如何在操作手册中运行mahout中的示例

本文介绍了如何在操作手册中运行mahout中的示例的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我试图在第7章中运行hello world示例。我在eclipse中创建了以下代码，然后将它打包到jar中： - $ / $>

package com.mycode.mahout import java.io.File; import java.io.IOException; import java.util.ArrayList; import java.util.List; 导入org.apache.hadoop.conf.Configuration; 导入org.apache.hadoop.fs.FileSystem; 导入org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.SequenceFile; import org.apache.hadoop.io.Text; import org.apache.mahout.clustering.WeightedVectorWritable; import org.apache.mahout.clustering.kmeans.Cluster; import org.apache.mahout.clustering.kmeans.KMeansDriver; 导入org.apache.mahoutmon.distance.EuclideanDistanceMeasure; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; public class SimpleKMeansClustering { public static final double [] [] points = {{1，1}，{2，1}，{1,2}， { 2，2}，{3，3}，{8,8}， {9,8}，{8,9}，{9,9}}; public static void writePointsToFile（List< Vector> points， String fileName， FileSystem fs， Configuration conf）throws IOException { Path path =新路径（文件名）; SequenceFile.Writer writer = new SequenceFile.Writer（fs，conf， path，LongWritable.class，VectorWritable.class）; 长recNum = 0; VectorWritable vec = new VectorWritable（）; for（Vector point：points）{ vec.set（point）; writer.append（new LongWritable（recNum ++），vec）; } writer.close（）; } public static List< Vector> getPoints（double [] [] raw）{ List< Vector> points = new ArrayList< Vector>（）; for（int i = 0; i< raw.length; i ++）{ double [] fr = raw [i]; Vector vec = new RandomAccessSparseVector（fr.length）; vec.assign（fr）; points.add（vec）; } 回报点; public static void main（String args []）throws Exception { int k = 2; List< Vector> vectors = getPoints（points）; 文件testData = new File（testdata）; if（！testData.exists（））{ testData.mkdir（）; } testData = new File（testdata / points）; if（！testData.exists（））{ testData.mkdir（）; } 配置conf = new Configuration（）; FileSystem fs = FileSystem.get（conf）; writePointsToFile（向量，testdata / points / file1，fs，conf）; 路径路径=新路径（testdata / clusters / part-00000）; SequenceFile.Writer writer = new SequenceFile.Writer（fs，conf， path，Text.class，Cluster.class）; for（int i = 0; i 我将它打包为myjob.jar

现在我该如何在群集上执行此操作？

我尝试了以下操作： -

hadoop jar myjob.jar com.mycode.mahout.SimpleKMeansClustering java -jar myjob.jar java -cp myjob。 jar

我得到follwing错误： - $ / b>

[root @ node1 tmp]＃hadoop jar mahoutfirst.jar com.mahout.emc.SimpleKMeansClustering 线程main中的异常java.lang.NoClassDefFoundError：org / apache / mahout / math / Vector`在java.lang.Class.forName0处使用（本地方法）$ b在java.lang.Class.forName（Class.java:270）处使用$ b $ org.apache.hadoop .util.RunJar.main（RunJar.java:201）导致：java.lang.ClassNotFoundException：org.apache.mahout.math.Vector $ b $ java.URLClassLoader $ 1.run（URLClassLoader .java：366）java.URLClassLoader $ 1.run（URLClassLoader.java:35 5）$ java.util.AccessController.doPrivileged（Native方法） ClassLoader.java:424） at java.lang.ClassLoader.loadClass（ClassLoader.java:357） ... 3 more

请指教使用mahout编写代码的正确方法是什么。 解决方案

虽然这很晚，但我面临类似的问题，下面的方法对我来说不起作用，因为我不想使用maven：

1）转到你的mahout安装目录&寻找* job.jar作为

ls / usr / lib / mahout / conf lib mahout-core-0.5 -cdh3u3-job.jar mahout-examples-0.5-cdh3u3-job.jar mahout-taste-webapp-0.5-cdh3u3.war

2）将mahout-examples-0.5-cdh3u3-job.jar复制到代码所在的目录中 3）使用jobJAR由Mahout提供的文件。它打包了所有的依赖关系。你也需要添加你的类。正如你已经使用hadoop和mahout库编译你的类，你已经准备好了你的.class文件。 $ b 4）将你的类文件添加到作业jar mahout-core-0.5- cdh3u3-job.jar在您的目录中：

jar uf mahout-core-0.5-cdh3u3-job.jar SimpleKMeansClustering.class

4）使用代码运行hadoop jar：

hadoop jar mahout-core-0.5-cdh3u3-job.jar SimpleKMeansClustering

5）在map-reduce作业结束时，您可以看到：

1.0：[1.000， 1.000]属于集群0 1.0：[2.000,1.000]属于集群0 1.0：[1.000,2000]属于集群0 1.0：[2.000,2.000]属于集群0 1.0：[3.000,3000]属于集群0 1.0：[8.000,8.000]属于集群1 1.0：[9.000,8.000]属于集群1 1.0：[8.000，9.000]属于群集1 1.0：[9.000,9.000]属于群集1

