:transparent; vertical-align:top; margin:0px; padding:2px 4px"> <pre><code><span style="background-color:transparent; line-height:1.3em; color:rgb(64,64,64); font-family:Verdana,Geneva,sans-serif; font-size:1.18em">好了，可以去写第一个wordcount的hbase mapreduce程序了。</span>
</code></pre> </td> 
  </tr> 
 </tbody> 
</table>

ps：另外详细的读写优化可以看之后的[一篇][Link 1]，更为详尽的内容看[这里][Link 2]

[Hadoop Wiki][]

[Login][]

# [Hbase][]/[MapReduce][] #

*  [FrontPage][Hadoop Wiki]
 *  [RecentChanges][]
 *  [FindPage][]
 *  [HelpContents][]
 *  [Hbase/MapReduce][Link 2]

*  Immutable Page
 *  [Info][]
 *  [Attachments][]
 *   
    
              More Actions:Raw Text
    
      Print View
    
     Render as Docbook
    
      Delete Cache
    
      \------------------------
    
     Check Spelling
    
      Like Pages
    
     Local Site Map
    
      \------------------------
    
      Rename Page
    
     Copy Page
    
      Delete Page
    
      \------------------------
    
     My Pages
    
      Subscribe User
    
      \------------------------
    
      Remove Spam
    
      Revert to this revision
    
     Package Pages
    
      Sync Pages
    
      \------------------------
    
     Load
    
      Save
    
     SlideShow

# Hbase, MapReduce and the CLASSPATH #

--------------------

**DEPRECATED!!! Instead see the new [mapreduce package description][] which supersedes the old [mapred package description][]**

--------------------

MapReduce jobs deployed to a mapreduce cluster do not by default have access to the hbase configuration under *$HBASE\_CONF\_DIR* nor to hbase classes.

You could add *hbase-site.xml* to $HADOOP\_HOME/conf and add hbase.jar to the $HADOOP\_HOME/lib and copy these changes across your cluster but the cleanest means of adding hbase configuration and classes to the cluster *CLASSPATH* is by uncommenting *HADOOP\_CLASSPATH* in *$HADOOP\_HOME/conf/hadoop-env.sh* and adding the path to the hbase jar and *$HBASE\_CONF\_DIR* directory. Then copy the amended configuration around the cluster. You'll probably need to restart the mapreduce cluster if you want it to notice the new configuration (You may not have to).

Below, find an example of how you would amend $HADOOP\_HOME/conf/hadoop-env.sh adding the hbase jar, conf. This example assumes you are using the hbase-0.2.0 release, with additional commented export commands for other releases/builds:

# Extra Java CLASSPATH elements.  Optional.
    # export HADOOP_CLASSPATH=
    # for hbase-0.2.0 release
    export HADOOP_CLASSPATH=$HBASE_HOME/build/hbase-0.2.0.jar:$HBASE_HOME/conf
    # for 0.16.0 release
    #export HADOOP_CLASSPATH=$HBASE_HOME/hadoop-0.16.0-hbase.jar:$HBASE_HOME/conf
    # for 0.16.0 developer build
    #export HADOOP_CLASSPATH=$HBASE_HOME/build/test:$HBASE_HOME/build/hadoop-0.16.0-hbase.jar:$HBASE_HOME/conf

Expand $HBASE\_HOME appropriately in the in accordance with your local environment. To use the developer versions of the HADOOP\_CLASSPATH, you first need to execute "ant" or "ant build" in $HBASE\_HOME. This will create some .jar files in the $HBASE\_HOME/build directory. To use the PerformanceEvaluation class from hbase test classes, you must use a developer build. Then, this is how you would run the [PerformanceEvaluation][] MR job to put up 4 clients:

> $HADOOP\_HOME/bin/hadoop org.apache.hadoop.hbase.PerformanceEvaluation sequentialWrite 4

The [PerformanceEvaluation][] class wil be found on the CLASSPATH because you added $HBASE\_HOME/build/test to HADOOP\_CLASSPATH

NOTE: While previous it used to be possible to bundle the hbase.jar up inside the job jar you submit to hadoop, as of 0.2.0RC2, this is no longer so. See [HBASE-797][].

