MapReduce处理通过采集的气象数据分析每年的最高温度

数据来源于：NCDC 美国国家气候数据中心

这里是五条源数据：

0029029070999991901010813004+64333+023450FM-12+000599999V0202301N011819999999N0000001N9-00331+99999103201ADDGF108991999999999999999999
0035029070999991901010820004+64333+023450FM-12+000599999V0202301N013919999999N0000001N9-00331+99999102991ADDGF108991999999999999999999MW1701
0029029070999991901010906004+64333+023450FM-12+000599999V0209991C000019999999N0000001N9-00501+99999102871ADDGF108991999999999999999999
0029029070999991901010913004+64333+023450FM-12+000599999V0209991C000019999999N0000001N9-00331+99999102661ADDGF108991999999999999999999
0029029070999991901010920004+64333+023450FM-12+000599999V0201801N009819999999N0000001N9-00281+99999102391ADDGF108991999999999999999999

对数据格式进行解释：

位置	数据	含义
1-4	0029
5-10	029070	USAF weather station identifie
11-15	99999	WBAN weather station identifier
16-23	19010108	观察日期
24-27	1300	观察时间
28	4
29-34	+64333	纬度（1000倍）
35-41	+023450	经度（1000倍）
42-46	FM-12
47-51	+0005	海拔
52-56	99999
57-60	V020
61-63	230	风向
64	1	质量代码
65	N
66-69	0118
70	1	质量代码
71-75	99999	云高（米）
76	9
77	9
78	N
79-84	000000	能见距离（米）
85	1	质量代码
86	N
87	9
88-92	-0033	空气温度(摄氏度*10)
93	1	质量代码
94-98	+9999	露点温度(摄氏度*10）
99	9	质量代码
100-104	10320	大气压(hectopascals x10)
105	1	质量代码

我的代码是和书上一样的。依旧用的是三个class的代码框架，不多说直接上代码
新建一个MapReduce项目
先写job的代码

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 MTJob {

    public static void main(String[] args) throws Exception {
        if (args.length != 2) {
            System.err.println("Usage: MaxTemperature <input path> <output path>");
            System.exit(-1);
        }
        @SuppressWarnings("deprecation")
        Job job = new Job();
        job.setJarByClass(MTJob.class);
        job.setJobName("Max temperature");

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        job.setMapperClass(MTMapper.class);
        job.setReducerClass(MTReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }   
}

然后就是mapper

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 MTMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

    private static final int MISSING = 9999;
    @Override

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        String line = value.toString();
        String year = line.substring(15, 19);
        int airTemperature;
        if (line.charAt(87) == '+') { // parseInt doesn't like leading plus signs

            airTemperature = Integer.parseInt(line.substring(88, 92));
        } else {

            airTemperature = Integer.parseInt(line.substring(87, 92));
        }
        String quality = line.substring(92, 93);
        if (airTemperature != MISSING && quality.matches("[01459]")) {
            context.write(new Text(year), new IntWritable(airTemperature));
        }
    }
}

最后是reducer

import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class MTReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    @Override
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int maxValue = Integer.MIN_VALUE;
        for (IntWritable value : values) {
            maxValue = Math.max(maxValue, value.get());
        }
        context.write(key, new IntWritable(maxValue));
    }
}

然后导出jar包