I am trying to run the hello world example in chapter 7. I created the following in eclipse and then packed it into a jar:-

package com.mycode.mahout import java.io.File; 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.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.SequenceFile; import org.apache.hadoop.io.Text; import org.apache.mahout.clustering.WeightedVectorWritable; import org.apache.mahout.clustering.kmeans.Cluster; import org.apache.mahout.clustering.kmeans.KMeansDriver; import org.apache.mahoutmon.distance.EuclideanDistanceMeasure; import org.apache.mahout.math.RandomAccessSparseVector; import org.apache.mahout.math.Vector; import org.apache.mahout.math.VectorWritable; public class SimpleKMeansClustering { public static final double[][] points = { {1, 1}, {2, 1}, {1, 2}, {2, 2}, {3, 3}, {8, 8}, {9, 8}, {8, 9}, {9, 9}}; public static void writePointsToFile(List<Vector> points, String fileName, FileSystem fs, Configuration conf) throws IOException { Path path = new Path(fileName); SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path, LongWritable.class, VectorWritable.class); long recNum = 0; VectorWritable vec = new VectorWritable(); for (Vector point : points) { vec.set(point); writer.append(new LongWritable(recNum++), vec); } writer.close(); } public static List<Vector> getPoints(double[][] raw) { List<Vector> points = new ArrayList<Vector>(); for (int i = 0; i < raw.length; i++) { double[] fr = raw[i]; Vector vec = new RandomAccessSparseVector(fr.length); vec.assign(fr); points.add(vec); } return points; } public static void main(String args[]) throws Exception { int k = 2; List<Vector> vectors = getPoints(points); File testData = new File("testdata"); if (!testData.exists()) { testData.mkdir(); } testData = new File("testdata/points"); if (!testData.exists()) { testData.mkdir(); } Configuration conf = new Configuration(); FileSystem fs = FileSystem.get(conf); writePointsToFile(vectors, "testdata/points/file1", fs, conf); Path path = new Path("testdata/clusters/part-00000"); SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path, Text.class, Cluster.class); for (int i = 0; i < k; i++) { Vector vec = vectors.get(i); Cluster cluster = new Cluster(vec, i, new EuclideanDistanceMeasure()); writer.append(new Text(cluster.getIdentifier()), cluster); } writer.close(); KMeansDriver.run(conf, new Path("testdata/points"), new Path("testdata/clusters"), new Path("output"), new EuclideanDistanceMeasure(), 0.001, 10, true, false); SequenceFile.Reader reader = new SequenceFile.Reader(fs, new Path("output/" + Cluster.CLUSTERED_POINTS_DIR + "/part-m-00000"), conf); IntWritable key = new IntWritable(); WeightedVectorWritable value = new WeightedVectorWritable(); while (reader.next(key, value)) { System.out.println(value.toString() + " belongs to cluster " + key.toString()); } reader.close(); } }

I packed it as myjob.jar

now how shall I execute this on my cluster ?

I tried following:-

hadoop jar myjob.jar com.mycode.mahout.SimpleKMeansClustering java -jar myjob.jar java -cp myjob.jar

I get follwing error:-

[root@node1 tmp]# hadoop jar mahoutfirst.jar com.mahout.emc.SimpleKMeansClustering 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:270) at org.apache.hadoop.util.RunJar.main(RunJar.java:201) Caused by: java.lang.ClassNotFoundException: org.apache.mahout.math.Vector at java.URLClassLoader$1.run(URLClassLoader.java:366) at java.URLClassLoader$1.run(URLClassLoader.java:355) at java.security.AccessController.doPrivileged(Native Method) at java.URLClassLoader.findClass(URLClassLoader.java:354) at java.lang.ClassLoader.loadClass(ClassLoader.java:424) at java.lang.ClassLoader.loadClass(ClassLoader.java:357) ... 3 more

Please advice what is the right way to run the code written using mahout.

解决方案

Even though this pretty late but I was facing similar issues and following approach does work for me as i don't wanted to use maven:

1) Go to your mahout installation directory & look for *job.jar as

ls /usr/lib/mahout/ conf lib mahout-core-0.5-cdh3u3-job.jar mahout-examples-0.5-cdh3u3-job.jar mahout-taste-webapp-0.5-cdh3u3.war

2) Copy mahout-examples-0.5-cdh3u3-job.jar to the directory where code resides

3) Use the "job" JAR file provided by Mahout. It packages up all the dependencies. You need to add your classes to it too. As you have compiled your class using hadoop and mahout libraries you have your .class file ready.

4) Add your class file to the job jar mahout-core-0.5-cdh3u3-job.jar in your directory:

jar uf mahout-core-0.5-cdh3u3-job.jar SimpleKMeansClustering.class

4) Run the hadoop jar as using your code:

hadoop jar mahout-core-0.5-cdh3u3-job.jar SimpleKMeansClustering

5) At the end of your map-reduce job you can see:

1.0: [1.000, 1.000] belongs to cluster 0 1.0: [2.000, 1.000] belongs to cluster 0 1.0: [1.000, 2.000] belongs to cluster 0 1.0: [2.000, 2.000] belongs to cluster 0 1.0: [3.000, 3.000] belongs to cluster 0 1.0: [8.000, 8.000] belongs to cluster 1 1.0: [9.000, 8.000] belongs to cluster 1 1.0: [8.000, 9.000] belongs to cluster 1 1.0: [9.000, 9.000] belongs to cluster 1