映射中的键类型不匹配:应为org.apache.hadoop.io.Text，收到的是org.apache.haoop.

Question

我正在尝试用java运行一个map/reducer。以下是我的文件

WordCount.java

package counter;

public class WordCount extends Configured implements Tool {
public int run(String[] arg0) throws Exception {
    Configuration conf = new Configuration();
    Job job = new Job(conf, "wordcount");
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    job.setMapperClass(WordCountMapper.class);
    job.setReducerClass(WordCountReducer.class);
    job.setInputFormatClass(TextInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);
    FileInputFormat.addInputPath(job, new Path("counterinput"));
    // Erase previous run output (if any)
    FileSystem.get(conf).delete(new Path("counteroutput"), true);
    FileOutputFormat.setOutputPath(job, new Path("counteroutput"));
    job.waitForCompletion(true);
    return 0;
}   
public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new Configuration(), new WordCount(), args);
    System.exit(res);
    }
}

WordCountMapper.java

public class WordCountMapper extends
Mapper<LongWritable, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();
    public void map(LongWritable key, Text value, OutputCollector<Text,IntWritable> output, Reporter reporter)
    throws IOException, InterruptedException {
        System.out.println("hi");
    String line = value.toString();
    StringTokenizer tokenizer = new StringTokenizer(line);
    while (tokenizer.hasMoreTokens()) {
        word.set(tokenizer.nextToken());
        output.collect(word, one);
        }
    }
}

WordCountReducer.java

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterator<IntWritable> values,
        OutputCollector<Text,IntWritable> output, Reporter reporter) throws IOException, InterruptedException {
        System.out.println("hello");
        int sum = 0;
        while (values.hasNext()) {
            sum += values.next().get();
        }
        output.collect(key, new IntWritable(sum));
    }
}

我得到以下错误

13/06/23 23:13:25 INFO jvm.JvmMetrics: Initializing JVM Metrics with  
processName=JobTracker, sessionId=
13/06/23 23:13:25 WARN mapred.JobClient: Use GenericOptionsParser for parsing the 
arguments. Applications should implement Tool for the same.
13/06/23 23:13:26 INFO input.FileInputFormat: Total input paths to process : 1
13/06/23 23:13:26 INFO mapred.JobClient: Running job: job_local_0001
13/06/23 23:13:26 INFO input.FileInputFormat: Total input paths to process : 1
13/06/23 23:13:26 INFO mapred.MapTask: io.sort.mb = 100
13/06/23 23:13:26 INFO mapred.MapTask: data buffer = 79691776/99614720
13/06/23 23:13:26 INFO mapred.MapTask: record buffer = 262144/327680
13/06/23 23:13:26 WARN mapred.LocalJobRunner: job_local_0001
java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.Text, 
recieved org.apache.hadoop.io.LongWritable
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:845)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:541)
at org.
apache.hadoop.mapreduce.TaskInputOutputContext.write(TaskInputOutputContext.java:80)
at org.apache.hadoop.mapreduce.Mapper.map(Mapper.java:124)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:144)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:621)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:305)
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:177)
13/06/23 23:13:27 INFO mapred.JobClient:  map 0% reduce 0%
13/06/23 23:13:27 INFO mapred.JobClient: Job complete: job_local_0001
13/06/23 23:13:27 INFO mapred.JobClient: Counters: 0

我认为它找不到Mapper和reducer类。我已经在主课堂上写了代码，它正在获取默认的Mapper和reducer类。

Answer 1

在代码中添加这两行：

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

您使用的是默认情况下发出LongWritable键和Text值的TextOutputFormat，但您发出的是Text作为键和IntWritable作为值。你需要把这件事告诉家庭工作者。

HTH

Answer 2

这可能不是你的问题，但我曾经遇到过这个愚蠢的问题。确保你没有混合新旧库，即mapred和mapreduce。在地图上标注@Overide并减少方法。如果您看到错误，说明您没有正确地重写这些方法。

Answer 3

由于我的代码中设置了不正确的Mapper class（打字错误：），我得到了类似的异常堆栈跟踪

job.setMapperClass(Mapper.class)  // Set to org.apache.hadoop.mapreduce.Mapper due to type

注意，我错误地使用了mapreduce包中的Mapper类，我将其更改为我的自定义Mapper类：

job.setMapperClass(LogProcMapperClass.class) // LogProcMapperClass is my custom mapper.

在我更正映射程序类之后，异常得到了解决。

Answer 4

从代码中删除它解决了问题

super.map(key, value, context);