第一个Hadoop程序——Hello Hadoop

于 2013-10-16 09:38:05 发布
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分类专栏： 大数据 文章标签： hadoop
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
本文链接：https://blog.youkuaiyun.com/panjunbiao/article/details/12773163
这篇博客介绍了如何开始使用Hadoop，包括下载Hadoop程序包，如何在CentOS服务器上安装，以及在Linux或Mac OS X环境中解压和验证hadoop二进制执行文件。作者还提到了MaxTemperatureMapper.java作为示例程序。

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本人原创，转载请注明出处：http://blog.youkuaiyun.com/panjunbiao/article/details/12773163


下载Hadoop程序包，下载地址：http://hadoop.apache.org/releases.html#Download

如果是在CentOS服务器安装，则执行：
yum install hadoop-1.2.1-1.x86_64.rpm

如果是在Linux或者Mac OS X开发环境下，可以下载bin或者源码包，然后解压缩即可。

验证hadoop二进制执行文件（假设放在~/Developments/toolkits/hadoop-1.2.1文件夹中）：
cd ~/Developments/toolkits/hadoop-1.2.1

执行hadoop程序：
bin/hadoop

Usage: hadoop [--config confdir] COMMAND
where COMMAND is one of:
  namenode -format     format the DFS filesystem
  secondarynamenode    run the DFS secondary namenode
  namenode             run the DFS namenode
  datanode             run a DFS datanode...
出现hadoop命令用法帮助，表示二进制文件可执行。

创建Hello Hadoop的Java项目：
按照《Hadoop权威指南（Hadoop: The Definitive Guide）》的例子，创建3个程序文件。
MaxTemperature.java 

/**
 * Created with IntelliJ IDEA.
 * User: james
 * Date: 8/27/13
 * Time: 11:33 AM
 * To change this template use File | Settings | File Templates.
 */
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);
    }
}

 MaxTemperatureMapper.java

/**
 * Created with IntelliJ IDEA.
 * User: james
 * Date: 8/27/13
 * Time: 11:28 AM
 * To change this template use File | Settings | File Templates.
 */
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));
        }
    }
}

MaxTemperatureReducer.java 

/**
 * Created with IntelliJ IDEA.
 * User: james
 * Date: 8/27/13
 * Time: 11:32 AM
 * To change this template use File | Settings | File Templates.
 */
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));
    }
}

需要将hadoop-core-1.2.1.jar文件添加到项目的库中，这个jar文件在解压缩的文件夹中 

编译之，假设项目编译到文件夹~/Developments/hello-hadoop/out/production/hello-hadoop/中，将这个文件夹位置输出到HADOOP_CLASSPATH：
export HADOOP_CLASSPATH=~/Developments/hello-hadoop/out/production/hello-hadoop/

另外还要注意定义JAVA_HOME，以Mac OS X为例：
export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk1.7.0_21.jdk/Contents/Home/

