hadoop+spark详细的部署过程
准备软件包1、hadoop-2.7.2.tar.gz
http://mirror.bit.edu.cn/apache/hadoop/common/
2、scala-2.10.4.tgz
http://www.scala-lang.org/download/2.10.4.html
3、spark-2.0.0-bin-hadoop2.7.tar
http://spark.apache.org/downloads.html
一、环境准备
3台Centos7的虚拟机：
172.16.92.115 spark01 #namenode
172.16.92.117 spark02 #datanode
172.16.92.80 spark03 #datanode
1.1、防火墙、selinux关闭
1.2、配置hosts设置ssh免密码登录，使三台机能够互访
# vi /etc/hosts
172.16.92.115 spark01 #namenode
172.16.92.117 spark02 #datanode
172.16.92.80 spark03 #datanode
# ssh-keygen
# ssh-copy-id spark01
# ssh-copy-id spark02
# ssh-copy-id spark03
# scp /etc/hosts spark02:/etc/
# scp /etc/hosts spark03:/etc/
# ssh-keygen
# ssh-copy-id spark01
# ssh-copy-id spark02
# ssh-copy-id spark03
# ssh-keygen
# ssh-copy-id spark01
# ssh-copy-id spark02
# ssh-copy-id spark03
1.3、安装JDK1.8
这里已安装
# yum list|grep jdk
Repodata is over 2 weeks old. Install yum-cron? Or run: yum
makecache fast
jdk1.8.0_91.x86_64 2000:1.8.0_91-fcs installed
1.4、配置环境变量
# export JAVA_HOME=/usr/java/jdk1.8.0_91
# export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar:$JAVA_HOME/lib:$CLASSPATH
# export PATH=$JAVA_HOME/bin:$PATH
二、开始安装hadoop
2.1、解压并创建hadoop文件目录
#tar -xzvf hadoop-2.7.2.tar.gz -C /opt
#cd /opt/hadoop-2.7.2
#mkdir -p dfs/name
#mkdir -p dfs/data
#mkdir tmp/
2.2、修改配置文件
主要是修改以下文件
etc/hadoop/slaves
etc/hadoop/core-site.xml
etc/hadoop/hdfs-site.xml
etc/hadoop/mapred-site.xml
etc/hadoop/yarn-site.xml
2.2.1、修改slaves文件（添加数据节点）
#vi etc/hadoop/slaves
spark02
spark03
2.2.2、修改core-site.xml文件（增加hadoop核心配置，hdfs文件端口是9000）
#vi etc/hadoop/core-site.xml
fs.defaultFS
hdfs://spark01:9000
io.file.buffer.size
131072#默认64MB，这里改为128MB
hadoop.tmp.dir
file:/opt/hadoop-2.7.2/tmp
Abasefor other temporary directories.
hadoop.proxyuser.spark.hosts
*
hadoop.proxyuser.spark.groups
*
2.2.3、
修改hdfs-site.xml 文件（增加hdfs配置信息、namenode、datanode端口和目录位置）
#vi etc/hadoop/hdfs-site.xml
dfs.namenode.secondary.http-address
spark01:9001
dfs.namenode.name.dir
file:/opt/hadoop-2.7.2/dfs/name
dfs.datanode.data.dir
file:/opt/hadoop-2.7.2/dfs/data
dfs.replication
2#复本个数==datanode个数
dfs.webhdfs.enabled
true
2.2.4、修改mapred-site.xml 文件（增加mapreduce配置、使用yarn框架、jobhistory使用地址以及web地址）
#vi etc/hadoop/mapred-site.xml
mapreduce.framework.name
yarn
mapreduce.jobhistory.address
spark01:10020
mapreduce.jobhistory.webapp.address
spark01:19888
# vi etc/hadoop/yarn-site.xml
yarn.nodemanager.aux-services
mapreduce_shuffle
yarn.nodemanager.aux-
services.mapreduce.shuffle.class
org.apache.hadoop.mapred.ShuffleHandler
yarn.resourcemanager.address
spark01:8032
yarn.resourcemanager.scheduler.address
spark01:8030
yarn.resourcemanager.resource-tracker.address
spark01:8035
yarn.resourcemanager.admin.address
spark01:8033
yarn.resourcemanager.webapp.address
spark01:8088
2.2.5、修改hadoop_env.sh配置文件的JAVA_HOME
# vi etc/hadoop/hadoop-env.sh
# The java implementation to use.
export JAVA_HOME=/usr/java/jdk1.8.0_91
2.3、将配置好的hadoop文件copy到其他的所有的slave机器
# scp -r /opt/hadoop-2.7.2/ spark02:/opt/
# scp -r /opt/hadoop-2.7.2/ spark03:/opt/
2.4、配置hadoop环境变量
# export HADOOP_HOME=/opt/hadoop-2.7.2
# export PATH=$HADOOP_HOME/bin:$PATH
# export HADOOP_HOME=/opt/hadoop-2.7.2
# export PATH=$HADOOP_HOME/bin:$PATH
# export HADOOP_HOME=/opt/hadoop-2.7.2
# export PATH=$HADOOP_HOME/bin:$PATH
注意：这里配置的是本地的环境变量，在hadoop中不一定会生效。
hadoop的环境变量在：etc/hadoop/hadoop-env.sh
spark的环境变量也一样，当使用spark-submit提交任务到集群，
如果要调用库等，需要在spark中配置环境变量，就是添加环境变量到spark-env.sh文件。
2.5、格式化namenode节点
# ./bin/hdfs namenode -format
17/07/18 09:52:36 INFO namenode.NameNode: STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: host = spark01/172.16.92.115
STARTUP_MSG: args = [-format]
STARTUP_MSG: version = 2.7.2
。。。。。。
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at spark01/172.16.92.115
************************************************************/
2.6、启动hadoop文件系统
# ./sbin/start-dfs.sh
Starting namenodes on
spark01: Warning: Permanently added the ECDSA host key for IP
address '172.16.92.115' to the list of known hosts.
