【HBase】HBase与MapReduce集成——从HDFS的文件读取数据到HBase

目录

• 需求
• 步骤
• 一、创建maven工程，导入jar包
• 二、开发MapReduce程序
• 三、结果

---

需求

将HDFS路径 /hbase/input/user.txt 文件的内容读取并写入到HBase 表myuser2中

首先在HDFS上准备些数据让我们用

hdfs dfs -mkdir -p /hbase/input
cd /export/servers/
vim user.txt

填写一下数据，注意是用 \t 分隔的

0007	zhangsan	18
0008	lisi	25
0009	wangwu	20

保存后上传到HDFS上就行

hdfs dfs -put user.txt /hbase/input

---

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方法类——HdfsReadHbaseWrite

package cn.itcast.mr.demo2;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class HdfsReadHbaseWrite extends Configured implements Tool {
    @Override
    public int run(String[] args) throws Exception {
        //获取Job对象
        Job job = Job.getInstance(super.getConf(), "hdfs->hbase");
        //获取输入数据和路径
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.setInputPaths(job, new Path("hdfs://node01:8020/hbase/input"));

        //自定义Map逻辑
        job.setMapperClass(HDFSReadMapper.class);
        //获取k2,v2输出类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(NullWritable.class);

        //自定义Reduce逻辑
        TableMapReduceUtil.initTableReducerJob("myuser2", HbaseWriteReducer.class, job);

        //设置reduceTask个数
        job.setNumReduceTasks(1);

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

    public static void main(String[] args) throws Exception {
        Configuration configuration = HBaseConfiguration.create();
        configuration.set("hbase.zookeeper.quorum", "node01:2181,node02:2181,node03:2181");
        int run = ToolRunner.run(configuration, new HdfsReadHbaseWrite(), args);
        System.exit(run);
    }
}

自定义Map逻辑，定义一个Mapper类——HDFSReadMapper

package cn.itcast.mr.demo2;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class HDFSReadMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        /*
        0007	zhangsan	18
        0008	lisi	25
        0009	wangwu	20
        我们要读取的数据都直接封装到了value中，所以直接拿到以后输出就行
         */
        context.write(value, NullWritable.get());
    }
}

自定义Reduce逻辑，定义一个Reducer类——HbaseWriteReducer

package cn.itcast.mr.demo2;

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.NullWritable;
import org.apache.hadoop.io.Text;

import java.io.IOException;

public class HbaseWriteReducer extends TableReducer<Text, NullWritable, ImmutableBytesWritable> {
    @Override
    protected void reduce(Text key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
        /*
        0007	zhangsan	18
        0008	lisi	25
        0009	wangwu	20
         */
        //先把拿到的数据分割一下
        String[] split = key.toString().split("\t");
        //拿到rowKey
        byte[] rowKey = split[0].getBytes();
        //拿到nameValue
        byte[] nameValue = split[1].getBytes();
        //拿到ageValue
        byte[] ageValue = split[2].getBytes();
        //创建put对象
        Put put = new Put(rowKey);
        //添加数据
        put.addColumn("f1".getBytes(), "name".getBytes(), nameValue);
        put.addColumn("f1".getBytes(), "age".getBytes(), ageValue);

        //构建ImmutableBytesWritable
        ImmutableBytesWritable immutableBytesWritable = new ImmutableBytesWritable();
        immutableBytesWritable.set(rowKey);
        //转换成k3,v3输出
        context.write(immutableBytesWritable, put);
    }
}

三、结果