Hadoop2.5.2 安装部署

0x00 平台环境

OS: CentOS-6.5-x86_64
JDK: jdk-8u111-linux-x64
Hadoop: hadoop-2.5.2

0x01 操作系统基本设置

1.1 网络配置

修改主机名

//查看当前主机名
# hostname
//修改当前主机名
vim /etc/sysconfig/network
NETWORKING 是否利用网络
GATEWAY 默认网关
IPGATEWAYDEV 默认网关的接口名
HOSTNAME 主机名
DOMAIN 域名

配置静态IP

# vim /etc/sysconfig/network-scripts/ifcfg-eth0
DEVICE 接口名(设备,网卡)
BOOTPROTO IP的配置方法(static:固定IP, dhcpHCP, none:手动)
HWADDR MAC地址
ONBOOT 系统启动的时候网络接口是否有效(yes/no)
TYPE 网络类型(通常是Ethemet)
NETMASK 网络掩码
IPADDR IP地址
IPV6INIT IPV6是否有效(yes/no)
GATEWAY 默认网关IP地址
DNS1 
DNS2

配置hosts文件

# vim /etc/hosts
192.168.1.2 master
192.168.1.3 slave1
192.168.1.4 slave2

1.2 关闭防火墙和SELinux

关闭防火墙

//临时关闭
# service iptables stop
//永久关闭
# chkconfig iptables off
# service ip6tables stop
# chkconfig ip6tables off

关闭SELinux

# vim /etc/sysconfig/selinux
SELINUX=enforcing -> SELINUX=disable

接着执行如下命令

# setenforce 0
# getenforce

1.3 建立一般用户hadoop

//新增用户
# useradd hadoop
//设置密码
# passwd hadoop
//根据提示输入两次密码

0x02 配置master免密钥登录slave

2.1 生成密钥

$ su hadoop
$ ssh-keygen -t rsa 

2.2 创建授权密钥

msaterid_rsa.pub追加到授权key中(只需要将master节点的公钥追加到authorized_keys

$ cat ~/.ssh/id_dsa.pub >> ~/.ssh/authorized_keys

更改authorized_keys的权限,也需要分别在slave节点操作

chomd 600 authorized_keys

2.3 将authorized_keys复制到所有slave节点

$ scp ~/.ssh/authorized_keys [email protected]:~/.ssh/ 
$ scp ~/.ssh/authorized_keys [email protected]:~/.ssh/   

2.4 测试master免密钥登陆所有slave节点

$ ssh slave1
$ ssh slave2

0x03 Hadoop 安装

3.1 解压

$ tar -zvxf hadoop-2.5.2.tar.gz  -C /home/hadoop/hadoop
$ chown -R hadoop:hadoop /home/hadoop

3.2 配置环境变量(在尾部追加

# vim  /etc/profile
# set hadoop environment
export HADOOP_HOME=/home/hadoop/hadoop
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_YARN_HOME=$HADOOP_HOME
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export CLASSPATH=.:$JAVA_HOME/lib:$HADOOP_HOME/lib:$CLASSPATH
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

使环境变量立即生效注意在哪个用户下执行该命令,环境变量在那个用户下生效

# su hadoop
$ source /etc/profile

0x04 配置hadoop文件

4.1 core-site.xml

注意:hadoop_tmp文件夹一定要配置在存储空间比较大的位置,否则会报错
可能出现的问题:
(1)Unhealthy Nodes 问题
http://blog.csdn.net/korder/article/details/46866271
(2)local-dirs turned bad
(3)Hadoop运行任务时一直卡在: INFO mapreduce.Job: Running job
http://www.bkjia.com/yjs/1030530.html


     
            fs.defaultFS
            hdfs://master:9000
     
     
            hadoop.tmp.dir
            file:/home/hadoop/hadoop/hadoop_tmp
            
     
     
            io.file.buffer.size
            131072
     
     
            hbase.rootdir
            hdfs://master:9000/hbase
     

4.2 hdfs-site.xml


       
               dfs.replication
               2
       
       
               dfs.namenode.secondary.http-address
               master:9001
       
       
              dfs.namenode.name.dir
              file:/home/hadoop/hadoop/dfs/name
              namenode上存储hdfs元数据
       
       
               dfs.datanode.data.dir
               file:/home/hadoop/hadoop/dfs/data
               datanode上数据块物理存储位置
       
       
               dfs.webhdfs.enabled
                true
       
 

注:访问namenode的 webhdfs 使用50070端口,访问datanode的webhdfs使用50075端口。要想不区分端口,直接使用namenode的IP和端口进行所有webhdfs操作,就需要在所有
datanode上都设置hdfs-site.xml中dfs.webhdfs.enabled为true。

