Hadoop2.x 集群搭建

Hadoop2.x 集群搭建

一些重复的细节参考Hadoop1.X集群完全分布式模式环境部署

1 HADOOP 集群搭建

1.1 集群简介

HADOOP 集群具体来说包含两个集群：HDFS 集群和YARN集群，两者逻辑上分离，但物理上常在一起.

  • HDFS集群：负责海量数据的存储，集群中的角色主要有 NameNode / DataNode
  • YARN集群：负责海量数据运算时的资源调度，集群中的角色主要有 ResourceManager /NodeManager

本集群搭建案例，以 5 节点为例进行搭建，角色分配如下：

结点 角色 IP
node1 NameNode
SecondaryNameNode		 192.168.33.200
node2 ResourceManager 192.168.33.201
node3 DataNode
NodeManager		 192.168.33.202
node4 DataNode
NodeManager		 192.168.33.203
node5 DataNode
NodeManager		 192.168.33.204

部署图如下：

屏幕快照 2017-04-19 下午3.21.40.png

1.2 服务器准备

本案例使用虚拟机服务器来搭建 HADOOP 集群，所用软件及版本：

★ paraller Desktop 12

★ Centos 6.5 64bit

1.3 网络环境准备

  • 采用 NAT 方式联网
  • 网关地址：192.168.33.1
  • 5个服务器节点 IP 地址：
    • 192.168.33.200,
    • 192.168.33.201,
    • 192.168.33.202,
    • 192.168.33.203,
    • 192.168.33.204
  • 子网掩码：255.255.255.0

1.4 服务器系统设置

  • 添加 HADOOP 用户
  • 为 HADOOP 用户分配 sudoer 权限
  • 设置主机名
    • node1
    • node2
    • node3
    • node4
    • node5
  • 配置内网域名映射：
    • 192.168.33.200--------node1
    • 192.168.33.201--------node2
    • 192.168.33.202--------node3
    • 192.168.33.203--------node4
    • 192.168.33.204--------node5
  • 配置 ssh 免密登陆
  • 配置防火墙

1.5 环境安装

  • 上传 jdk 安装包
  • 规划安装目录 /home/hadoop/apps/jdk_1.7.65
  • 解压安装包
  • 配置环境变量 /etc/profile

1.6 HADOOP 安装部署

  • 上传 HADOOP 安装包
  • 规划安装目录 /home/hadoop/apps/hadoop-2.6.1
  • 解压安装包
  • 修改配置文件 $HADOOP_HOME/etc/hadoop/

最简化配置如下：

vi hadoop-env.sh

/home/hd2/tmp目录要先建好

# The java implementation to use.

export JAVA_HOME=/usr/local/jdk1.7.0_65

vi core-site.xml

<configuration> 

<property> 
<name>fs.defaultFS</name> 
<value>hdfs://node1:9000</value> 
</property> 

<property> 
<name>hadoop.tmp.dir</name> 
<value>/home/hd2/tmp</value> 
</property> 

<property>
  <name>hadoop.logfile.size</name>
    <value>10000000</value>
      <description>The max size of each log file</description>
      </property>

<property>
  <name>hadoop.logfile.count</name>
    <value>10</value>
      <description>The max number of log files</description>
      </property>

</configuration>

vi hdfs-site.xml

<configuration>
 
<property> 
<name>dfs.namenode.name.dir</name> 
<value>/home/hd2/data/name</value> 
</property> 

<property> 
<name>dfs.datanode.data.dir</name> 
<value>/home/hd2/data/data</value> 
</property>

<property> 
<name>dfs.replication</name> 
<value>3</value> 
</property>

<property> 
<name>dfs.secondary.http.address</name> 
<value>node1:50090</value> 
</property> 
ca

</configuration>

vi mapred-site.xml

<configuration> 

<property> 
<name>mapreduce.framework.name</name> 
<value>yarn</value> 
</property>

</configuration>

vi yarn-site.xml

<configuration> 
<property> 
<name>yarn.resourcemanager.hostname</name> 
<value>node1</value> 
</property>

<property> 
<name>yarn.nodemanager.aux-services</name> 
<value>mapreduce_shuffle</value> 
</property> 
</configuration>

vi salves

node1 
node2 
node3
node4
node5

1.7 启动集群

初始化 HDFS

bin/hadoop namenode -format

启动 HDFS

sbin/start-dfs.sh

启动 YARN

sbin/start-yarn.sh

1.8 验证集群

浏览器访问http://192.168.33.200:50070

1.9 用worldcount程序测试集群

1.建立一个测试的目录

[hd2@node1 hadoop-2.4.1]$ hadoop fs -mkdir input

2.检验input文件夹是否创建成功

[hd2@node1 hadoop-2.4.1]$ hadoop fs -ls 
Found 1 items  
drwxr-xr-x   - root supergroup          0 2014-08-18 09:02 input

