CentOS搭建Hadoop3高可用集群
- 其他
- 2025-08-15 01:21:02
Hadoop FullyDistributed Mode 完全分布式 spark101spark102spark103192.168.171.101192.168.171.102192.168.171.103namenodenamenodejournalnodejournalnodejournalnodedatanodedatanodedatanodenodemanagernodemanagernodemanagerrecource managerrecource managerjob historyjob logjob logjob log 1. 准备 1.1 升级操作系统和软件 yum -y update
升级后建议重启
1.2 安装常用软件 yum -y install gcc gcc-c++ autoconf automake cmake make rsync vim man zip unzip net-tools zlib zlib-devel openssl openssl-devel pcre-devel tcpdump lrzsz tar wget openssh-server 1.3 修改主机名 hostnamectl set-hostname spark01 hostnamectl set-hostname spark02 hostnamectl set-hostname spark03 1.4 修改IP地址 vim /etc/sysconfig/network-scripts/ifcfg-ens160网卡 配置文件示例
TYPE="Ethernet" PROXY_METHOD="none" BROWSER_ONLY="no" BOOTPROTO="none" DEFROUTE="yes" IPV4_FAILURE_FATAL="no" IPV6INIT="yes" IPV6_AUTOCONF="yes" IPV6_DEFROUTE="yes" IPV6_FAILURE_FATAL="no" IPV6_ADDR_GEN_MODE="stable-privacy" NAME="ens32" DEVICE="ens32" ONBOOT="yes" IPADDR="192.168.171.101" PREFIX="24" GATEWAY="192.168.171.2" DNS1="192.168.171.2" IPV6_PRIVACY="no" 1.5 关闭防火墙 sed -i 's/SELINUX=enforcing/SELINUX=disabled/g' /etc/selinux/configsetenforce 0 systemctl stop firewalld systemctl disable firewalld 1.6 修改hosts配置文件 vim /etc/hosts修改内容如下：
192.168.171.101 spark01 192.168.171.102 spark02 192.168.171.103 spark03 1.7 上传软件配置环境变量在所有主机节点创建软件目录
mkdir -p /opt/soft以下操作在 hadoop101 主机上完成
进入软件目录
cd /opt/soft下载 JDK
wget download.oracle /otn/java/jdk/8u391-b13/b291ca3e0c8548b5a51d5a5f50063037/jdk-8u391-linux-x64.tar.gz?AuthParam=1698206552_11c0bb831efdf87adfd187b0e4ccf970下载 zookeeper
wget dlcdn.apache.org/zookeeper/zookeeper-3.8.3/apache-zookeeper-3.8.3-bin.tar.gz下载 hadoop
wget dlcdn.apache.org/hadoop/common/hadoop-3.3.5/hadoop-3.3.5.tar.gz解压 JDK 修改名称
解压 zookeeper 修改名称
解压 hadoop 修改名称
tar -zxvf jdk-8u391-linux-x64.tar.gz -C /opt/soft/ mv jdk1.8.0_391/ jdk-8 tar -zxvf apache-zookeeper-3.8.3-bin.tar.gz mv apache-zookeeper-3.8.3-bin zookeeper-3 tar -zxvf hadoop-3.3.5.tar.gz -C /opt/soft/ mv hadoop-3.3.5/ hadoop-3配置环境变量
vim /etc/profile.d/my_env.sh编写以下内容：
export JAVA_HOME=/opt/soft/jdk-8 # export set JAVA_OPTS="--add-opens java.base/java.lang=ALL-UNNAMED" export ZOOKEEPER_HOME=/opt/soft/zookeeper-3 export HDFS_NAMENODE_USER=root export HDFS_SECONDARYNAMENODE_USER=root export HDFS_DATANODE_USER=root export HDFS_ZKFC_USER=root export HDFS_JOURNALNODE_USER=root export HADOOP_SHELL_EXECNAME=root export YARN_RESOURCEMANAGER_USER=root export YARN_NODEMANAGER_USER=root export HADOOP_HOME=/opt/soft/hadoop-3 export HADOOP_INSTALL=$HADOOP_HOME export HADOOP_MAPRED_HOME=$HADOOP_HOME export HADOOP_COMMON_HOME=$HADOOP_HOME export HADOOP_HDFS_HOME=$HADOOP_HOME export YARN_HOME=$HADOOP_HOME export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop export PATH=$PATH:$JAVA_HOME/bin:$ZOOKEEPER_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin生成新的环境变量
注意：分发软件和配置文件后 在所有主机执行该步骤
