1、集群部署概述
1.1 Hadoop简介
研发要做数据挖掘统计,需要Hadoop环境,便开始了本次安装测试,仅仅使用了3台虚拟机做测试工作。 简介……此处省略好多……,可自行查找 ……
从你找到的内容可以总结看到,NameNode和JobTracker负责分派任务,DataNode和TaskTracker负责数据计算和存储。这样集群中可以有一台NameNode+JobTracker,N多台DataNode和TaskTracker。
1.2版本信息
本次测试安装所需软件版本信息如表1-1所示。
表1-1:软件版本信息
名称 |
版本信息 |
操作系统 |
CentOS-6.8-x86_64-bin-DVD1.iso |
Java |
jdk-8u121-linux-x64.tar.gz |
Hadoop |
hadoop-3.0.0-alpha2.tar.gz |
1.3测试环境说明
本实验环境是在虚拟机中安装测试的,Hadoop集群中包括1个Master,2个Salve,节点之间内网互通,虚拟机主机名和IP地址如表1-2所示。
主机名 |
模拟外网IP地址(eth1) |
备注 |
master |
192.168.24.15 |
NameNode+JobTracker |
slave1 |
192.168.24.16 |
DataNode+TaskTracker |
slave2 |
192.168.24.17 |
DataNode+TaskTracker |
### 说明:文档出现的灰色阴影部分内容为文件编辑内容或操作显示内容。
2、操作系统设置
1、安装常用软件
### 由于操作系统是最小化安装,所以安装一些常用的软件包
# yum install gcc gcc-c++ openssh-clients vimmake ntpdate unzip cmake tcpdump openssl openssl-devel lzo lzo-devel zlibzlib-devel snappy snappy-devel lz4 lz4-devel bzip2 bzip2-devel cmake wget
2、修改主机名
# vim /etc/sysconfig/network # 其他两个节点分别是:slave1和slave2
NETWORKING=yes
HOSTNAME=master
3、配置hosts文件
# vim /etc/hosts # master和slave服务器上均添加以下配置内容
10.0.24.15 master
10.0.24.16 slave1
10.0.24.17 slave2
4、创建账号
# useradd hadoop
5、文件句柄设置
# vim/etc/security/limits.conf
* soft nofile 65000
* hard nofile 65535
$ ulimit -n # 查看
6、系统内核参数调优sysctl.conf
net.ipv4.ip_forward = 0
net.ipv4.conf.default.rp_filter = 1
net.ipv4.conf.default.accept_source_route = 0
kernel.sysrq = 0
kernel.core_uses_pid = 1
net.ipv4.tcp_syncookies = 1
kernel.msgmnb = 65536
kernel.msgmax = 65536
kernel.shmmax = 68719476736
kernel.shmall = 4294967296
net.ipv4.tcp_max_tw_buckets = 60000
net.ipv4.tcp_sack = 1
net.ipv4.tcp_window_scaling = 1
net.ipv4.tcp_rmem = 4096 87380 4194304
net.ipv4.tcp_wmem = 4096 16384 4194304
net.core.wmem_default = 8388608
net.core.rmem_default = 8388608
net.core.rmem_max = 16777216
net.core.wmem_max = 16777216
net.core.netdev_max_backlog = 262144
net.core.somaxconn = 262144
net.ipv4.tcp_max_orphans = 3276800
net.ipv4.tcp_max_syn_backlog = 262144
net.ipv4.tcp_timestamps = 0
net.ipv4.tcp_synack_retries = 1
net.ipv4.tcp_syn_retries = 1
net.ipv4.tcp_tw_recycle = 1
net.ipv4.tcp_tw_reuse = 1
net.ipv4.tcp_mem = 94500000 915000000 927000000
net.ipv4.tcp_fin_timeout = 1
net.ipv4.tcp_keepalive_time = 1200
net.ipv4.tcp_max_syn_backlog = 65536
net.ipv4.tcp_timestamps = 0
net.ipv4.tcp_synack_retries = 2
net.ipv4.tcp_syn_retries = 2
net.ipv4.tcp_tw_recycle = 1
#net.ipv4.tcp_tw_len = 1
net.ipv4.tcp_tw_reuse = 1
#net.ipv4.tcp_fin_timeout = 30
#net.ipv4.tcp_keepalive_time = 120
net.ipv4.ip_local_port_range = 1024 65535
7、关闭SELINUX
# vim /etc/selinux/config
#SELINUX=enforcing
#SELINUXTYPE=targeted
SELINUX=disabled
# reboot # 重启服务器生效
8、配置ssh
# vim /etc/ssh/sshd_config # 去掉以下内容前“#”注释
HostKey /etc/ssh/ssh_host_rsa_key
RSAAuthentication yes
PubkeyAuthentication yes
AuthorizedKeysFile .ssh/authorized_keys
# /etc/init.d/sshd restart
9、配置master和slave间无密码互相登录
(1)maseter和slave服务器上均生成密钥
# su - hadoop
$ssh-keygen -b 1024 -t rsa
Generating public/private rsa key pair.
