Hadoop集群部署实战(cdh发行版)

内容简介：Hadoop集群部署实战(cdh发行版)

一、概要

由于工作需要，最近一段时间开始接触学习hadoop相关的东西，目前公司的实时任务和离线任务都跑在一个hadoop集群，离线任务的特点就是每天定时跑，任务跑完了资源就空闲了，为了合理的利用资源，我们打算在搭一个集群用于跑离线任务，计算节点和储存节点分离，计算节点结合aws的Auto Scaling（自动扩容、缩容服务）以及竞价实例，动态调整，在跑任务的时候拉起一批实例，任务跑完就自动释放掉服务器，本文记录下hadoop集群的搭建过程，方便自己日后查看，也希望能帮到初学者，本文所有软件都是通过yum安装，大家也可以下载相应的二进制文件进行安装，使用哪种方式安装，从属个人习惯。

二、环境

1、角色介绍

10.10.103.246 NameNode zkfc journalNode QuorumaPeerMain DataNode ResourceManager NodeManager WebAppProxyServer JobHistoryServer
10.10.103.144 NameNode zkfc journalNode QuorumaPeerMain DataNode ResourceManager NodeManager WebAppProxyServer
10.10.103.62       zkfc journalNode QuorumaPeerMain DataNode             NodeManager

2、基础环境说明

a、系统版本

我们用的是aws的ec2，用的aws自己定制过的系统，不过和redhat基本相同，内核版本：4.9.20-10.30.amzn1.x86_64

b、 java 版本

java version "1.8.0_121"

c、hadoop版本

hadoop-2.6.0

d、cdh版本

cdh5.11.0

e、关于主机名，因为我这里用的aws的ec2，默认已有主机名，并且内网可以解析，故就不单独做主机名的配置了，如果你的主机名内网不能解析，请一定要配置主机名，集群内部通讯很多组件使用的是主机名

三、配置部署

1、设置yum源

vim /etc/yum.repos.d/cloudera.repo
   
[cloudera-cdh5-11-0]
# Packages for Cloudera's Distribution for Hadoop, Version 5.11.0, on RedHat or CentOS 6 x86_64
name=Cloudera's Distribution for Hadoop, Version 5.11.0
baseurl=http://archive.cloudera.com/cdh5/redhat/6/x86_64/cdh/5.11.0/
gpgkey=http://archive.cloudera.com/cdh5/redhat/6/x86_64/cdh/RPM-GPG-KEY-cloudera   
gpgcheck=1
[cloudera-gplextras5b2]
# Packages for Cloudera's GPLExtras, Version 5.11.0, on RedHat or CentOS 6 x86_64
name=Cloudera's GPLExtras, Version 5.11.0
baseurl=http://archive.cloudera.com/gplextras5/redhat/6/x86_64/gplextras/5.11.0/
gpgkey=http://archive.cloudera.com/gplextras5/redhat/6/x86_64/gplextras/RPM-GPG-KEY-cloudera   
gpgcheck=1

PS：我这里安装的5.11.0，如果想安装低版本或者高版本，根据自己的需求修改版本号即可

2、安装配置zookeeper集群

yum -y  install zookeeper zookeeper-server

vi /etc/zookeeper/conf/zoo.cfg

tickTime=2000
initLimit=10
syncLimit=5
dataDir=/data/zookeeper
clientPort=2181
maxClientCnxns=0
server.1=10.10.103.144:2888:3888
server.2=10.10.103.226:2888:3888
server.3=10.10.103.62:2888:3888
autopurge.snapRetainCount=3
autopurge.purgeInterval=1

mkdir /data/zookeeper           #创建datadir目录
/etc/init.d/zookeeper-server init    #所有节点先初始化
echo 1 > /data/zookeeper/myid      #10.10.103.144上操作
echo 2 > /data/zookeeper/myid      #10.10.103.226上操作
echo 3 > /data/zookeeper/myid      #10.10.103.62上操作
/etc/init.d/zookeeper-server       #启动服务
/usr/lib/zookeeper/bin/zkServer.sh status  #查看所有节点状态，其中只有一个节点是Mode: leader就正常 了