spark01: starting namenode, logging to /opt/hadoop-2.7.2/logs/hadoop-root-namenode-spark01.out
spark03: starting datanode, logging to /opt/hadoop-2.7.2/logs/hadoop-root-datanode-spark03.out
spark02: starting datanode, logging to /opt/hadoop-2.7.2/logs/hadoop-root-datanode-spark02.out
Starting secondary namenodes
spark01: starting secondarynamenode, logging to /opt/hadoop-2.7.2/logs/hadoop-root-secondarynamenode-spark01.out
2.7、查看进程jps
# jps
25954 Jps
25749 SecondaryNameNode
25533 NameNode
为了使用hadoop命令方便，加个环境变量
# vi /etc/profile
export PATH=$PATH:/opt/hadoop-2.7.2/bin
2.8、其他命令
2.8.1、关闭文件系统
#./sbin/stop-dfs.sh 2.8.2、开启或者关闭hadoop所有服务
#./sbin/start-all.sh
#./sbin/stop-all.sh
用浏览器输入以下地址查看hadoop集群
http://spark01:50070/
三、安装Spark2.0.0
已经在3个节点中安装hadoop集群
3.1.1、安装scala
# tar -zxvf scala-2.10.4.tgz -C /usr/local 3.1.2、配置scala环境变量（spark是用scala开发的）
# vi /etc/profile
export SCALA_HOME=/usr/local/scala-2.10.4
export PATH=${JAVA_HOME}/bin:${HADOOP_HOME}/bin:${SCALA_HOME}/bin:$PATH
# source /etc/profile
3.1.3、测试scala运行环境
# scala
Welcome to Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server
VM, Java 1.8.0_91).
Type in expressions to have them evaluated.
Type :help for more information.
scala> 9*3
res0: Int = 27
scala> exit
3.2、安装spark
3.2.1、解压spark包
# tar -zxvf spark-2.0.0-bin-hadoop2.7.tar -C /usr/local/ 3.2.2、配置本地环境变量
# vi /etc/profile
export SPARK_HOME=/usr/local/spark-2.0.0-bin-hadoop2.7/
export PATH=${JAVA_HOME}/bin:${HADOOP_HOME}/bin:${SCALA_HOME}/bin:${SPARK_HOME}/bin:$PATH
# source /etc/profile
3.2.3、配置Spark环境变量
# cd $SPARK_HOME/conf
# cp spark-env.sh.template spark-env.sh
# vi spark-env.sh
export JAVA_HOME=/usr/java/jdk1.8.0_91
export SCALA_HOME=/usr/local/scala-2.10.4
export SPARK_MASTER_IP=spark01 #Spark集群的主节点的主机名
export SPARK_WORKER_CORES=1 #Spark集群工作节点的cpu核数
export SPARK_WORKER_MEMORY=512M #Spark集群工作节点的可用内存
export HADOOP_CONF_DIR=/opt/hadoop-2.7.2/etc/hadoop
3.2.4、配置工作节点
# cp slaves.template slaves
# vi slaves
spark01
spark02
spark03
3.2.5、将配置好的hadoop文件copy到其他的所有的slave机器
# scp -r /usr/local/spark-2.0.0-bin-hadoop2.7/ spark02:/usr/local/
# scp -r /usr/local/spark-2.0.0-bin-hadoop2.7/ spark03:/usr/local/
3.2.6、启动Spark
# cd $SPARK_HOME/sbin
# ./start-all.sh
starting org.apache.spark.deploy.master.Master, logging to
/usr/local/spark-2.0.0-bin-hadoop2.7//logs/spark-root-org.apache.spark.deploy.master.Master-1-spark01.out
localhost: Warning: Permanently added 'localhost' (ECDSA) to the list of known hosts.
spark02: starting org.apache.spark.deploy.worker.Worker, logging
to /usr/local/spark-2.0.0-bin-hadoop2.7/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-spark02.out
spark01: starting org.apache.spark.deploy.worker.Worker, logging
to /usr/local/spark-2.0.0-bin-hadoop2.7/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-spark01.out
localhost: starting org.apache.spark.deploy.worker.Worker,
logging to /usr/local/spark-2.0.0-bin-hadoop2.7/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-spark01.out
spark03: starting org.apache.spark.deploy.worker.Worker, logging
to /usr/local/spark-2.0.0-bin-hadoop2.7/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-spark03.out
3.2.7、jps查看进程，多了一个Master和Worker进程
# jps
25749 SecondaryNameNode
13413 Worker
13719 Jps
13322 Master
13434 Worker
25533 NameNode
查看spark集群
http://spark01:8080/
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