java.lang.NoClassDefFoundError: орг/Apache/Обще/регистрация/LogFactory в Hadoop 1.2.1 алгоритм Kmean

Question

Я пытаюсь запустить пример из http://codingwiththomas.blogspot.kr/2011/05/k-means-clustering-with-mapreduce.html но имеет ошибку

log4j:WARN Error during default initialization 
java.lang.NoClassDefFoundError: org/apache/log4j/AppenderSkeleton 
    at java.lang.ClassLoader.findBootstrapClass(Native Method) 
    at java.lang.ClassLoader.findBootstrapClassOrNull(ClassLoader.java:1070) 
    at java.lang.ClassLoader.loadClass(ClassLoader.java:414) 
    at java.lang.ClassLoader.loadClass(ClassLoader.java:412) 
    at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308) 
    at java.lang.ClassLoader.loadClass(ClassLoader.java:358) 
    at java.lang.Class.forName0(Native Method) 
    at java.lang.Class.forName(Class.java:190) 
    at org.apache.log4j.helpers.Loader.loadClass(Loader.java:179) 
    at org.apache.log4j.helpers.OptionConverter.instantiateByClassName(OptionConverter.java:320) 
    at org.apache.log4j.helpers.OptionConverter.instantiateByKey(OptionConverter.java:121) 
    at org.apache.log4j.PropertyConfigurator.parseAppender(PropertyConfigurator.java:664) 
    at org.apache.log4j.PropertyConfigurator.parseCategory(PropertyConfigurator.java:647) 
    at org.apache.log4j.PropertyConfigurator.configureRootCategory(PropertyConfigurator.java:544) 
    at org.apache.log4j.PropertyConfigurator.doConfigure(PropertyConfigurator.java:440) 
    at org.apache.log4j.PropertyConfigurator.doConfigure(PropertyConfigurator.java:476) 
    at org.apache.log4j.helpers.OptionConverter.selectAndConfigure(OptionConverter.java:471) 
    at org.apache.log4j.LogManager.<clinit>(LogManager.java:125) 
    at org.apache.log4j.Logger.getLogger(Logger.java:105) 
    at org.apache.commons.logging.impl.Log4JLogger.getLogger(Log4JLogger.java:289) 
    at org.apache.commons.logging.impl.Log4JLogger.<init>(Log4JLogger.java:109) 
    at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method) 
    at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:57) 
    at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45) 
    at java.lang.reflect.Constructor.newInstance(Constructor.java:526) 
    at org.apache.commons.logging.impl.LogFactoryImpl.createLogFromClass(LogFactoryImpl.java:1116) 
    at org.apache.commons.logging.impl.LogFactoryImpl.discoverLogImplementation(LogFactoryImpl.java:914) 
    at org.apache.commons.logging.impl.LogFactoryImpl.newInstance(LogFactoryImpl.java:604) 
    at org.apache.commons.logging.impl.LogFactoryImpl.getInstance(LogFactoryImpl.java:336) 
    at org.apache.commons.logging.impl.LogFactoryImpl.getInstance(LogFactoryImpl.java:310) 
    at org.apache.commons.logging.LogFactory.getLog(LogFactory.java:685) 
    at com.clustering.mapreduce.KMeansClusteringJob.<clinit>(KMeansClusteringJob.java:22) 

Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/commons/logging/LogFactory 
    at org.apache.hadoop.conf.Configuration.<clinit>(Configuration.java:146) 
    at com.clustering.mapreduce.KMeansClusteringJob.main(KMeansClusteringJob.java:28) 

хотя я добавить всю баночку файл в hadoop/lib и hadoop.core.1.2.1.jar в моем проекте. Мой код

public class KMeansClusteringJob { 

private static final Log LOG = LogFactory.getLog(KMeansClusteringJob.class); 

public static void main(String[] args) throws IOException, 
     InterruptedException, ClassNotFoundException { 

    int iteration = 1; 
    Configuration conf = new Configuration(); 
    conf.set("num.iteration", iteration + ""); 

    Path in = new Path("files/clustering/import/data"); 
    Path center = new Path("files/clustering/import/center/cen.seq"); 
    conf.set("centroid.path", center.toString()); 
    Path out = new Path("files/clustering/depth_1"); 

    Job job = new Job(conf); 
    job.setJobName("KMeans Clustering"); 

    job.setMapperClass(KMeansMapper.class); 
    job.setReducerClass(KMeansReducer.class); 
    job.setJarByClass(KMeansMapper.class); 