3、安装

a、10.10.103.246和10.10.103.144安装

yum -y install hadoop hadoop-client hadoop-hdfs hadoop-hdfs-namenode hadoop-hdfs-zkfc hadoop-hdfs-journalnode hadoop-hdfs-datanode hadoop-mapreduce-historyserver hadoop-yarn-nodemanager hadoop-yarn-proxyserver  hadoop-yarn hadoop-mapreduce hadoop-yarn-resourcemanager hadoop-lzo* impala-lzo

b、10.10.103.62上安装

yum -y install hadoop hadoop-client hadoop-hdfs hadoop-hdfs-journalnode hadoop-hdfs-datanode  hadoop-lzo* impala-lzo hadoop-yarn hadoop-mapreduce hadoop-yarn-nodemanager

PS：

1、一般小公司，计算节点(ResourceManager)和储存节点(NameNode)的主节点部署在两台服务器上做HA，计算节点（NodeManager）和储存节点（DataNode）部署在多台服务器上，每台服务器上都启动NodeManager和DataNode服务。

2、如果大集群，可能需要计算资源和储存资源分离，集群的各个角色都有服务器单独部署，个人建议划分如下:

a、储存节点

NameNode：

需要安装hadoop hadoop-client hadoop-hdfs hadoop-hdfs-namenode hadoop-hdfs-zkfc hadoop-lzo* impala-lzo

DataNode：

需要安装hadoop hadoop-client hadoop-hdfs hadoop-hdfs-datanode  hadoop-lzo* impala-lzo

QJM集群：

需要安装hadoop hadoop-hdfs  hadoop-hdfs-journalnode zookeeper zookeeper-server

b、计算节点

ResourceManager：

需要安装hadoop hadoop-client hadoop-yarn hadoop-mapreduce hadoop-yarn-resourcemanager

WebAppProxyServer：

需要安装 hadoop hadoop-yarn hadoop-mapreduce hadoop-yarn-proxyserver

JobHistoryServer：

需要安装 hadoop hadoop-yarn hadoop-mapreduce hadoop-mapreduce-historyserver

NodeManager：

需要安装hadoop hadoop-client hadoop-yarn hadoop-mapreduce hadoop-yarn-nodemanager

4、配置

a、创建目录并设置权限

mkdir -p /data/hadoop/dfs/nn             #datanode上操作
chown hdfs:hdfs /data/hadoop/dfs/nn/ -R  #datanode上操作
mkdir -p /data/hadoop/dfs/dn             #namenode上操作
chown hdfs:hdfs /data/hadoop/dfs/dn/ -R  #namenode上操作
mkdir -p /data/hadoop/dfs/jn             #journalnode上操作
chown hdfs:hdfs /data/hadoop/dfs/jn/ -R  #journalnode上操作
mkdir /data/hadoop/yarn -p               #nodemanager上操作
chown yarn:yarn  /data/hadoop/yarn  -R   #nodemanager上操作

b、撰写配置文件

5、服务启动

a、启动journalnode(三台服务器上都启动)

/etc/init.d/hadoop-hdfs-journalnode start

b、格式化namenode(在其中一台namenode10.10.103.246上操作)

sudo -u hdfs hadoop namenode -format

c、初始化zk中HA的状态(在其中一台namenode10.10.103.246上操作)

sudo -u hdfs hdfs zkfc -formatZK

d、初始化共享Edits文件(在其中一台namenode10.10.103.246上操作)

sudo -u hdfs hdfs namenode -initializeSharedEdits

e、启动10.10.103.246上namenode

/etc/init.d/hadoop-hdfs-namenode start

f、同步源数据并启动10.10.103.144上namenode

sudo -u hdfs hdfs namenode -bootstrapStandby
/etc/init.d/hadoop-hdfs-namenode start

g、在两台namenode上启动zkfc

/etc/init.d/hadoop-hdfs-zkfc start

h、启动datanode(所有机器上操作)