# Hbase as MapReduce job data source and sink #

Hbase can be used as a data source, [TableInputFormat][], and data sink, [TableOutputFormat][], for mapreduce jobs. Writing mapreduce jobs that read or write hbase, you'll probably want to subclass [TableMap][] and/or [TableReduce][]. See the do-nothing passthrough classes [IdentityTableMap][] and [IdentityTableReduce][] for basic usage. For a more involved example, see [BuildTableIndex][] from the same package or review the org.apache.hadoop.hbase.mapred.TestTableMapReduce unit test.

Running mapreduce jobs that have hbase as source or sink, you'll need to specify source/sink table and column names in your configuration.

Reading from hbase, the TableInputFormat asks hbase for the list of regions and makes a map-per-region. Writing, it may make sense to avoid the reduce step and write back into hbase from inside your map. You'd do this when your job does not need the sort and collation that MR does inside in its reduce; on insert, hbase sorts so no point double-sorting (and shuffling data around your MR cluster) unless you need to. If you do not need the reduce, you might just have your map emit counts of records processed just so the framework can emit that nice report of records processed when the job is done. See example code below. If running the reduce step makes sense in your case, its better to have lots of reducers so load is spread across the hbase cluster.

## Example to bulk import/load a text file into an HTable ##

Here's a sample program from [Allen Day][] that takes an HDFS text file path and an HBase table name as inputs, and loads the contents of the text file to the table.

package com.spicylogic.hbase;
    import java.io.IOException;
    import java.util.Iterator;
    import java.util.Map;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.hbase.HBaseConfiguration;
    import org.apache.hadoop.hbase.client.HTable;
    import org.apache.hadoop.hbase.io.BatchUpdate;
    import org.apache.hadoop.hbase.mapred.TableOutputFormat;
    import org.apache.hadoop.hbase.mapred.TableReduce;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.MapWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapred.JobClient;
    import org.apache.hadoop.mapred.JobConf;
    import org.apache.hadoop.mapred.MapReduceBase;
    import org.apache.hadoop.mapred.Mapper;
    import org.apache.hadoop.mapred.OutputCollector;
    import org.apache.hadoop.mapred.Reducer;
    import org.apache.hadoop.mapred.Reporter;
    import org.apache.hadoop.mapred.lib.NullOutputFormat;
    import org.apache.hadoop.util.Tool;
    import org.apache.hadoop.util.ToolRunner;
    
    public class BulkImport implements Tool {
      private static final String NAME = "BulkImport";
      private Configuration conf;
    
      public static class InnerMap extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text> {
        private HTable table;
        private HBaseConfiguration HBconf;
    
        public void map(LongWritable key, Text value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
          if ( table == null )
            throw new IOException("table is null");
          
          String [] splits = value.toString().split("\t");
          if ( splits.length != 4 )
            return;
    
          String rowID     = splits[0];
          int timestamp    = Integer.parseInt( splits[1] );
          String colID     = splits[2];
          String cellValue = splits[3];
    
          reporter.setStatus("Map emitting cell for row='" + rowID + "', column='" + colID + "', time='" + timestamp + "'");
    
          BatchUpdate bu = new BatchUpdate( rowID );
          if ( timestamp > 0 )
            bu.setTimestamp( timestamp );
    
          bu.put(colID, cellValue.getBytes());      
          table.commit( bu );      
        }
        public void configure(JobConf job) {
          HBconf = new HBaseConfiguration();
          try {
            table = new HTable( HBconf, job.get("input.table") );
          } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
          }
        }
      }
      
      
      public JobConf createSubmittableJob(String[] args) {
        JobConf c = new JobConf(getConf(), BulkImport.class);
        c.setJobName(NAME);
        c.setInputPath(new Path(args[0]));
    
        c.set("input.table", args[1]);
        c.setMapperClass(InnerMap.class);
        c.setNumReduceTasks(0);
        c.setOutputFormat(NullOutputFormat.class);
        return c;
      }
      
