搭建好了Eclipse的开发环境，接下来就是Helloword，hadoop 的HelloWord是一个Wordcount的例子，就是统计单词在不同的文档里出现的次数。
我这边准备了三个文档：（存入hdfs 的文件系统中)
[root@bigdata2 hadoop-1.0.1]# ./bin/hadoop fs -cat /user/root/in/helloword.txt Warning: $HADOOP_HOME is deprecated. Hello,Word! [root@bigdata2 hadoop-1.0.1]# ./bin/hadoop fs -cat /user/root/in/input1.txt Warning: $HADOOP_HOME is deprecated. hello,word ! what's your name ? haow are you ? are you ok ? are you ok ? [root@bigdata2 hadoop-1.0.1]# ./bin/hadoop fs -cat /user/root/in/input2.txt Warning: $HADOOP_HOME is deprecated. hello,mobile. hello,word ! what's your name ? haow are you ? are you ok ? are you ok ?
WordCount.java
package wordcount;
import java.io.IOException;
import java.text.SimpleDateFormat;
import java.util.ArrayList;
import java.util.Date;
import java.util.Iterator;
import java.util.List;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
public class WordCount {
public static class MapClass extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
String line = value.toString();
StringTokenizer itr = new StringTokenizer(line);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
output.collect(word, one);
}
}
}
/**
* A reducer class that just emits the sum of the input values.
*/
public static class Reduce extends MapReduceBase implements
Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
}
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("wordcount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(MapClass.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
String outFileExt = "_" + new SimpleDateFormat("yyyyMMddHHmmss").format(new Date());
FileInputFormat.setInputPaths(conf,new Path("hdfs://192.168.1.2:9000/user/root/in/"));
FileOutputFormat.setOutputPath(conf, new Path("hdfs://192.168.1.2:9000/user/root/out/"+outFileExt));
JobClient.runJob(conf);
}
}
直接运行
结果 写道
! 2
? 8
Hello,Word! 1
are 6
haow 2
hello,mobile. 1
hello,word 2
name 2
ok 4
what's 2
you 6
your 2
? 8
Hello,Word! 1
are 6
haow 2
hello,mobile. 1
hello,word 2
name 2
ok 4
what's 2
you 6
your 2
代码解释：
JobConf conf = new JobConf(WordCount.class);
conf.setJobName("wordcount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(MapClass.class);
conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
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JobConf 负责读取配置文件（主要包括：core-site.xml,hdfs-site.xml,mapred-site.xml等）
conf.setJobName("wordcount");主要用来设置JOB名称，便于页面监控
InputFormat 主要负责调用getRecodeReader()方法生成RecordReader对象，RecordReader对象则调用CreatKey和CreatValue方法生产可以供Map处理的<Key,Value>键值对
InputFormat方法有很多重写版本，支持不同的数据源，例如FileInputFormat，DbInputFormat等
OutputFormat这负责输出的格式应为Key和value 是Object类型，那么内部会转为String来输出。
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter
Map函数产生<Key，ValueList>类型的键值对，交由Reduce函数进行处理
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
output.collect(key, new IntWritable(sum));
Reduce函数则负责将Value的值做Count，计算出次数，然后将结果输出。