Enter file in which to save the key(/root/.ssh/id_rsa): <–直接输入回车
Enter passphrase (empty for no passphrase): <–直接输入回车
Enter same passphrase again: <–直接输入回车
Your identification has been saved in/root/.ssh/id_rsa.
Your public key has been saved in/root/.ssh/id_rsa.pub.
The key fingerprint is: ……
注意:在程序提示输入 passphrase 时直接输入回车,表示无证书密码。
(2)maseter和slave服务器上hadoop用户下均创建authorized_keys文件
$ cd .ssh
$ vim authorized_keys # 添加master和salve服务器上hadoop用户下id_rsa.pub文件内容
ssh-rsa AAAAB3Nza…省略…HxNDk= [email protected]
ssh-rsa AAAAB3Nza…省略…7CmlRs= [email protected]
ssh-rsa AAAAB3Nza…省略…URmXD0= [email protected]
$ chmod 644 authorized_keys
$ ssh -p2221 [email protected] $ ssh -p2221 slave1 # 分别测试ssh连通性
3、Java环境安装
### Hadoop集群均需安装Java环境
# mkdir /data && cd /data
# tar zxf jdk-8u121-linux-x64.tar.gz
# ln -sv jdk1.8.0_121 jdk
# chown -R root. jdk*
# cat >> /etc/profile.d/java.sh<<‘EOF‘
# Set jave environment
export JAVA_HOME=/data/jdk
export CLASSPATH=.:$JAVA_HOME/lib:$JAVA_HOME/jre/lib
export PATH=$PATH:$JAVA_HOME/bin:$JAVA_HOME/jre/bin
EOF
# source /etc/profile # 及时生效 # java -version或# javac-version # 查看版本信息
4、Hadoop集群安装
4.1 master上安装Hadoop
# cd /data
# hadoop-3.0.0-alpha2.tar.gz
# ln -sv hadoop-3.0.0-alpha2 hadoop # mkdir -p /data/hadoop/logs # chown -Rhadoop:hadoop /data/hadoop/logs
# mkdir -p /data/hadoop/tmp # 配置文件core-site.xml中配置使用
# mkdir -p /data/{hdfsname1,hdfsname2}/hdfs/name
# mkdir -p /data/{hdfsdata1,hdfsdata2}/hdfs/data
# chown -R hadoop:hadoop /data/hdfs*
# 以上四个文件目录hadfs-site.xml中配置使用
# cat >> /etc/profile.d/hadoop.sh<<‘EOF‘
# Set hadoop environment
export HADOOP_HOME=/data/hadoop
export PATH=$PATH:$HADOOP_HOME/bin
EOF
# source /etc/profile
# chown -R hadoop:hadoop hadoop*
4.2 master上配置Hadoop
# cd /data/hadoop/etc/hadoop
4.2.1 hadoop-env.sh
# vim hadoop-env.sh # master和slave末行均添加
# Set jave environment
export JAVA_HOME=/data/jdk
export HADOOP_SSH_OPTS="-p 2221"
4.2.2 core-site.xml
# vim core-site.xml
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://master:9000</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>/data/hadoop/tmp</value>
</property>
<property>
<name>io.compression.codecs</name>
<value>org.apache.hadoop.io.compress.DefaultCodec,com.hadoop.compression.lzo.LzoCodec,com.hadoop.compression.lzo.LzopCodec,org.apache.hadoop.io.compress.GzipCodec,org.apache
.hadoop.io.compress.BZip2Codec</value>
</property>
<property>
<name>io.compression.codec.lzo.class</name>
<value>com.hadoop.compression.lzo.LzoCodec</value>
</property>
</configuration>
### 说明:
第1个<property>:定义hdfsnamenode的主机名和端口,本机,主机名在/etc/hosts设置
第2个<property>:定义如没有配置hadoop.tmp.dir参数,此时系统默认的临时目录为:/tmp/hadoo-hadoop。而这个目录在每次重启后都会被删掉,必须重新执行format才行,否则会出错。默认是NameNode、DataNode、JournalNode等存放数据的公共目录。用户也可以自己单独指定这三类节点的目录。这里的/data/hadoop/tmp目录与文件都是自己创建的,配置后在格式化namenode的时候也会自动创建。
第3个<property>:定义hdfs使用压缩(本次测试暂时关闭了本项目,可以注释掉)
第4个<property>:定义压缩格式和解码器类(本次测试暂时关闭了本项目,可以注释掉)
4.2.3 hdfs-site.xml
# vim hdfs-site.xml
<configuration>
<property>
<name>dfs.name.dir</name>
<value>file:///data/hdfsname1/hdfs/name,file:// /data/hdfsname2/hdfs/name</value>
<description> </description>
</property>
<property>
<name>dfs.data.dir</name>
<value>file:///data/hdfsdata1/hdfs/data,file:///data/hdfsdata2/hdfs/data</value>
