【HBase】HBase与MapReduce的集成案例
HBase与MapReducer集成官方帮助文档：http://archive.cloudera.com/cdh5/cdh/5/hbase-1.2.0-cdh5.14.0/book.html
需求
在HBase先创建一张表myuser2 —— create 'myuser2','f1'，然后读取myuser表中的数据，将myuser表中f1列族下name列和age列的数据写入到表myuser2中
步骤
一、创建maven工程，导入jar包
<repositories>
<repository>
<id>cloudera</id>
<url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
</repository>
</repositories>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.6.0-mr1-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>1.2.0-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>1.2.0-cdh5.14.0</version>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.12</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.testng</groupId>
<artifactId>testng</artifactId>
<version>6.14.3</version>
<scope>test</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.0</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>UTF-8</encoding>
<!-- <verbal>true</verbal>-->
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.2</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*/RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
二、开发MapReduce程序
定义一个main类——HbaseReadWrite
package cn.itcast.mr.demo1;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.hbase.HBaseConfiguration;
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 HbaseReadWrite extends Configured implements Tool {
@Override
public int run(String[] args) throws Exception {
//创建Job对象
Job job = Job.getInstance(super.getConf(), "HbaseMapReduce");
//创建Scan对象，这里如果不设置过滤器，就是全表查询，因为在Mapper类中已经设置了判断条件，所以这里不需要设置过滤器
Scan scan = new Scan();
/**
* 这是自定义Map逻辑的工具类
* 这里需要五个参数：
* tablename 就是 要读取数据的表名
* scan 就是 HBASE 在java代码 实现增删改查时用来设置过滤器，获取数据等的
* 接着就是自己定义的Mapper类，k2和v2的输出类型
* 最后是Job对象
*/
TableMapReduceUtil.initTableMapperJob("myuser",scan,HbaseReadMapper.class, Text.class, Put.class,job);
/**
* 这是自定义Reduce逻辑的工具类
* 这里只需要三个参数即可
* tablename 就是要写入数据的表名
* 然后一个自定义的reduce类和job对象
*/
TableMapReduceUtil.initTableReducerJob("myuser2",HbaseWriteReducer.class,job);
//提交任务
boolean b = job.waitForCompletion(true);
return b?0:1;
}
/**
* main方法，负责run的退出
* @param args
* @throws Exception
*/
public static void main(String[] args) throws Exception {
Configuration configuration = HBaseConfiguration.create();
//一定记得要在configuration中设置zookeeper的地址，否则无法连接
configuration.set("hbase.zookeeper.quorum","node01:2181,node02:2181,node03:2181");
int run = ToolRunner.run(configuration, new HbaseReadWrite(), args);
System.exit(run);
}
}
自定义Mapper逻辑，定义一个Mapper类——HbaseReadMapper
package cn.itcast.mr.demo1;
import org.apache.hadoop.hbase.Cell;
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;
import java.util.List;
public class HbaseReadMapper extends TableMapper<Text, Put> {
/**
*
* @param key ke2输出类型为Text，因为是rowKey
* @param result v2输出类型为Put，因为Hbase插入数据都是Put对象
* @param context
* @throws IOException
* @throws InterruptedException
*/
@Override
protected void map(ImmutableBytesWritable key, Result result, Context context) throws IOException, InterruptedException {
//获取Hbase表中rowKey的字节
byte[] rowKeyBytes = key.get();
//将rowKey字节转换为字符串，因为k2输出类型为Text
String rowKey = Bytes.toString(rowKeyBytes);
//新建Put对象
Put put = new Put(rowKeyBytes);
//获取Hbase所有数据
List<Cell> cells = result.listCells();
//循环遍历到每一条数据
for (Cell cell : cells) {
//获取cell的列族
byte[] family = cell.getFamily();
//获取cell的列
byte[] qualifier = cell.getQualifier();
//判断cell的列族和列值，拿到需要的数据
if ("f1".equals(Bytes.toString(family))){
if ("name".equals(Bytes.toString(qualifier)) || "age".equals(Bytes.toString(qualifier))){
put.add(cell);
}
}
}
//判断Put是否为空
if (!put.isEmpty()){
context.write(new Text(rowKey),put);
}
}
}
自定义Reducer逻辑，定义一个Reducer类——HbaseWriterReduce
package cn.itcast.mr.demo1;
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;
public class HbaseWriteReducer extends TableReducer<Text, Put, ImmutableBytesWritable> {
/**
*
* @param key 输入值，k2为Text，也就是rowKey
* @param values 输入值，v2为Put
* @param context
* @throws IOException
* @throws InterruptedException
*/
@Override
protected void reduce(Text key, Iterable<Put> values, Context context) throws IOException, InterruptedException {
// ImmutableBytesWritable是用来封装rowKey的
ImmutableBytesWritable immutableBytesWritable = new ImmutableBytesWritable();
// key就是rowKey
immutableBytesWritable.set(key.getBytes());
// 循环遍历拿到每一个put对象，输出即可
for (Put put : values) {
context.write(immutableBytesWritable,put);
}
}
}
三、运行结果
