一般的mapreduce的wordcount程序如下：

public class WcMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

    @Override
    protected void map(LongWritable key, Text value, Context ctx) throws IOException, InterruptedException {

        String[] words = value.toString().split(" ");
        for (int i = 0; i < words.length; i++) {
            ctx.write(new Text(words[i]), new LongWritable(1L));
        }
    }
}

public class WcReduer extends Reducer<Text, LongWritable, Text, LongWritable> {

    LongWritable count = new LongWritable();
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context ctx) throws IOException, InterruptedException {
        Iterator<LongWritable> itr = values.iterator();
        long sum = 0L;
        while (itr.hasNext()) {
            sum = sum + itr.next().get();
        }
        count.set(sum);
        ctx.write(key, count);
    }
}

驅動作業代碼：

public class JobClient {

    public static void main(String[] args) throws Exception {

        Job job = Job.getInstance();
        job.setJarByClass(JobClient.class);
        job.setMapperClass(WcMapper.class);
        job.setReducerClass(WcReduer.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        job.setJobName("wordcount");
        FileInputFormat.addInputPath(job, new Path("/daxin/hadoop-mapreduce/words"));
        FileOutputFormat.setOutputPath(job, new Path("/daxin/hadoop-mapreduce/wordcount-result"));
        job.waitForCompletion(true);
    }
}

提交作業會報錯：

Error: java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.LongWritable, received org.apache.hadoop.io.Text
	at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1072)
	at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:715)
	at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
	at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112)
	at com.daxin.blog.WcMapper.map(WcMapper.java:20)
	at com.daxin.blog.WcMapper.map(WcMapper.java:13)
	at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)
	at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)
	at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
	at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
	at java.security.AccessController.doPrivileged(Native Method)
	at javax.security.auth.Subject.doAs(Subject.java:422)
	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
	at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)　

通過異常信息我們可以定位錯誤在源碼中的位置：org.apache.hadoop.mapred.MapTask.MapOutputBuffer#collect，具體關鍵源碼如下:

 public synchronized void collect(K key, V value, final int partition
                                     ) throws IOException {
      reporter.progress();
      if (key.getClass() != keyClass) {
        throw new IOException("Type mismatch in key from map: expected "
                              + keyClass.getName() + ", received "
                              + key.getClass().getName());
      }
      if (value.getClass() != valClass) {
        throw new IOException("Type mismatch in value from map: expected "
                              + valClass.getName() + ", received "
                              + value.getClass().getName());
      }
   .....
}

此處key.getClass可以確定是Text，需要確定keyClass是什麽類型。下面就將確定一下keyClass類型，可以發現keyClass賦值源碼：

 keyClass = (Class<K>)job.getMapOutputKeyClass();

　getMapOutputKeyClass源碼：

  public Class<?> getMapOutputKeyClass() {
    Class<?> retv = getClass(JobContext.MAP_OUTPUT_KEY_CLASS, null, Object.class);
    if (retv == null) {
      retv = getOutputKeyClass();
    }
    return retv;
  }　

其中MAP_OUTPUT_KEY_CLASS則是獲取map輸出的key的類型，由於我們驅動代碼沒有設置因此此處得到的值為默認值null，接下在調用getOutputKeyClass方法：

  public Class<?> getOutputKeyClass() {
    return getClass(JobContext.OUTPUT_KEY_CLASS,
                    LongWritable.class, Object.class);
  }

 public static final String OUTPUT_KEY_CLASS = "mapreduce.job.output.key.class";

通過獲取OUTPUT_KEY_CLASS的類型，OUTPUT_KEY_CLASS表示的是作業的key的輸出類型，但是由於我們沒有設置因此獲取默認值為LongWritable。但是實際上我們的MapTask輸出的key為Text，因而報如上類型不匹配錯誤。同理Map的value也有類似問題。為了解決此問題就需要顯式的設置MapTask的Key、Value輸出類型。代碼如下：

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

mapreduce設置setMapOutputKeyClass與setMapOutputValueClass原因