<description> </description>
</property>
<property>
<name>dfs.replication</name>
<value>2</value>
</property>
<property>
<name>dfs.datanode.du.reserved</name>
<value>1073741824</value>
</property>
<property>
<name>dfs.block.size</name>
<value>134217728</value>
</property>
<property>
<name>dfs.permissions</name>
<value>false</value>
</property>
</configuration>
第1个<property>:定义hdfs Namenode持久存储名字空间、事务日志路径。多路径可以使用“,”分割,这里配置模拟了多磁盘挂载。
第2个<property>:定义本地文件系统上DFS数据节点应存储其块的位置。可以逗号分隔目录列表,则数据将存储在所有命名的目录中,通常在不同的设备上。
第3个<property>:定义DataNode存储block的副本数量。默认值是3个,我们现在有2个 DataNode,该值不大2即可,份数越多越安全,但速度越慢。
第4个<property>:定义du操作返回。
第5个<property>:定义hdfs的存储块大小,默认64M,我用的128M。
第6个<property>:权限设置,最好不要。
4.2.4 mapred-site.xml
# cp -a mapred-site.xml.templatemapred-site.xml
# vim mapred-site.xml
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.application.classpath</name>
<value>
/data/hadoop/etc/hadoop,
/data/hadoop/share/hadoop/common/*,
/data/hadoop/share/hadoop/common/lib/*,
/data/hadoop/share/hadoop/hdfs/*,
/data/hadoop/share/hadoop/hdfs/lib/*,
/data/hadoop/share/hadoop/mapreduce/*,
/data/hadoop/share/hadoop/mapreduce/lib/*,
/data/hadoop/share/hadoop/yarn/*,
/data/hadoop/share/hadoop/yarn/lib/*
</value>
</property>
</configuration>
###说明:
上面的mapreduce.application.classpath一开始没有配置,导致使用mapreduce时报错
Error: Could not find or load main classorg.apache.hadoop.mapreduce.v2.app.MRAppMaster
4.2.5 yarn-site.xml
# vim yarn-site.xml
<configuration>
<property>
<name>yarn.resourcemanager.hostname</name>
<value>master</value>
</property>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>
第1个<property>:定义指的是运行ResourceManager机器所在的节点.
第2个<property>:定义在hadoop2.2.0版本中是mapreduce_shuffle,一定要看清楚。
### 注意:本次测试使用了默认文件,没有添加任何内容。
4.2.6 workers
# vim workers # 配置slave的主机名,否则slave节点不启动
slave1
slave2
4.3 slava上安装Hadoop
复制主节点master上的hadoop安装配置环境到所有的slave上,切记:目标路径要与master保持一致。
$ scp -P2221 [email protected]:/home/hadoop
$ scp -P2221 [email protected]:/home/hadoop
4.4配置防火墙
### 实验时可以关闭防火墙,避免不必要的麻烦,等后续陆续调试
4.5 Hadoop启动及其验证
4.5.1 master上格式化HDFS文件系统
### 注意回到master服务器上执行如下操作:
# su - hadoop
$ /data/hadoop/bin/hdfsnamenode -format # 显示如下内容:
2017-03-15 19:02:50,062 INFO namenode.NameNode:STARTUP_MSG:
/************************************************************
STARTUP_MSG: Starting NameNode
STARTUP_MSG: user = hadoop
STARTUP_MSG: host = master/10.0.24.15
STARTUP_MSG: args = [-format]
STARTUP_MSG: version = 3.0.0-alpha2
……此处省略好多……
Re-format filesystem in Storage Directory/data/hdfsname1/hdfs/name ? (Y or N) y
Re-format filesystem in Storage Directory/data/hdfsname2/hdfs/name ? (Y or N) y
……此处省略好多……
2017-03-15 19:03:48,703 INFO namenode.FSImage:Allocated new BlockPoolId: BP-1344030132-10.0.24.15-1489575828688
……此处省略好多……
2017-03-15 19:03:48,999 INFO util.ExitUtil: Exitingwith status 0
2017-03-15 19:03:49,002 INFO namenode.NameNode:SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode atmaster/10.0.24.15
************************************************************/
4.5.2 启动校验停止集群
$ cd /data/hadoop/sbin # master服务器上操作
(1)$ ./start-all.sh # 启动 # 显示内容:WARNING WARN暂时没有解决,详见5、FAQ内
WARNING: Attempting to start all Apache Hadoopdaemons as hadoop in 10 seconds.
