操作系统环境准备 （1）安装VMWare 下载地址：http://pan.baidu.com/s/1bniBipD 密码：pbdw 安装过程略 1 2 3 （2）下载操作系统并安装 Ubuntu 10.04操作系统下载地址：

链接：http://pan.baidu.com/s/1kTy9Umj 密码：2w5b 1 CentOS 6.5下载地址：

下载地址：http://pan.baidu.com/s/1mgkuKdi 密码：xtm5 1 2 本实验要求装三台：CentOS 6.5，可以分别安装，也可以安装完一台后克隆两台，具体过程略。初学者，建议三台分别安装。安装后如下图所示：

（3）CentOS 6.5网络配置 安装好的虚拟机一般默认使用的是NAT（关于NAT、桥接等虚拟机网络连接方式参见本人博客：http://blog.csdn.net/lovehuangjiaju/article/details/48183485），由于三台机器之间需要互通之外，还需要与本机连通，因此采用将网络连接方式设置为Bridged（三台机器相同的设置），如下图所法：

修改主机名 （1）修改centos_salve01虚拟机主机名：

vim /etc/sysconfig/network 1 /etc/sysconfig/network修改后的内容如下：

（2）vim /etc/sysconfig/network命令修改centos_slave02虚拟机主机名 /etc/sysconfig/network修改后的内容如下：

（3）vim /etc/sysconfig/network命令修改centos_slave03虚拟机主机名 /etc/sysconfig/network修改后的内容如下：

修改主机IP地址 在大家在配置时，修改/etc/sysconfig/network-scripts/ifcfg-eth0文件对应的BOOTPROT=static、IPADDR、NETMASK、GATEWAY及DNS1信息即可

（1）修改centos_salve01虚拟机主机IP地址：

vim /etc/sysconfig/network-scripts/ifcfg-eth0

修改后内容如下：

DEVICE="eth0" BOOTPROTO="static" HWADDR="00:0c:29:3f:69:4d" IPV6INIT="yes" NM_CONTROLLED="yes" ONBOOT="yes" TYPE="Ethernet" UUID="5315276c-db0d-4061-9c76-9ea86ba9758e" IPADDR="192.168.1.111" NETMASK="255.255.255.0" GATEWAY="192.168.1.1" DNS1="8.8.8.8"

（2）修改centos_salve02虚拟机主机IP地址：

vim /etc/sysconfig/network-scripts/ifcfg-eth0

修改后内容如下：

DEVICE="eth0" BOOTPROTO="static" HWADDR="00:0c:29:64:f9:80" IPV6INIT="yes" NM_CONTROLLED="yes" ONBOOT="yes" TYPE="Ethernet" UUID="5315276c-db0d-4061-9c76-9ea86ba9758e" IPADDR="192.168.1.112" NETMASK="255.255.255.0" GATEWAY="192.168.1.1" DNS1="8.8.8.8"

（3）修改centos_salve03虚拟机主机IP地址：

vim /etc/sysconfig/network-scripts/ifcfg-eth0 1 修改后内容如下：

DEVICE="eth0" BOOTPROTO="static" HWADDR="00:0c:29:1e:80:b1" IPV6INIT="yes" NM_CONTROLLED="yes" ONBOOT="yes" TYPE="Ethernet" UUID="5315276c-db0d-4061-9c76-9ea86ba9758e" IPADDR="192.168.1.113" NETMASK="255.255.255.0" GATEWAY="192.168.1.1" DNS1="8.8.8.8"

/etc/sysconfig/network-scripts/ifcfg-eth0文件内容解析：

DEVICE=eth0 //指出设备名称 BOOTPROT=static //启动类型 dhcp|static，使用桥接模式，必须是static HWADDR=00:06:5B:FE:DF:7C //硬件Mac地址 IPADDR=192.168.0.2 //IP地址 NETMASK=255.255.255.0 //子网掩码 NETWORK=192.168.0.0 //网络地址 GATEWAY=192.168.0.1 //网关地址 ONBOOT=yes //是否启动应用 TYPE=Ethernet //网络类型

设置完成后，使用

service network restart

命令重新启动网络，配置即可生效。

设置主机名与IP地址映射 （1）修改centos_salve01主机名与IP地址映射

vim /etc/hosts

设置内容如下：

127.0.0.1 slave01.example.com localhost localhost.localdomain localhost4 localhost4.localdomain4 slave01.example.com 192.168.1.111 slave01.example.com 192.168.1.112 slave02.example.com 192.168.1.113 slave03.example.com

