驱动类要改的东西不是很大
到此就算是完成了。
package qf.com.mr;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.FloatWritable;
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.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
/*
*类说明：将汽车生产流水线的三个时间段的记录算一个平均值
*
*l z w y
*L_1 393 430 276
*L_2 388 560 333
*L_3 450 600 312
*
*生产线 生产平均值
*L_1 *
*L_2 *
*/
public class Avg {
public static class MyMapper extends Mapper<LongWritable, Text, Text, FloatWritable>{
public static Text k = new Text();
public static FloatWritable v = new FloatWritable();
//setup 只在map阶段执行之前执行一次（用于初始化等一次性操作）
@Override
protected void setup(Mapper<LongWritable, Text, Text, FloatWritable>.Context context)
throws IOException, InterruptedException {
}
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FloatWritable>.Context context)
throws IOException, InterruptedException {
//1.从输入数据中获取每一个文件中的每一行的值
String line = value.toString();
//2.对每一行的数据进行切分（有的不用）
String [] words = line.split("\t");
//3.按照业务循环处理
String lineName = words[0];
int z = Integer.parseInt(words[1]);
int w = Integer.parseInt(words[2]);
int y = Integer.parseInt(words[3]);
k.set(lineName);
float avg = (float)((z+w+y)*1.0/(words.length-1));
v.set(avg);
context.write(k, v);
}
//在整个map执行之后 一般作用于最后的结果
@Override
protected void cleanup(Mapper<LongWritable, Text, Text, FloatWritable>.Context context)
throws IOException, InterruptedException {
}
}
public static class MyReducer extends Reducer<Text, FloatWritable, Text, Text>{
//在reduce阶段执行之前 执行 仅一次
@Override
protected void setup(Reducer<Text, FloatWritable, Text, Text>.Context context)
throws IOException, InterruptedException {
context.write(new Text("生产线 生产平均值"), new Text(""));
}
@Override
protected void reduce(Text key, Iterable<FloatWritable> values, Context context)
throws IOException, InterruptedException {
context.write(key, new Text(values.iterator().next().get()+""));
}
//在reduce执行之后执行 仅执行一次
@Override
protected void cleanup(Reducer<Text, FloatWritable, Text, Text>.Context context)
throws IOException, InterruptedException {
}
}
//驱动
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//1.获取配置对象信息
Configuration conf = new Configuration();
//2.对conf进行设置（没有就不用）
//3.获取job对象 （注意导入的包）
Job job = Job.getInstance(conf, "myAvg");
//4.设置job的运行主类
job.setJarByClass(Avg.class);
//System.out.println("jiazai finished");
//5.对map阶段进行设置
job.setMapperClass(MyMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FloatWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));//具体路径从控制台输入
//System.out.println("map finished");
//6.对reduce阶段进行设置
job.setReducerClass(MyReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//System.out.println("reduce finished");
//7.提交job并打印信息
int isok = job.waitForCompletion(true) ? 0 : 1;
//退出job
System.exit(isok);
System.out.println("all finished");
}
}
