Spark1.2集群环境搭建(Standalone+HA) 4G内存5个节点也是蛮拼的

准备工作：1、笔记本4G内存 ，操作系统WIN7 
2、工具VMware Workstation
3、虚拟机：CentOS6.4共五台
4、搭建好Hadoop集群( 方便Spark可从HDSF上读取文件，进行实验测试)

实验环境：Hadoop HA集群：

Ip

hostname
role
192.168.249.130
SY-0130
ActiveNameNode
192.168.249.131 
SY-0131
StandByNameNode
192.168.249.132
SY-0132
DataNode1
192.168.249.133
SY-0133
DataNode2

Spark HA集群：
Ip
hostname
role
192.168.249.134
SY-0134
Master
192.168.249.130
SY-0130
StandBy Master
192.168.249.131
SY-0131
worker
192.168.249.132
SY-0132
worker
192.168.249.133
SY-0133
worker










实验环境仅作学习用，4G内存确实蛮拼的，资源非常有限。下周换上几台台式机作集群。
上述SY-0134是新克隆的虚拟机，作为Spark的环境中的Master，原属于Hadoop集群中的4个节点分别作为StandByMaster 和 Worker角色。
关于虚拟机环境设置、网络配置、Hadoop集群搭建参见《Hadoop2.6集群环境搭建》 
本文重点关注Spark1.2环境、Zookeeper环境简易搭建，仅作学习与实验原型，且不涉及太多理论知识。

软件安装：(注：用户hadoop登录SY-0134)
1、在节点SY-0134，hadoop用户目录创建toolkit 文件夹，用来保存所有软件安装包，建立labsp文件作为本次实验环境目录。
[hadoop@SY-0134 ~]$ mkdir labsp
[hadoop@SY-0134~]$ mkdir toolkit
我将下载的软件包存放在toolkit中如下
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[hadoop@SY-0134 toolkit]$ ls
hadoop-2.5.2.tar.gz  hadoop-2.6.0.tar.gz  jdk-7u71-linux-i586.gz  scala-2.10.3.tgz  spark-1.2.0-bin-hadoop2.3.tgz  zookeeper-3.4.6.tar.gz　




2、这次实验我下载的Spark包是spark-1.2.0-bin-hadoop2.3.tgz ，Scala版本是2.10.3，Zookeeper是3.4.6。这里需要注意的是，Spark和Scala有版本对应关系，可在Spark官网介绍中找到Spark版本支持的Scala版本。
3、JDK安装及环境变量设置
[hadoop@SY-0134 ~]$ mkdir lab
#我将jdk7安装在lab目录
[hadoop@SY-0134 jdk1.7.0_71]$ pwd
/home/hadoop/lab/jdk1.7.0_71
#环境变量设置：
[hadoop@SY-0134 ~]$ vi .bash_profile
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# User specific environment and startup programs

export JAVA_HOME=/home/hadoop/lab/jdk1.7.0_71

PATH=$JAVA_HOME/binPATHHOME/bin
export PATH

export CLASSPATH=.JAVA_HOME/lib/dt.jarJAVA_HOME/lib/tools.jar




#设置生效
[hadoop@SY-0130 ~]$ source .bash_profile
4、Scala安装及环境变量设置
我将scala解压到/home/hadoop/labsp/scala-2.10.3位置。
修改.bash_profile文件
增加：export SCALA_HOME=/home/hadoop/labsp/scala-2.10.3
修改：PATH=$JAVA_HOME/binPATHHOME/binSCALA_HOME/bin
#设置生效
[hadoop@SY-0130 ~]$ source .bash_profile
检验Scala是否安装好：
[hadoop@SY-0134 ~]$ scala
Welcome to Scala version 2.10.3 (Java HotSpot(TM) Client VM, Java 1.7.0_71).
上述显示安装成功。
5、Spark安装及环境配置
   我将spark解压到/home/hadoop/labsp/spark1.2_hadoop2.3位置。下载的这个包是预编译包。
   修改.bash_profile文件
   增加：export SPARK_HOME=/home/hadoop/labsp/spark1.2_hadoop2.3

