1.实现方法
Hbase对MapReduce提供支持，它实现了TableMapper类和TableReducer类，我们只需要继承这两个类即可。
1、写个mapper继承TableMapper<Text, IntWritable>
参数：Text：mapper的输出key类型； IntWritable：mapper的输出value类型。
其中的map方法如下：
map(ImmutableBytesWritable key, Result value,Context context) 参数：key：rowKey； value： Result ，一行数据； context上下文
2、写个reduce继承TableReducer<Text, IntWritable, ImmutableBytesWritable>
参数：Text:reducer的输入key； IntWritable：reduce的输入value；
ImmutableBytesWritable：reduce输出到hbase中的rowKey类型。
其中的reduce方法如下：
reduce(Text key, Iterable<IntWritable> values,Context context) 参数： key：reduce的输入key； values：reduce的输入value；
2.表
1、建立数据来源表‘word’，包含一个列族‘content’
向表中添加数据，在列族中放入列‘info’，并将短文数据放入该列中，如此插入多行，行键为不同的数据即可
2、建立输出表‘stat’，包含一个列族‘content’
3、通过Mr操作Hbase的‘word’表，对‘content：info’中的短文做词频统计，并将统计结果写入‘stat’表的‘content：info中’，行键为单词
3.实现
package com.itcast.hbase; import java.io.IOException; import java.util.ArrayList; import java.util.List; import org.apache.hadoop.conf.Configuration; 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.HTable; import org.apache.hadoop.hbase.client.Put; import org.apache.hadoop.hbase.client.Result; import org.apache.hadoop.hbase.client.Scan; import org.apache.hadoop.hbase.io.ImmutableBytesWritable; import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil; import org.apache.hadoop.hbase.mapreduce.TableMapper; 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.Text; import org.apache.hadoop.mapreduce.Job; /** * mapreduce操作hbase * @author wilson * */ public class HBaseMr { /** * 创建hbase配置 */ static Configuration config = null; static { config = HBaseConfiguration.create(); config.set("hbase.zookeeper.quorum", "slave1,slave2,slave3"); config.set("hbase.zookeeper.property.clientPort", "2181"); } /** * 表信息 */ public static final String tableName = "word";//表名1 public static final String colf = "content";//列族 public static final String col = "info";//列 public static final String tableName2 = "stat";//表名2 /** * 初始化表结构，及其数据 */ public static void initTB() { HTable table=null; HBaseAdmin admin=null; try { admin = new HBaseAdmin(config);//创建表管理 /*删除表*/ if (admin.tableExists(tableName)||admin.tableExists(tableName2)) { System.out.println("table is already exists!"); admin.disableTable(tableName); admin.deleteTable(tableName); admin.disableTable(tableName2); admin.deleteTable(tableName2); } /*创建表*/ HTableDescriptor desc = new HTableDescriptor(tableName); HColumnDescriptor family = new HColumnDescriptor(colf); desc.addFamily(family); admin.createTable(desc); HTableDescriptor desc2 = new HTableDescriptor(tableName2); HColumnDescriptor family2 = new HColumnDescriptor(colf); desc2.addFamily(family2); admin.createTable(desc2); /*插入数据*/ table = new HTable(config,tableName); table.setAutoFlush(false); table.setWriteBufferSize(5); List<Put> lp = new ArrayList<Put>(); Put p1 = new Put(Bytes.toBytes("1")); p1.add(colf.getBytes(), col.getBytes(), ("The Apache Hadoop software library is a framework").getBytes()); lp.add(p1); Put p2 = new Put(Bytes.toBytes("2"));p2.add(colf.getBytes(),col.getBytes(),("The common utilities that support the other Hadoop modules").getBytes()); lp.add(p2); Put p3 = new Put(Bytes.toBytes("3")); p3.add(colf.getBytes(), col.getBytes(),("Hadoop by reading the documentation").getBytes()); lp.add(p3); Put p4 = new Put(Bytes.toBytes("4")); p4.add(colf.getBytes(), col.getBytes(),("Hadoop from the release page").getBytes()); lp.add(p4); Put p5 = new Put(Bytes.toBytes("5")); p5.add(colf.getBytes(), col.getBytes(),("Hadoop on the mailing list").getBytes()); lp.add(p5); table.put(lp); table.flushCommits(); lp.clear(); } catch (Exception e) { e.printStackTrace(); } finally { try { if(table!=null){ table.close(); } } catch (IOException e) { e.printStackTrace(); } } } /** * MyMapper 继承 TableMapper * TableMapper<Text,IntWritable> * Text:输出的key类型， * IntWritable：输出的value类型 */ public static class MyMapper extends TableMapper<Text, IntWritable> { private static IntWritable one = new IntWritable(1); private static Text word = new Text(); @Override //输入的类型为：key：rowKey； value：一行数据的结果集Result protected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException { //获取一行数据中的colf：col String words = Bytes.toString(value.getValue(Bytes.toBytes(colf), Bytes.toBytes(col)));// 表里面只有一个列族，所以我就直接获取每一行的值 //按空格分割 String itr[] = words.toString().split(" "); //循环输出word和1 for (int i = 0; i < itr.length; i++) { word.set(itr[i]); context.write(word, one); } } } /** * MyReducer 继承 TableReducer * TableReducer<Text,IntWritable> * Text:输入的key类型， * IntWritable：输入的value类型， * ImmutableBytesWritable：输出类型，表示rowkey的类型 */ public static class MyReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable> { @Override protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { //对mapper的数据求和 int sum = 0; for (IntWritable val : values) {//叠加 sum += val.get(); } // 创建put，设置rowkey为单词 Put put = new Put(Bytes.toBytes(key.toString())); // 封装数据 put.add(Bytes.toBytes(colf), Bytes.toBytes(col),Bytes.toBytes(String.valueOf(sum))); //写到hbase,需要指定rowkey、put context.write(new ImmutableBytesWritable(Bytes.toBytes(key.toString())),put); } } public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { config.set("df.default.name", "hdfs://master:9000/");//设置hdfs的默认路径 config.set("hadoop.job.ugi", "hadoop,hadoop");//用户名，组 config.set("mapred.job.tracker", "master:9001");//设置jobtracker在哪 //初始化表 initTB();//初始化表 //创建job Job job = new Job(config, "HBaseMr");//job job.setJarByClass(HBaseMr.class);//主类 //创建scan Scan scan = new Scan(); //可以指定查询某一列 scan.addColumn(Bytes.toBytes(colf), Bytes.toBytes(col)); //创建查询hbase的mapper，设置表名、scan、mapper类、mapper的输出key、mapper的输出value TableMapReduceUtil.initTableMapperJob(tableName, scan, MyMapper.class,Text.class, IntWritable.class, job); //创建写入hbase的reducer，指定表名、reducer类、job TableMapReduceUtil.initTableReducerJob(tableName2, MyReducer.class, job); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