（2）修改centos_salve02主机名与IP地址映射

vim /etc/hosts 1 设置内容如下：

127.0.0.1 slave02.example.com localhost localhost.localdomain localhost4 localhost4.localdomain4 ::1 slave02.example.com 192.168.1.111 slave01.example.com 192.168.1.112 slave02.example.com 192.168.1.113 slave03.example.com

（3）修改centos_salve03主机名与IP地址映射

vim /etc/hosts 1 设置内容如下：

127.0.0.1 slave03.example.com localhost localhost.localdomain localhost4 localhost4.localdomain4 ::1 slave03.example.com 192.168.1.111 slave01.example.com 192.168.1.112 slave02.example.com 192.168.1.113 slave03.example.com

修改主机DNS 采用下列命令设置各主机DNS（三台机器进行相同的设置)

vim /etc/resolv.conf 1 设置后的内容：

Generated by NetworkManager

search example.com nameserver 8.8.8.8

8.8.8.8为Google提供的DNS服务器

网络连通测试 前面所有的配置完成后，重启centos_salve01、centos_salve02、centos_salve03使主机名设置生效，然后分别在三台机器上作如下测试命令： 下面只给出在centos_salve01虚拟机上的测试

[root@slave01 ~]# ping slave02.example.com PING slave02.example.com (192.168.1.112) 56(84) bytes of data. 64 bytes from slave02.example.com (192.168.1.112): icmp_seq=1 ttl=64 time=0.417 ms 64 bytes from slave02.example.com (192.168.1.112): icmp_seq=2 ttl=64 time=0.355 ms 64 bytes from slave02.example.com (192.168.1.112): icmp_seq=3 ttl=64 time=0.363 ms ^C — slave02.example.com ping statistics — 3 packets transmitted, 3 received, 0% packet loss, time 2719ms rtt min/avg/max/mdev = 0.355/0.378/0.417/0.031 ms [root@slave01 ~]# ping slave03.example.com PING slave03.example.com (192.168.1.113) 56(84) bytes of data. 64 bytes from slave03.example.com (192.168.1.113): icmp_seq=1 ttl=64 time=0.386 ms 64 bytes from slave03.example.com (192.168.1.113): icmp_seq=2 ttl=64 time=0.281 ms ^C — slave03.example.com ping statistics — 2 packets transmitted, 2 received, 0% packet loss, time 1799ms rtt min/avg/max/mdev = 0.281/0.333/0.386/0.055 ms

测试外网的连通性（我在装的时候，8.8.8.8，已经被禁用….心中一万头cnm）：

[root@slave01 ~]# ping www.baidu.com ping: unknown host www.baidu.com [root@slave01 ~]# ping 8.8.8.8 PING 8.8.8.8 (8.8.8.8) 56(84) bytes of data. From 192.168.1.111 icmp_seq=2 Destination Host Unreachable From 192.168.1.111 icmp_seq=3 Destination Host Unreachable From 192.168.1.111 icmp_seq=4 Destination Host Unreachable From 192.168.1.111 icmp_seq=6 Destination Host Unreachable From 192.168.1.111 icmp_seq=7 Destination Host Unreachable From 192.168.1.111 icmp_seq=8 Destination Host Unreachable

（4）SSH完密码登录

(1) OpenSSH安装

如果大家在配置时，ping 8.8.8.8能够ping通，则主机能够正常上网；如果不能上网，则将网络连接方式重新设置为NAT，并修改网络配置文件为dhcp方式。在保证网络连通的情况下执行下列命令：

yum install openssh-server 1

(2) 无密码登录实现

使用以下命令生成相应的密钥（三台机器进行相同的操作）

ssh-keygen -t rsa 1 执行过程一直回车即可

[root@slave01 ~]# ssh-keygen -t rsa Generating public/private rsa key pair. Enter file in which to save the key (/root/.ssh/id_rsa): Enter passphrase (empty for no passphrase): Enter same passphrase again: Your identification has been saved in /root/.ssh/id_rsa. Your public key has been saved in /root/.ssh/id_rsa.pub. The key fingerprint is: 4e:2f:39:ed:f4:32:2e:a3:55:62:f5:8a:0d:c5:2c:16 root@slave01.example.com The key’s randomart image is:

