Hadoop之MapReduce单元测试-Mysql Tutorial-php.cn

Hadoop之MapReduce单元测试

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Release： 2016-06-07 16:31:12

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通常情况下，我们需要用小数据集来单元测试我们写好的map函数和reduce函数。而一般我们可以使用Mockito框架来模拟OutputCollector对象（Hadoop版本号小于0.20.0)和Context对象(大于等于0.20.0)。下面是一个简单的WordCount例子：(使用的是新API）在开始之

通常情况下，我们需要用小数据集来单元测试我们写好的map函数和reduce函数。而一般我们可以使用Mockito框架来模拟OutputCollector对象（Hadoop版本号小于0.20.0)和Context对象(大于等于0.20.0)。

下面是一个简单的WordCount例子：(使用的是新API）

在开始之前，需要导入以下包：

1.Hadoop安装目录下和lib目录下的所有jar包。

2.JUnit4

3.Mockito

map函数：

public class WordCountMapper extends Mapper {
	private static final IntWritable one = new IntWritable(1);
	private Text word = new Text();
	@Override
	protected void map(LongWritable key, Text value,Context context)
			throws IOException, InterruptedException {
		String line = value.toString();		// 该行的内容
		String[] words = line.split(";");	// 解析该行的单词
		for(String w : words) {
			word.set(w);
			context.write(word,one);
		}
	}
}

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?reduce函数：

public class WordCountReducer extends Reducer {
	@Override
	protected void reduce(Text key, Iterable values,Context context)
			throws IOException, InterruptedException {
		int sum = 0;
		Iterator iterator = values.iterator();		// key相同的值集合
		while(iterator.hasNext()) {
			int one = iterator.next().get();
			sum += one;
		}
		context.write(key, new IntWritable(sum));
	}
}

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?测试代码类：

public class WordCountMapperReducerTest {
	@Test
	public void processValidRecord() throws IOException, InterruptedException {
		WordCountMapper mapper = new WordCountMapper();
		Text value = new Text("hello");
		org.apache.hadoop.mapreduce.Mapper.Context context = mock(Context.class);
		mapper.map(null, value, context);
		verify(context).write(new Text("hello"), new IntWritable(1));
	}
	@Test
	public void processResult() throws IOException, InterruptedException {
		WordCountReducer reducer = new WordCountReducer();
		Text key = new Text("hello");
		// {"hello",[1,1,2]}
		Iterable values = Arrays.asList(new IntWritable(1),new IntWritable(1),new IntWritable(2));
		org.apache.hadoop.mapreduce.Reducer.Context context = mock(org.apache.hadoop.mapreduce.Reducer.Context.class);
		reducer.reduce(key, values, context);
		verify(context).write(key, new IntWritable(4));		// {"hello",4}
	}
}

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具体就是给map函数传入一行数据-"hello"

map函数对数据进行处理，输出{"hello",0}

reduce函数接受map函数的输出数据，对相同key的值求和，并输出。

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