MapReduce UnitTest

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<LongWritable, Text, Text, IntWritable> {

    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);
        }
    }
}

 reduce函数：

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values,Context context)
            throws IOException, InterruptedException {

        int sum = 0;
        Iterator<IntWritable> 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));
    }

}

 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<IntWritable> 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}
    }
}

 

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。