hadoop自带一个wordcount的示例代码，用于计算单词个数。我将其单独移出来，
测试成功。源码如下：
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package org.apache.hadoop.examples;
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{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word = new Text(itr.nextToken()); //to unitest,should be new Text word.set(itr.nextToken())
context.write(word, new IntWritable(1));
}
}
}
public static class IntSumReducer
extends Reducer {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable 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 ");
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);
}
}
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现在我想对其进行单元测试。一种方式，是job执行完了后，读取输出目录中的文件，确认计数是否正确。但这样的情况如果失败，也不知道是哪里失败。我们需要对map和reduce单独进行测试。
tomwhite的书《hadoop权威指南》有提到如何用Mockito进行单元测试，我们依照原书对温度的单元测试来对wordcount进行单元测试。(原书第二版的示例已经过时，可以参考英文版第三版或我的程序)。
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package org.apache.hadoop.examples;
/* author zhouhh
* date:2012.8.7
*/
import static org.mockito.Mockito.*;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;
import org.apache.hadoop.io.*;
import org.junit.*;
public class WordCountTest {
@Test
public void testWordCountMap() throws IOException, InterruptedException
{
WordCount w = new WordCount();
WordCount.TokenizerMapper mapper = new WordCount.TokenizerMapper();
Text value = new Text("a b c b a a");
@SuppressWarnings("unchecked")
WordCount.TokenizerMapper.Context context = mock(WordCount.TokenizerMapper.Context.class);
mapper.map(null, value, context);
verify(context,times(3)).write(new Text("a"), new IntWritable(1));
verify(context).write(new Text("c"), new IntWritable(1));
//verify(context).write(new Text("cc"), new IntWritable(1));
}
@Test
public void testWordCountReduce() throws IOException, InterruptedException
{
WordCount.IntSumReducer reducer = new WordCount.IntSumReducer();
WordCount.IntSumReducer.Context context = mock(WordCount.IntSumReducer.Context.class);
Text key = new Text("a");
List values = new ArrayList();
values.add(new IntWritable(1));
values.add(new IntWritable(1));
reducer.reduce(key, values, context);
verify(context).write(new Text("a"), new IntWritable(2));
}
public static void main(String[] args) {
//
try {
//
WordCountTest t = new WordCountTest();
//
//
//t.testWordCountMap();
//
t.testWordCountReduce();
//
} catch (IOException e) {
//
// TODO Auto-generated catch block
//
e.printStackTrace();
//
} catch (InterruptedException e) {
//
// TODO Auto-generated catch block
//
e.printStackTrace();
//
}
}
}
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verify(context)只检查一次的写，如果多次写，需用verify(contex,times(n))检查,否则会失败。
执行时在测试文件上点run as JUnit Test，会得到测试结果是否通过。
本示例程序在hadoop1.0.3环境中测试通过。Mockito也在hadoop的lib中自带，打包在mockito-all-1.8.5.jar
最新内容请见作者的GitHub页：http://qaseven.github.io/
