Hadoop3.3.0--Linux编译安装
Hadoop3.3.0--Linux编译安装
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基础环境:Centos 7.7
编译环境软件安装目录
mkdir -p /export/server
一、Hadoop编译安装(选做)
可以直接使用课程提供已经编译好的安装包。
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安装编译相关的依赖
yum install gcc gcc-c++ make autoconf automake libtool curl lzo-devel zlib-devel openssl openssl-devel ncurses-devel snappy snappy-devel bzip2 bzip2-devel lzo lzo-devel lzop libXtst zlib -y yum install -y doxygen cyrus-sasl* saslwrapper-devel*
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手动安装cmake
#yum卸载已安装cmake 版本低 yum erase cmake #解压 tar zxvf CMake-3.19.4.tar.gz #编译安装 cd /export/server/CMake-3.19.4 ./configure make && make install #验证 [root@node4 ~]# cmake -version cmake version 3.19.4 #如果没有正确显示版本 请断开SSH连接 重写登录
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手动安装snappy
#卸载已经安装的 rm -rf /usr/local/lib/libsnappy* rm -rf /lib64/libsnappy* #上传解压 tar zxvf snappy-1.1.3.tar.gz #编译安装 cd /export/server/snappy-1.1.3 ./configure make && make install #验证是否安装 [root@node4 snappy-1.1.3]# ls -lh /usr/local/lib |grep snappy -rw-r--r-- 1 root root 511K Nov 4 17:13 libsnappy.a -rwxr-xr-x 1 root root 955 Nov 4 17:13 libsnappy.la lrwxrwxrwx 1 root root 18 Nov 4 17:13 libsnappy.so -> libsnappy.so.1.3.0 lrwxrwxrwx 1 root root 18 Nov 4 17:13 libsnappy.so.1 -> libsnappy.so.1.3.0 -rwxr-xr-x 1 root root 253K Nov 4 17:13 libsnappy.so.1.3.0
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安装配置JDK 1.8
#解压安装包 tar zxvf jdk-8u65-linux-x64.tar.gz #配置环境变量 vim /etc/profile export JAVA_HOME=/export/server/jdk1.8.0_241 export PATH=$PATH:$JAVA_HOME/bin export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar source /etc/profile #验证是否安装成功 java -version java version "1.8.0_241" Java(TM) SE Runtime Environment (build 1.8.0_241-b07) Java HotSpot(TM) 64-Bit Server VM (build 25.241-b07, mixed mode)
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安装配置maven
#解压安装包 tar zxvf apache-maven-3.5.4-bin.tar.gz #配置环境变量 vim /etc/profile export MAVEN_HOME=/export/server/apache-maven-3.5.4 export MAVEN_OPTS="-Xms4096m -Xmx4096m" export PATH=:$MAVEN_HOME/bin:$PATH source /etc/profile #验证是否安装成功 [root@node4 ~]# mvn -v Apache Maven 3.5.4 #添加maven 阿里云仓库地址 加快国内编译速度 vim /export/server/apache-maven-3.5.4/conf/settings.xml <mirrors> <mirror> <id>alimaven</id> <name>aliyun maven</name> <url>http://maven.aliyun.com/nexus/content/groups/public/</url> <mirrorOf>central</mirrorOf> </mirror> </mirrors>
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安装ProtocolBuffer 3.7.1
#卸载之前版本的protobuf #解压 tar zxvf protobuf-3.7.1.tar.gz #编译安装 cd /export/server/protobuf-3.7.1 ./autogen.sh ./configure make && make install #验证是否安装成功 [root@node4 protobuf-3.7.1]# protoc --version libprotoc 3.7.1
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编译hadoop
#上传解压源码包 tar zxvf hadoop-3.3.0-src.tar.gz #编译 cd /root/hadoop-3.3.0-src mvn clean package -Pdist,native -DskipTests -Dtar -Dbundle.snappy -Dsnappy.lib=/usr/local/lib #参数说明: Pdist,native :把重新编译生成的hadoop动态库; DskipTests :跳过测试 Dtar :最后把文件以tar打包 Dbundle.snappy :添加snappy压缩支持【默认官网下载的是不支持的】 Dsnappy.lib=/usr/local/lib :指snappy在编译机器上安装后的库路径
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编译之后的安装包路径
/root/hadoop-3.3.0-src/hadoop-dist/target
二、Hadoop集群分布式安装
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集群规划
主机 角色 node1 NN DN RM NM node2 SNN DN NM node3 DN NM -
基础环境
3台机器都需要操作
# 主机名 cat /etc/hostname # hosts映射 vim /etc/hosts 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 ::1 localhost localhost.localdomain localhost6 localhost6.localdomain6 192.168.88.151 node1.itcast.cn node1 192.168.88.152 node2.itcast.cn node2 192.168.88.153 node3.itcast.cn node3 # JDK 1.8安装 上传 jdk-8u241-linux-x64.tar.gz到/export/server/目录下 cd /export/server/ tar zxvf jdk-8u241-linux-x64.tar.gz #配置环境变量 vim /etc/profile export JAVA_HOME=/export/server/jdk1.8.0_241 export PATH=$PATH:$JAVA_HOME/bin export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar #重新加载环境变量文件 source /etc/profile # 集群时间同步 ntpdate ntp5.aliyun.com # 防火墙关闭 firewall-cmd --state #查看防火墙状态 systemctl stop firewalld.service #停止firewalld服务 systemctl disable firewalld.service #开机禁用firewalld服务 # ssh免密登录(只需要配置node1至node1、node2、node3即可) #node1生成公钥私钥 (一路回车) ssh-keygen #node1配置免密登录到node1 node2 node3 ssh-copy-id node1 ssh-copy-id node2 ssh-copy-id node3
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上传Hadoop安装包到node1 /export/server
hadoop-3.3.0-Centos7-64-with-snappy.tar.gz tar zxvf hadoop-3.3.0-Centos7-64-with-snappy.tar.gz
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修改配置文件(配置文件路径 hadoop-3.3.0/etc/hadoop)
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hadoop-env.sh
#文件最后添加 export JAVA_HOME=/export/server/jdk1.8.0_241 export HDFS_NAMENODE_USER=root export HDFS_DATANODE_USER=root export HDFS_SECONDARYNAMENODE_USER=root export YARN_RESOURCEMANAGER_USER=root export YARN_NODEMANAGER_USER=root
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core-site.xml