4.3 mapred-site.xml


       
             mapreduce.framework.name
             yarn
      
      
             mapreduce.jobhistory.address
             master:10020
      
      
             mapreduce.jobhistory.webapp.address
             master:19888
      
      
             mapreduce.jobtracker.http.address
             NameNode:50030
      
 

jobhistory是Hadoop自带一个历史服务器,记录Mapreduce历史作业。默认情况下,jobhistory没有启动,可用以下命令启动:

$ sbin/mr-jobhistory-daemon.sh start historyserver

4.4 yarn-site.xml


       
              yarn.nodemanager.aux-services
              mapreduce_shuffle
       
       
               yarn.resourcemanager.address
               master:8032
       
       
              yarn.resourcemanager.scheduler.address
              master:8030
       
       
              yarn.resourcemanager.resource-tracker.address
              master:8031
       
       
               yarn.resourcemanager.admin.address
               master:8033
       
       
               yarn.resourcemanager.webapp.address
               master:8088
       
       
               yarn.resourcemanager.zk-address
               master:2181,slave1L2181,slave2:2181
       
       
               yarn.log-aggregation-enable
               true
       

4.5 修改slaves文件,添加datanode节点hostname到slaves文件中

slave1
slave2

4.6 hadoop-env.sh

vim /home/hadoop/hadoop/etc/hadoop/hadoop-env.sh
export JAVA_HOME=${JAVA_HOME} -> export JAVA_HOME=/usr/java
export HADOOP_COMMON_LIB_NATIVE_DIR=/home/hadoop/hadoop/lib/native

4.7 复制

最后,将整个/home/hadoop/hadoop文件夹及其子文件夹使用scp复制到slave相同目录中:

$ scp -r /home/hadoop/hadoop hadoop@slave1:/home/hadoop/
$ scp -r /home/hadoop/hadoop hadoop@slave2:/home/hadoop/

