1. 数据提炼、探索数据、挖掘数据
2. map=切碎，reduce=合并
3. 重新启动后会清空tmp目录，默认namenode会存在这里，须要在core-site.xml文件里添加（别忘了创建目录，没权限的话，须要用root创建并把权限改成777）：

   <property>
        <name>hadoop.tmp.dir</name>
        <value>/usr/local/hadoop/tmp</value>
   </property>

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);
	}
}

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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开发过程中遇到的问题：

1. Job被标记为已作废，那应该用什么我还没有查到
2. 乱码问题，hadoop默认是utf8格式的，假设读取的是gbk的须要进行处理
3. 这类样例网上挺少的，有也是老版的，新版的资料没有，我全然是拼凑出来的，非常多地方还不甚了解，须要进一步学习官方资料
4. 搜索资料时，有资料说不建议採用这样的方式处理实际的大数据问题，原因就是并发过高，会瞬间秒杀掉数据库，一般都会採用导成文本文件的形式