hadoop2.2.0 编译运行wordcount，因为hadoop2.2.0不支持eclipse的插件，所以运行wordcount，需要手动编译并将wordcount打包成jar包来运行，下面记录一下编译运行的过程，希望能给大家有些帮助。
1、首先介绍下hadoop的版本问题，当前Hadoop版本比较混乱，让很多用户不知所措。实际上，当前Hadoop只有两个版本：Hadoop 1.0和Hadoop 2.0，其中，Hadoop 1.0由一个分布式文件系统HDFS和一个离线计算框架MapReduce组成，而Hadoop 2.0则包含一个支持NameNode横向扩展的HDFS，一个资源管理系统YARN和一个运行在YARN上的离线计算框架MapReduce。相比于Hadoop 1.0，Hadoop 2.0功能更加强大，且具有更好的扩展性、性能，并支持多种计算框架。由于hadoop 2.0不用于hadoop 1.0的API，所以，从hadoop 1.0升级到hadoop 2.0需要重写mapreduce程序，关于从Hadoop 1.0升级到2.0（1）参考链接： http://dongxicheng.org/mapreduce-nextgen/hadoop-upgrade-to-version-2/ hadoop 2.2.0新功能介绍 参考链接http://docs.aws.amazon.com/zh_cn/ElasticMapReduce/latest/DeveloperGuide/emr-hadoop-2.2.0-features.html
2、然后就是准备程序WordCount.java在/root/test/下：
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
// value已经是文件内容的一行
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
3、新建bin文件夹在/root/test/下，将WordCount编译成class文件，命令如下：
[email protected]:/home/ubuntu/software/cdh5-hadoop/share/hadoop# javac -classpath common/hadoop-common-2.2.0-cdh5.0.0-beta-2.jar:common/lib/commons-cli-1.2.jar:common/lib/hadoop-annotations-2.2.0-cdh5.0.0-beta-2.jar:mapreduce/hadoop-mapreduce-client-core-2.2.0-cdh5.0.0-beta-2.jar -d /root/test/bin/ /root/test/WordCount.java
4、将class文件打包成jar包，命令如下：
[email protected]:~/test# jar -cvf WordCount.jar com/du/simple/*.class
5、运行jar文件
[email protected]:~/test# hadoop jar WordCount.jar com/du/simple/WordCount /user/root/input /user/root/output
6、查看运行结果
[email protected]:~/hadoop/WordCount# hadoop fs -cat output/part-r-00000
好的，到此打完收功！