跟Hadoop的无缝集成使得使用MapReduce对HBase的数据进行分布式计算非常方便，本文将介绍HBase下 MapReduce开发要点。很好理解本文前提是你对Hadoop MapReduce有一定的了解，如果你是初次接触Hadoop MapReduce编程，可以参考 这篇文章来建立基本概念。
一、Java代码
package hbase;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.hbase.HBaseConfiguration;import org.apache.hadoop.hbase.HColumnDescriptor;import org.apache.hadoop.hbase.HTableDescriptor;import org.apache.hadoop.hbase.client.HBaseAdmin;import org.apache.hadoop.hbase.client.Put;import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;import org.apache.hadoop.hbase.mapreduce.TableReducer;import org.apache.hadoop.hbase.util.Bytes;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;public class WordCountHBase { public static class Map extends Mapper { private IntWritable i = new IntWritable(1); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String s[] = value.toString().trim().split(" "); // 将输入的每行以空格分开 for (String m : s) { context.write(new Text(m), i); } } } public static class Reduce extends TableReducer { public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable i : values) { sum += i.get(); } Put put = new Put(Bytes.toBytes(key.toString())); // Put实例化，每一个词存一行 put.add(Bytes.toBytes("content"), Bytes.toBytes("count"), Bytes.toBytes(String.valueOf(sum))); // 列族为content，列为count，列值为数目 context.write(NullWritable.get(), put); } } public static void createHBaseTable(String tableName) throws IOException { HTableDescriptor htd = new HTableDescriptor(tableName); HColumnDescriptor col = new HColumnDescriptor("content"); htd.addFamily(col); Configuration conf = HBaseConfiguration.create(); conf.set("hbase.zookeeper.quorum", "libin2"); HBaseAdmin admin = new HBaseAdmin(conf); if (admin.tableExists(tableName)) { System.out.println("table exists, trying to recreate table......"); admin.disableTable(tableName); admin.deleteTable(tableName); } System.out.println("create new table:" + tableName); admin.createTable(htd); } public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { String tableName = "WordCount"; Configuration conf = new Configuration(); conf.set(TableOutputFormat.OUTPUT_TABLE, tableName); createHBaseTable(tableName); String input = args[0]; Job job = new Job(conf, "WordCount table with " + input); job.setJarByClass(WordCountHBase.class); job.setNumReduceTasks(3); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TableOutputFormat.class); FileInputFormat.addInputPath(job, new Path(input)); System.exit(job.waitForCompletion(true) ? 0 : 1); }} 二、把java代码打成jar包
如果同时用到了两个jar包，需要在两个jar包之间加一个":"分隔符。
三、运行程序
运行WordCountHBase.jar可能会报错：java.lang.NoClassDefFoundError: org/apache/hadoop/hbase/HTableDescriptor
解决方法（把hbase的核心jar包和hbase自带的Zookeeperjar包拷贝到hadoop的安装目录\lib下，然后重启服务）：
然后再次执行
四、查看HBase表中的数据
如果表中有保存好的MapReduce处理后的数据，说明成功！本文通过实例分析演示了使用MapReduce分析HBase的数据，需要注意的这只是一种常规的方式（分析表中的数据存到另外的表中），实际上不局限于此，不过其他方式跟此类似。