因为师妹毕设需要，实验室的集群原先的Mahout 0.11.1跟教程上关于随机森林算法有些出入，由于急于使用，因此想换回低版本。在Cloudera公司的CDN 5.3下用的就是Mahout 0.9版本，为了能够实验室的Hadoop 2.6集群环境下使用Mahout 0.9，需要对Mahout 0.9进行编译，因为Mahout 0.9官方提供的版本上只能兼容hadoop 1.x系列，因此需要使用源代码重新进行编译（参考：mahout, https://mahout.apache.org/developers/buildingmahout.html）。
为了方便大家，特将编译之后可用于Hadoop 2.x版本的Mahout提供下载：http://pan.baidu.com/s/1eRRBP2U
如果想自行编译，请参考以下教程：
第一步：Mahout0.9源码修改（Mahout源码打patch）
Mahout官方下载地址：http://archive.apache.org/dist/mahout/
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。
下载：1329-3.patch拷贝至服务器上；
https://issues.apache.org/jira/secure/attachment/12630146/1329-3.patch
下载：mahout-distribution-0.9-src.tar.gz源码解压至服务器上；
到源码根目录下执行以下命令打patch：
patch -p0 < ../1329-3.patch
提示如下：
patching file core/pom.xml patching file integration/pom.xml patching file pom.xml
第二步：Mahout0.9源码编译（Hadoop2.x）
执行（前提已安装好Maven环境，文章末尾介绍Maven环境的安装）：
mvn package -Prelease -Dhadoop2 -Dhadoop2.version=2.2.0 -DskipTests=true
然后就是漫长的等待过程……
直到：
[INFO] Reactor Summary: [INFO] [INFO] Mahout Build Tools ................................. SUCCESS [ 1.680 s] [INFO] Apache Mahout ...................................... SUCCESS [ 2.056 s] [INFO] Mahout Math ........................................ SUCCESS [ 24.012 s] [INFO] Mahout Core ........................................ SUCCESS [ 32.697 s] [INFO] Mahout Integration ................................. SUCCESS [ 7.977 s] [INFO] Mahout Examples .................................... SUCCESS [ 20.199 s] [INFO] Mahout Release Package ............................. SUCCESS [ 34.697 s] [INFO] Mahout Math/Scala wrappers ......................... SUCCESS [ 4.728 s] [INFO] ------------------------------------------------------------------------ [INFO] BUILD SUCCESS [INFO] ------------------------------------------------------------------------ [INFO] Total time: 02:08 min [INFO] Finished at: 2016-03-31T14:56:44+08:00 [INFO] Final Memory: 87M/1320M [INFO] ------------------------------------------------------------------------
出现上述信息说明编译成功。
查看编译部署二进制包
进入到源代码的distribution/target目录。
mahout-distribution-0.9.tar.gz为安装部署包
第三步：Mahout0.9安装部署
下载编译好的mahout-distribution-0.9.tar.gz部署包，直接解压即可。
移动到相应的目录。
vi /etc/profile 配置环境变量
添加：
# set mahout environment export MAHOUT_HOME=/usr/local/hadoop-2.6.0/mahout-0.9 export PATH=$PATH:$MAHOUT_HOME/bin export CLASSPATH=.:$CLASSPATH:$MAHOUT_HOME/lib
保存退出，输入source /etc/profile命令使之立即生效
Mahout配置
vi mahout-distribution-0.9/bin/mahout进入配置界面
添加：
MAHOUT_JAVA_HOME=/usr/local/jdk1.7.0_03
如果要设置伪分布式 添加
MAHOUT_LOCAL=true
如果不设置 将默认为在hadoop上运行。
输入mahout --help测试下Mahout是否安装成功
MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.
Running on hadoop, using /usr/local/hadoop/hadoop-2.2.0/bin/hadoop and HADOOP_CONF_DIR=/usr/local/hadoop/hadoop-2.2.0/conf
MAHOUT-JOB: /usr/lib/mahout-distribution-0.9/mahout-examples-0.9-job.jar
Unknown program '--help' chosen.
