MAPREDUCE并行程序开发
求每年最高气温
原始数据如下:
2014010114 2014010216 2014010317 2014010410 2014010506 2012010609 2012010732 2012010812 2012010919 2012011023 2001010116 2001010212 2001010310 2001010411 2001010529 2013010619 2013010722 2013010812 2013010929 2013011023 2008010105 2008010216 2008010337 2008010414 2008010516 2007010619 2007010712 2007010812 2007010999 2007011023 2010010114 2010010216 2010010317 2010010410 2010010506 2015010649 2015010722 2015010812 2015010999 2015011023 |
参考代码
import java.io.IOException;
import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; 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; public class Temperature { /** * 四个泛型类型分别代表: * KeyIn Mapper的输入数据的Key,这里是每行文字的起始位置(0,11,...) * ValueIn Mapper的输入数据的Value,这里是每行文字 * KeyOut Mapper的输出数据的Key,这里是每行文字中的“年份” * ValueOut Mapper的输出数据的Value,这里是每行文字中的“气温” */ static class TempMapper extends Mapper<LongWritable, Text, Text, IntWritable> { @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 打印样本: Before Mapper: 0, 2000010115 System.out.print("Before Mapper: " + key + ", " + value); String line = value.toString(); String year = line.substring(0, 4); int temperature = Integer.parseInt(line.substring(8)); context.write(new Text(year), new IntWritable(temperature)); // 打印样本: After Mapper:2000, 15 System.out.println( "======" + "After Mapper:" + new Text(year) + ", " + new IntWritable(temperature)); } }
/** * 四个泛型类型分别代表: * KeyIn Reducer的输入数据的Key,这里是每行文字中的“年份” * ValueIn Reducer的输入数据的Value,这里是每行文字中的“气温” * KeyOut Reducer的输出数据的Key,这里是不重复的“年份” * ValueOut Reducer的输出数据的Value,这里是这一年中的“最高气温” static class TempReducer 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; StringBuffer sb = new StringBuffer(); //取values的最大值 for (IntWritable value : values) { maxValue = Math.max(maxValue, value.get()); sb.append(value).append(", "); } // 打印样本: Before Reduce: 2000, 15, 23, 99, 12, 22, System.out.print("Before Reduce: " + key + ", " + sb.toString()); context.write(key, new IntWritable(maxValue)); // 打印样本: After Reduce: 2000, 99 System.out.println( "======" + "After Reduce: " + key + ", " + maxValue); } }
public static void main(String[] args) throws Exception { //输入路径 String dst = "hdfs://localhost:9000/intput.txt"; //输出路径,必须是不存在的,空文件加也不行。 String dstOut = "hdfs://localhost:9000/output"; Configuration hadoopConfig = new Configuration();
hadoopConfig.set("fs.hdfs.impl", org.apache.hadoop.hdfs.DistributedFileSystem.class.getName() ); hadoopConfig.set("fs.file.impl", org.apache.hadoop.fs.LocalFileSystem.class.getName() ); Job job = new Job(hadoopConfig);
//如果需要打成jar运行,需要下面这句 //job.setJarByClass(NewMaxTemperature.class);
//job执行作业时输入和输出文件的路径 FileInputFormat.addInputPath(job, new Path(dst)); FileOutputFormat.setOutputPath(job, new Path(dstOut));
//指定自定义的Mapper和Reducer作为两个阶段的任务处理类 job.setMapperClass(TempMapper.class); job.setReducerClass(TempReducer.class);
//设置最后输出结果的Key和Value的类型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); //执行job,直到完成 job.waitForCompletion(true); System.out.println("Finished"); } } |
将程序发布为jar包,并上传到hadoop平台运行。