/etc/init.d/hadoop-hdfs-journalnode start

i、在10.10.103.246上启动WebAppProxyServer、JobHistoryServer、httpfs

/etc/init.d/hadoop-yarn-proxyserver start
/etc/init.d/hadoop-mapreduce-historyserver start
/etc/init.d/hadoop-httpfs start

j、在所有机器上启动nodemanager

/etc/init.d/hadoop-yarn-nodemanager restart

四、功能验证

1、hadoop功能

a、查看hdfs根目录

[root@ip-10-10-103-246 ~]# hadoop fs -ls /
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
Found 3 items
drwxr-xr-x   - hdfs   hdfs          0 2017-05-11 11:40 /tmp
drwxrwx---   - mapred hdfs          0 2017-05-11 11:28 /user
drwxr-xr-x   - yarn   hdfs          0 2017-05-11 11:28 /var

b、上传一个文件到根目录

[root@ip-10-10-103-246 ~]# hadoop fs -put /tmp/test.txt  /
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
[root@ip-10-10-103-246 ~]# hadoop fs -ls /               
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
Found 4 items
-rw-r--r--   2 root   hdfs         22 2017-05-11 15:47 /test.txt
drwxr-xr-x   - hdfs   hdfs          0 2017-05-11 11:40 /tmp
drwxrwx---   - mapred hdfs          0 2017-05-11 11:28 /user
drwxr-xr-x   - yarn   hdfs          0 2017-05-11 11:28 /var

c、直接删除文件不放回收站

[root@ip-10-10-103-246 ~]# hadoop fs -rm -skipTrash /test.txt
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
Deleted /test.txt

d、跑一个wordcount用例

[root@ip-10-10-103-246 ~]# hadoop fs -put /tmp/test.txt /user/hdfs/rand/
Java HotSpot(TM) 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
[root@ip-10-10-103-246 conf]# sudo -u hdfs  hadoop  jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples-2.6.0-cdh5.11.0.jar wordcount /user/hdfs/rand/ /tmp
OpenJDK 64-Bit Server VM warning: ignoring option MaxPermSize=128m; support was removed in 8.0
17/05/11 11:40:08 INFO client.ConfiguredRMFailoverProxyProvider: Failing over to 10.10.103.246
17/05/11 11:40:09 INFO input.FileInputFormat: Total input paths to process : 1
17/05/11 11:40:09 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library
17/05/11 11:40:09 INFO lzo.LzoCodec: Successfully loaded & initialized native-lzo library [hadoop-lzo rev 674c65bbf0f779edc3e00a00c953b121f1988fe1]
17/05/11 11:40:09 INFO mapreduce.JobSubmitter: number of splits:1
17/05/11 11:40:09 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494472050574_0003
17/05/11 11:40:09 INFO impl.YarnClientImpl: Submitted application application_1494472050574_0003
17/05/11 11:40:09 INFO mapreduce.Job: The url to track the job: http://10.10.103.246:8100/proxy/application_1494472050574_0003/
17/05/11 11:40:09 INFO mapreduce.Job: Running job: job_1494472050574_0003
17/05/11 11:40:15 INFO mapreduce.Job: Job job_1494472050574_0003 running in uber mode : false
17/05/11 11:40:15 INFO mapreduce.Job:  map 0% reduce 0%
17/05/11 11:40:20 INFO mapreduce.Job:  map 100% reduce 0%
17/05/11 11:40:25 INFO mapreduce.Job:  map 100% reduce 100%
17/05/11 11:40:25 INFO mapreduce.Job: Job job_1494472050574_0003 completed successfully
17/05/11 11:40:25 INFO mapreduce.Job: Counters: 53
        File System Counters
                FILE: Number of bytes read=1897
                FILE: Number of bytes written=262703
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=6431
                HDFS: Number of bytes written=6219
                HDFS: Number of read operations=6
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters
                Launched map tasks=1
                Launched reduce tasks=1
                Data-local map tasks=1
                Total time spent by all maps in occupied slots (ms)=2592
                Total time spent by all reduces in occupied slots (ms)=5360
                Total time spent by all map tasks (ms)=2592
                Total time spent by all reduce tasks (ms)=2680
                Total vcore-milliseconds taken by all map tasks=2592
                Total vcore-milliseconds taken by all reduce tasks=2680
                Total megabyte-milliseconds taken by all map tasks=3981312
                Total megabyte-milliseconds taken by all reduce tasks=8232960
        Map-Reduce Framework
                Map input records=102
                Map output records=96
                Map output bytes=6586
                Map output materialized bytes=1893
                Input split bytes=110
                Combine input records=96
                Combine output records=82
                Reduce input groups=82
                Reduce shuffle bytes=1893
                Reduce input records=82
                Reduce output records=82
                Spilled Records=164
                Shuffled Maps =1
                Failed Shuffles=0
                Merged Map outputs=1
                GC time elapsed (ms)=120
                CPU time spent (ms)=1570
                Physical memory (bytes) snapshot=501379072
                Virtual memory (bytes) snapshot=7842639872
                Total committed heap usage (bytes)=525860864
                Peak Map Physical memory (bytes)=300183552
                Peak Map Virtual memory (bytes)=3244224512
                Peak Reduce Physical memory (bytes)=201195520
                Peak Reduce Virtual memory (bytes)=4598415360
        Shuffle Errors
                BAD_ID=0
                CONNECTION=0
                IO_ERROR=0
                WRONG_LENGTH=0
                WRONG_MAP=0
                WRONG_REDUCE=0
        File Input Format Counters
                Bytes Read=6321
        File Output Format Counters
                Bytes Written=6219
[root@ip-10-10-103-246 conf]#

