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

时间：2022-10-11 02:09:59

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

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

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