概述
一、hadoop2.6单机版安装配置
环境:
jdk版本:1.8(已完成安装及环境配置,路径:/usr/java/jdk1.8.0_65)
hadoop版本:2.6.0
spark版本:1.4.1
0、创建目录
[root@localhost ~]# mkdir /usr/local/hadoop
1、下载hadoop
hadoop-2.6.0.tar.gz
2、解压tar包
[root@localhost hadoop]# tar -zxvf hadoop-2.6.0.tar.gz
3、profile环境变量配置
jdk的位置
[root@localhost lib]# cd /usr/java/jdk1.8.0_65
[root@localhost jdk1.8.0_65]# vim /etc/profile
#HADOOP VARIABLES START
export JAVA_HOME=/usr/java/jdk1.8.0_65
export HADOOP_INSTALL=/usr/local/hadoop/hadoop-2.6.0
export PATH=$PATH:$HADOOP_INSTALL/bin
export PATH=$PATH:$HADOOP_INSTALL/sbin
export HADOOP_MAPRED_HOME=$HADOOP_INSTALL
export HADOOP_COMMON_HOME=$HADOOP_INSTALL
export HADOOP_HDFS_HOME=$HADOOP_INSTALL
export YARN_HOME=$HADOOP_INSTALL
#HADOOP VARIABLES END
[root@localhost hadoop-2.6.0]# source ~/.bashrc #让配置文件即刻生效
--------------------------------------- 或者更全面的可配置为如下:
#HADOOP VARIABLES START
export JAVA_HOME=/usr/java/jdk1.8.0_65
export HADOOP_HOME=/usr/local/hadoop/hadoop-2.6.0
export PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin:
$HADOOP_HOME/sbin
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export YARN_HOME=$HADOOP_HOME
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path-$HADOOP_HOME/lib"
#HADOOP VARIABLES END
4、修改hadoop-env.sh
位置为:[root@localhost hadoop]# pwd
/usr/local/hadoop/hadoop-2.6.0/etc/hadoop
[root@localhost hadoop]# vim hadoop-env.sh
#export JAVA_HOME=${JAVA_HOME} #修改前
export JAVA_HOME=/usr/java/jdk1.8.0_65 #修改后
5、修改core-site.xml
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/local/hadoop/hadoop-2.6.0/tmp</value>
</property>
</configuration>
6、修改mapred-site.xml.template并修改为mapred-site.xml
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
7、修改yarn-site.xml
<configuration>
<!-- Site specific YARN configuration properties -->
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>
8、修改hdfs-site.xml
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>/usr/local/hadoop/hadoop-2.6.0/dfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>/usr/local/hadoop/hadoop-2.6.0/dfs/data</value>
</property>
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
</configuration>
9、修改masters和slaves
masters好像不存在,⾃自⾏行添加
插入:
[root@localhost hadoop]# vim slaves
localhost
[root@localhost hadoop]# vim masters
localhost
10、添加临时⽬目录
[root@localhost hadoop]# cd /usr/local/hadoop/hadoop-2.6.0
[root@localhost hadoop-2.6.0]# mkdir tmp dfs dfs/name dfs/data
11.初始化hdfs
[root@localhost hadoop-2.6.0]# hdfs namenode -format
12.启动hadoop
[root@localhost hadoop-2.6.0]# start-dfs.sh
[root@localhost hadoop-2.6.0]# start-yarn.sh
13、jps
[root@localhost hadoop-2.6.0]# jps
21008 DataNode
21152 SecondaryNameNode
20897 NameNode
21297 ResourceManager
21449 NodeManager
21690 Jps
14、基于hadoop2.6的spark环境变量配置
[root@localhost hadoop]# pwd #版本间较大的改动!,1.X版本不同
/usr/local/hadoop/hadoop-2.6.0/etc/hadoop #hadoop位置
[root@localhost conf]# pwd
/usr/local/spark/spark-1.4.1-bin-hadoop2.6/conf
[root@localhost conf]# vim spark-env.sh #配置环境变量
export SCALA_HOME=/usr/local/scala/scala-2.11.7