source /etc/profile 2. zookeeper 2.1 编辑配置文件 cd $ZOOKEEPER_HOME/conf vim zoo.cfg # 心跳单位，2s tickTime=2000 # zookeeper-3初始化的同步超时时间，10个心跳单位，也即20s initLimit=10 # 普通同步：发送一个请求并得到响应的超时时间，5个心跳单位也即10s syncLimit=5 # 内存快照数据的存储位置 dataDir=/home/zookeeper-3/data # 事务日志的存储位置 dataLogDir=/home/zookeeper-3/datalog # 当前zookeeper-3节点的端口 clientPort=2181 # 单个客户端到集群中单个节点的并发连接数，通过ip判断是否同一个客户端，默认60 maxClientCnxns=1000 # 保留7个内存快照文件在dataDir中，默认保留3个 autopurge.snapRetainCount=7 # 清除快照的定时任务，默认1小时，如果设置为0，标识关闭清除任务 autopurge.purgeInterval=1 #允许客户端连接设置的最小超时时间，默认2个心跳单位 minSessionTimeout=4000 #允许客户端连接设置的最大超时时间，默认是20个心跳单位，也即40s， maxSessionTimeout=300000 #zookeeper-3 3.5.5启动默认会把AdminService服务启动，这个服务默认是8080端口 admin.serverPort=9001 #集群地址配置 server.1=spark01:2888:3888 server.2=spark02:2888:3888 server.3=spark03:2888:3888 tickTime=2000 initLimit=10 syncLimit=5 dataDir=/home/zookeeper-3/data dataLogDir=/home/zookeeper-3/datalog clientPort=2181 maxClientCnxns=1000 autopurge.snapRetainCount=7 autopurge.purgeInterval=1 minSessionTimeout=4000 maxSessionTimeout=300000 admin.serverPort=9001 server.1=spark01:2888:3888 server.2=spark02:2888:3888 server.3=spark03:2888:3888 2.2 保存后根据配置文件创建目录在每台服务器上执行
mkdir -p /home/zookeeper-3/data mkdir -p /home/zookeeper-3/datalog 2.3 myidspark01
echo 1 > /home/zookeeper-3/data/myid more /home/zookeeper-3/data/myidspark02
echo 2 > /home/zookeeper-3/data/myid more /home/zookeeper-3/data/myidspark03
echo 3 > /home/zookeeper-3/data/myid more /home/zookeeper-3/data/myid 2.4 编写zookeeper-3开机启动脚本在/etc/systemd/system/文件夹下创建一个启动脚本zookeeper-3.service
注意：在每台服务器上编写
cd /etc/systemd/system vim zookeeper.service内容如下：
[Unit] Description=zookeeper After=syslog.target network.target [Service] Type=forking # 指定zookeeper-3 日志文件路径，也可以在zkServer.sh 中定义 Environment=ZOO_LOG_DIR=/home/zookeeper-3/datalog # 指定JDK路径，也可以在zkServer.sh 中定义 Environment=JAVA_HOME=/opt/soft/jdk-8 ExecStart=/opt/soft/zookeeper-3/bin/zkServer.sh start ExecStop=/opt/soft/zookeeper-3/bin/zkServer.sh stop Restart=always User=root Group=root [Install] WantedBy=multi-user.target [Unit] Description=zookeeper After=syslog.target network.target [Service] Type=forking Environment=ZOO_LOG_DIR=/home/zookeeper-3/datalog Environment=JAVA_HOME=/opt/soft/jdk-8 ExecStart=/opt/soft/zookeeper-3/bin/zkServer.sh start ExecStop=/opt/soft/zookeeper-3/bin/zkServer.sh stop Restart=always User=root Group=root [Install] WantedBy=multi-user.target systemctl daemon-reload # 等所有主机配置好后再执行以下命令 systemctl start zookeeper systemctl enable zookeeper systemctl status zookeeper 3. hadoop修改配置文件
cd $HADOOP_HOME/etc/hadoop hadoop-env.shcore-site.xmlhdfs-site.xmlworkersmapred-site.xmlyarn-site.xml hadoop-env.sh 文件末尾追加 export JAVA_HOME=/opt/soft/jdk-8 # export HADOOP_OPTS="--add-opens java.base/java.lang=ALL-UNNAMED" export HDFS_NAMENODE_USER=root export HDFS_SECONDARYNAMENODE_USER=root export HDFS_DATANODE_USER=root export HDFS_ZKFC_USER=root export HDFS_JOURNALNODE_USER=root export HADOOP_SHELL_EXECNAME=root export YARN_RESOURCEMANAGER_USER=root export YARN_NODEMANAGER_USER=root core-site.xml <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl"?> <!-- 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:// .