Hadoop2.5.2 从hdfs 地图reduce导出数据到多个hbase表

hadoop和hbase配置好正常运行时的进程情况，jps后查看

60559 HRegionServer

7329 Main

20653 Jps

29355 HQuorumPeer

16221 ResourceManager

29417 HMaster

16538 NodeManager

15750 NameNode

15880 DataNode

16046 SecondaryNameNode

网上很多例子都是基于hadoop 0.9x 的，新版hadoop函数有变。

例子是从 hadoop hdfs上读取文件，map reduce后写入多个hbase 表

故重新测试例子如下环境：

hadoop 2.5.2

hbase 1.1.4

有一种场景：例如需要分析日志，统计后，存储到hbase 结果集表和索引表：

例子中没用新版hbase函数,若用新版函数请参考修改

http://bobboy007.iteye.com/admin/blogs/2289537

package jyw.test;

import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;

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.MultiTableOutputFormat;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
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.Text;
import org.apache.hadoop.io.NullWritable;
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;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
import org.apache.hadoop.io.Writable;
/*
 * 测试reduce写入多个表
 * */
public class HBaseMultiTableOutputReduce {

	// 实现 Map 类
	public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
		private final static IntWritable one = new IntWritable(1);
		private Text word = new Text();

		public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
			StringTokenizer itr = new StringTokenizer(value.toString());
			while (itr.hasMoreTokens()) {
				word.set(itr.nextToken());
				context.write(word, one);
			}
		}
	}

	/* 实现 Reduce 类
	 * map 的输出类型
	 * map的输出值类型
	 * reduce的输出类型
	 * reduce的输出类型
	 * 查是否有setup,clear方法，测试到 myql
 */
	public static class Reduce extends Reducer<Text, IntWritable, Writable, Put> {

		public void reduce(Text key, Iterable<IntWritable> values, Context context) {
			ImmutableBytesWritable putTable1 = new ImmutableBytesWritable(Bytes.toBytes("wordcount"));
			ImmutableBytesWritable putTable2 = new ImmutableBytesWritable(Bytes.toBytes("wordcount1"));
			int sum = 0;

			Iterator<IntWritable> iterator = values.iterator();
			while (iterator.hasNext()) {
				sum += iterator.next().get();
			}

			// Put 实例化，每个词存一行
			Put put = new Put(Bytes.toBytes(key.toString()));
			// 列族为 content，列修饰符为 count，列值为数目
			put.add(Bytes.toBytes("content"), Bytes.toBytes("count"), Bytes.toBytes(String.valueOf(sum)));

			try {
				context.write(putTable1, put);
				context.write(putTable2, put);
			} catch (Exception e) {
				e.printStackTrace();
			}
			// context.write(NullWritable.get(), put);
		}
	}

	// 创建 HBase 数据表
	public static void createHBaseTable(String tableName) throws IOException {
		// 创建表描述
		HTableDescriptor htd = new HTableDescriptor(tableName);
		// 创建列族描述
		HColumnDescriptor col = new HColumnDescriptor("content");
		htd.addFamily(col);

		// 配置 HBase
		Configuration conf = HBaseConfiguration.create();

		// conf.set("hbase.zookeeper.quorum","127.0.0.1");
		// conf.set("hbase.zookeeper.property.clientPort", "2181");
		HBaseAdmin hAdmin = new HBaseAdmin(conf);

		if (hAdmin.tableExists(tableName)) {
			System.out.println("该数据表已经存在，正在重新创建。");
			// hAdmin.disableTable(tableName);
			// hAdmin.deleteTable(tableName);
		} else {

			System.out.println("创建表：" + tableName);
			hAdmin.createTable(htd);
		}
	}

	public static void main(String[] args) throws Exception {
		String tableName1 = "wordcount";
		String tableName2 = "wordcount1";
		// 第一步：创建数据库表
		HBaseMultiTableOutputReduce.createHBaseTable(tableName1);
		HBaseMultiTableOutputReduce.createHBaseTable(tableName2);
		// 第二步：进行 MapReduce 处理
		// 配置 MapReduce
		Configuration conf = new Configuration();
		// 这几句话很关键
		// conf.set("mapred.job.tracker", "master:9001");
		// conf.set("hbase.zookeeper.quorum","master");
		// conf.set("hbase.zookeeper.property.clientPort", "2181");
		// conf.set(TableOutputFormat.OUTPUT_TABLE, tableName);

		Job job = new Job(conf, "multi output Count");
		job.setJarByClass(HBaseMultiTableOutputReduce.class);

		// 设置 Map 和 Reduce 处理类
		job.setMapperClass(Map.class);
		job.setReducerClass(Reduce.class);

		// 设置输出类型
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(IntWritable.class);

		// 设置输入和输出格式
		job.setInputFormatClass(TextInputFormat.class);
		// job.setOutputFormatClass(TableOutputFormat.class);
		job.setOutputFormatClass(MultiTableOutputFormat.class);

		// 设置输入目录
		FileInputFormat.addInputPath(job, new Path("hdfs://192.168.0.42:9000/user/jiayongwei/input/"));
		System.exit(job.waitForCompletion(true) ? 0 : 1);

	}
}