Hadoop 实例1---通过采集的气象数据分析每年的最高温度

最新推荐文章于 2025-06-30 16:13:19 发布

garychenqin

最新推荐文章于 2025-06-30 16:13:19 发布

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文章标签： hadoop 数据分析实例 91

本文链接：https://blog.youkuaiyun.com/garychenqin/article/details/48212091

本文概述了大数据处理领域的关键技术，包括数据收集、清洗、分析、存储等环节，涉及Hadoop、Spark、Flink等分布式计算框架，以及数据挖掘、机器学习、人工智能等高级分析方法。

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1.原始数据分析

0067011990999991950051507004888888889999999N9+00001+9999999999999999999999
0067011990999991950051512004888888889999999N9+00221+9999999999999999999999
0067011990999991950051518004888888889999999N9-00111+9999999999999999999999
0067011990999991949032412004888888889999999N9+01111+9999999999999999999999
0067011990999991950032418004888888880500001N9+00001+9999999999999999999999
0067011990999991950051507004888888880500001N9+00781+9999999999999999999999

数据说明：
1.第15-19个字符是year
2.第45-50位是温度表示，+表示零上, -表示零下，且温度的值不能是9999，9999表示异常数据第50位值只能是0、1、4、5、9几个数字

2.写Mapper程序：

package cn.edu.bjut.temperautre;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class TemperatureMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

    private static final Integer ERROR_TEMPER = 9999;

    @Override
    protected void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String content = value.toString();
        String year = content.substring(15, 19);    //获取year

        Integer temperature  = null;    //获取温度
        if('+' == content.charAt(45)) {
            temperature = Integer.parseInt(content.substring(46, 50));
        } else {
            temperature = Integer.parseInt(content.substring(45, 50));
        }

        if(temperature <= ERROR_TEMPER && content.substring(50, 51).matches("[01459]")) {
            context.write(new Text(year), new IntWritable(temperature));
        }
    }

}

3.写Reducer程序：

package cn.edu.bjut.temperautre;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class TemperatureReducer extends
        Reducer<Text, IntWritable, Text, IntWritable> {

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {

        int maxTemperature = Integer.MIN_VALUE;

        for(IntWritable intWritable : values) {
            maxTemperature = Math.max(maxTemperature, intWritable.get());
        }

        context.write(key, new IntWritable(maxTemperature));

    }
}

4.写主程序：

package cn.edu.bjut.temperautre;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MainJob {
    public static void main(String[] args) throws Exception {

        if(2 != args.length) {
            System.out.println("Usage: MaxTemperature<input path> <output path>");
            System.exit(-1);
        }

        Configuration conf = new Configuration();
        Job job = new Job(conf, "temperature");
        job.setJarByClass(MainJob.class);

        job.setMapperClass(TemperatureMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        job.setReducerClass(TemperatureReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));

        Path outPath = new Path(args[1]);
        FileSystem fileSystem = FileSystem.get(conf);
        if(fileSystem.exists(outPath)) {
            fileSystem.delete(outPath, true);
        }

        FileOutputFormat.setOutputPath(job, outPath);

        System.exit(job.waitForCompletion(true)?1:0);
    }
}

5.打成jar，只需要打包对应的源代码即可，上传到linux服务器
6.创建文件source.txt 并把需要分析的文本数据copy到该文件中，执行 hadoop fs -put source.txt /user/root/data/1/source.txt 将文件存放在hdfs中。（这里所提供的目录仅供参考，可以自己定义的）
7.hadoop jar one.jar /user/root/data/1 /result_one