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. --> <!-- Put site-specific property overrides in this file. --> <configuration> <property> <name>fs.defaultFS</name> <value>hdfs://lihaozhe</value> </property> <property> <name>hadoop.tmp.dir</name> <value>/home/hadoop/data</value> </property> <property> <name>ha.zookeeper.quorum</name> <value>spark01:2181,spark02:2181,spark03:2181</value> </property> <property> <name>hadoop.http.staticuser.user</name> <value>root</value> </property> <property> <name>dfs.permissions.enabled</name> <value>false</value> </property> <property> <name>hadoop.proxyuser.root.hosts</name> <value>*</value> </property> <property> <name>hadoop.proxyuser.root.groups</name> <value>*</value> </property> </configuration> hdfs-site.xml <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl"?> <!-- 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:// .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. --> <!-- Put site-specific property overrides in this file. --> <configuration> <property> <name>dfs.nameservices</name> <value>lihaozhe</value> </property> <property> <name>dfs.ha.namenodes.lihaozhe</name> <value>nn1,nn2</value> </property> <property> <name>dfs.namenode.rpc-address.lihaozhe.nn1</name> <value>spark01:8020</value> </property> <property> <name>dfs.namenode.rpc-address.lihaozhe.nn2</name> <value>spark02:8020</value> </property> <property> <name>dfs.namenode.http-address.lihaozhe.nn1</name> <value>spark01:9870</value> </property> <property> <name>dfs.namenode.http-address.lihaozhe.nn2</name> <value>spark02:9870</value> </property> <property> <name>dfs.namenode.shared.edits.dir</name> <value>qjournal://spark01:8485;spark02:8485;spark03:8485/lihaozhe</value> </property> <property> <name>dfs.client.failover.proxy.provider.lihaozhe</name> <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value> </property> <property> <name>dfs.ha.fencing.methods</name> <value>sshfence</value> </property> <property> <name>dfs.ha.fencing.ssh.private-key-files</name> <value>/root/.ssh/id_rsa</value> </property> <property> <name>dfs.journalnode.edits.dir</name> <value>/home/hadoop/journalnode/data</value> </property> <property> <name>dfs.ha.automatic-failover.enabled</name> <value>true</value> </property> <property> <name>dfs.safemode.threshold.pct</name> <value>1</value> </property> </configuration> workers spark01 spark02 spark03 mapred-site.xml <?xml version="1.0"?> <?xml-stylesheet type="text/xsl" href="configuration.xsl"?> <!-- 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:// .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. --> <!-- Put site-specific property overrides in this file. --> <configuration> <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> <property> <name>mapreduce.application.classpath</name> <value>$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*:$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*</value> </property> <!-- yarn历史服务端口 --> <property> <name>mapreduce.jobhistory.address</name> <value>spark01:10020</value> </property> <!