HBase-API操作之与MR交互（二）

# 前言

目标：实现将 HDFS 中的数据写入到 HBase 表中

1. 构建 Mapper 用于读取 HDFS 中的文件数据

   	package com.yaindream.mr1;
   	import org.apache.hadoop.io.LongWritable;
   	import org.apache.hadoop.io.Text;
   	import org.apache.hadoop.mapreduce.Mapper;
   	import java.io.IOException;
   	/**
   	* Created with IntelliJ IDEA.
   	* User: WangYu
   	* Date: 2020/6/15
   	* Time: 19:11
   	* Description:
   	*/
   	public class FruitMapper extends Mapper<LongWritable, Text, LongWritable, Text> {
   	@Override
   	protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
   	context.write(key, value);
   	}
   	}

2. 构建 Reducer 类用于对读入的数据进行处理

   	package com.yaindream.mr1;
   	import org.apache.hadoop.hbase.client.Put;
   	import org.apache.hadoop.hbase.mapreduce.TableReducer;
   	import org.apache.hadoop.hbase.util.Bytes;
   	import org.apache.hadoop.io.LongWritable;
   	import org.apache.hadoop.io.NullWritable;
   	import org.apache.hadoop.io.Text;
   	import java.io.IOException;
   	/**
   	* Created with IntelliJ IDEA.
   	* User: WangYu
   	* Date: 2020/6/15
   	* Time: 19:11
   	* Description:
   	*/
   	public class FruitReducer extends TableReducer<LongWritable, Text, NullWritable> {
   	@Override
   	protected void reduce(LongWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
   	// 1. 遍历 values
   	for (Text value : values) {
   	// 2. 获取每一行数据
   	String[] fields = value.toString().split("\t");
   	// 3. 构建 put 对象
   	Put put = new Put(Bytes.toBytes(fields[0]));
   	// 4. 给 put 对象赋值
   	put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("name"), Bytes.toBytes(fields[1]));
   	put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("color"), Bytes.toBytes(fields[2]));
   	// 5. 写出
   	context.write(NullWritable.get(), put);
   	}
   	}
   	}

3. 构建 Driver 类组装 Job

   	package com.yaindream.mr1;
   	import org.apache.hadoop.conf.Configuration;
   	import org.apache.hadoop.fs.Path;
   	import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
   	import org.apache.hadoop.io.LongWritable;
   	import org.apache.hadoop.io.Text;
   	import org.apache.hadoop.mapreduce.Job;
   	import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
   	import org.apache.hadoop.util.Tool;
   	import org.apache.hadoop.util.ToolRunner;
   	/**
   	* Created with IntelliJ IDEA.
   	* User: WangYu
   	* Date: 2020/6/15
   	* Time: 19:12
   	* Description:
   	*/
   	public class FruitDriver implements Tool {
   	// 定义一个 Configuration
   	private Configuration configuration = null;
   	public int run(String[] args) throws Exception {
   	// 1. 获取 Job 对象
   	Job job = Job.getInstance(configuration);
   	// 2. 设置驱动类路径
   	job.setJarByClass(FruitDriver.class);
   	// 3. 设置 Mapper & Mapper 输出的 KV 类型
   	job.setMapperClass(FruitMapper.class);
   	job.setMapOutputKeyClass(LongWritable.class);
   	job.setMapOutputValueClass(Text.class);
   	// 4. 设置 Reducer 类
   	TableMapReduceUtil.initTableReducerJob(args[1], FruitReducer.class, job);
   	// 5. 设置输入参数
   	FileInputFormat.setInputPaths(job, new Path(args[0]));
   	// 6. 提交任务
   	boolean result = job.waitForCompletion(true);
   	return result ? 0 : 1;
   	}
   	public void setConf(Configuration conf) {
   	configuration = conf;
   	}
   	public Configuration getConf() {
   	return configuration;
   	}
   	public static void main(String[] args) {
   	try {
   	Configuration configuration = new Configuration();
   	int run = ToolRunner.run(configuration, new FruitDriver(), args);
   	System.exit(run);
   	} catch (Exception e) {
   	e.printStackTrace();
   	}
   	}
   	}

4. 通过 IDEA 将程序打包成 Jar 包。
   

5. 在 HBase 上创建 fruit2 表

hbase(main):001:0> create 'fruit2','info'

6. 将 Jar 包上传到集群上，然后运行。yarn 命令是 /opt/module/hadoop-2.7.2/bin/yarn 。如果配置了环境变量就可以直接使用。

[root@hadoop02 hbase-1.3.1]# yarn jar hbase-demo-1.0-SNAPSHOT.jar com.yaindream.mr1.FruitDriver /fruit.tsv fruit1

7. 运行结果