3.建立测试文件

[hd2@node1 hadoop-2.4.1]$ vi test.txt

hello hadoop

hello World

Hello Java

Hey man

i am a programmer

4.将测试文件放到测试目录中

[hd2@node1 hadoop-2.4.1]$ hadoop fs -put test.txt input/

5.检验test.txt文件是否已经导入

[hd2@node1 hadoop-2.4.1]$ hadoop fs -ls input/
Found 1 items  
-rw-r--r--   1 root supergroup         62 2014-08-18 09:03 input/test.txt

6.执行wordcount程序

[hd2@node1 hadoop-2.4.1]$ hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.4.1.jar wordcount input/ output/

执行过程

17/04/19 21:07:19 INFO client.RMProxy: Connecting to ResourceManager at node1/192.168.33.200:8032
17/04/19 21:07:19 INFO input.FileInputFormat: Total input paths to process : 2
17/04/19 21:07:20 INFO mapreduce.JobSubmitter: number of splits:2
17/04/19 21:07:20 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1492605823444_0003
17/04/19 21:07:20 INFO impl.YarnClientImpl: Submitted application application_1492605823444_0003
17/04/19 21:07:20 INFO mapreduce.Job: The url to track the job: http://node1:8088/proxy/application_1492605823444_0003/
17/04/19 21:07:20 INFO mapreduce.Job: Running job: job_1492605823444_0003
17/04/19 21:07:26 INFO mapreduce.Job: Job job_1492605823444_0003 running in uber mode : false
17/04/19 21:07:26 INFO mapreduce.Job:  map 0% reduce 0%
17/04/19 21:07:33 INFO mapreduce.Job:  map 100% reduce 0%
17/04/19 21:07:40 INFO mapreduce.Job:  map 100% reduce 100%
17/04/19 21:07:42 INFO mapreduce.Job: Job job_1492605823444_0003 completed successfully
17/04/19 21:07:42 INFO mapreduce.Job: Counters: 50
        File System Counters
                FILE: Number of bytes read=68
                FILE: Number of bytes written=279333
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=246
                HDFS: Number of bytes written=25
                HDFS: Number of read operations=9
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters 
                Launched map tasks=2
                Launched reduce tasks=1
                Data-local map tasks=1
                Rack-local map tasks=1
                Total time spent by all maps in occupied slots (ms)=8579
                Total time spent by all reduces in occupied slots (ms)=5101
                Total time spent by all map tasks (ms)=8579
                Total time spent by all reduce tasks (ms)=5101
                Total vcore-seconds taken by all map tasks=8579
                Total vcore-seconds taken by all reduce tasks=5101
                Total megabyte-seconds taken by all map tasks=8784896
                Total megabyte-seconds taken by all reduce tasks=5223424
        Map-Reduce Framework
                Map input records=2
                Map output records=6
                Map output bytes=62
                Map output materialized bytes=74
                Input split bytes=208
                Combine input records=6
                Combine output records=5
                Reduce input groups=3
                Reduce shuffle bytes=74
                Reduce input records=5
                Reduce output records=3
                Spilled Records=10
                Shuffled Maps =2
                Failed Shuffles=0
                Merged Map outputs=2
                GC time elapsed (ms)=430
                CPU time spent (ms)=1550
                Physical memory (bytes) snapshot=339206144
                Virtual memory (bytes) snapshot=1087791104
                Total committed heap usage (bytes)=242552832
        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=38
        File Output Format Counters 
                Bytes Written=25

执行结果

[hd2@node1 hadoop-2.4.1]$ hadoop fs -ls /user/hd2/out/
Found 2 items
-rw-r--r--   3 hd2 supergroup          0 2017-04-19 21:07 /user/hd2/out/_SUCCESS
-rw-r--r--   3 hd2 supergroup         25 2017-04-19 21:07 /user/hd2/out/part-r-00000
[hd2@node1 hadoop-2.4.1]$ hadoop fs -cat /user/hd2/out/part-r-00000
hadoop  2
hello   3
world   1
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原文地址：https://www.cnblogs.com/cmi-sh-love/p/6736042.html