HBase表数据的转移之使用自定义MapReduce
目标：将fruit表中的一部分数据，通过MR迁入到fruit_mr表中
Step1、构建ReadFruitMapper类，用于读取fruit表中的数据
package com.z.hbase_mr;
import java.io.IOException;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
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;
public class ReadFruitMapper extends TableMapper<ImmutableBytesWritable, Put> {
@Override
protected void map(ImmutableBytesWritable key, Result value, Context context)
throws IOException, InterruptedException {
//将fruit的name和color提取出来，相当于将每一行数据读取出来放入到Put对象中。
Put put = new Put(key.get());
//遍历添加column行
for(Cell cell: value.rawCells()){
//添加/克隆列族:info
if("info".equals(Bytes.toString(CellUtil.cloneFamily(cell)))){
//添加/克隆列：name
if("name".equals(Bytes.toString(CellUtil.cloneQualifier(cell)))){
//将该列cell加入到put对象中
put.add(cell);
//添加/克隆列:color
}else if("color".equals(Bytes.toString(CellUtil.cloneQualifier(cell)))){
//向该列cell加入到put对象中
put.add(cell);
}
}
}
//将从fruit读取到的每行数据写入到context中作为map的输出
context.write(key, put);
}
}
Step2、构建WriteFruitMRReducer类，用于将读取到的fruit表中的数据写入到fruit_mr表中
package com.z.hbase_mr;
import java.io.IOException;
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;
public class WriteFruitMRReducer extends TableReducer<ImmutableBytesWritable, Put, NullWritable> {
@Override
protected void reduce(ImmutableBytesWritable key, Iterable<Put> values, Context context)
throws IOException, InterruptedException {
//读出来的每一行数据写入到fruit_mr表中
for(Put put: values){
context.write(NullWritable.get(), put);
}
}
}
Step3、构建Fruit2FruitMRJob extends Configured implements Tool，用于组装运行Job任务
//组装Job
public int run(String[] args) throws Exception {
//得到Configuration
Configuration conf = this.getConf();
//创建Job任务
Job job = Job.getInstance(conf, this.getClass().getSimpleName());
job.setJarByClass(Fruit2FruitMRJob.class);
//配置Job
Scan scan = new Scan();
scan.setCacheBlocks(false);
scan.setCaching(500);
//设置Mapper，注意导入的是mapreduce包下的，不是mapred包下的，后者是老版本
TableMapReduceUtil.initTableMapperJob(
"fruit", //数据源的表名
scan, //scan扫描控制器
ReadFruitMapper.class,//设置Mapper类
ImmutableBytesWritable.class,//设置Mapper输出key类型
Put.class,//设置Mapper输出value值类型
job//设置给哪个JOB
);
//设置Reducer
TableMapReduceUtil.initTableReducerJob("fruit_mr", WriteFruitMRReducer.class, job);
//设置Reduce数量，最少1个
job.setNumReduceTasks(1);
boolean isSuccess = job.waitForCompletion(true);
if(!isSuccess){
throw new IOException("Job running with error");
}
return isSuccess ? 0 : 1;
}
Step4、主函数中调用运行该Job任务
public static void main( String[] args ) throws Exception{
Configuration conf = HBaseConfiguration.create();
int status = ToolRunner.run(conf, new Fruit2FruitMRJob(), args);
System.exit(status);
}