   修改：PATH=$JAVA_HOME/binPATHHOME/binSCALA_HOME/bin:$SPARK_HOME/bin

#设置生效
[hadoop@SY-0130 ~]$ source .bash_profile
#修改spark-env.sh
[hadoop@SY-0134 conf]$ pwd
/home/hadoop/labsp/spark1.2_hadoop2.3/conf
[hadoop@SY-0134 conf]$vi spark-env.sh
核心配置：
export JAVA_HOME=/home/hadoop/lab/jdk1.7.0_71
export SCALA_HOME=/home/hadoop/labsp/scala-2.10.3
export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=SY-0134:2181,SY-0130:2181,SY-0131:2181,SY-0132:2181,SY-0133:2181 -Dspark.deploy.zookeeper.dir=/spark"
至此JDK，Scala，Spark 安装及环境变量设置好，当然上述配置步骤也可一次修改完成。
6、Zookeeper安装
  我将zookeeper解压到/home/hadoop/labsp/zookeeper-3.4.6位置。
  #配置zoo.cfg文件
  [hadoop@SY-0134 zookeeper-3.4.6]$ pwd
  /home/hadoop/labsp/zookeeper-3.4.6
  [hadoop@SY-0134 zookeeper-3.4.6]$ mkdir data
  [hadoop@SY-0134 zookeeper-3.4.6]$ mkdir datalog
  [hadoop@SY-0134 zookeeper-3.4.6]$ cd conf
  [hadoop@SY-0134 conf]$ cp zoo_sample.cfg  zoo.cfg
  [hadoop@SY-0134 conf]$ vi zoo.cfg
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# The number of milliseconds of each tick
tickTime=2000
# The number of ticks that the initial 
# synchronization phase can take
initLimit=10
# The number of ticks that can pass between 
# sending a request and getting an acknowledgement
syncLimit=5
# the directory where the snapshot is stored.
# do not use /tmp for storage, /tmp here is just 
# example sakes.
dataDir=/home/hadoop/labsp/zookeeper-3.4.6/data
dataLogDir=/home/hadoop/labsp/zookeeper-3.4.6/datalog
# the port at which the clients will connect
clientPort=2181
# the maximum number of client connections.
# increase this if you need to handle more clients
#maxClientCnxns=60
#
# Be sure to read the maintenance section of the 
# administrator guide before turning on autopurge.
#
# http://zookeeper.apache.org/doc/current/zookeeperAdmin.html#sc_maintenance
#
# The number of snapshots to retain in dataDir
#autopurge.snapRetainCount=3
# Purge task interval in hours
# Set to "0" to disable auto purge feature
#autopurge.purgeInterval=1
server.1=SY-0134:2888:3888
server.2=SY-0130:2888:3888
server.3=SY-0131:2888:3888
server.4=SY-0132:2888:3888
server.5=SY-0133:2888:3888