0x05 运行Hadoop

5.1 格式化(*确保配置文件中各文件夹已经创建

$ hdfs namenode –format

成功后显示信息

************************************************************/
17/09/09 04:27:03 INFO namenode.NameNode: registered UNIX signal handlers for [TERM, HUP, INT]
17/09/09 04:27:03 INFO namenode.NameNode: createNameNode [-format]
17/09/09 04:27:04 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Formatting using clusterid: CID-243cecfb-c003-4213-8112-b5f227616e39
17/09/09 04:27:04 INFO namenode.FSNamesystem: No KeyProvider found.
17/09/09 04:27:04 INFO namenode.FSNamesystem: fsLock is fair:true
17/09/09 04:27:04 INFO blockmanagement.DatanodeManager: dfs.block.invalidate.limit=1000
17/09/09 04:27:04 INFO blockmanagement.DatanodeManager: dfs.namenode.datanode.registration.ip-hostname-check=true
17/09/09 04:27:04 INFO blockmanagement.BlockManager: dfs.namenode.startup.delay.block.deletion.sec is set to 000:00:00:00.000
17/09/09 04:27:04 INFO blockmanagement.BlockManager: The block deletion will start around 2017 Sep 09 04:27:04
17/09/09 04:27:04 INFO util.GSet: Computing capacity for map BlocksMap
17/09/09 04:27:04 INFO util.GSet: VM type       = 64-bit
17/09/09 04:27:04 INFO util.GSet: 2.0% max memory 889 MB = 17.8 MB
17/09/09 04:27:04 INFO util.GSet: capacity      = 2^21 = 2097152 entries
17/09/09 04:27:04 INFO blockmanagement.BlockManager: dfs.block.access.token.enable=false
17/09/09 04:27:04 INFO blockmanagement.BlockManager: defaultReplication         = 2
17/09/09 04:27:04 INFO blockmanagement.BlockManager: maxReplication             = 512
17/09/09 04:27:04 INFO blockmanagement.BlockManager: minReplication             = 1
17/09/09 04:27:04 INFO blockmanagement.BlockManager: maxReplicationStreams      = 2
17/09/09 04:27:04 INFO blockmanagement.BlockManager: replicationRecheckInterval = 3000
17/09/09 04:27:04 INFO blockmanagement.BlockManager: encryptDataTransfer        = false
17/09/09 04:27:04 INFO blockmanagement.BlockManager: maxNumBlocksToLog          = 1000
17/09/09 04:27:04 INFO namenode.FSNamesystem: fsOwner             = hadoop (auth:SIMPLE)
17/09/09 04:27:04 INFO namenode.FSNamesystem: supergroup          = supergroup
17/09/09 04:27:04 INFO namenode.FSNamesystem: isPermissionEnabled = false
17/09/09 04:27:04 INFO namenode.FSNamesystem: HA Enabled: false
17/09/09 04:27:04 INFO namenode.FSNamesystem: Append Enabled: true
17/09/09 04:27:05 INFO util.GSet: Computing capacity for map INodeMap
17/09/09 04:27:05 INFO util.GSet: VM type       = 64-bit
17/09/09 04:27:05 INFO util.GSet: 1.0% max memory 889 MB = 8.9 MB
17/09/09 04:27:05 INFO util.GSet: capacity      = 2^20 = 1048576 entries
17/09/09 04:27:05 INFO namenode.NameNode: Caching file names occuring more than 10 times
17/09/09 04:27:05 INFO util.GSet: Computing capacity for map cachedBlocks
17/09/09 04:27:05 INFO util.GSet: VM type       = 64-bit
17/09/09 04:27:05 INFO util.GSet: 0.25% max memory 889 MB = 2.2 MB
17/09/09 04:27:05 INFO util.GSet: capacity      = 2^18 = 262144 entries
17/09/09 04:27:05 INFO namenode.FSNamesystem: dfs.namenode.safemode.threshold-pct = 0.9990000128746033
17/09/09 04:27:05 INFO namenode.FSNamesystem: dfs.namenode.safemode.min.datanodes = 0
17/09/09 04:27:05 INFO namenode.FSNamesystem: dfs.namenode.safemode.extension     = 30000
17/09/09 04:27:05 INFO namenode.FSNamesystem: Retry cache on namenode is enabled
17/09/09 04:27:05 INFO namenode.FSNamesystem: Retry cache will use 0.03 of total heap and retry cache entry expiry time is 600000 millis
17/09/09 04:27:05 INFO util.GSet: Computing capacity for map NameNodeRetryCache
17/09/09 04:27:05 INFO util.GSet: VM type       = 64-bit
17/09/09 04:27:05 INFO util.GSet: 0.029999999329447746% max memory 889 MB = 273.1 KB
17/09/09 04:27:05 INFO util.GSet: capacity      = 2^15 = 32768 entries
17/09/09 04:27:05 INFO namenode.NNConf: ACLs enabled? false
17/09/09 04:27:05 INFO namenode.NNConf: XAttrs enabled? true
17/09/09 04:27:05 INFO namenode.NNConf: Maximum size of an xattr: 16384
17/09/09 04:27:05 INFO namenode.FSImage: Allocated new BlockPoolId: BP-706635769-192.168.32.100-1504902425219
17/09/09 04:27:05 INFO common.Storage: Storage directory /home/hadoop/cloud/hadoop/dfs/name has been successfully formatted.
17/09/09 04:27:05 INFO namenode.FSImageFormatProtobuf: Saving image file /home/hadoop/cloud/hadoop/dfs/name/current/fsimage.ckpt_0000000000000000000 using no compression
17/09/09 04:27:05 INFO namenode.FSImageFormatProtobuf: Image file /home/hadoop/cloud/hadoop/dfs/name/current/fsimage.ckpt_0000000000000000000 of size 323 bytes saved in 0 seconds.
17/09/09 04:27:05 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
17/09/09 04:27:05 INFO util.ExitUtil: Exiting with status 0
17/09/09 04:27:05 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at master/192.168.32.100
************************************************************/

5.2 启动Hadoop

$ start-dfs.sh   
$ start-yarn.sh  
//可以用一条命令来代替:
$ start-all.sh

5.3 使用jps命令查看进程

(1) master主节点进程:

8193 Jps
7943 ResourceManager
7624 NameNode
7802 SecondaryNameNode

(2) slave数据节点进程:

1413 DataNode
1512 NodeManager
1626 Jps

5.4 通过浏览器查看集群运行状态

概览:http://172.16.1.156:50070/
集群:http://172.16.1.156:8088/
JobHistory:http://172.16.1.156:19888

jobhistory是Hadoop自带一个历史服务器,记录Mapreduce历史作业。默认情况下,jobhistory没有启动,可用以下命令启动:

$ sbin/mr-jobhistory-daemon.sh start historyserver

0x06 测试Hadoop(运行wordcount)

6.1 建立文件

$ vi wordcount.txt
hello you
hello me
hello everyone

6.2 在HDFS上建立目录

$ hadoop fs -mkdir /data/wordcount    
$ hadoop fs –mkdir /output/  

目录/data/wordcount用来存放Hadoop自带WordCount例子的数据文件,运行这个MapReduce任务结果输出到/output/wordcount目录中。