WARNING: This is not a recommended productiondeployment configuration.
WARNING: Use CTRL-C to abort.
Starting namenodes on [master]
Starting datanodes
Starting secondary namenodes [master]
2017-03-21 18:51:03,092 WARN util.NativeCodeLoader:Unable to load native-hadoop library for your platform... using builtin-javaclasses where applicable
Starting resourcemanager
Starting nodemanagers
(2)$ /data/jdk1.8.0_121/bin/jps # master上查看进程
9058 SecondaryNameNode
9272 ResourceManager
9577 RunJar
8842 NameNode
9773 Jps
(3)$ /data/jdk1.8.0_121/bin/jps # slave1\slave2上查看进程
5088 DataNode
5340 Jps
5213 NodeManager
(4)$ ./stop-all.sh # master服务器上操作停止集群
WARNING: Stopping all Apache Hadoop daemons as hadoopin 10 seconds.
WARNING: Use CTRL-C to abort.
Stopping namenodes on [master]
Stopping datanodes
Stopping secondary namenodes [master]
2017-03-21 18:57:20,746 WARN util.NativeCodeLoader:Unable to load native-hadoop library for your platform... using builtin-javaclasses where applicable
Stopping nodemanagers
slave1: WARNING: nodemanager did not stop gracefullyafter 5 seconds: Trying to kill with kill -9
slave2: WARNING: nodemanager did not stop gracefullyafter 5 seconds: Trying to kill with kill -9
Stopping resourcemanager
(5)$ /data/jdk1.8.0_121/bin/jps # 再次查看进程都已经正常关闭
11500 Jps
(6)Web页面
1)http://192.168.24.15:8088
2)http://192.168.24.15:9870
4.5.3 Mapreduce程序测试
$ cd /data/hadoop/bin
1、第一种测试方法:
$ hadoop jar../share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.0-alpha2.jar pi 1 1 #说明成功
Number of Maps = 1
Samples per Map = 1
Wrote input for Map #0
Starting Job
2017-04-01 05:34:34,150 INFO client.RMProxy:Connecting to ResourceManager at master/192.168.24.15:8032
2017-04-01 05:34:35,765 INFO input.FileInputFormat:Total input files to process : 1
2017-04-01 05:34:35,876 INFO mapreduce.JobSubmitter:number of splits:1
2017-04-01 05:34:35,926 INFOConfiguration.deprecation:yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead,use yarn.system-metrics-publisher.enabled
2017-04-01 05:34:36,402 INFO mapreduce.JobSubmitter:Submitting tokens for job: job_1490957345671_0007
2017-04-01 05:34:36,939 INFO impl.YarnClientImpl:Submitted application application_1490957345671_0007
2017-04-01 05:34:37,085 INFO mapreduce.Job: The urlto track the job: http://master:8088/proxy/application_1490957345671_0007/
2017-04-01 05:34:37,086 INFO mapreduce.Job: Runningjob: job_1490957345671_0007
2017-04-01 05:34:47,336 INFO mapreduce.Job: Jobjob_1490957345671_0007 running in uber mode : false
2017-04-01 05:34:47,340 INFO mapreduce.Job: map 0% reduce 0%
2017-04-01 05:34:57,496 INFO mapreduce.Job: map 100% reduce 0%
2017-04-01 05:35:05,574 INFO mapreduce.Job: map 100% reduce 100%
2017-04-01 05:35:05,588 INFO mapreduce.Job: Jobjob_1490957345671_0007 completed successfully
2、第二种测试方式:
(1)生成HDFS请求目录执行MapReduce任务
$ hdfs dfs -mkdir /user
$ hdfs dfs -mkdir /user/hduser
(2)将输入文件拷贝到分布式文件系统
$ hdfs dfs -mkdir /user/hduser/input
$ hdfs dfs -put ../etc/hadoop/yarn-site.xml/user/hduser/input
(2)运行提供的示例程序
$ hadoop jar../share/hadoop/mapreduce/hadoop-mapreduce-examples-3.0.0-alpha2.jar grep/user/hduser/input output ‘dfs[a-z.]+‘……省略……
2017-03-31 10:58:46,650 INFO mapreduce.Job: map 100% reduce 100%
2017-03-31 10:58:46,664 INFO mapreduce.Job: Jobjob_1490957345671_0003 completed successfully
2017-03-31 10:58:46,860 INFO mapreduce.Job: Counters:49
……省略……
http://192.168.24.15:9870里可以看到:
### 由于博客文字限制,只能分开写了:
Hadoop 3.0.0-alpha2安装(二)链接:
http://laowafang.blog.51cto.com/251518/1912345
刘政委 2017-04-01