      static int printUsage() {
        System.err.println("Usage: " + NAME + " <input> <table_name>");
        System.err.println("\twhere <input> is a tab-delimited text file with 4 columns.");
        System.err.println("\t\tcolumn 1 = row ID");
        System.err.println("\t\tcolumn 2 = timestamp (use a negative value for current time)");
        System.err.println("\t\tcolumn 3 = column ID");
        System.err.println("\t\tcolumn 4 = cell value");
        return -1;
      } 
    
      public int run(@SuppressWarnings("unused") String[] args) throws Exception {
        // Make sure there are exactly 3 parameters left.
        if (args.length != 2) {
          return printUsage();
        }
        JobClient.runJob(createSubmittableJob(args));
        return 0;
      }
    
      public Configuration getConf() {
        return this.conf;
      } 
    
      public void setConf(final Configuration c) {
        this.conf = c;
      }
    
      public static void main(String[] args) throws Exception {
        int errCode = ToolRunner.run(new Configuration(), new BulkImport(), args);
        System.exit(errCode);
      }
    }

## Example to map rows/column families between two HTables ##

Here another sample program from [Allen Day][] that will iterate over all rows in one table for specified column families and insert those rows/columns to a second table.

package com.spicylogic.hbase;
    import java.io.IOException;
    
    public class BulkCopy extends TableMap<Text, Text> implements Tool {
      static final String NAME = "bulkcopy";  
      private Configuration conf;
      
      public void map(ImmutableBytesWritable row, RowResult value, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
        HTable table = new HTable(new HBaseConfiguration(), conf.get("output.table"));
        if ( table == null ) {
          throw new IOException("output table is null");
        }
    
        BatchUpdate bu = new BatchUpdate( row.get() );
    
        boolean content = false;
        for (Map.Entry<byte [], Cell> e: value.entrySet()) {
          Cell cell = e.getValue();
          if (cell != null && cell.getValue().length > 0) {
            bu.put(e.getKey(), cell.getValue());
          }
        }
        table.commit( bu );
      }
    
      public JobConf createSubmittableJob(String[] args) throws IOException {
        JobConf c = new JobConf(getConf(), BulkExport.class);
        //table = new HTable(new HBaseConfiguration(), args[2]);
        c.set("output.table", args[2]);
        c.setJobName(NAME);
        // Columns are space delimited
        StringBuilder sb = new StringBuilder();
        final int columnoffset = 3;
        for (int i = columnoffset; i < args.length; i++) {
          if (i > columnoffset) {
            sb.append(" ");
          }
          sb.append(args[i]);
        }
        // Second argument is the table name.
        TableMap.initJob(args[1], sb.toString(), this.getClass(),
        Text.class, Text.class, c);
        c.setReducerClass(IdentityReducer.class);
        // First arg is the output directory.
        c.setOutputPath(new Path(args[0]));
        return c;
      }
      
      static int printUsage() {
        System.out.println(NAME +" <outputdir> <input tablename> <output tablename> <column1> [<column2>...]");
        return -1;
      }
      
      public int run(final String[] args) throws Exception {
        // Make sure there are at least 3 parameters
        if (args.length < 3) {
          System.err.println("ERROR: Wrong number of parameters: " + args.length);
          return printUsage();
        }
        JobClient.runJob(createSubmittableJob(args));
        return 0;
      }
    
      public Configuration getConf() {
        return this.conf;
      }
    
      public void setConf(final Configuration c) {
        this.conf = c;
      }
    
      public static void main(String[] args) throws Exception {
        //String[] aa = {"/tmp/foobar", "M2", "M3", "R:"};
        int errCode = ToolRunner.run(new HBaseConfiguration(), new BulkCopy(), args);
        System.exit(errCode);
      }
    }

## Sample running HBase inserts out of Map Task ##

Here's sample code from Andrew Purtell that does HBase insert inside in the mapper rather than via [TableReduce][TableReduce 1].

public class MyMap 
      extends TableMap<ImmutableBytesWritable,MapWritable> // or whatever
    {
      private HTable table;
    
      public void configure(JobConf job) {
        super.configure(job);
        try {
          HBaseConfiguration conf = new HBaseConfiguration(job);
          table = new HTable(conf, "mytable");
        } catch (Exception) {
          // can't do anything about this now
        }
      }
    
      public void map(ImmutableBytesWritable key, RowResult value,
        OutputCollector<ImmutableBytesWritable,MapWritable> output,
        Reporter reporter) throws IOException
      {
        // now we can report an exception opening the table
        if (table == null)
          throw new IOException("could not open mytable");
    
        // ...
    