2、namenode高可用验证

查看http://10.10.103.246:50070

查看http://10.10.103.144:50070

停掉10.10.103.246节点的namenode进程，查看10.10.103.144节点是否会提升为active节点

3、resourcemanager高可用验证

查看http://10.10.103.246:8088

查看http://10.10.103.144:8088

在浏览器输入http://10.10.103.144:8088，会跳转到 http://ip-10-10-103-246.ec2.internal:8088/ ，ip-10-10-103-246.ec2.internal是10.10.103.246的主机名，说明resourcemanager高可用配置ok，停掉10.10.103.144的

resourcemanager进程，在浏览器输入http://10.10.103.144:8088，就不会在跳转了，说明10.10.103.144已经切成了master。

五、总结

1、hadoop集群能成本部署完成，这才是开始，后期的维护，业务方问题的解决这些经验需要一点一点积累，多出差多折腾总是好的。

2、对应上面部署的集群后期需要扩容，直接把10.10.103.62这台机器做个镜像，用镜像启动服务器即可，服务会自动启动并且加入到集群

3、云上hadoop集群的成本优化，这里只针对aws而言

a、冷数据存在在s3上，hdfs可以直接支持s3，在hdfs-site.xml里面添加s3的key参数(fs.s3n.awsAccessKeyId和fs.s3n.awsSecretAccessKey)即可，需要注意的是程序上传、下载的逻辑需要多加几个重试机制，s3有时候不稳定会导致上传或者下载不成功

b、使用Auto Scaling服务结合竞价实例，配置扩展策略，比如当cpu大于50%的时候就扩容5台服务器，当cpu小于10%的时候就缩容5台服务器，当然你可以配置更多阶梯级的扩容、缩容策略，Auto Scaling还有一个计划任务的功能，你可以向设置crontab一样设置，让Auto Scaling帮你扩容、缩容服务器

本文出自 “�潘吭宋�男” 博客，谢绝转载！

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持 码农网

查看所有标签

猜你喜欢:

• docker部署rabbitmq集群
• 部署Ceph集群－－jluocc
• 部署高可用Kubernetes集群
• Eureka使用及集群部署
• 使用Docker部署RabbitMQ集群
• GreenPlum 5.10.0 集群部署

本站部分资源来源于网络，本站转载出于传递更多信息之目的，版权归原作者或者来源机构所有，如转载稿涉及版权问题，请联系我们。