第一步：创建表

注意：两张表的列族一定要相同

/**
 create 'user','f1'
 put 'user','rk001','f1:name','tony'
 put 'user','rk001','f1:age','12'
 put 'user','rk001','f1:address','beijing'
 put 'user','rk002','f1:name','wangwu'
 create 'user2','f1'
 */

第二步：创建maven工程并导入jar包

pom.xml文件内容如下：

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>Hadoop</groupId>
    <artifactId>HbaseTang</artifactId>
    <version>1.0-SNAPSHOT</version>
    <packaging>jar</packaging>
    <properties>
        <hadoop.version>2.7.3</hadoop.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>commons-cli</groupId>
            <artifactId>commons-cli</artifactId>
            <version>1.2</version>
        </dependency>
        <dependency>
            <groupId>commons-logging</groupId>
            <artifactId>commons-logging</artifactId>
            <version>1.1.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>2.7.3</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-app</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-mapreduce-client-hs</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.2.1</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-common</artifactId>
            <version>1.2.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.2.1</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
    </dependencies>

</project>

.assets/image-20200213103813808.png)

第三步：开发MR程序实现功能

（1）自定义map类

import org.apache.hadoop.hbase.Cell; import org.apache.hadoop.hbase.CellUtil; import org.apache.hadoop.hbase.client.Put; import org.apache.hadoop.hbase.client.Result; import org.apache.hadoop.hbase.io.ImmutableBytesWritable; import org.apache.hadoop.hbase.mapreduce.TableMapper; import org.apache.hadoop.hbase.util.Bytes; import org.apache.hadoop.io.Text;

import java.io.IOException;

/** * myuser f1: name&age => myuser2 f1 */ public class HBaseReadMapper extends TableMapper<Text, Put> { /** *

* @param key rowkey

 * @param value rowkey此行的数据 Result类型

 * @param context

 * @throws IOException

 * @throws InterruptedException
 */
@Override

protected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {

* ```
 /**
 *  ImmutableBytesWritable key:Mapper接收数据值是Put对象，key是hbase中一条数据Put对应的rowkey(可序列化)
 *  Result value：hbase中读取的result对象
     *  获取rowkey的字节数组
     */
    ```

 * //获得roweky的字节数组
 byte[] rowkey_bytes = key.get();
String rowkeyStr = Bytes.toString(rowkey_bytes);
Text text = new Text(rowkeyStr);`

    //输出数据 -> 写数据 -> Put 构建Put对象
    Put put = new Put(rowkey_bytes);
    //获取一行中所有的Cell对象`
Cell[] cells = value.rawCells();
    //将f1 : name& age输出`
    for(Cell cell: cells) {`
        //当前cell是否是f1
        //列族`
    byte[] family_bytes = CellUtil.cloneFamily(cell);
        String familyStr = Bytes.toString(family_bytes);
        if("f1".equals(familyStr)) {
            //在判断是否是name | age
            byte[] qualifier_bytes = CellUtil.cloneQualifier(cell);
            String qualifierStr = Bytes.toString(qualifier_bytes);
        if("name".equals(qualifierStr)) {
             put.add(cell);
         }
         if("age".equals(qualifierStr)) {`
             `put.add(cell);`
         }
     }
 }

 `//判断是否为空；不为空，才输出`
 `if(!put.isEmpty()){`
     `context.write(text, put);`

} } }

（2）自定义reduce类

package com.kaikeba.hbase.demo01;

import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.io.Text;
import java.io.IOException;
/**
 * TableReducer第三个泛型包含rowkey信息
 */

public class HBaseWriteReducer extends TableReducer<Text, Put, ImmutableBytesWritable> {
    //将map传输过来的数据，写入到hbase表
    /**
     Text:map端输出键类型
     Put:map端输出值类型
     ImmutableBytesWritable:reduce端输出键类型
    */
    @Override
    protected void reduce(Text key, Iterable<Put> values, Context context) throws IOException, InterruptedException {
        /**
        *Text key:接收map端输出键
        *Iterable<Put> values:接收map端输出值，put对象封装成的迭代器
        */
        //rowkey
        ImmutableBytesWritable immutableBytesWritable = new ImmutableBytesWritable();
        immutableBytesWritable.set(key.toString().getBytes());
        //遍历put对象，并输出
        for(Put put: values) {
            context.write(immutableBytesWritable, put);
        }
    }
}

（3）main入口类

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class HBaseMR extends Configured implements Tool {
    public static void main(String[] args) throws Exception {
        Configuration configuration = HBaseConfiguration.create();
        //设定绑定的zk集群
        configuration.set("hbase.zookeeper.quorum", "node01:2181,node02:2181,node03:2181");

        int run = ToolRunner.run(configuration, new HBaseMR(), args);
        System.exit(run);
    }

    @Override
    public int run(String[] args) throws Exception {
        Job job = Job.getInstance(super.getConf());
        job.setJarByClass(HBaseMR.class);

        //mapper
        TableMapReduceUtil.initTableMapperJob(TableName.valueOf("myuser"), new Scan(),HBaseReadMapper.class, Text.class, Put.class, job);
        //reducer
        TableMapReduceUtil.initTableReducerJob("myuser2", HBaseWriteReducer.class, job);

        boolean b = job.waitForCompletion(true);
        return b? 0: 1;
    }
}

第四步：打成jar包提交到集群运行

打包：

.assets/image-20200213105050592.png)

.assets/image-20200213105111803.png)

执行命令：

hadoop jar HbaseTang-1.0-SNAPSHOT.jar mapreduce_hbase.HbaseMR

执行结果：

.assets/image-20200213101255568.png)

ITPub博客

Hadoop整合Hbase案例讲解