-- yarn历史服务web访问端口 --> <property> <name>mapreduce.jobhistory.webapp.address</name> <value>spark01:19888</value> </property> </configuration> yarn-site.xml <?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:// .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> <!-- Site specific YARN configuration properties --> <property> <name>yarn.resourcemanager.ha.enabled</name> <value>true</value> </property> <property> <name>yarn.resourcemanager.cluster-id</name> <value>cluster1</value> </property> <property> <name>yarn.resourcemanager.ha.rm-ids</name> <value>rm1,rm2</value> </property> <property> <name>yarn.resourcemanager.hostname.rm1</name> <value>spark01</value> </property> <property> <name>yarn.resourcemanager.hostname.rm2</name> <value>spark02</value> </property> <property> <name>yarn.resourcemanager.webapp.address.rm1</name> <value>spark01:8088</value> </property> <property> <name>yarn.resourcemanager.webapp.address.rm2</name> <value>spark02:8088</value> </property> <property> <name>yarn.resourcemanager.zk-address</name> <value>spark01:2181,spark02:2181,spark03:2181</value> </property> <property> <name>yarn.nodemanager.aux-services</name> <value>mapreduce_shuffle</value> </property> <property> <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name> <value>org.apache.hadoop.mapred.ShuffleHandler</value> </property> <property> <name>yarn.nodemanager.env-whitelist</name> <value>JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_MAPRED_HOME</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://spark01:19888/jobhistory/logs</value> </property> <!-- 保存的时间7天 --> <property> <name>yarn.log-aggregation.retain-seconds</name> <value>604800</value> </property> </configuration> 4. 配置ssh免密钥登录创建本地秘钥并将公共秘钥写入认证文件
ssh-keygen -t rsa -P '' -f ~/.ssh/id_rsa ssh-copy-id root@spark01 ssh-copy-id root@spark02 ssh-copy-id root@spark03 ssh root@spark01 exit ssh root@spark02 exit ssh root@spark03 exit 5. 分发软件和配置文件 scp -r /etc/profile.d root@spark02:/etc scp -r /etc/profile.d root@spark03:/etc scp -r /opt/soft/zookeeper-3 root@spark02:/opt/soft scp -r /opt/soft/zookeeper-3 root@spark03:/opt/soft scp -r /opt/soft/hadoop-3/etc/hadoop/* root@spark02:/opt/soft/hadoop-3/etc/hadoop/ scp -r /opt/soft/hadoop-3/etc/hadoop/* root@spark03:/opt/soft/hadoop-3/etc/hadoop/ 6. 在各服务器上使环境变量生效 source /etc/profile 7. 启动zookeeper 7.1 myidspark01
echo 1 > /home/zookeeper-3/data/myid more /home/zookeeper-3/data/myidspark02
echo 2 > /home/zookeeper-3/data/myid more /home/zookeeper-3/data/myidspark03
echo 3 > /home/zookeeper-3/data/myid more /home/zookeeper-3/data/myid 7.2 启动服务在各节点执行以下命令