    SequenceFileInputFormat.addInputPath(job, in); 
    FileSystem fs = FileSystem.get(conf); 
    if (fs.exists(out)) 
     fs.delete(out, true); 

    if (fs.exists(center)) 
     fs.delete(out, true); 

    if (fs.exists(in)) 
     fs.delete(out, true); 

    final SequenceFile.Writer centerWriter = SequenceFile.createWriter(fs, 
      conf, center, ClusterCenter.class, IntWritable.class); 
    final IntWritable value = new IntWritable(0); 
    centerWriter.append(new ClusterCenter(new Vector(1, 1)), value); 
    centerWriter.append(new ClusterCenter(new Vector(5, 5)), value); 
    centerWriter.close(); 

    final SequenceFile.Writer dataWriter = SequenceFile.createWriter(fs, 
      conf, in, ClusterCenter.class, Vector.class); 
    dataWriter 
      .append(new ClusterCenter(new Vector(0, 0)), new Vector(1, 2)); 
    dataWriter.append(new ClusterCenter(new Vector(0, 0)), 
      new Vector(16, 3)); 
    dataWriter 
      .append(new ClusterCenter(new Vector(0, 0)), new Vector(3, 3)); 
    dataWriter 
      .append(new ClusterCenter(new Vector(0, 0)), new Vector(2, 2)); 
    dataWriter 
      .append(new ClusterCenter(new Vector(0, 0)), new Vector(2, 3)); 
    dataWriter.append(new ClusterCenter(new Vector(0, 0)), 
      new Vector(25, 1)); 
    dataWriter 
      .append(new ClusterCenter(new Vector(0, 0)), new Vector(7, 6)); 
    dataWriter 
      .append(new ClusterCenter(new Vector(0, 0)), new Vector(6, 5)); 
    dataWriter.append(new ClusterCenter(new Vector(0, 0)), new Vector(-1, 
      -23)); 
    dataWriter.close(); 

    SequenceFileOutputFormat.setOutputPath(job, out); 
    job.setInputFormatClass(SequenceFileInputFormat.class); 
    job.setOutputFormatClass(SequenceFileOutputFormat.class); 

    job.setOutputKeyClass(ClusterCenter.class); 
    job.setOutputValueClass(Vector.class); 

    job.waitForCompletion(true); 

    long counter = job.getCounters() 
      .findCounter(KMeansReducer.Counter.CONVERGED).getValue(); 
    iteration++; 
    while (counter > 0) { 
     conf = new Configuration(); 
     conf.set("centroid.path", center.toString()); 
     conf.set("num.iteration", iteration + ""); 
     job = new Job(conf); 
     job.setJobName("KMeans Clustering " + iteration); 

     job.setMapperClass(KMeansMapper.class); 
     job.setReducerClass(KMeansReducer.class); 
     job.setJarByClass(KMeansMapper.class); 

     in = new Path("files/clustering/depth_" + (iteration - 1) + "/"); 
     out = new Path("files/clustering/depth_" + iteration); 

     SequenceFileInputFormat.addInputPath(job, in); 
     if (fs.exists(out)) 
      fs.delete(out, true); 

     SequenceFileOutputFormat.setOutputPath(job, out); 
     job.setInputFormatClass(SequenceFileInputFormat.class); 
     job.setOutputFormatClass(SequenceFileOutputFormat.class); 
     job.setOutputKeyClass(ClusterCenter.class); 
     job.setOutputValueClass(Vector.class); 

     job.waitForCompletion(true); 
     iteration++; 
     counter = job.getCounters() 
       .findCounter(KMeansReducer.Counter.CONVERGED).getValue(); 
    } 

    Path result = new Path("files/clustering/depth_" + (iteration - 1) 
      + "/"); 

    FileStatus[] stati = fs.listStatus(result); 
    for (FileStatus status : stati) { 
     if (!status.isDir()) { 
      Path path = status.getPath(); 
      LOG.info("FOUND " + path.toString()); 
      SequenceFile.Reader reader = new SequenceFile.Reader(fs, path, 
        conf); 
      ClusterCenter key = new ClusterCenter(); 
      Vector v = new Vector(); 
      while (reader.next(key, v)) { 
       LOG.info(key + "/" + v); 
      } 
      reader.close(); 
     } 
    } 
} 

Если требуется применение более баночка

Answer 1

Ошибка явно просит апач-достояние лесосечного баночку. Добавьте этот JAR в свой класс.