export JAVA_HOME=/usr/java/jdk1.8.0_65
export SPARK_MASTER_IP=192.168.31.157
export SPARK_WORKER_MEMORY=512m
export master=spark://192.168.31.157:7070
export HADOOP_CONF_DIR=/usr/local/hadoop/hadoop-2.6.0/etc/hadoop #新添加的部分
15、验证
[root@localhost sbin]# cd /usr/local/spark/spark-1.4.1-bin-hadoop2.6/sbin/
[root@localhost sbin]# pwd
/usr/local/spark/spark-1.4.1-bin-hadoop2.6/sbin
[root@localhost sbin]# ./start-all.sh
[root@localhost sbin]# cd /usr/local/hadoop/hadoop-2.6.0/sbin/
[root@localhost sbin]# pwd
/usr/local/hadoop/hadoop-2.6.0/sbin
[root@localhost sbin]# ./start-all.sh
[root@localhost sbin]# jps
5171 NameNode
6259 Worker
6071 Master
6328 Jps
5290 DataNode
5581 ResourceManager
5855 NodeManager
5439 SecondaryNameNode
二、hbase1.1.2单机版安装配置
0、创建目录
[root@localhost ~]# mkdir /usr/local/hbase
1、下载hbase
hbase-1.1.2-bin.tar.gz
2、解压tar包
[root@localhost hbase]# tar -zxvf hbase-1.1.2-bin.tar.gz
3、修改环境变量
[root@localhost conf]# pwd
/usr/local/hbase/hbase-1.1.2/conf
[root@localhost conf]# vim hbase-env.sh
export JAVA_HOME=/usr/java/jdk1.8.0_65/
修改$JAVA_HOME为jdk安装⽬目录,这⾥里是/usr/java/jdk1.8.0_65/
4、修改hbase-site.xml
[root@localhost conf]# vim hbase-site.xml
添加:
<configuration>
<property>
<name>hbase.rootdir</name>
<value>hdfs://localhost:9000/hbase</value>
</property>
<property>
<name>hbase.cluster.distributed</name>
<value>true</value>
</property>
</configuration>
5、启动hbase
[root@localhost bin]# pwd
/usr/local/hbase/hbase-1.1.2/bin
[root@localhost bin]# ./start-hbase.sh
6、进入hbase shell
[root@localhost bin]# ./hbase shell
7、查看进程
[root@localhost bin]# jps
9298 HMaster
5171 NameNode
6259 Worker
8357 HQuorumPeer
6071 Master
9415 HRegionServer
5290 DataNode
9931 Jps
5581 ResourceManager
5855 NodeManager
5439 SecondaryNameNode
至此,hadoop和hbase都安装完成了,不过这只是单机版也可以说是伪分布式配
置
spark与hadoop相关问题解决
问题一:spark问题
[root@B sbin]# pwd
/usr/local/spark/spark-1.4.1-bin-hadoop2.6/sbin
[root@B sbin]# ./start-all.sh
org.apache.spark.deploy.master.Master running as process 3941. Stop it first.
B: ssh: connect to host B port 22: No route to host
[root@B sbin]# jps
4661 Jps
3941 Master
原因:IP地址变更所致
问题解决1:
检查1:
[root@B sbin]# vim /etc/sysconfig/network
NETWORKING=yes
HOSTNAME=B.localdomain
检查2:
[root@B sbin]# vim /etc/hosts
127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.31.132 B B.localdomain
检查3:
[root@B sbin]# vim /usr/local/spark/spark-1.4.1-bin-hadoop2.6/conf/slaves
B
检查4:
[root@B sbin]# vim /usr/local/spark/spark-1.4.1-bin-hadoop2.6/conf/spark-env.sh
export SCALA_HOME=/usr/local/scala/scala-2.11.7
export JAVA_HOME=/usr/java/jdk1.8.0_65
export SPARK_MASTER_IP=192.168.31.132
export SPARK_WORKER_MEMORY=512m
export master=spark://192.168.31.132:7070
验证:
[root@B sbin]# ./start-all.sh
[root@B sbin]# jps
3941 Master
4984 Jps
4909 Worker
问题2:hbase、spark、hadoop同时启动时,进程 HRegionServer、Worker、Worker、SecondaryNameNode等启动不了的问题
启动顺序:
可以是:spark->Hadoop->hbase ,启动不了时,尝试关闭后重启。尤其是hadoop,有些进程可能会一次启动不来。
最后
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