systemctl daemon-reload systemctl start zookeeper systemctl enable zookeeper systemctl status zookeeper 7.3 验证 jps zkServer.sh status 8. Hadoop初始化 1. 启动三个zookeeper：zkServer.sh start 2. 启动三个JournalNode： hadoop-daemon.sh start journalnode 或者 hdfs --daemon start journalnode 3. 在其中一个namenode上格式化：hdfs namenode -format 4. 把刚刚格式化之后的元数据拷贝到另外一个namenode上 a) 启动刚刚格式化的namenode : hadoop-daemon.sh start namenode 或者 hdfs --daemon start namenode b) 在没有格式化的namenode上执行：hdfs namenode -bootstrapStandby c) 启动第二个namenode: hadoop-daemon.sh start namenode 或者 hdfs --daemon start namenode 5. 在其中一个namenode上初始化 hdfs zkfc -formatZK 6. 停止上面节点：stop-dfs.sh 7. 全面启动：start-all.sh 8. 启动resourcemanager节点 yarn-daemon.sh start resourcemanager 或者 start-yarn.sh http://dl.bintray /sequenceiq/sequenceiq-bin/hadoop-native-64-2.5.0.tar 不需要执行第 8 步 9. 启动历史服务 mapred --daemon start historyserver 10 11 12 不需要执行 10、安全模式 hdfs dfsadmin -safemode enter hdfs dfsadmin -safemode leave 11、查看哪些节点是namenodes并获取其状态 hdfs getconf -namenodes hdfs haadmin -getServiceState spark01 12、强制切换状态 hdfs haadmin -transitionToActive --forcemanual spark01 重点提示： # 关机之前 依关闭服务 stop-yarn.sh stop-dfs.sh # 开机后 依次开启服务 start-dfs.sh start-yarn.sh或者
# 关机之前关闭服务 stop-all.sh # 开机后开启服务 start-all.sh #jps 检查进程正常后开启胡哦关闭在再做其它操作 9. 修改windows下hosts文件C:\Windows\System32\drivers\etc\hosts
追加以下内容：
192.168.171.101 hadoop101 192.168.171.102 hadoop102 192.168.171.103 hadoop103Windows11 注意 修改权限
开始搜索 cmd
找到命令头提示符 以管理身份运行
进入 C:\Windows\System32\drivers\etc 目录
cd drivers/etc去掉 hosts文件只读属性
attrib -r hosts打开 hosts 配置文件
start hosts追加以下内容后保存
192.168.171.101 spark01 192.168.171.102 spark02 192.168.171.103 spark03 10. 测试 12.1 浏览器访问hadoop集群浏览器访问: http://spark01:9870
浏览器访问:http://spark01:8088
浏览器访问:http://spark01:19888/
12.2 测试 hdfs本地文件系统创建 测试文件 wcdata.txt
vim wcdata.txt Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase HiveHadoop Spark HBase StormHBase Hadoop Hive FlinkHBase Flink Hive StormHive Flink HadoopHBase Hive Spark HBaseHive Flink Storm Hadoop HBase SparkFlinkHBase StormHBase Hadoop Hive在 HDFS 上创建目录 /wordcount/input
hdfs dfs -mkdir -p /wordcount/input查看 HDFS 目录结构
hdfs dfs -ls / hdfs dfs -ls /wordcount hdfs dfs -ls /wordcount/input上传本地测试文件 wcdata.txt 到 HDFS 上 /wordcount/input
hdfs dfs -put wcdata.txt /wordcount/input检查文件是否上传成功
hdfs dfs -ls /wordcount/input hdfs dfs -cat /wordcount/input/wcdata.txt 12.2 测试 mapreduce计算 PI 的值
hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.5.jar pi 10 10单词统计
hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.5.jar wordcount /wordcount/input/wcdata.txt /wordcount/result hdfs dfs -ls /wordcount/result hdfs dfs -cat /wordcount/result/part-r-00000 11. 元数据hadoop101
cd /home/hadoop_data/dfs/name/current ls看到如下内容:
edits_0000000000000000001-0000000000000000009 edits_inprogress_0000000000000000299 fsimage_0000000000000000298 VERSION edits_0000000000000000010-0000000000000000011 fsimage_0000000000000000011 fsimage_0000000000000000298.md5 edits_0000000000000000012-0000000000000000298 fsimage_0000000000000000011.md5 seen_txid查看fsimage
hdfs oiv -p XML -i fsimage_0000000000000000011将元数据内容按照指定格式读取后写入到新文件中
hdfs oiv -p XML -i fsimage_0000000000000000011 -o /opt/soft/fsimage.xml查看edits
将元数据内容按照指定格式读取后写入到新文件中
hdfs oev -p XML -i edits_inprogress_0000000000000000299 -o /opt/soft/edit.xmlCentOS搭建Hadoop3高可用集群由讯客互联其他栏目发布，感谢您对讯客互联的认可，以及对我们原创作品以及文章的青睐，非常欢迎各位朋友分享到个人网站或者朋友圈，但转载请说明文章出处“CentOS搭建Hadoop3高可用集群”
![[NKU]C++基础课（四）补充：继承](/0pic/pp_28.jpg)