生成的文件分别为/root/.ssh/id_rsa（私钥）、/root/.ssh/id_rsa.pub（公钥）

完成后将公钥拷贝到要免登陆的机器上（三台可进行相同操作）：

ssh-copy-id -i slave01.example.com ssh-copy-id -i slave02.example.com ssh-copy-id -i slave03.example.com

Hadoop 2.4.1集群搭建 集群搭建相关软件下载地址：

链接：http://pan.baidu.com/s/1sjIG3b3 密码：38gh 1 下载后将所有软件都放置在E盘的share目录下:

设置share文件夹为虚拟机的共享目录，如下图所示：

在linux系统中，采用

[root@slave01 /]# cd /mnt/hgfs/share [root@slave01 share]# ls 1 2 命令可以切换到该目录下，如下图

Spark官方要求的JDK、Scala版本

Spark runs on Java 7+, Python 2.6+ and R 3.1+. For the Scala API, Spark 1.5.0 uses Scala 2.10. You will need to use a compatible Scala version (2.10.x). 1 （1）JDK 1.8 安装 在根目录下创建sparkLearning目前，后续所有相关软件都放置在该目录下，代码如下：

[root@slave01 /]# mkdir /sparkLearning [root@slave01 /]# ls bin etc lib media proc selinux sys var boot hadoopLearning lib64 mnt root sparkLearning tmp dev home lost+found opt sbin srv usr

将共享目录中的jdk安装包复制到/sparkLearning目录

[root@slave01 share]# cp /mnt/hgfs/share/jdk-8u40-linux-x64.gz /sparkLearning/ [root@slave01 share]# cd /sparkLearning/ //解压 [root@slave01 sparkLearning]# tar -zxvf jdk-8u40-linux-x64.gz

设置环境变量：

[root@slave01 sparkLearning]# vim /etc/profile 1 在文件最后添加：

export JAVA_HOME=/sparkLearning/jdk1.8.0_40 export PATH=$JAV{A}_{H}OME/bin:$PATH

测试配置是否成功：

//使修改后的配置生效 [root@slave01 sparkLearning]# source /etc/profile //环境变量是否已经设置 [root@slave01 sparkLearning]# $JAVA_HOME bash: /sparkLearning/jdk1.8.0_40: is a directory //测试java是否安装配置成功 [root@slave01 sparkLearning]# java -version java version “1.8.0_40” Java™ SE Runtime Environment (build 1.8.0_40-b25) Java HotSpot™ 64-Bit Server VM (build 25.40-b25, mixed mode)

（2）Scala 2.10.4 安装 //复制文件到sparkLearning目录下 [root@slave01 sparkLearning]# cp /mnt/hgfs/share/scala-2.10.4.tgz . //解压 [root@slave01 sparkLearning]# tar -zxvf scala-2.10.4.tgz > /dev/null

[root@slave01 sparkLearning]# vim /etc/profile

将/etc/profile文件末尾内容修改如下：

export JAVA_HOME=/sparkLearning/jdk1.8.0_40 export SCALA_HOME=/sparkLearning/scala-2.10.4 export PATH=$JAV{A}_{H}OME/bin:${SCALA_HOME}/bin:$PATH

测试Scala是否安装成功

[root@slave01 sparkLearning]# source /etc/profile [root@slave01 sparkLearning]# $SCALA_HOME bash: /sparkLearning/scala-2.10.4: is a directory [root@slave01 sparkLearning]# scala -version Scala code runner version 2.10.4 – Copyright 2002-2013, LAMP/EPFL

（3）Zookeeper-3.4.5 集群搭建 [root@slave01 sparkLearning]# cp /mnt/hgfs/share/zookeeper-3.4.5.tar.gz . [root@slave01 sparkLearning]# tar -zxvf zookeeper-3.4.5.tar.gz > /dev/null

[root@slave01 sparkLearning]# cp zookeeper-3.4.5/conf/zoo_sample.cfg zoo.cfg [root@slave01 sparkLearning]# vim zoo.cfg

修改dataDir为：

dataDir=/sparkLearning/zookeeper-3.4.5/zookeeper_data

在文件末尾添加如下内容：

server.1=slave01.example.com:2888:3888 server.2=slave02.example.com:2888:3888 server.3=slave03.example.com:2888:3888