        // commit the result
        BatchUpdate update = new BatchUpdate();
        // ...
        table.commit(update);
      }
    }

This assumes that you do this when setting up your job: JobConf conf = new JobConf(new HBaseConfiguration());

Or maybe something like this:

*  \{ \{ \{ [JobConf][] conf = new [JobConf][](new Configuration());

conf.set("hbase.master", myMaster);\}\}\}

# Sample MR+HBase Jobs #

A [students/classes example][students_classes example] by Naama Kraus.

## Sample MR Bulk Uploader ##

Read the class comment below for specification of inputs, prerequisites, etc. In particular, note that the class comment says that this code is for hbase 0.1.x.

package org.apache.hadoop.hbase.mapred;
    
    import java.io.IOException;
    import java.util.Iterator;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.MapWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapred.JobClient;
    import org.apache.hadoop.mapred.JobConf;
    import org.apache.hadoop.mapred.MapReduceBase;
    import org.apache.hadoop.mapred.Mapper;
    import org.apache.hadoop.mapred.OutputCollector;
    import org.apache.hadoop.mapred.Reporter;
    import org.apache.hadoop.util.Tool;
    import org.apache.hadoop.util.ToolRunner;
    
    /**
     * Sample uploader.
     * 
     * This is EXAMPLE code.  You will need to change it to work for your context.
     * 
     * Uses TableReduce to put the data into hbase. Change the InputFormat to suit
     * your data. Use the map to massage the input so it fits hbase.  Currently its
     * just a pass-through map.  In the reduce, you need to output a row and a
     * map of columns to cells.  Change map and reduce to suit your input.
     * 
     * <p>The below is wired up to handle an input whose format is a text file
     * which has a line format as follow:
     * <pre>
     * row columnname columndata
     * </pre>
     * 
     * <p>The table and columnfamily we're to insert into must preexist.
     * 
     * <p> To run, edit your hadoop-env.sh and add hbase classes and conf to your
     * HADOOP_CLASSPATH.  For example:
     * <pre>
     * export HADOOP_CLASSPATH=/Users/stack/Documents/checkouts/hbase/branches/0.1/build/classes:/Users/stack/Documents/checkouts/hbase/branches/0.1/conf
     * </pre>
     * <p>Restart your MR cluster after making the following change (You need to 
     * be running in pseudo-distributed mode at a minimum for the hadoop to see
     * the above additions to your CLASSPATH).
     * 
     * <p>Start up your hbase cluster.
     * 
     * <p>Next do the following to start the MR job:
     * <pre>
     * ./bin/hadoop org.apache.hadoop.hbase.mapred.SampleUploader /tmp/input.txt TABLE_NAME
     * </pre>
     * 
     * <p>This code was written against hbase 0.1 branch.
     */
    public class SampleUploader extends MapReduceBase
    implements Mapper<LongWritable, Text, Text, MapWritable>, Tool {
      private static final String NAME = "SampleUploader";
      private Configuration conf;
    
      public JobConf createSubmittableJob(String[] args) {
        JobConf c = new JobConf(getConf(), SampleUploader.class);
        c.setJobName(NAME);
        c.setInputPath(new Path(args[0]));
        c.setMapperClass(this.getClass());
        c.setMapOutputKeyClass(Text.class);
        c.setMapOutputValueClass(MapWritable.class);
        c.setReducerClass(TableUploader.class);
        TableReduce.initJob(args[1], TableUploader.class, c);
        return c;
      } 
    
      public void map(LongWritable k, Text v,
        OutputCollector<Text, MapWritable> output, Reporter r)
      throws IOException {
        // Lines are space-delimited; first item is row, next the columnname and
        // then the third the cell value.
        String tmp = v.toString();
        if (tmp.length() == 0) {
          return;
        }
        String [] splits = v.toString().split(" ");
        MapWritable mw = new MapWritable();
        mw.put(new Text(splits[1]),
          new ImmutableBytesWritable(splits[2].getBytes()));
        String row = splits[0];
        r.setStatus("Map emitting " + row + " for record " + k.toString());
        output.collect(new Text(row), mw);
      }
      
