HBase with MapReduce （Read and Write）

上面一篇文章仅仅是介绍如何通过mapReduce来对HBase进行读的过程，下面将要介绍的是利用mapreduce进行读写的过程，前面我们已经知道map实际上是读过程，reduce是写的过程，然而map也可以实现写入的过程，因此可以通过map实现读写的过程。具体实现如下所示：

（1）map的实现

package com.datacenter.HbaseMapReduce.ReadWrite;

import java.io.IOException;

import org.apache.hadoop.hbase.KeyValue;
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;

public class ReadWriteHbaseMap extends TableMapper<ImmutableBytesWritable, Put> {

	@Override
	protected void map(ImmutableBytesWritable row, Result value, Context context)
			throws IOException, InterruptedException {
		// TODO Auto-generated method stub
		context.write(row, resultToPut(row, value));
	}

	private static Put resultToPut(ImmutableBytesWritable key, Result result)
			throws IOException {
		Put put = new Put(key.get());
		for (KeyValue kv : result.raw()) {
			put.add(kv);
		}
		return put;
	}
}

 

（2）主类的main的实现

package com.datacenter.HbaseMapReduce.ReadWrite;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.HConnection;
import org.apache.hadoop.hbase.client.HConnectionManager;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.mapreduce.Job;

public class ReadWriteHbase {
	static String rootdir = "hdfs://hadoop3:8020/hbase";
	static String zkServer = "hadoop3";
	static String port = "2181";

	private static Configuration conf;
	private static HConnection hConn = null;

	public static void HbaseUtil(String rootDir, String zkServer, String port) {

		conf = HBaseConfiguration.create();// 获取默认配置信息
		conf.set("hbase.rootdir", rootDir);
		conf.set("hbase.zookeeper.quorum", zkServer);
		conf.set("hbase.zookeeper.property.clientPort", port);

		try {
			hConn = HConnectionManager.createConnection(conf);
		} catch (IOException e) {
			// TODO Auto-generated catch block
			e.printStackTrace();
		}
	}

	public static void main(String[] args) throws Exception {
		// TODO Auto-generated method stub
		HbaseUtil(rootdir, zkServer, port);

		// Configuration config = HBaseConfiguration.create();

		Job job = new Job(conf, "ExampleReadWrite");
		job.setJarByClass(ReadWriteHbase.class); // class that contains mapper

		Scan scan = new Scan();
		scan.setCaching(500); // 1 is the default in Scan, which will be bad for
								// MapReduce jobs
		scan.setCacheBlocks(false); // don't set to true for MR jobs
		// set other scan attrs

		TableMapReduceUtil.initTableMapperJob("score", // input table
				scan, // Scan instance to control CF and attribute selection
				ReadWriteHbaseMap.class, // mapper class
				null, // mapper output key
				null, // mapper output value
				job);
		TableMapReduceUtil.initTableReducerJob("liujiyu", // output table
				null, // reducer class
				job);
		job.setNumReduceTasks(0);

		boolean b = job.waitForCompletion(true);
		if (!b) {
			throw new IOException("error with job!");
		}
	}

}

 注意：上面虽然利用TableMapReduceUtil来初始化输出的表，但是我们的reduce个数是0，job.setNumReduceTasks(0)。