Hadoop2.4.1入门实例：MaxTemperature

注意：以下内容在2.x版本与1.x版本同样适用，已在2.4.1与1.2.0进行测试。

一、前期准备

1、创建伪分布Hadoop环境，请参考官方文档。或者http://blog.csdn.net/jediael_lu/article/details/38637277

2、准备数据文件如下sample.txt：

123456798676231190101234567986762311901012345679867623119010123456798676231190101234561+00121534567890356

123456798676231190101234567986762311901012345679867623119010123456798676231190101234562+01122934567890456

123456798676231190201234567986762311901012345679867623119010123456798676231190101234562+02120234567893456

123456798676231190401234567986762311901012345679867623119010123456798676231190101234561+00321234567803456

123456798676231190101234567986762311902012345679867623119010123456798676231190101234561+00429234567903456

123456798676231190501234567986762311902012345679867623119010123456798676231190101234561+01021134568903456

123456798676231190201234567986762311902012345679867623119010123456798676231190101234561+01124234578903456

123456798676231190301234567986762311905012345679867623119010123456798676231190101234561+04121234678903456

123456798676231190301234567986762311905012345679867623119010123456798676231190101234561+00821235678903456

二、编写代码

1、创建Map

package org.jediael.hadoopDemo.maxtemperature;

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 MaxTemperatureMapper 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));
		}
	}
}

2、创建Reduce

package org.jediael.hadoopDemo.maxtemperature;

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

public class MaxTemperatureReducer 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));
	}
}

3、创建main方法

package org.jediael.hadoopDemo.maxtemperature;

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

4、导出成MaxTemp.jar，并上传至运行程序的服务器。

三、运行程序

1、创建input目录并将sample.txt复制到input目录

hadoop fs -put sample.txt /

2、运行程序

export HADOOP_CLASSPATH=MaxTemp.jar

hadoop org.jediael.hadoopDemo.maxtemperature.MaxTemperature /sample.txt output10

注意输出目录不能已经存在，否则会创建失败。

3、查看结果

（1）查看结果

[jediael@jediael44 code]$  hadoop fs -cat output10/*

14/07/09 14:51:35 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

1901    42

1902    212

1903    412

1904    32

1905    102

（2）运行时输出

[jediael@jediael44 code]$  hadoop org.jediael.hadoopDemo.maxtemperature.MaxTemperature /sample.txt output10

14/07/09 14:50:40 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

14/07/09 14:50:41 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032

14/07/09 14:50:42 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.

14/07/09 14:50:43 INFO input.FileInputFormat: Total input paths to process : 1

14/07/09 14:50:43 INFO mapreduce.JobSubmitter: number of splits:1

14/07/09 14:50:44 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1404888618764_0001

14/07/09 14:50:44 INFO impl.YarnClientImpl: Submitted application application_1404888618764_0001

14/07/09 14:50:44 INFO mapreduce.Job: The url to track the job: http://jediael44:8088/proxy/application_1404888618764_0001/

14/07/09 14:50:44 INFO mapreduce.Job: Running job: job_1404888618764_0001

14/07/09 14:50:57 INFO mapreduce.Job: Job job_1404888618764_0001 running in uber mode : false

14/07/09 14:50:57 INFO mapreduce.Job:  map 0% reduce 0%

14/07/09 14:51:05 INFO mapreduce.Job:  map 100% reduce 0%

14/07/09 14:51:15 INFO mapreduce.Job:  map 100% reduce 100%

14/07/09 14:51:15 INFO mapreduce.Job: Job job_1404888618764_0001 completed successfully

14/07/09 14:51:16 INFO mapreduce.Job: Counters: 49

        File System Counters

                FILE: Number of bytes read=94

                FILE: Number of bytes written=185387

                FILE: Number of read operations=0

                FILE: Number of large read operations=0

                FILE: Number of write operations=0

                HDFS: Number of bytes read=1051

                HDFS: Number of bytes written=43

                HDFS: Number of read operations=6

                HDFS: Number of large read operations=0

                HDFS: Number of write operations=2

        Job Counters 

                Launched map tasks=1

                Launched reduce tasks=1

                Data-local map tasks=1

                Total time spent by all maps in occupied slots (ms)=5812

                Total time spent by all reduces in occupied slots (ms)=7023

                Total time spent by all map tasks (ms)=5812

                Total time spent by all reduce tasks (ms)=7023

                Total vcore-seconds taken by all map tasks=5812

                Total vcore-seconds taken by all reduce tasks=7023

                Total megabyte-seconds taken by all map tasks=5951488

                Total megabyte-seconds taken by all reduce tasks=7191552

        Map-Reduce Framework

                Map input records=9

                Map output records=8

                Map output bytes=72

                Map output materialized bytes=94

                Input split bytes=97

                Combine input records=0

                Combine output records=0

                Reduce input groups=5

                Reduce shuffle bytes=94

                Reduce input records=8

                Reduce output records=5

                Spilled Records=16

                Shuffled Maps =1

                Failed Shuffles=0

                Merged Map outputs=1

                GC time elapsed (ms)=154

                CPU time spent (ms)=1450

                Physical memory (bytes) snapshot=303112192

                Virtual memory (bytes) snapshot=1685733376

                Total committed heap usage (bytes)=136515584

        Shuffle Errors

                BAD_ID=0

                CONNECTION=0

                IO_ERROR=0

                WRONG_LENGTH=0

                WRONG_MAP=0

                WRONG_REDUCE=0

        File Input Format Counters 

                Bytes Read=954

        File Output Format Counters 

                Bytes Written=43