//配置slave02.example.com上的myid [root@slave01 /]# ssh salve02.example.com [root@slave02 ~]# echo 2 > /sparkLearning/zookeeper-3.4.5/zookeeper_data/myid [root@slave02 ~]# more /sparkLearning/zookeeper-3.4.5/zookeeper_data/myid 2 //配置slave03.example.com上的myid [root@slave02 ~]# ssh slave03.example.com Last login: Fri Sep 18 01:33:29 2015 from slave01.example.com [root@slave03 ~]# echo 3 > /sparkLearning/zookeeper-3.4.5/zookeeper_data/myid [root@slave03 ~]# more /sparkLearning/zookeeper-3.4.5/zookeeper_data/myid

如此便完成配置，下面对集群进行测试：

//在slave03.example.com主机上 [root@slave03 ~]# cd /sparkLearning/zookeeper-3.4.5/bin [root@slave03 bin]# ls README.txt zkCli.cmd zkEnv.cmd zkServer.cmd zkCleanup.sh zkCli.sh zkEnv.sh zkServer.sh

//启动slave03.example.com上的ZooKeeper [root@slave03 bin]# ./zkServer.sh start JMX enabled by default Using config: /sparkLearning/zookeeper-3.4.5/bin/…/conf/zoo.cfg Starting zookeeper … STARTED [root@slave03 bin]# ./zkServer.sh status JMX enabled by default Using config: /sparkLearning/zookeeper-3.4.5/bin/…/conf/zoo.cfg Mode: leader

//在slave02.example.com主机上 [root@slave02 bin]# ./zkServer.sh start JMX enabled by default Using config: /sparkLearning/zookeeper-3.4.5/bin/…/conf/zoo.cfg Starting zookeeper … STARTED //查看zookeeper集群状态，如果Mode显示为follower或leader则表明配置成功 [root@slave02 bin]# ./zkServer.sh status JMX enabled by default Using config: /sparkLearning/zookeeper-3.4.5/bin/…/conf/zoo.cfg Mode: follower

//在slave01.example.com主机上 [root@slave01 bin]# ./zkServer.sh start JMX enabled by default Using config: /sparkLearning/zookeeper-3.4.5/bin/…/conf/zoo.cfg Starting zookeeper … STARTED [root@slave01 bin]# ./zkServer.sh status JMX enabled by default Using config: /sparkLearning/zookeeper-3.4.5/bin/…/conf/zoo.cfg Mode: follower

//在slave03.example.com主机上zookeeper状态 [root@slave03 bin]# ./zkServer.sh status JMX enabled by default Using config: /sparkLearning/zookeeper-3.4.5/bin/…/conf/zoo.cfg Mode: leader （4）Hadoop 2.4.1 集群搭建 （1）Hadoop 2.4.1基本目录浏览 root@slave01 bin]# cp /mnt/hgfs/share/hadoop-2.4.1.tar.gz /sparkLearning/ [root@slave01 bin]# cd /sparkLearning/ [root@slave01 sparkLearning]# tar -zxvf hadoop-2.4.1.tar.gz > /dev/null [root@slave01 sparkLearning]# cd hadoop-2.4.1 [root@slave01 hadoop-2.4.1]# ls bin include libexec NOTICE.txt sbin etc lib LICENSE.txt README.txt share cd [root@slave01 hadoop-2.4.1]# cd etc/hadoop/ [root@slave01 hadoop]# ls capacity-scheduler.xml hdfs-site.xml mapred-site.xml.template configuration.xsl httpfs-env.sh slaves container-executor.cfg httpfs-log4j.properties ssl-client.xml.example core-site.xml httpfs-signature.secret ssl-server.xml.example hadoop-env.cmd httpfs-site.xml yarn-env.cmd hadoop-env.sh log4j.properties yarn-env.sh hadoop-metrics2.properties mapred-env.cmd yarn-site.xml hadoop-metrics.properties mapred-env.sh hadoop-policy.xml mapred-queues.xml.template

（2）将Hadoop 2.4.1添加到环境变量 使用命令：vim /etc/profile 将环境变量信息修改如下：

export JAVA_HOME=/sparkLearning/jdk1.8.0_40 export SCALA_HOME=/sparkLearning/scala-2.10.4 export HADOOP_HOME=/sparkLearning/hadoop-2.4.1 export PATH=$JAV{A}_{H}OME/bin:${SCALA_HOME}/bin:$HADOO{P}_{H}OME/bin:${HADOOP_HOME}/sbin:$PATH

