Win7中使用Eclipse连接虚拟机中的Ubuntu中的Hadoop2.

Win7中使用Eclipse连接虚拟机中的Ubuntu中的Hadoop2.4<3>

• 经过前几天的学习，基本上可以小试牛刀编写一些小程序玩一玩了，在此之前做几项准备工作

• 答：

• 大数据，我的第一反应是现有关系型数据库中的数据怎么跟hadoop结合使用，网上搜了一些资料，使用的是DBInputFormat，那就简单编写一个从数据库读取数据，然后经过处理后，生成文件的小例子吧
• 数据库弄的简单一点吧，id是数值整型、test是字符串型，需求很简单，统计TEST字段出现的数量

• 数据读取类：

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.lib.db.DBWritable;

public class DBRecoder implements Writable, DBWritable{
	String test;
	int id;
	@Override
	public void write(DataOutput out) throws IOException {
		out.writeUTF(test);
		out.writeInt(id);
	}
	@Override
	public void readFields(DataInput in) throws IOException {
		test = in.readUTF();
		id = in.readInt();
	}
	@Override
	public void readFields(ResultSet arg0) throws SQLException {
		test = arg0.getString("test");
		id = arg0.getInt("id");
	}
	@Override
	public void write(PreparedStatement arg0) throws SQLException {
		arg0.setString(1, test);
		arg0.setInt(2, id);
	}
}

• mapreduce操作类

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class DataCountTest {
	public static class TokenizerMapper extends Mapper<LongWritable, DBRecoder, Text, IntWritable> {
		public void map(LongWritable key, DBRecoder value, Context context) throws IOException, InterruptedException {
			context.write(new Text(value.test), new IntWritable(1));
		}
	}

	public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
		private IntWritable result = new IntWritable();

		public void reduce(Text key, Iterable<IntWritable> values,
				Context context) throws IOException, InterruptedException {
			int sum = 0;
			for (IntWritable val : values) {
				sum += val.get();
			}
			result.set(sum);
			context.write(key, result);
		}
	}

	public static void main(String[] args) throws Exception {
		args = new String[1];
		args[0] = "hdfs://192.168.203.137:9000/user/chenph/output1111221";

		Configuration conf = new Configuration();
		
        DBConfiguration.configureDB(conf, "oracle.jdbc.driver.OracleDriver",  
                "jdbc:oracle:thin:@192.168.101.179:1521:orcl", "chenph", "chenph");  
		
		String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

		Job job = new Job(conf, "DB count");
		
		job.setJarByClass(DataCountTest.class);
		job.setMapperClass(TokenizerMapper.class);
		job.setReducerClass(IntSumReducer.class); 
		job.setOutputKeyClass(Text.class); 
		job.setOutputValueClass(IntWritable.class);
		job.setMapOutputKeyClass(Text.class);  
		job.setMapOutputValueClass(IntWritable.class);  
        String[] fields1 = { "id", "test"};  
        DBInputFormat.setInput(job, DBRecoder.class, "t1", null, "id",  fields1);  

		FileOutputFormat.setOutputPath(job, new Path(otherArgs[0]));
		
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}

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