下载天气数据（ http://hadoopbook.com/code.html ），上面有1901年和1902年的天气例子数据。

进入hadoop文件夹：
cd ~/Developments/toolkits/hadoop-1.2.1

执行例子程序（这个MaxTemperature是hadoop程序通过HADOOP_CLASSPATH查找到的）：

bin/hadoop MaxTemperature 1901 output 

2013-10-15 17:56:40.412 java[5522:1703] Unable to load realm info from SCDynamicStore
13/10/15 17:56:41 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
13/10/15 17:56:41 WARN mapred.JobClient: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
13/10/15 17:56:41 WARN mapred.JobClient: No job jar file set.  User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
13/10/15 17:56:41 INFO input.FileInputFormat: Total input paths to process : 1
13/10/15 17:56:41 WARN snappy.LoadSnappy: Snappy native library not loaded
13/10/15 17:56:42 INFO mapred.JobClient: Running job: job_local1783370164_0001
13/10/15 17:56:42 INFO mapred.LocalJobRunner: Waiting for map tasks
13/10/15 17:56:42 INFO mapred.LocalJobRunner: Starting task: attempt_local1783370164_0001_m_000000_0
13/10/15 17:56:42 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/10/15 17:56:42 INFO mapred.MapTask: Processing split: file:/Users/james/Developments/hello-hadoop/out/production/hello-hadoop/1901:0+888190
13/10/15 17:56:42 INFO mapred.MapTask: io.sort.mb = 100
13/10/15 17:56:42 INFO mapred.MapTask: data buffer = 79691776/99614720
13/10/15 17:56:42 INFO mapred.MapTask: record buffer = 262144/327680
13/10/15 17:56:42 INFO mapred.MapTask: Starting flush of map output
13/10/15 17:56:42 INFO mapred.MapTask: Finished spill 0
13/10/15 17:56:42 INFO mapred.Task: Task:attempt_local1783370164_0001_m_000000_0 is done. And is in the process of commiting
13/10/15 17:56:42 INFO mapred.LocalJobRunner:
13/10/15 17:56:42 INFO mapred.Task: Task 'attempt_local1783370164_0001_m_000000_0' done.
13/10/15 17:56:42 INFO mapred.LocalJobRunner: Finishing task: attempt_local1783370164_0001_m_000000_0
13/10/15 17:56:42 INFO mapred.LocalJobRunner: Map task executor complete.
13/10/15 17:56:42 INFO mapred.Task:  Using ResourceCalculatorPlugin : null
13/10/15 17:56:42 INFO mapred.LocalJobRunner:
13/10/15 17:56:42 INFO mapred.Merger: Merging 1 sorted segments
13/10/15 17:56:42 INFO mapred.Merger: Down to the last merge-pass, with 1 segments left of total size: 72206 bytes
13/10/15 17:56:42 INFO mapred.LocalJobRunner:
13/10/15 17:56:42 INFO mapred.Task: Task:attempt_local1783370164_0001_r_000000_0 is done. And is in the process of commiting
13/10/15 17:56:42 INFO mapred.LocalJobRunner:
13/10/15 17:56:42 INFO mapred.Task: Task attempt_local1783370164_0001_r_000000_0 is allowed to commit now
13/10/15 17:56:42 INFO output.FileOutputCommitter: Saved output of task 'attempt_local1783370164_0001_r_000000_0' to output
13/10/15 17:56:42 INFO mapred.LocalJobRunner: reduce > reduce
13/10/15 17:56:42 INFO mapred.Task: Task 'attempt_local1783370164_0001_r_000000_0' done.
13/10/15 17:56:43 INFO mapred.JobClient:  map 100% reduce 100%
13/10/15 17:56:43 INFO mapred.JobClient: Job complete: job_local1783370164_0001
13/10/15 17:56:43 INFO mapred.JobClient: Counters: 17
13/10/15 17:56:43 INFO mapred.JobClient:   File Output Format Counters
13/10/15 17:56:43 INFO mapred.JobClient:     Bytes Written=21
13/10/15 17:56:43 INFO mapred.JobClient:   File Input Format Counters
13/10/15 17:56:43 INFO mapred.JobClient:     Bytes Read=888190
13/10/15 17:56:43 INFO mapred.JobClient:   FileSystemCounters
13/10/15 17:56:43 INFO mapred.JobClient:     FILE_BYTES_READ=1848986
13/10/15 17:56:43 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=245951
13/10/15 17:56:43 INFO mapred.JobClient:   Map-Reduce Framework
13/10/15 17:56:43 INFO mapred.JobClient:     Reduce input groups=1
13/10/15 17:56:43 INFO mapred.JobClient:     Map output materialized bytes=72210
13/10/15 17:56:43 INFO mapred.JobClient:     Combine output records=0
13/10/15 17:56:43 INFO mapred.JobClient:     Map input records=6565
13/10/15 17:56:43 INFO mapred.JobClient:     Reduce shuffle bytes=0
13/10/15 17:56:43 INFO mapred.JobClient:     Reduce output records=1
13/10/15 17:56:43 INFO mapred.JobClient:     Spilled Records=13128
13/10/15 17:56:43 INFO mapred.JobClient:     Map output bytes=59076
13/10/15 17:56:43 INFO mapred.JobClient:     Total committed heap usage (bytes)=331350016
13/10/15 17:56:43 INFO mapred.JobClient:     SPLIT_RAW_BYTES=141
13/10/15 17:56:43 INFO mapred.JobClient:     Map output records=6564
13/10/15 17:56:43 INFO mapred.JobClient:     Combine input records=0
13/10/15 17:56:43 INFO mapred.JobClient:     Reduce input records=6564

查看输出结果
ls output/ 

_SUCCESS     part-r-00000


vi output/part-r-00000 

1901    317

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