hadoop2.2+mahout0.9实战 博客分类： MahoutHadoop HadoopMahout

 

版本：hadoop2.2.0，mahout0.9。

使用mahout的org.apache.mahout.cf.taste.hadoop.item.RecommenderJob进行测试。

首先说明下，如果使用官网提供的下载hadoop2.2.0以及mahout0.9进行调用mahout的相关算法会报错。一般报错如下：

java.lang.IncompatibleClassChangeError: Found interface org.apache.hadoop.mapreduce.JobContext, but class was expected
at org.apache.mahout.common.HadoopUtil.getCustomJobName(HadoopUtil.java:174)
at org.apache.mahout.common.AbstractJob.prepareJob(AbstractJob.java:614)
at org.apache.mahout.cf.taste.hadoop.preparation.PreparePreferenceMatrixJob.run(PreparePreferenceMatrixJob.java:73)
at org.apache.hadoop.util.ToolRunner.run(ToolRunner.java:70)

这个是因为目前mahout只支持hadoop1 的缘故。在这里可以找到解决方法：https://issues.apache.org/jira/browse/MAHOUT-1329。主要就是修改pom文件，修改mahout的依赖。

大家可以下载修改后的源码包（http://download.csdn.net/detail/fansy1990/7165957）自己编译mahout，或者直接下载已经编译好的jar包（http://download.csdn.net/detail/fansy1990/7166017、http://download.csdn.net/detail/fansy1990/7166055）。

接着，按照这篇文章建立eclipse的环境：http://blog.csdn.net/fansy1990/article/details/22896249。环境配置好了之后，需要添加mahout的jar包，下载前面提供的jar包，然后导入到java工程中。

编写下面的java代码：

package fz.hadoop2.util;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.yarn.conf.YarnConfiguration;

public class Hadoop2Util {
private static Configuration conf=null;

private static final String YARN_RESOURCE="node31:8032";
private static final String DEFAULT_FS="hdfs://node31:9000";

public static Configuration getConf(){
if(conf==null){
conf = new YarnConfiguration();
conf.set("fs.defaultFS", DEFAULT_FS);
conf.set("mapreduce.framework.name", "yarn");
conf.set("yarn.resourcemanager.address", YARN_RESOURCE);
}
return conf;
}
}

package fz.mahout.recommendations;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.cf.taste.hadoop.item.RecommenderJob;
import org.junit.After;
import org.junit.Before;
import org.junit.Test;

import fz.hadoop2.util.Hadoop2Util;
/**
 * 测试mahout org.apache.mahout.cf.taste.hadoop.item.RecommenderJob
 * environment： 
 * mahout0.9
 * hadoop2.2
 * @author fansy
 *
 */
public class RecommenderJobTest{
//RecommenderJob rec = null;
Configuration conf =null;
@Before
public void setUp(){
//	rec= new RecommenderJob();
conf= Hadoop2Util.getConf();
System.out.println("Begin to test...");
}

@Test
public void testMain() throws Exception{
String[] args ={
 "-i","hdfs://node31:9000/input/user.csv",  
         "-o","hdfs://node31:9000/output/rec001",  
         "-n","3","-b","false","-s","SIMILARITY_EUCLIDEAN_DISTANCE",  
         "--maxPrefsPerUser","7","--minPrefsPerUser","2",  
         "--maxPrefsInItemSimilarity","7",  
         "--outputPathForSimilarityMatrix","hdfs://node31:9000/output/matrix/rec001",
         "--tempDir","hdfs://node31:9000/output/temp/rec001"}; 
ToolRunner.run(conf, new RecommenderJob(), args);
}

@After
public void cleanUp(){

}
}

在前面下载好了mahout的jar包后，需要把这些jar包放入hadoop2的lib目录（share/hadoop/mapreduce/lib，注意不一定一定要这个路径，其他hadoop lib也可以）。然后运行RecommenderJobTest即可。

输入文件如下：

1,101,5.0
1,102,3.0
1,103,2.5
2,101,2.0
2,102,2.5
2,103,5.0
2,104,2.0
3,101,2.5
3,104,4.0
3,105,4.5
3,107,5.0
4,101,5.0
4,103,3.0
4,104,4.5
4,106,4.0
5,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5
5,106,4.0

输出文件为：

最后一个MR日志：

2014-04-09 13:03:09,301 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - io.sort.factor is deprecated. Instead, use mapreduce.task.io.sort.factor
2014-04-09 13:03:09,301 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.map.child.java.opts is deprecated. Instead, use mapreduce.map.java.opts
2014-04-09 13:03:09,302 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - io.sort.mb is deprecated. Instead, use mapreduce.task.io.sort.mb
2014-04-09 13:03:09,302 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - mapred.task.timeout is deprecated. Instead, use mapreduce.task.timeout
2014-04-09 13:03:09,317 INFO  [main] client.RMProxy (RMProxy.java:createRMProxy(56)) - Connecting to ResourceManager at node31/192.168.0.31:8032
2014-04-09 13:03:09,460 INFO  [main] input.FileInputFormat (FileInputFormat.java:listStatus(287)) - Total input paths to process : 1
2014-04-09 13:03:09,515 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:submitJobInternal(394)) - number of splits:1
2014-04-09 13:03:09,531 INFO  [main] Configuration.deprecation (Configuration.java:warnOnceIfDeprecated(840)) - fs.default.name is deprecated. Instead, use fs.defaultFS
2014-04-09 13:03:09,547 INFO  [main] mapreduce.JobSubmitter (JobSubmitter.java:printTokens(477)) - Submitting tokens for job: job_1396479318893_0015
2014-04-09 13:03:09,602 INFO  [main] impl.YarnClientImpl (YarnClientImpl.java:submitApplication(174)) - Submitted application application_1396479318893_0015 to ResourceManager at node31/192.168.0.31:8032
2014-04-09 13:03:09,604 INFO  [main] mapreduce.Job (Job.java:submit(1272)) - The url to track the job: http://node31:8088/proxy/application_1396479318893_0015/
2014-04-09 13:03:09,604 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1317)) - Running job: job_1396479318893_0015
2014-04-09 13:03:24,170 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1338)) - Job job_1396479318893_0015 running in uber mode : false
2014-04-09 13:03:24,170 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) -  map 0% reduce 0%
2014-04-09 13:03:32,299 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) -  map 100% reduce 0%
2014-04-09 13:03:41,373 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1345)) -  map 100% reduce 100%
2014-04-09 13:03:42,404 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1356)) - Job job_1396479318893_0015 completed successfully
2014-04-09 13:03:42,485 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1363)) - Counters: 43
File System Counters
FILE: Number of bytes read=306
FILE: Number of bytes written=163713
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=890
HDFS: Number of bytes written=192
HDFS: Number of read operations=10
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters 
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=5798
Total time spent by all reduces in occupied slots (ms)=6179
Map-Reduce Framework
Map input records=7
Map output records=21
Map output bytes=927
Map output materialized bytes=298
Input split bytes=131
Combine input records=0
Combine output records=0
Reduce input groups=5
Reduce shuffle bytes=298
Reduce input records=21
Reduce output records=5
Spilled Records=42
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=112
CPU time spent (ms)=1560
Physical memory (bytes) snapshot=346509312
Virtual memory (bytes) snapshot=1685782528
Total committed heap usage (bytes)=152834048
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters 
Bytes Read=572
File Output Format Counters 
Bytes Written=192

说明：由于只测试了一个协同过滤算法的程序，其他的算法并没有测试，如果其他算法在此版本上有问题，也是可能有的。

 

 http://blog.csdn.net/fansy1990