6.3 上传文件

$ hadoop fs -put wordcount.txt/data/wordcount/

6.4 执行wordcount程序

$ hadoop jar /home/hadoop/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.5.1.jar wordcount /data/wordcount /output/wordcount/

6.5 查看结果

# hadoop fs -text /output/wordcount/part-r-00000  
everyone  1 
hello  3  
me    1  
you   1 

0x07 搭建中遇到的问题

7.1 在配置环境变量过程可能遇到输入命令ls命令不能识别问题:ls -bash: ls: command not found

原因:在设置环境变量时,编辑profile文件没有写正确,将export PATH=\(JAVA_HOME/bin:\)PATH中冒号误写成分号 ,导致在命令行下ls等命令不能够识别。解决方案:export PATH=/usr/local/sbin:/usr/local/bin:/sbin:/bin:/usr/sbin:/usr/bin:/root/bin

7.2 nodemanager死掉

在主机上启动hadoop集群,然后使用jps查看主从机上进程状态,能够看到主机上的resourcemanager和各个从机上的nodemanager,但是过一段时间后,从机上的nodemanager就没有了,主机上的resourcemanager还在。

原因是防火墙处于开启状态:
注:nodemanager启动后要通过心跳机制定期与RM通信,否则RM会认为NM死掉,会停止NM服务。

7.3 SSH连接慢的问题

sshd服务中设置了UseDNS yes,当配置的DNS服务器出现无法访问的问题,可能会造成连接该服务器需要等待10到30秒的时间。由于使用UseDNS,sshd服务器会反向解析连接客户端的ip,即使是在局域网中也会。
当平时连接都是很快,突然变的异常的慢,可能是sshd服务的服务器上配置的DNS失效,例如DNS配置的是外网的,而此时外面故障断开。终极解决方案是不要使用UseDNS,在配置文件/etc/sshd_config(有些linux发行版在/etc/ssh/sshd_config)中找到UseDNS 设置其值为 no,如果前面有#号,需要去掉,重启sshd服务器即可。

vim /etc/ssh/sshd_config
UseDNS no

7.4 重新格式化HDFS文件系统后报错

FATAL org.apache.hadoop.hdfs.server.namenode.NameNode: Exception in namenode join java.io.IOException: There appears to be a gap in the edit log. We expected txid 176531929, but got txid 176533587.
原因:是因为namenode和datenode数据不一致引起的
解决办法:删除master slave节点dataname文件夹下的内容,即可解决。缺点是数据不可恢复。
另一种解决办法:http://blog.csdn.net/amber_amber/article/details/46896719
参考链接:
https://yq.aliyun.com/articles/36274
https://taoistwar.gitbooks.io/spark-operationand-maintenance-management/content/spark_relate_software/hadoop_2x_install.html

7.5 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable

I assume you're running Hadoop on 64bit CentOS. The reason you saw that warning is the native Hadoop library $HADOOP_HOME/lib/native/libhadoop.so.1.0.0 was actually compiled on 32 bit.Anyway, it's just a warning, and won't impact Hadoop's functionalities.

http://stackoverflow.com/questions/19943766/hadoop-unable-to-load-native-hadoop-library-for-your-platform-warning

(1)简便的解决方法是:(后来我发现这两步都要做)
下载64位的库,解压到hadoop-2.7.0/lib/native/,不在有警告
下载地址:http://dl.bintray.com/sequenceiq/sequenceiq-bin/
(2)修改hadoop-env.sh

export HADOOP_OPTS="$HADOOP_OPTS -Djava.library.path=/usr/local/hadoop/lib/native"
export HADOOP_COMMON_LIB_NATIVE_DIR="/usr/local/hadoop/lib/native/"

7.6 hadoop提交jar包卡住不会往下执行的解决方案,卡在此处:

INFO mapreduce.Job: Running job: job_1474517485267_0001
这里我们在集群的yarn-site.xml中添加配置


    yarn.nodemanager.resource.memory-mb
    4096


    yarn.scheduler.minimum-allocation-mb
    2048


    yarn.nodemanager.vmem-pmem-ratio
    2.1

重新启动集群,运行jar包即可

但是,并没有解决我的问题,我的问题是Unhealthy Nodes,最后才发现!!可能不添加上述配置原来配置也是对的。
http://www.voidcn.com/blog/gamer_gyt/article/p-6209546.html

2017年1月22日, 星期日

  • 2017-06-02 更新
    增加操作系统基本设置部分
    修改部分配置文件内容

转载于:https://www.cnblogs.com/ning-wang/p/6414203.html

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