【HBase】HBase与MapReduce的集成案例

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);

        }

    }

}

巴特西

【HBase】HBase与MapReduce的集成案例

目录

需求

步骤

一、创建maven工程，导入jar包

二、开发MapReduce程序

三、运行结果

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