mapreduce实现读取hbase表数据保存到hdfs

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/u013803572/article/details/86178599

mapreduce实现读取hbase表数据保存到hdfs

• 参考资料 https://hbase.apache.org/book.html#mapreduce.example

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.CellUtil;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class Hbase2Hdfs {

	public static void main(String[] args) throws Exception {

		Configuration config = HBaseConfiguration.create();
		Job job = Job.getInstance(config, "Hbase2HdfsMrTest");
		job.setJarByClass(Hbase2Hdfs.class);

		String hbaseTableName = "wordcount";

		Scan scan = new Scan();
		scan.setCaching(500);
		scan.setCacheBlocks(false);

		TableMapReduceUtil.initTableMapperJob(hbaseTableName, scan, MyMapper.class, Text.class, Text.class, job);
		FileOutputFormat.setOutputPath(job, new Path("/tmp/mr/mySummaryFile"));
		boolean b = job.waitForCompletion(true);
		if (b) {
			System.out.println("hbase to hdfs ok");
		}

	}

	/**
	 * 只有mapper，没有reducer，将mapper的输出直接保存到文件中
	 * @author caoxiangqian
	 *
	 */
	public static class MyMapper extends TableMapper<Text, Text> {

		@Override
		protected void map(ImmutableBytesWritable key, Result value,
				Mapper<ImmutableBytesWritable, Result, Text, Text>.Context context)
				throws IOException, InterruptedException {

			for (Cell cell : value.rawCells()) {
				String family = Bytes.toString(CellUtil.cloneFamily(cell));
				String qualifier = Bytes.toString(CellUtil.cloneQualifier(cell));
				String row = Bytes.toString(CellUtil.cloneRow(cell));
				String rowValue = null;
				if ("count".equals(qualifier)) {
					rowValue = Bytes.toLong(CellUtil.cloneValue(cell)) + "";
				} else {
					rowValue = Bytes.toString(CellUtil.cloneValue(cell));
				}
				System.out.println(
						"family: " + family + ", qualifier:" + qualifier + ", rowKey:" + row + ", value: " + rowValue);

				context.write(new Text(
						"rowKey=" + row + ",family=" + family + ",qualifier=" + qualifier + ",value=" + rowValue),
						new Text(""));
			}

		}

	}
}

• 查看输出结果

[hadoop@hadoop001 mrtest]$ hdfs dfs -text /tmp/mr/mySummaryFile/*
rowKey=a,family=cf,qualifier=count,value=4	
rowKey=a,family=cf,qualifier=word,value=a	
rowKey=am,family=cf,qualifier=count,value=10	
rowKey=am,family=cf,qualifier=word,value=am	
rowKey=boy,family=cf,qualifier=count,value=2	
rowKey=boy,family=cf,qualifier=word,value=boy	
rowKey=caoxiangqian,family=cf,qualifier=count,value=1	
rowKey=caoxiangqian,family=cf,qualifier=word,value=caoxiangqian	
rowKey=go,family=cf,qualifier=count,value=6918	
rowKey=go,family=cf,qualifier=word,value=go	
rowKey=good,family=cf,qualifier=count,value=3	
rowKey=good,family=cf,qualifier=word,value=good	
rowKey=haha,family=cf,qualifier=count,value=3624	
rowKey=haha,family=cf,qualifier=word,value=haha	
rowKey=hello,family=cf,qualifier=count,value=7398	
rowKey=hello,family=cf,qualifier=word,value=hello		

• 如果只有mapper可能无法控制输出的文件数量，可以加一个reducer，并设置reducer的数量来控制输出文件的数量