CentOS 搭建 Hadoop3 高可用集群

guduadmin114小时前

Hadoop FullyDistributed Mode 完全分布式

spark101	spark102	spark103
192.168.171.101	192.168.171.102	192.168.171.103
namenode	namenode
journalnode	journalnode	journalnode
datanode	datanode	datanode
nodemanager	nodemanager	nodemanager
recource manager	recource manager
job history
job log	job log	job 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 https://download.oracle.com/otn/java/jdk/8u391-b13/b291ca3e0c8548b5a51d5a5f50063037/jdk-8u391-linux-x64.tar.gz?AuthParam=1698206552_11c0bb831efdf87adfd187b0e4ccf970

下载 zookeeper

wget https://dlcdn.apache.org/zookeeper/zookeeper-3.8.3/apache-zookeeper-3.8.3-bin.tar.gz

下载 hadoop

wget https://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 myid

spark01

echo 1 > /home/zookeeper-3/data/myid
more /home/zookeeper-3/data/myid

spark02

echo 2 > /home/zookeeper-3/data/myid
more /home/zookeeper-3/data/myid

spark03

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.sh
core-site.xml
hdfs-site.xml
workers
mapred-site.xml
yarn-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






  
    fs.defaultFS
    hdfs://lihaozhe
  
  
    hadoop.tmp.dir
    /home/hadoop/data
  
  
    ha.zookeeper.quorum
    spark01:2181,spark02:2181,spark03:2181
  
  
    hadoop.http.staticuser.user
    root
  
  
    dfs.permissions.enabled
    false
  
  
    hadoop.proxyuser.root.hosts
    *
  
  
    hadoop.proxyuser.root.groups
    *
  
  
    hadoop.proxyuser.root.users
    *

hdfs-site.xml






  
    dfs.nameservices
    lihaozhe
  
  
    dfs.ha.namenodes.lihaozhe
    nn1,nn2
  
  
    dfs.namenode.rpc-address.lihaozhe.nn1
    spark01:8020
  
  
    dfs.namenode.rpc-address.lihaozhe.nn2
    spark02:8020
  
  
    dfs.namenode.http-address.lihaozhe.nn1
    spark01:9870
  
  
    dfs.namenode.http-address.lihaozhe.nn2
    spark02:9870
  
  
    dfs.namenode.shared.edits.dir
    qjournal://spark01:8485;spark02:8485;spark03:8485/lihaozhe
  
  
    dfs.client.failover.proxy.provider.lihaozhe
    org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider
  
  
    dfs.ha.fencing.methods
    sshfence
  
  
    dfs.ha.fencing.ssh.private-key-files
    /root/.ssh/id_rsa
  
  
    dfs.journalnode.edits.dir
    /home/hadoop/journalnode/data
  
  
    dfs.ha.automatic-failover.enabled
    true
  
  
    dfs.safemode.threshold.pct
    1

workers

spark01
spark02
spark03

mapred-site.xml






  
    mapreduce.framework.name
    yarn
  
  
    mapreduce.application.classpath
    $HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*:$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*
  
  
  
    mapreduce.jobhistory.address
    spark01:10020
  
  
  
    mapreduce.jobhistory.webapp.address
    spark01:19888

yarn-site.xml




  
  
    yarn.resourcemanager.ha.enabled
    true
  
  
    yarn.resourcemanager.cluster-id
    cluster1
  
  
    yarn.resourcemanager.ha.rm-ids
    rm1,rm2
  
  
    yarn.resourcemanager.hostname.rm1
    spark01
  
  
    yarn.resourcemanager.hostname.rm2
    spark02
  
  
    yarn.resourcemanager.webapp.address.rm1
    spark01:8088
  
  
    yarn.resourcemanager.webapp.address.rm2
    spark02:8088
  
  
    yarn.resourcemanager.zk-address
    spark01:2181,spark02:2181,spark03:2181
  
  
    yarn.nodemanager.aux-services
    mapreduce_shuffle
  
  
    yarn.nodemanager.aux-services.mapreduce.shuffle.class
    org.apache.hadoop.mapred.ShuffleHandler
  
  
    yarn.nodemanager.env-whitelist
    JAVA_HOME,HADOOP_COMMON_HOME,HADOOP_HDFS_HOME,HADOOP_CONF_DIR,CLASSPATH_PREPEND_DISTCACHE,HADOOP_YARN_HOME,HADOOP_MAPRED_HOME
  
  
  
    yarn.nodemanager.pmem-check-enabled
    false
  
  
  
    yarn.nodemanager.vmem-check-enabled
    false
  
  
  
    yarn.log-aggregation-enable
    true
  
  
  
    yarn.log.server.url
    http://spark01:19888/jobhistory/logs
  
  
  
    yarn.log-aggregation.retain-seconds
    604800

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 myid

spark01

echo 1 > /home/zookeeper-3/data/myid
more /home/zookeeper-3/data/myid

spark02

echo 2 > /home/zookeeper-3/data/myid
more /home/zookeeper-3/data/myid

spark03

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.com/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 nn1
hdfs haadmin -getServiceState nn2
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	hadoop103

Windows11 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

CentOS 搭建 Hadoop3 高可用集群,hadoop namenode,第6张

CentOS 搭建 Hadoop3 高可用集群,hadoop datanodes,第7张

浏览器访问:http://spark01:8088

CentOS 搭建 Hadoop3 高可用集群,hadoop resourcemanager,第8张

浏览器访问:http://spark01:19888/

CentOS 搭建 Hadoop3 高可用集群,hadoop historyserver,第9张

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