Hadoop经典案例Spark实现（一）——通过采集的气象数据分析每年的最高温度

Hadoop经典案例Spark实现（一）——通过采集的气象数据分析每年的最高温度

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： 
第15-19个字符是year
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、首先MapReduce实现

1） map 任务

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 NewMaxTemperatureMapper extendsMapper<LongWritable, Text, Text, IntWritable> {private static final int MISSING = 9999;@Overridepublic void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException {String line = value.toString();System.out.println("key: " + key);String year = line.substring(15, 19);int airTemperature;if (line.charAt(45) == '+') {airTemperature = Integer.parseInt(line.substring(46, 50));} else {airTemperature = Integer.parseInt(line.substring(45, 50));}String quality = line.substring(50, 51);System.out.println("quality: " + quality);if (airTemperature != MISSING && quality.matches("[01459]")) {context.write(new Text(year), new IntWritable(airTemperature));}}}

2）reduce任务

import java.io.IOException;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;public class NewMaxTemperatureReducer extendsReducer<Text, IntWritable, Text, IntWritable> {@Overrideprotected 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));}}

3）Job提交

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 NewMaxTemperature {public static void main(String[] args)throws Exception {if (args.length != 2) {System.err.print("Usage: MaxTemperature<input path> <output path>");System.exit(-1);}Job job = new Job();job.setJarByClass(NewMaxTemperature.class);FileInputFormat.addInputPath(job, new Path(args[0]));FileOutputFormat.setOutputPath(job, new Path(args[1]));job.setMapperClass(NewMaxTemperatureMapper.class);job.setReducerClass(NewMaxTemperatureReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);System.exit(job.waitForCompletion(true) ? 0 : 1);}}

3、Spark代码实现Scala版本

val one = sc.textFile("/tmp/hadoop/one")val yearAndTemp = one.filter(line => { val quality = line.substring(50, 51); var airTemperature = 0 if(line.charAt(45)=='+'){  airTemperature = line.substring(46, 50).toInt }else{  airTemperature = line.substring(45, 50).toInt } airTemperature != 9999 && quality.matches("[01459]")}).map{line =>{ val year = line.substring(15,19) var airTemperature = 0 if(line.charAt(45)=='+'){  airTemperature = line.substring(46, 50).toInt }else{  airTemperature = line.substring(45, 50).toInt }  (year,airTemperature)}}val res = yearAndTemp.reduceByKey( (x,y)=> if(x>y) x else y)res.collect.foreach(x=>println("year : " + x._1+", max : "+x._2))

上面为了过滤非法的数据，在map前先做了filter过滤。

mapreduce与spark执行的任务结果是一样的

year : 1949, max : 111
year : 1950, max : 78