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