写在前边的话

       hadoop2.7完全分布式安装请参考：点击阅读，继任该篇博客之后，诞生了下面的这一篇博客

       基本环境：

              CentOS 6.5，Hadoop 2.7，Java 1.7

              Hive 2.0.0，Zookeeper 3.4.8， Hbase 1.2.2

       预安装

              Scala  2.11

              Spark 2.0

              Sqoop 1.4.6

              Mahout 0.12.2 

一：安装 Scala 2.11.X

      下载：点击进入下载  （我使用的是Scala 2.11.8）

    1：解压到指定目录，并重命名文件夹

            sudo tar -zxvf /home/master/下载/scala-2.11.8.tar.gz -C /opt/

            sudo mv scala-2.11.8/ /opt/scala

    2：修改环境变量

            sudo vim /etc/profile 加入如下代码：

#scala
export SCALA_HOME=/opt/scala
export PATH=$PATH:$SCALA_HOME/bin

           source /etc/profile

     3：每台机器上都部署scala

           执行 sudo scp -r /opt/scala/ slave1:/opt/scala

                   sudo scp -r /opt/scala/ slave2:/opt/scala

           分别在各个节点上修改环境变量即可

     4：运行scala

           终端直接输入scala即可

二：安装 Spark 2.0

       下载：点击进入下载 （这里建议不要安装最新版Spark，具体看评论）

     1：解压到指定目录，并重命名文件夹

            [master@master1 opt]$ sudo tar -zxvf /home/master/下载/spark-2.0.0-bin-hadoop2.7.tgz -C .

            [master@master1 opt]$ sudo mv spark-2.0.0-bin-hadoop2.7/ spark

     2：配置环境变量

            sudo vim /etc/profile ，加入

#spark home  
export SPARK_HOME=/opt/spark  
export PATH=$SPARK_HOME/bin:$PATH 

    3：配置spark-env.sh

            复制 ：sudo cp spark-env.sh.template spark-env.sh

            加入以下：

export SCALA_HOME=/opt/scala
export JAVA_HOME=/opt/java
export SPARK_MASTER_IP=192.168.48.130
export SPARK_WORKER_MEMORY=1g
export HADOOP_CONF_DIR=/opt/hadoop/etc/hadoop

     4：将 slaves.template 拷贝到 slaves， 编辑内容为

            master1

            slave1

            slave2

      5：将spark目录拷贝到各个节点

           sudo scp -r /opt/spark/ slave1:/opt/spark

           sudo scp -r /opt/spark/ slave2:/opt/spark

           并修改各个节点的环境变量

      6：启动 spark

            启动master： sbin/start-master.sh

            启动salve： sbin/start-slaves.sh

            如遇到权限不足问题，直接给每台机器上的spark目录赋予 777 的权限即可

      7：web界面

            http://192.168.48.130:8080/

   

      8：shell 界面

               bin/spark-shell

   

三：安装  Sqoop 1.4.6

        下载：点击进入下载  

     1：解压到指定目录，并重命名

 sudo tar -zxvf /home/master/下载/sqoop-1.4.6.tar.gz -C 

 sudo mv sqoop-1.99.6/ sqoop

     2：配置环境变量

           sudo vim /etc/profile

#sqoop
export SQOOP_HOME=/opt/sqoop
export PATH = $SQOOP_HOME/bin:$PATH

        保存生效：source /etc/profile

     3：复制Mysql-jdbc 包到sqoop/lib目录下

               sudo cp /home/master/下载/mysql-connector-java-5.1.39-bin.jar /opt/sqoop/lib/

     4：修改bin/configure-sqoop文件

              此时如果没有启用hbase,zookeeper等组件，将相应的信息注释，如果启用了，就pass，直接进入下一步

      5：sqoop help 查看帮助

            

四：安装  Mahout 0.12.2

        下载：点击进入下载

       1：解压到指定目录，并重命名

               注意路径问题

[master@master1 opt]$ sudo tar -zxvf /home/master/桌面/apache-mahout-distribution-0.12.0.tar.gz -C .

[master@master1 opt]$ sudo mv apache-mahout-distribution-0.12.0/ mahout

        2：配置环境变量

                 sudo vim  /etc/profile ，加入以下内容：

<span style="font-size:14px;">#mahout home
export MAHOUT_HOME=/opt/mahout
export PATH=$MAHOUT_HOME/bin:$PATH
</span>

               保存生效：source /etc/profile

        3：启动mahout

              进入mahout安装目录，执行：bin/mahout

[master@master1 mahout]$ bin/mahout
Running on hadoop, using /opt/hadoop/bin/hadoop and HADOOP_CONF_DIR=
MAHOUT-JOB: /opt/mahout/mahout-examples-0.12.0-job.jar
An example program must be given as the first argument.
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
  cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
  cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
  describe: : Describe the fields and target variable in a data set
  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
  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

五：额外补充

      1：出现 sudo: command not found时

           执行 export PATH=$PATH:/bin:/usr/bin:/usr/local/bin 即可

      2：这里我们发现除了spark的分布式安装以外要把安装包拷贝到各个节点之外，sqoop和mahout并不需要，只需要在master主机上部署即可，我的理解是sqoop只是进行数据传输的，数据可以是HDFS上的，也可以是Hive，或者Hbase上的，而本身他们已经是分布式的了，所以这里自然不需要将其拷贝到各个节点，而mahout也一样吧，只要运行在分布式的平台上即可，其所依赖的数据也是在hdfs或者hive上，故也不需要将其拷贝到各个节点

      3：那么问题来了，我们是否可以将sqoop或者mahout部署到slave节点上呢？答案是肯定的吧，因为每台机器之间是可以互相通过ssh访问的，sqoop使用时可以直接加上对应的IP地址即可，而mahout就可以直接使用了

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