MapReduce实验——分析年气象数据最高温度,最低温度

65 阅读 0 评论 43 点赞

我是靠谱客的博主体贴夕阳，最近开发中收集的这篇文章主要介绍MapReduce实验——分析年气象数据最高温度,最低温度，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

分析年气象数据最高温度

Map类

package MaxTemperature_05;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class MyMap extends
Mapper<LongWritable,Text, Text, IntWritable> {
private static final int MISSING = 9999;
@Override
protected void map(LongWritable key,Text value,Context context) throws IOException, InterruptedException {
//读取一条数据
String line = value.toString();
//获取年份
String year = line.substring(15,19);
int airTem;
if(line.charAt(45) == '+'){
//判断温度正负
airTem = Integer.parseInt(line.substring(46,50));
}else{
airTem = Integer.parseInt(line.substring(45,50));
}
String quality = line.substring(50,51);
System.out.println("quality:"+quality);
//判断温度是否异常
if(airTem != MISSING && quality.matches("[01459]]")){
context.write(new Text(year),new IntWritable(airTem));
}
}
}

Reduce类

package MaxTemperature_05;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class MyReduce extends Reducer<Text, IntWritable,Text,IntWritable> {
protected void reduce(Text key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
//把Integer.MIN_VALUE作为maxValue的初始值
int maxValue = Integer.MIN_VALUE;
int minValue = Integer.MAX_VALUE;
//循环取出最大值,最小值
for (IntWritable value:values) {
maxValue = Math.max(maxValue,value.get());
//another type
//if(value.get() > maxValue){
//
maxValue = value.get();
//}
minValue = Math.min(minValue,value.get());
//another type
//if(value.get() < minValue){
//
minValue= value.get();
//}
}
//最高温度
context.write(key,new IntWritable(maxValue));
//最低温度
//context.write(key,new IntWritable(minValue));
}
}

Job类

package MaxTemperature_05;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.io.Text;
import java.io.IOException;
public class TestJob {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration conf = new Configuration();
//1、获取作业对象
Job job = Job.getInstance(conf);
//2、设置主类
job.setJarByClass(TestJob.class);
//3、设置job参数
job.setMapperClass(MyMap.class);
job.setReducerClass(MyReduce.class);
//4 set map reduce output type
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//5、设置job输入输出
FileInputFormat.setInputPaths(job,new Path("file:///simple/source.txt"));
FileOutputFormat.setOutputPath(job,new Path("file:///simple/output"));
//6 commit job
System.out.println(job.waitForCompletion(true) ? 0 : 1);;
}
}