如何构建/运行这个简单的 Mahout 程序而不出现异常？

我想运行我在 Mahout In Action 中找到的这段代码：

package org.help;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
import org.apache.mahout.math.DenseVector;
import org.apache.mahout.math.NamedVector;
import org.apache.mahout.math.VectorWritable;

public class SeqPrep {

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

        List<NamedVector> apples = new ArrayList<NamedVector>();

        NamedVector apple;

        apple = new NamedVector(new DenseVector(new double[]{0.11, 510, 1}), "small round green apple");        

        apples.add(apple);

        Configuration conf = new Configuration();
        FileSystem fs = FileSystem.get(conf);
        Path path = new Path("appledata/apples");

        SequenceFile.Writer writer = new SequenceFile.Writer(fs,  conf, path, Text.class, VectorWritable.class);

        VectorWritable vec = new VectorWritable();
        for(NamedVector vector : apples){
            vec.set(vector);
            writer.append(new Text(vector.getName()), vec);
        }
        writer.close();

        SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path("appledata/apples"), conf);

        Text key = new Text();
        VectorWritable value = new VectorWritable();
        while(reader.next(key, value)){
            System.out.println(key.toString() + " , " + value.get().asFormatString());
        }
        reader.close();

    }

}

我用以下方法编译它：

$ javac -classpath :/usr/local/hadoop-1.0.3/hadoop-core-1.0.3.jar:/home/hduser/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT.jar:/home/hduser/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT-job.jar:/home/hduser/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT-sources.jar -d myjavac/ SeqPrep.java

我把它罐装：

$ jar -cvf SeqPrep.jar -C myjavac/ .

现在我想在本地 hadoop 节点上运行它。我试过了：

 hadoop jar SeqPrep.jar org.help.SeqPrep

但我得到：

Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/mahout/math/Vector
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:247)
    at org.apache.hadoop.util.RunJar.main(RunJar.java:149)

所以我尝试使用 libjars 参数：

$ hadoop jar SeqPrep.jar org.help.SeqPrep -libjars /home/hduser/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT.jar -libjars /home/hduser/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT-job.jar -libjars /home/hduser/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT-sources.jar -libjars /home/hduser/mahout/trunk/math/target/mahout-math-0.8-SNAPSHOT.jar -libjars /home/hduser/mahout/trunk/math/target/mahout-math-0.8-SNAPSHOT-sources.jar

并遇到了同样的问题。我不知道还能尝试什么。

我的最终目标是能够将 hadoop fs 上的 .csv 文件读入稀疏矩阵，然后将其乘以随机向量。

edit:看起来 Razvan 明白了（注意：请参阅下面的另一种方法来执行此操作，并且不会干扰您的 hadoop 安装）。以供参考：

$ find /usr/local/hadoop-1.0.3/. |grep mah
/usr/local/hadoop-1.0.3/./lib/mahout-core-0.8-SNAPSHOT-tests.jar
/usr/local/hadoop-1.0.3/./lib/mahout-core-0.8-SNAPSHOT.jar
/usr/local/hadoop-1.0.3/./lib/mahout-core-0.8-SNAPSHOT-job.jar
/usr/local/hadoop-1.0.3/./lib/mahout-core-0.8-SNAPSHOT-sources.jar
/usr/local/hadoop-1.0.3/./lib/mahout-math-0.8-SNAPSHOT-sources.jar
/usr/local/hadoop-1.0.3/./lib/mahout-math-0.8-SNAPSHOT-tests.jar
/usr/local/hadoop-1.0.3/./lib/mahout-math-0.8-SNAPSHOT.jar

进而：

$hadoop jar SeqPrep.jar org.help.SeqPrep

small round green apple , small round green apple:{0:0.11,1:510.0,2:1.0}

edit:我试图在不将 mahout jar 复制到 hadoop lib/ 的情况下执行此操作

$ rm /usr/local/hadoop-1.0.3/lib/mahout-*

然后当然：

hadoop jar SeqPrep.jar org.help.SeqPrep

Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/mahout/math/Vector
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:247)
    at org.apache.hadoop.util.RunJar.main(RunJar.java:149)
Caused by: java.lang.ClassNotFoundException: org.apache.mahout.math.Vector
    at java.net.URLClassLoader$1.run(URLClassLoader.java:202)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:190)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:306)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:247)

当我尝试使用 mahout 作业文件时：

$hadoop jar ~/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT-job.jar org.help.SeqPrep

Exception in thread "main" java.lang.ClassNotFoundException: org.help.SeqPrep
    at java.net.URLClassLoader$1.run(URLClassLoader.java:202)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:190)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:306)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:247)
    at java.lang.Class.forName0(Native Method)
    at java.lang.Class.forName(Class.java:247)
    at org.apache.hadoop.util.RunJar.main(RunJar.java:149)

如果我尝试包含我制作的 .jar 文件：

$ hadoop jar ~/mahout/trunk/core/target/mahout-core-0.8-SNAPSHOT-job.jar SeqPrep.jar org.help.SeqPrep

Exception in thread "main" java.lang.ClassNotFoundException: SeqPrep.jar

edit:显然我一次只能发送一个jar到hadoop。这意味着我需要将我制作的类添加到 mahout 核心作业文件中：

~/mahout/trunk/core/target$ cp mahout-core-0.8-SNAPSHOT-job.jar mahout-core-0.8-SNAPSHOT-job.jar_backup

~/mahout/trunk/core/target$ cp ~/workspace/seqprep/bin/org/help/SeqPrep.class .

~/mahout/trunk/core/target$ jar uf mahout-core-0.8-SNAPSHOT-job.jar SeqPrep.class

进而：

~/mahout/trunk/core/target$ hadoop jar mahout-core-0.8-SNAPSHOT-job.jar org.help.SeqPrep

Exception in thread "main" java.lang.ClassNotFoundException: org.help.SeqPrep

edit:好的，现在我可以做到这一点，而不会弄乱我的 hadoop 安装。我在之前的编辑中错误地更新了 .jar。它应该是：

~/mahout/trunk/core/target$ jar uf mahout-core-0.8-SNAPSHOT-job.jar org/help/SeqPrep.class

then:

~/mahout/trunk/core/target$ hadoop jar mahout-core-0.8-SNAPSHOT-job.jar org.help.SeqPrep

small round green apple , small round green apple:{0:0.11,1:510.0,2:1.0}

您需要使用 Mahout 提供的“job”JAR 文件。它打包了所有依赖项。您还需要将您的课程添加到其中。这就是所有 Mahout 示例的工作方式。您不应该将 Mahout jar 放入 Hadoop 库中，因为这样会将程序“安装”在 Hadoop 中太深。