<!-- 设置默认使用的文件系统 Hadoop支持file、HDFS、GFS、ali|Amazon云等文件系统 --> <property> <name>fs.defaultFS</name> <value>hdfs://node1:8020</value> </property> <!-- 设置Hadoop本地保存数据路径 --> <property> <name>hadoop.tmp.dir</name> <value>/export/data/hadoop-3.3.0</value> </property> <!-- 设置HDFS web UI用户身份 --> <property> <name>hadoop.http.staticuser.user</name> <value>root</value> </property> <!-- 整合hive 用户代理设置 --> <property> <name>hadoop.proxyuser.root.hosts</name> <value>*</value> </property> <property> <name>hadoop.proxyuser.root.groups</name> <value>*</value> </property> <!-- 文件系统垃圾桶保存时间 --> <property> <name>fs.trash.interval</name> <value>1440</value> </property>
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hdfs-site.xml
<!-- 设置SNN进程运行机器位置信息 --> <property> <name>dfs.namenode.secondary.http-address</name> <value>node2:9868</value> </property>
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mapred-site.xml
<!-- 设置MR程序默认运行模式: yarn集群模式 local本地模式 --> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> <!-- MR程序历史服务地址 --> <property> <name>mapreduce.jobhistory.address</name> <value>node1:10020</value> </property> <!-- MR程序历史服务器web端地址 --> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>node1:19888</value> </property> <property> <name>yarn.app.mapreduce.am.env</name> <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value> </property> <property> <name>mapreduce.map.env</name> <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value> </property> <property> <name>mapreduce.reduce.env</name> <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value> </property>
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yarn-site.xml
<!-- 设置YARN集群主角色运行机器位置 --> <property> <name>yarn.resourcemanager.hostname</name> <value>node1</value> </property> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <!-- 是否将对容器实施物理内存限制 --> <property> <name>yarn.nodemanager.pmem-check-enabled</name> <value>false</value> </property> <!-- 是否将对容器实施虚拟内存限制。 --> <property> <name>yarn.nodemanager.vmem-check-enabled</name> <value>false</value> </property> <!-- 开启日志聚集 --> <property> <name>yarn.log-aggregation-enable</name> <value>true</value> </property> <!-- 设置yarn历史服务器地址 --> <property> <name>yarn.log.server.url</name> <value>http://node1:19888/jobhistory/logs</value> </property> <!-- 历史日志保存的时间 7天 --> <property> <name>yarn.log-aggregation.retain-seconds</name> <value>604800</value> </property>
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workers
node1.itcast.cn node2.itcast.cn node3.itcast.cn
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分发同步hadoop安装包
cd /export/server scp -r hadoop-3.3.0 root@node2:$PWD scp -r hadoop-3.3.0 root@node3:$PWD
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将hadoop添加到环境变量(3台机器)
vim /etc/profile export HADOOP_HOME=/export/server/hadoop-3.3.0 export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin source /etc/profile #别忘了scp给其他两台机器哦
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Hadoop集群启动
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(首次启动)格式化namenode
hdfs namenode -format
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脚本一键启动
[root@node1 ~]# start-dfs.sh Starting namenodes on [node1] Last login: Thu Nov 5 10:44:10 CST 2020 on pts/0 Starting datanodes Last login: Thu Nov 5 10:45:02 CST 2020 on pts/0 Starting secondary namenodes [node2] Last login: Thu Nov 5 10:45:04 CST 2020 on pts/0 [root@node1 ~]# start-yarn.sh Starting resourcemanager Last login: Thu Nov 5 10:45:08 CST 2020 on pts/0 Starting nodemanagers Last login: Thu Nov 5 10:45:44 CST 2020 on pts/0
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Web UI页面
- HDFS集群:http://node1:9870/
- YARN集群:http://node1:8088/
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错误1:运行hadoop3官方自带mr示例出错。
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错误信息
Error: Could not find or load main class org.apache.hadoop.mapreduce.v2.app.MRAppMaster Please check whether your etc/hadoop/mapred-site.xml contains the below configuration: <property> <name>yarn.app.mapreduce.am.env</name> <value>HADOOP_MAPRED_HOME=${full path of your hadoop distribution directory}</value> </property> <property> <name>mapreduce.map.env</name> <value>HADOOP_MAPRED_HOME=${full path of your hadoop distribution directory}</value> </property> <property> <name>mapreduce.reduce.env</name> <value>HADOOP_MAPRED_HOME=${full path of your hadoop distribution directory}</value> </property>
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解决 mapred-site.xml,增加以下配置
<property> <name>yarn.app.mapreduce.am.env</name> <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value> </property> <property> <name>mapreduce.map.env</name> <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value> </property> <property> <name>mapreduce.reduce.env</name> <value>HADOOP_MAPRED_HOME=${HADOOP_HOME}</value> </property>
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