Valid program names are:
arff.vector: : Generate Vectors from an ARFF file or directory
baumwelch: : Baum-Welch algorithm for unsupervised HMM training
canopy: : Canopy clustering
cat: : Print a file or resource as the logistic regression models would see it
cleansvd: : Cleanup and verification of SVD output
clusterdump: : Dump cluster output to text
clusterpp: : Groups Clustering Output In Clusters
cmdump: : Dump confusion matrix in HTML or text formats
concatmatrices: : Concatenates 2 matrices of same cardinality into a single matrix
cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes
fkmeans: : Fuzzy K-means clustering
hmmpredict: : Generate random sequence of observations by given HMM
itemsimilarity: : Compute the item-item-similarities for item-based collaborative filtering
kmeans: : K-means clustering
lucene.vector: : Generate Vectors from a Lucene index
lucene2seq: : Generate Text SequenceFiles from a Lucene index
matrixdump: : Dump matrix in CSV format
matrixmult: : Take the product of two matrices
parallelALS: : ALS-WR factorization of a rating matrix
qualcluster: : Runs clustering experiments and summarizes results in a CSV
recommendfactorized: : Compute recommendations using the factorization of a rating matrix
recommenditembased: : Compute recommendations using item-based collaborative filtering
regexconverter: : Convert text files on a per line basis based on regular expressions
resplit: : Splits a set of SequenceFiles into a number of equal splits
rowid: : Map SequenceFile<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>}
rowsimilarity: : Compute the pairwise similarities of the rows of a matrix
runAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression model
runlogistic: : Run a logistic regression model against CSV data
seq2encoded: : Encoded Sparse Vector generation from Text sequence files
seq2sparse: : Sparse Vector generation from Text sequence files
seqdirectory: : Generate sequence files (of Text) from a directory
seqdumper: : Generic Sequence File dumper
seqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archives
seqwiki: : Wikipedia xml dump to sequence file
spectralkmeans: : Spectral k-means clustering
split: : Split Input data into test and train sets
splitDataset: : split a rating dataset into training and probe parts
ssvd: : Stochastic SVD
streamingkmeans: : Streaming k-means clustering
svd: : Lanczos Singular Value Decomposition
testnb: : Test the Vector-based Bayes classifier
trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model
trainlogistic: : Train a logistic regression using stochastic gradient descent
trainnb: : Train the Vector-based Bayes classifier
transpose: : Take the transpose of a matrix
validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data set
vecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectors
vectordump: : Dump vectors from a sequence file to text
viterbi: : Viterbi decoding of hidden states from given output states sequence
看到以上，代表安装成功，上面提示：Running on hadoop，下面是Mahout支持的函数列表
附：Maven配置
Manen官方下载页面：http://maven.apache.org/download.cgi
选择相应版本下载，上传服务器，解压并拷贝至相关目录。
配置环境变量：
# set maven environment export MAVEN_HOME=/usr/local/maven-3.3.9 export PATH=$MAVEN_HOME/bin:$PATH
source /etc/profile是配置生效
输入mvn -v测试
出现：
Apache Maven 3.3.9 (bb52d8502b132ec0a5a3f4c09453c07478323dc5; 2015-11-11T00:41:47+08:00) Maven home: /usr/local/maven-3.3.9 Java version: 1.7.0_79, vendor: Oracle Corporation Java home: /usr/java/jdk1.7.0_79/jre Default locale: zh_CN, platform encoding: UTF-8 OS name: "linux", version: "2.6.32-573.el6.x86_64", arch: "amd64", family: "unix"
说明安装成功。
给maven添加本地仓库：
修改conf下面的settings.xml文件
找到第53行，把注释去掉，修改成：
/usr/data/maven/maven-dependcies
由于maven国外服务器可能连不上，先给maven配置一下国内镜像，在maven目录下，conf/settings.xml,在<mirrors></mirros>里添加，原本的不要动：
<id>nexus-osc</id>
<mirrorOf>*</mirrorOf>
<name>Nexusosc</name>
<url>http://maven.oschina.net/content/groups/public/</url>同样，在<profiles></profiles>内新添加:
<id>jdk-1.7</id>
<activation>
<jdk>1.4</jdk>
</activation>
<repositories>
<repository>
<id>nexus</id>
<name>localprivatenexus</name>
<url>http://maven.oschina.net/content/groups/public/</url>
<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</repository>
</repositories>
<pluginRepositories>
<pluginRepository>
<id>nexus</id>
<name>localprivatenexus</name>
<url>http://maven.oschina.net/content/groups/public/</url>
<releases>
<enabled>true</enabled>
</releases>
<snapshots>
<enabled>false</enabled>
</snapshots>
</pluginRepository>
</pluginRepositories>截至目前，配置完成。