      public static class TableUploader
      extends TableReduce<Text, MapWritable> {
        @Override
        public void reduce(Text k, Iterator<MapWritable> v,
          OutputCollector<Text, MapWritable> output, Reporter r)
        throws IOException {
          while (v.hasNext()) {
            r.setStatus("Reducer committing " + k);
            output.collect(k, v.next());
          }
        }
      }
      
      static int printUsage() {
        System.out.println(NAME + " <input> <table_name>");
        return -1;
      } 
        
      public int run(@SuppressWarnings("unused") String[] args) throws Exception {
        // Make sure there are exactly 2 parameters left.
        if (args.length != 2) {
          System.out.println("ERROR: Wrong number of parameters: " +
            args.length + " instead of 2.");
          return printUsage();
        }
        JobClient.runJob(createSubmittableJob(args));
        return 0;
      }
        
      public Configuration getConf() {
        return this.conf;
      } 
      
      public void setConf(final Configuration c) {
        this.conf = c;
      }
    
      public static void main(String[] args) throws Exception {
        int errCode = ToolRunner.run(new Configuration(), new SampleUploader(),
          args);
        System.exit(errCode);
      }
    }

Hbase/MapReduce (last edited 2009-12-12 17:30:56 by [tuxracer69][])

*  [MoinMoin Powered][]
 *  [Python Powered][]
 *  [GPL licensed][]
 *  [Valid HTML 4.01][]

[hbase MapReduce]: http://blog.pureisle.net/archives/1938.html
[Link 1]: http://blog.pureisle.net/?p=1930
[Link 2]: http://wiki.apache.org/hadoop/Hbase/MapReduce
[Hadoop Wiki]: http://wiki.apache.org/hadoop/FrontPage
[Login]: http://wiki.apache.org/hadoop/Hbase/MapReduce?action=login
[Hbase]: http://wiki.apache.org/hadoop/Hbase
[MapReduce]: http://wiki.apache.org/hadoop/Hbase/MapReduce?action=fullsearch&value=linkto%3A%22Hbase%2FMapReduce%22&context=180
[RecentChanges]: http://wiki.apache.org/hadoop/RecentChanges
[FindPage]: http://wiki.apache.org/hadoop/FindPage
[HelpContents]: http://wiki.apache.org/hadoop/HelpContents
[Info]: http://wiki.apache.org/hadoop/Hbase/MapReduce?action=info
[Attachments]: http://wiki.apache.org/hadoop/Hbase/MapReduce?action=AttachFile
[mapreduce package description]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapreduce/package-summary.html#package_description
[mapred package description]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/package-summary.html#package_description
[PerformanceEvaluation]: http://wiki.apache.org/hadoop/PerformanceEvaluation
[HBASE-797]: https://issues.apache.org/jira/browse/HBASE-797
[TableInputFormat]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/TableInputFormat.html
[TableOutputFormat]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/TableOutputFormat.html
[TableMap]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/TableMap.html
[TableReduce]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/TableReduce.html
[IdentityTableMap]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/IdentityTableMap.html
[IdentityTableReduce]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/IdentityTableReduce.html
[BuildTableIndex]: http://hadoop.apache.org/hbase/docs/current/api/org/apache/hadoop/hbase/mapred/BuildTableIndex.html
[Allen Day]: http://spicylogic.com/allenday/blog
[TableReduce 1]: http://wiki.apache.org/hadoop/TableReduce
[JobConf]: http://wiki.apache.org/hadoop/JobConf
[students_classes example]: http://www.nabble.com/Re%3A-Map-Reduce-over-HBase---sample-code-p18253120.html
[tuxracer69]: http://wiki.apache.org/hadoop/tuxracer69
[MoinMoin Powered]: http://moinmo.in/
[Python Powered]: http://moinmo.in/Python
[GPL licensed]: http://moinmo.in/GPL
[Valid HTML 4.01]: http://validator.w3.org/check?uri=referer