（3）将Hadoop 2.4.1添加到环境变量 使用命令：vim hadoop-env.sh 将环境变量信息修改如下，在export JAVA_HOME修改为：

export JAVA_HOME=/sparkLearning/jdk1.8.0_40

（4）修改core-site.xml文件 利用vim core-site.xml命令，文件内容如下：

<property> <name>fs.defaultFS</name> <value>hdfs://ns1</value> </property> <!-- 指定hadoop临时目录 --> <property> <name>hadoop.tmp.dir</name> <value>/sparkLearning/hadoop-2.4.1/tmp</value> </property> <!-- 指定zookeeper地址 --> <property> <name>ha.zookeeper.quorum</name> <value>slave01.example.com:2181,slave02.example.com:2181,slave03.example.com:2181</value> </property> </configuration>

（5）修改hdfs-site.xml文件 vim hdfs-site.xml内容如下：

<configuration> <!--指定hdfs的nameservice为ns1，需要和core-site.xml中的保持一致 --> <property> <name>dfs.nameservices</name> <value>ns1</value> </property> <!-- ns1下面有两个NameNode，分别是nn1，nn2 --> <property> <name>dfs.ha.namenodes.ns1</name> <value>nn1,nn2</value> </property> <!-- nn1的RPC通信地址 --> <property> <name>dfs.namenode.rpc-address.ns1.nn1</name> <value>slave01.example.com:9000</value> </property> <!-- nn1的http通信地址 --> <property> <name>dfs.namenode.http-address.ns1.nn1</name> <value>slave01.example.com:50070</value> </property> <!-- nn2的RPC通信地址 --> <property> <name>dfs.namenode.rpc-address.ns1.nn2</name> <value>slave02.example.com:9000</value> </property> <!-- nn2的http通信地址 --> <property> <name>dfs.namenode.http-address.ns1.nn2</name> <value>slave02.example.com:50070</value> </property> <!-- 指定NameNode的元数据在JournalNode上的存放位置 --> <property> <name>dfs.namenode.shared.edits.dir</name> <value>qjournal://slave01.example.com:8485;slave02.example.com:8485;slave03.example.com:8485/ns1</value> </property> <!-- 指定JournalNode在本地磁盘存放数据的位置 --> <property> <name>dfs.journalnode.edits.dir</name> <value>/sparkLearning/hadoop-2.4.1/journal</value> </property> <!-- 开启NameNode失败自动切换 --> <property> <name>dfs.ha.automatic-failover.enabled</name> <value>true</value> </property> <!-- 配置失败自动切换实现方式 --> <property> <name>dfs.client.failover.proxy.provider.ns1</name> <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value> </property> <!-- 配置隔离机制方法，多个机制用换行分割，即每个机制暂用一行--> <property> <name>dfs.ha.fencing.methods</name> <value> sshfence shell(/bin/true) </value> </property> <!-- 使用sshfence隔离机制时需要ssh免登陆 --> <property> <name>dfs.ha.fencing.ssh.private-key-files</name> <value>/home/hadoop/.ssh/id_rsa</value> </property> <!-- 配置sshfence隔离机制超时时间 --> <property> <name>dfs.ha.fencing.ssh.connect-timeout</name> <value>30000</value> </property> </configuration>

（4）修改mapred-site.xml文件 [root@slave01 hadoop]# cp mapred-site.xml.template mapred-site.xml 1 vim mapred-site.xml修改文件内容如下:

<configuration> <!-- 指定mr框架为yarn方式 --> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> </configuration>

（6）修改yarn-site.xml文件

<?xml version="1.0"?> <!-- Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. See accompanying LICENSE file. --> <configuration> <!-- 开启RM高可靠 --> <property> <name>yarn.resourcemanager.ha.enabled</name> <value>true</value> </property> <!-- 指定RM的cluster id --> <property> <name>yarn.resourcemanager.cluster-id</name> <value>SparkCluster</value> </property> <!-- 指定RM的名字 --> <property> <name>yarn.resourcemanager.ha.rm-ids</name> <value>rm1,rm2</value> </property> <!-- 分别指定RM的地址 --> <property> <name>yarn.resourcemanager.hostname.rm1</name> <value>slave01.example.com</value> </property> <property> <name>yarn.resourcemanager.hostname.rm2</name> <value>slave02.example.com</value> </property> <!-- 指定zk集群地址 --> <property> <name>yarn.resourcemanager.zk-address</name> <value> </value> </property> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> </configuration>

