命令行实现hadoop上传JAR文件，运行《hadoop权威指南第二版》测试最大气温的应用

本人的配置环境如下：

Ubuntu13.10

hadoop1.1.2

jdk8

1.按照《hadoop权威指南第二版》中查找最高气温，程序如下：

package com.sun.hadoop.mapreduce;

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.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

public class NewMaxTemperature {

static class NewMaxTemperatureMapper 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 {
// TODO Auto-generated method stub
String line = value.toString();
String year = line.substring(15, 19);
int airTemperature;
if (line.charAt(87) == '+') {
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));
}
}
}

static class NewMaxTemperatureReduce extends
Reducer<Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterable<IntWritable> values,
Context content) throws IOException, InterruptedException {
// TODO Auto-generated method stub
int maxValue = Integer.MIN_VALUE;
for (IntWritable value : values) {
maxValue = Math.max(maxValue, value.get());
}
content.write(key, new IntWritable(maxValue));
}
}

public static void main(String[] args) throws Exception {
// TODO Auto-generated method stub
if (args.length != 2) {
System.out
.println("Usage:MaxTemperature <input path> <output path>");
System.exit(-1);
}
Job job = new Job();
job.setJarByClass(NewMaxTemperature.class);

FileInputFormat.addInputPath(job,
new org.apache.hadoop.fs.Path(args[0]));
org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.setOutputPath(
job, new org.apache.hadoop.fs.Path(args[1]));

job.setMapperClass(NewMaxTemperatureMapper.class);
job.setReducerClass(NewMaxTemperatureReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

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

}

2.生成JAR文件，直接用Eclipse生成

右键JAVA工程-->Export-->java-->jar file

3.上传到hadoop安装目录下

我的路径为:/usr/local/hadoop

4.把需要测试的数据添加到hdfs下(可以从网上下载：https://github.com/tomwhite/hadoop-book)

在hadoop目录下: bin/hadoop dfs -put /home/test/sample.txt /input/sample.txt

5.运行JAR文件

在hadoop目录下：bin/hadoop jar newtemperature.jar com.sun.hadoop.mapreduce.NewMaxTemperature /input/sample.txt /output/

其中：newtemperature.jar为生成的jar包名，com.sun.hadoop.mapreduce为包名，NewMaxTemperature为类名，如果没有用包，直接为 类名，不用加包名。

如果出现以下结果，表明运行成功：

14/06/24 19:28:45 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
14/06/24 19:28:45 INFO input.FileInputFormat: Total input paths to process : 1
14/06/24 19:28:45 INFO util.NativeCodeLoader: Loaded the native-hadoop library
14/06/24 19:28:45 WARN snappy.LoadSnappy: Snappy native library not loaded
14/06/24 19:28:46 INFO mapred.JobClient: Running job: job_201406241439_0006
14/06/24 19:28:47 INFO mapred.JobClient:  map 0% reduce 0%
14/06/24 19:28:52 INFO mapred.JobClient:  map 100% reduce 0%
14/06/24 19:28:59 INFO mapred.JobClient:  map 100% reduce 33%
14/06/24 19:29:01 INFO mapred.JobClient:  map 100% reduce 100%
14/06/24 19:29:01 INFO mapred.JobClient: Job complete: job_201406241439_0006
14/06/24 19:29:01 INFO mapred.JobClient: Counters: 29
14/06/24 19:29:01 INFO mapred.JobClient:   Map-Reduce Framework
14/06/24 19:29:01 INFO mapred.JobClient:     Spilled Records=16
14/06/24 19:29:01 INFO mapred.JobClient:     Map output materialized bytes=94
14/06/24 19:29:01 INFO mapred.JobClient:     Reduce input records=8
14/06/24 19:29:01 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=3776598016
14/06/24 19:29:01 INFO mapred.JobClient:     Map input records=8
14/06/24 19:29:01 INFO mapred.JobClient:     SPLIT_RAW_BYTES=97
14/06/24 19:29:01 INFO mapred.JobClient:     Map output bytes=72
14/06/24 19:29:01 INFO mapred.JobClient:     Reduce shuffle bytes=94
14/06/24 19:29:01 INFO mapred.JobClient:     Physical memory (bytes) snapshot=270008320
14/06/24 19:29:01 INFO mapred.JobClient:     Reduce input groups=1
14/06/24 19:29:01 INFO mapred.JobClient:     Combine output records=0
14/06/24 19:29:01 INFO mapred.JobClient:     Reduce output records=1
14/06/24 19:29:01 INFO mapred.JobClient:     Map output records=8
14/06/24 19:29:01 INFO mapred.JobClient:     Combine input records=0
14/06/24 19:29:01 INFO mapred.JobClient:     CPU time spent (ms)=2070
14/06/24 19:29:01 INFO mapred.JobClient:     Total committed heap usage (bytes)=240123904
14/06/24 19:29:01 INFO mapred.JobClient:   File Input Format Counters
14/06/24 19:29:01 INFO mapred.JobClient:     Bytes Read=1080
14/06/24 19:29:01 INFO mapred.JobClient:   FileSystemCounters
14/06/24 19:29:01 INFO mapred.JobClient:     HDFS_BYTES_READ=1177
14/06/24 19:29:01 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=104412
14/06/24 19:29:01 INFO mapred.JobClient:     FILE_BYTES_READ=94
14/06/24 19:29:01 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=9
14/06/24 19:29:01 INFO mapred.JobClient:   Job Counters
14/06/24 19:29:01 INFO mapred.JobClient:     Launched map tasks=1
14/06/24 19:29:01 INFO mapred.JobClient:     Launched reduce tasks=1
14/06/24 19:29:01 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=8655
14/06/24 19:29:01 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
14/06/24 19:29:01 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=4923
14/06/24 19:29:01 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
14/06/24 19:29:01 INFO mapred.JobClient:     Data-local map tasks=1
14/06/24 19:29:01 INFO mapred.JobClient:   File Output Format Counters
14/06/24 19:29:01 INFO mapred.JobClient:     Bytes Written=9