hadoop项目之求出每年二月的最高气温（Combiner优化）

hadoop项目之求出每年二月的最高气温（Combiner优化）

一、项目结构

一、java实现随机生成日期和气温

package com.shujia.weather;import java.io.BufferedWriter;import java.io.FileWriter;import java.io.IOException;import java.text.DateFormat;import java.text.ParseException;import java.text.SimpleDateFormat;import java.util.Date;public class RandomWeather {    public static void main(String[] args) throws ParseException, IOException {        //创建日期格式        DateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");        long start = sdf.parse("2000-01-01 00:00:00").getTime();        long end = sdf.parse("2022-12-31 00:00:00").getTime();        long difference=end - start;        BufferedWriter bw = new BufferedWriter(new FileWriter("F:\\software\\IdeaProjects\\bigdata19-project\\biddata19-mapreduce\\src\\data\\weather.txt"));        for (int i=0;i<10000;i++){            //随机生成时间            Date date = new Date(start + (long) (Math.random() * difference));            //随机生成一个温度            int temperature = -20+(int) (Math.random()*60);            //打印//            System.out.println(date+"\t"+temperature);            bw.write(sdf.format(date)+"\t"+temperature);//将结果写入文件            bw.newLine();            bw.flush();        }        bw.close();    }}

二、将这个weather.txt文件上传到虚拟机后再上传到hadoop

1、通过xftp上传文件2、通过命令上传到hadoophadoop fs -put weather.txt /路径

三、项目实现

package com.shujia.weather;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;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;import java.io.IOException;class WeatherMapper extends Mapper<LongWritable,Text,Text,LongWritable>{    /*    2022-06-12 02:40:26	21    2002-01-03 03:49:27	-13    2001-04-21 19:19:22	-16    2005-01-18 01:52:15	10    求出每年二月份的最高气温     */    @Override    protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context) throws IOException, InterruptedException {        String line = value.toString();        String[] str = line.split("\t");        String temperature = str[1];        String[] strings = str[0].split("-");        String Month = strings[1];        if ("02".equals(Month)){            context.write(new Text(strings[0]+"-"+Month),new LongWritable(Long.parseLong(temperature)));        }    }}class WeatherReducer extends Reducer<Text,LongWritable,Text,LongWritable>{    @Override    protected void reduce(Text key, Iterable<LongWritable> values, Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {        long max=0L;        for (LongWritable value : values) {            long l = value.get();            if (l>max){                max=l;            }        }        context.write(key,new LongWritable(max));    }}public class WeatherDemo {    public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {        Configuration conf = new Configuration();        Job job = Job.getInstance(conf);        job.setCombinerClass(WeatherReducer.class);//Combiner优化        job.setJarByClass(WeatherDemo.class);        job.setMapperClass(WeatherMapper.class);        job.setReducerClass(WeatherReducer.class);        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(LongWritable.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(LongWritable.class);        FileInputFormat.setInputPaths(job,new Path(args[0]));        FileOutputFormat.setOutputPath(job,new Path(args[1]));        job.waitForCompletion(true);    }}

优化前

优化后

减少了reduce 从map拉取数据的过程，提高计算效率。

hadoop 的计算特点：将计算任务向数据靠拢，而不是将数据向计算靠拢。

注意：将reduce端的聚合操作，放到map 进行执行。适合求和，计数，等一些等幂操作。不适合求平均值，次幂等类似操作