对Mapreduce代码进行单元测试

　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：

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);

}

　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)。

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));

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();

// }

}

　　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

posted on 2014-11-26 14:19 顺其自然EVO 阅读(305) 评论(0) 编辑收藏所属分类: 测试学习专栏

新用户注册刷新评论列表


只有注册用户登录后才能发表评论。




网站导航: 博客园 IT新闻 Chat2DB C++博客博问管理
相关文章: 如何进行Web服务的性能测试？利用drozer进行Android渗透测试 Appium Android Bootstrap源码分析之命令解析执行 Fliptest—iOS 的应用A/B测试框架 iOS功能测试工具 Frank iOS单元测试框架Kiwi for iOS Appium Android Bootstrap之控件AndroidElement 移动应用测试框架—Calabash Android 简介 Appium Server源码分析之作为Bootstrap客户端移植MonkeyRunner的图片对比功能实现-Appium篇

qileilove

对Mapreduce代码进行单元测试

导航

统计

常用链接

留言簿(55)

随笔分类

随笔档案

文章分类

文章档案

搜索

最新评论

阅读排行榜

评论排行榜