（7）修改slaves文件 slave01.example.com slave02.example.com slave03.example.com

（8）配置文件拷贝到其它服务器 //slave01.example.com上的配置文件拷贝到slave02.example.com [root@slave01 hadoop]# scp -r /etc/profile slave02.example.com:/etc/profile profile 100% 2027 2.0KB/s 00:00 [root@slave01 hadoop]# scp -r /sparkLearning/hadoop-2.4.1 slave02.example.com:/sparkLearning/

//slave01.example.com上的配置文件拷贝到slave03.example.com [root@slave01 hadoop]# scp -r /etc/profile slave03.example.com:/etc/profile profile 100% 2027 2.0KB/s 00:00 [root@slave01 hadoop]# scp -r /sparkLearning/hadoop-2.4.1 slave03.example.com:/sparkLearning/

（9）启动journalnode //使用下列命令启动journalnode [root@slave01 hadoop]# hadoop-daemons.sh start journalnode slave02.example.com: starting journalnode, logging to /sparkLearning/hadoop-2.4.1/logs/hadoop-root-journalnode-slave02.example.com.out slave03.example.com: starting journalnode, logging to /sparkLearning/hadoop-2.4.1/logs/hadoop-root-journalnode-slave03.example.com.out slave01.example.com: starting journalnode, logging to /sparkLearning/hadoop-2.4.1/logs/hadoop-root-journalnode-slave01.example.com.out //JournalNode进程存在，启动成功 [root@slave01 hadoop]# jps 11261 JournalNode 11295 Jps [root@slave01 hadoop]# ssh slave02.example.com Last login: Fri Sep 18 05:33:05 2015 from slave01.example.com [root@slave02 ~]# jps 6598 JournalNode 6795 Jps [root@slave02 ~]# ssh slave03.example.com Last login: Fri Sep 18 05:33:26 2015 from slave02.example.com [root@slave03 ~]# jps 5876 JournalNode 6047 Jps [root@slave03 ~]#

（10）格式化HDFS 登录slave02.example.com服务器，执行下列命令

[root@slave02 ~]# hdfs namenode -format //下面是执行结果 15/09/18 06:05:26 INFO namenode.NameNode: STARTUP_MSG: /************************************************************ STARTUP_MSG: Starting NameNode STARTUP_MSG: host = slave02.example.com/127.0.0.1 STARTUP_MSG: args = [-format] STARTUP_MSG: version = 2.4.1 STARTUP_MSG: classpath = /sparkLearning/hadoop-2.4.1/etc/hadoop:/sparkLearning/hadoop-…省略无关信息… STARTUP_MSG: build = http://svn.apache.org/repos/asf/hadoop/common -r 1604318; compiled by ‘jenkins’ on 2014-06-21T05:43Z STARTUP_MSG: java = 1.8.0_40 …省略… /sparkLearning/hadoop-2.4.1/tmp/dfs/name has been successfully formatted. 15/09/18 06:05:30 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0 15/09/18 06:05:30 INFO util.ExitUtil: Exiting with status 0 15/09/18 06:05:30 INFO namenode.NameNode: SHUTDOWN_MSG: /************************************************************ SHUTDOWN_MSG: Shutting down NameNode at slave02.example.com/127.0.0.1 ************************************************************/

（11）格式化HDFS信息复制到slave03.example.com服务器 [root@slave02 ~]# scp -r /sparkLearning/hadoop-2.4.1/tmp/ slave01.example.com:/sparkLearning/hadoop-2.4.1/ fsimage_0000000000000000000.md5 100% 62 0.1KB/s 00:00 seen_txid 100% 2 0.0KB/s 00:00 fsimage_0000000000000000000 100% 350 0.3KB/s 00:00 VERSION 100% 200 0.2KB/s 00:00