#配置myid文件
[hadoop@SY-0134 data]$ pwd
/home/hadoop/labsp/zookeeper-3.4.6/data
输入1进入SY-0134的zookeeper中的myid文件
echo "1"> home/hadoop/labsp/zookeeper-3.4.6/data/myid
7、SSH免密码登录
虽然在Hadoop集群中，SY-0130，能够免密码登录到SY-0131，SY-0132，SY-0133 。
但是在本次Spark集群中，Master为SY-0134 ，他需要能够免密码登录到SY-0130，SY-0131，SY-0132，SY-0133。
#我是先在SY-0134中，生成公钥。
[hadoop@SY-0134 ~]$ ssh-keygen -t rsa
[hadoop@SY-0134 ~]$ cd .ssh
[hadoop@SY-0134 .ssh]$ ls
id_rsa  id_rsa.pub  known_hosts
#将id_rsa.pub文件拷贝给SY-0130
[hadoop@SY-0134 .ssh]$ scp id_rsa.pub hadoop@SY-0130:~/.ssh/authorized_keys
#在SY-0130中，生成公钥。
[hadoop@SY-0130 ~]$ ssh-keygen -t rsa
[hadoop@SY-0130 ~]$ cd .ssh
[hadoop@SY-0130 .ssh]$ ls
authorized_keys     id_rsa  id_rsa.pub  known_hosts
#将id_rsa.pub文件的内容追加写入到authorized_keys中。稍微有点特殊。
[hadoop@SY-0130 .ssh]$ cat id_rsa.pub >>authorized_keys
#将SY-0130下的authorized_keys文件使用SCP命令复制到SY-0131，SY-0132，SY-0133 。
8、其他节点Spark，Scala, zookeeper安装
  上述7步仅完成了SY-0134 ，Spark，Scala, Zookeeper的安装，须将三个安装文件目录SCP命令拷贝到SY-0130,SY-0131,SY-132,SY-0133目录，并且同样设置环境变量。
  [hadoop@SY-0134 labsp]$ ls
  scala-2.10.3  spark1.2_hadoop2.3  zookeeper-3.4.6
另外一点，Zoookeeper 的Server在不同节点上，myid文件内容不一样。
echo "1"> home/hadoop/labsp/zookeeper-3.4.6/data/myid   #SY-0134
echo "2"> home/hadoop/labsp/zookeeper-3.4.6/data/myid   #SY-0130
echo "3"> home/hadoop/labsp/zookeeper-3.4.6/data/myid   #SY-0131
echo "4"> home/hadoop/labsp/zookeeper-3.4.6/data/myid   #SY-0132
echo "5"> home/hadoop/labsp/zookeeper-3.4.6/data/myid   #SY-0133
集群启动测试：1、在5个节点上分别启动zookeeper .
[hadoop@SY-0134 zookeeper-3.4.6]$ bin/zkServer.sh start
[hadoop@SY-0130 zookeeper-3.4.6]$ bin/zkServer.sh start
[hadoop@SY-0131 zookeeper-3.4.6]$ bin/zkServer.sh start
[hadoop@SY-0132 zookeeper-3.4.6]$ bin/zkServer.sh start
[hadoop@SY-0133 zookeeper-3.4.6]$ bin/zkServer.sh start

2、在SY-0134启动 Spark Master
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[hadoop@SY-0134 spark1.2_hadoop2.3]$ sbin/start-all.sh
starting org.apache.spark.deploy.master.Master, logging to /home/hadoop/labsp/spark1.2_hadoop2.3/sbin/../logs/spark-hadoop-org.apache.spark.deploy.master.Master-1-SY-0134.out
SY-0133: starting org.apache.spark.deploy.worker.Worker, logging to /home/hadoop/labsp/spark1.2_hadoop2.3/sbin/../logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-SY-0133.out
SY-0132: starting org.apache.spark.deploy.worker.Worker, logging to /home/hadoop/labsp/spark1.2_hadoop2.3/sbin/../logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-SY-0132.out
SY-0131: starting org.apache.spark.deploy.worker.Worker, logging to /home/hadoop/labsp/spark1.2_hadoop2.3/sbin/../logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-SY-0131.out





3、在SY-0130启动 Standby Spark Master
[hadoop@SY-0130 spark1.2_hadoop2.3]$ sbin/start-master.sh
starting org.apache.spark.deploy.master.Master, logging to /lab/labsp/spark1.2_hadoop2.3/sbin/../logs/spark-hadoop-org.apache.spark.deploy.master.Master-1-SY-0130.out
 
 
有了这样的实验环境，就可以继续深入学习Spark运行架构、SparkSQL等知识啦. 


转自：http://www.cnblogs.com/xiejin/p/4213082.html