（12）格式化ZK(在slave02.example.com上执行即可) [root@slave02 hadoop]# hdfs zkfc -formatZK Java HotSpot™ 64-Bit Server VM warning: You have loaded library /sparkLearning/hadoop-2.4.1/lib/native/libhadoop.so which might have disabled stack guard. The VM will try to fix the stack guard now. …省略无关信息… //执行成功 15/09/18 06:14:22 INFO ha.ActiveStandbyElector: Successfully created /hadoop-ha/ns1 in ZK. 15/09/18 06:14:22 INFO zookeeper.ZooKeeper: Session: 0x34fe096c3ca0000 closed 15/09/18 06:14:22 INFO zookeeper.ClientCnxn: EventThread shut down

（13）启动HDFS(在slave02.example.com上执行) [root@slave02 hadoop]# start-dfs.sh [root@slave02 hadoop]# jps 7714 QuorumPeerMain 6598 JournalNode 8295 DataNode 8202 NameNode 8716 Jps 8574 DFSZKFailoverController

[root@slave02 hadoop]# ssh slave01.example.com Last login: Thu Aug 27 06:24:16 2015 from slave01.example.com [root@slave01 ~]# jps 13744 DataNode 13681 NameNode 11862 QuorumPeerMain 14007 Jps 13943 DFSZKFailoverController 13851 JournalNode

[root@slave03 ~]# jps 5876 JournalNode 7652 Jps 7068 DataNode 6764 QuorumPeerMain

（14）启动YARN(在slave01.example.com上执行) //slave01.example.com [root@slave01 ~]# start-yarn.sh …输出省略… [root@slave01 ~]# jps 14528 Jps 13744 DataNode 13681 NameNode 14228 NodeManager 11862 QuorumPeerMain 13943 DFSZKFailoverController 14138 ResourceManager 13851 JournalNode

//slave02.example.com [root@slave02 ~]# jps 11216 Jps 10656 JournalNode 7714 QuorumPeerMain 11010 NodeManager 10427 DataNode 10844 DFSZKFailoverController 10334 NameNode

//slave03.example.com [root@slave03 ~]# jps 8610 JournalNode 8791 NodeManager 8503 DataNode 9001 Jps 6764 QuorumPeerMain

（15）查看hadoop运行管理界面 打开浏览器，输入http://slave01.example.com:8088/，可以得到hadoop集群管理界面:

输入http://slave01.example.com:50070 可以得到HDFS管理界面

至此Hadoop集群配置成功

Spark 1.5.0 集群部署 （1）将Spark添加到环境变量 [root@slave01 hadoop]# cp /mnt/hgfs/share/spark-1.5.0-bin-hadoop2.4.tgz /sparkLearning/

[root@slave01 sparkLearning]# tar -zxvf spark-1.5.0-bin-hadoop2.4.tgz > /dev/null

[root@slave01 sparkLearning]# vim /etc/profile

将/etc/profile内容修改如下：

export JAVA_HOME=/sparkLearning/jdk1.8.0_40 export SCALA_HOME=/sparkLearning/scala-2.10.4 export HADOOP_HOME=/sparkLearning/hadoop-2.4.1 export SPARK_HOME=/sparkLearning/spark-1.5.0-bin-hadoop2.4 export PATH=$JAV{A}_{H}OME/bin:${SCALA_HOME}/bin:$HADOO{P}_{H}OME/bin:${HADOOP_HOME}/sbin:$SPAR{K}_{H}OME/bin:${SPARK_HOME}/sbin:$PATH

（2）将Spark添加到环境变量 [root@slave01 sparkLearning]# cd spark-1.5.0-bin-hadoop2.4/conf [root@slave01 conf]# ls docker.properties.template metrics.properties.template spark-env.sh.template fairscheduler.xml.template slaves.template log4j.properties.template spark-defaults.conf.template

//复制模板文件 [root@slave01 conf]# cp spark-env.sh.template spark-env.sh [root@slave01 conf]# vim spark-env.sh

在spark-env.sh文件中添加如下内容：

export JAVA_HOME=/sparkLearning/jdk1.8.0_40 export SCALA_HOME=/sparkLearning/scala-2.10.4 export HADOOP_CONF_DIR=/sparkLearning/hadoop-2.4.1/etc/hadoop

[root@slave01 conf]# cp slaves.template slaves [root@slave01 conf]# vim slaves

slaves文件内容如下：

A Spark Worker will be started on each of the machines listed below.

slave01.example.com slave02.example.com slave03.example.com

（3）将配置信息复制到其它服务器 [root@slave01 sparkLearning]# scp /etc/profile slave02.example.com:/etc/profile profile 100% 2123 2.1KB/s 00:00 [root@slave01 sparkLearning]# scp /etc/profile slave03.example.com:/etc/profile profile 100% 2123 2.1KB/s 00:00 [root@slave01 sparkLearning]# vim /etc/profile [root@slave01 sparkLearning]# scp -r spark-1.5.0-bin-hadoop2.4 slave02.example.com:/sparkLearning/ …执行过程省略… [root@slave01 sparkLearning]# scp -r spark-1.5.0-bin-hadoop2.4 slave03.example.com:/sparkLearning/ …执行过程省略…

（4）启动Spark集群 因为本人机器上装了Ambari Server，占用了8080端口，而Spark Master默认端是8080，因此将sbin/start-master.sh中的SPARK_MASTER_WEBUI_PORT修改为8888

if [ “$SPARK_MASTER_WEBUI_PORT” = “” ]; then SPARK_MASTER_WEBUI_PORT=8888

[root@slave01 sbin]# ./start-all.sh starting org.apache.spark.deploy.master.Master, logging to /sparkLearning/spark-1.5.0-bin-hadoop2.4/sbin/…/logs/spark-root-org.apache.spark.deploy.master.Master-1-slave01.example.com.out slave03.example.com: starting org.apache.spark.deploy.worker.Worker, logging to /sparkLearning/spark-1.5.0-bin-hadoop2.4/sbin/…/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-slave03.example.com.out slave02.example.com: starting org.apache.spark.deploy.worker.Worker, logging to /sparkLearning/spark-1.5.0-bin-hadoop2.4/sbin/…/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-slave02.example.com.out slave01.example.com: starting org.apache.spark.deploy.worker.Worker, logging to /sparkLearning/spark-1.5.0-bin-hadoop2.4/sbin/…/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-slave01.example.com.out

[root@slave01 sbin]# jps 13744 DataNode 13681 NameNode 14228 NodeManager 16949 Master 11862 QuorumPeerMain 13943 DFSZKFailoverController 14138 ResourceManager 13851 JournalNode 17179 Jps 17087 Worker

浏览器中输入slave01.example.com:8888

但是在启动过程中出现了错误，查看日志文件

[root@slave02 logs]# more spark-root-org.apache.spark.deploy.worker.Worker-1-slave02.example.com.out 1 2 日志内容中包括下列错误：

akka.actor.ActorNotFound: Actor not found for: ActorSelection[Anchor(akka.tcp:// sparkMaster@slave01.example.com:7077/), Path(/user/Master)] at akka.actor.ActorSelectionKaTeX parse error: Can't use function '$' in math mode at position 8: anonfun$̲resolveOne$1.ap…anonfun$resolveOne$1.apply(ActorSelection.scala:63)atscala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)atakka.dispatch.BatchingExecutor$AbstractBatch.processBatch(BatchingExe cutor.scala:55) at akka.dispatch.BatchingExecutor$Batch.run(BatchingExecutor.scala:73)atakka.dispatch.ExecutionContexts$sameThreadExecutionContext$.unbatchedExecute(Future.scala:74)atakka.dispatch.BatchingExecutor$class.execute(BatchingExecutor.scala:1 20) at akka.dispatch.ExecutionContexts$sameThreadExecutionContext$.execute(F uture.scala:73) at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala :40) at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)atakka.pattern.PromiseActorRef.$bang(AskSupport.scala:266) at akka.actor.EmptyLocalActorRef.specialHandle(ActorRef.scala:533) at akka.actor.DeadLetterActorRef.specialHandle(ActorRef.scala:569) …省略…

没找到具体原因，在ubuntu 10.04服务器上进行相同的配置，集群却搭建成功

（5）测试Spark集群 采用下列命上传spark-1.5.0-bin-hadoop2.4目录下的README.md文件到相应的根目录。

hadoop dfs -put README.md 进入/spark-1.5.0-bin-hadoop2.4/bin目录，启动./spark-shell，如下图所示：

执行REDME.md文件的wordcount操作：

scala> val textCount = sc.textFile(“README.md”).filter(line => line.contains(“Spark”)).count() 至此，Spark 1.5集群搭建成功