Hadoop2.6版本集群环境搭建

118次阅读

共计 10542 个字符，预计需要花费 27 分钟才能阅读完成。

一、环境说明

1、机器：一台物理机和一台虚拟机

2、linux 版本：[spark@S1PA11 ~]$ cat /etc/issue
Red Hat Enterprise Linux Server release 5.4 (Tikanga)

3、JDK: [spark@S1PA11 ~]$ java -version
java version “1.6.0_27”
Java(TM) SE Runtime Environment (build 1.6.0_27-b07)
Java HotSpot(TM) 64-Bit Server VM (build 20.2-b06, mixed mode)

4、集群节点：两个 S1PA11（Master）,S1PA222(Slave)

二、准备工作

1、安装 Java JDK 前一篇文章撰写了：http://www.linuxidc.com/Linux/2015-01/111464.htm

2、ssh 免密码验证：http://www.linuxidc.com/Linux/2015-01/111465.htm

3、下载 Hadoop 版本：http://mirror.bit.edu.cn/apache/hadoop/common/

三、安装 Hadoop

这是下载后的 hadoop-2.6.0.tar.gz 压缩包，

1、解压 tar -xzvf hadoop-2.6.0.tar.gz

2、move 到指定目录下：[spark@S1PA11 software]$ mv hadoop-2.6.0 ~/opt/

3、进入 hadoop 目前 [spark@S1PA11 opt]$ cd hadoop-2.6.0/
[spark@S1PA11 hadoop-2.6.0]$ ls
bin dfs etc include input lib libexec LICENSE.txt logs NOTICE.txt README.txt sbin share tmp

配置之前，先在本地文件系统创建以下文件夹：~/hadoop/tmp、~/dfs/data、~/dfs/name。主要涉及的配置文件有 7 个：都在 /hadoop/etc/hadoop 文件夹下，可以用 gedit 命令对其进行编辑。

~/hadoop/etc/hadoop/hadoop-env.sh
~/hadoop/etc/hadoop/yarn-env.sh
~/hadoop/etc/hadoop/slaves
~/hadoop/etc/hadoop/core-site.xml
~/hadoop/etc/hadoop/hdfs-site.xml
~/hadoop/etc/hadoop/mapred-site.xml
~/hadoop/etc/hadoop/yarn-site.xml

4、进去 hadoop 配置文件目录

[spark@S1PA11 hadoop-2.6.0]$ cd etc/hadoop/
[spark@S1PA11 hadoop]$ ls
capacity-scheduler.xml hadoop-env.sh httpfs-env.sh kms-env.sh mapred-env.sh ssl-client.xml.example
configuration.xsl hadoop-metrics2.properties httpfs-log4j.properties kms-log4j.properties mapred-queues.xml.template ssl-server.xml.example
container-executor.cfg hadoop-metrics.properties httpfs-signature.secret kms-site.xml mapred-site.xml yarn-env.cmd
core-site.xml hadoop-policy.xml httpfs-site.xml log4j.properties mapred-site.xml.template yarn-env.sh
hadoop-env.cmd hdfs-site.xml kms-acls.xml mapred-env.cmd slaves yarn-site.xml

4.1、配置 hadoop-env.sh 文件 –> 修改 JAVA_HOME

# The java implementation to use.
export JAVA_HOME=/home/spark/opt/java/jdk1.6.0_37

4.2、配置 yarn-env.sh 文件 –>> 修改 JAVA_HOME

# some Java parameters

export JAVA_HOME=/home/spark/opt/java/jdk1.6.0_37

4.3、配置 slaves 文件 –>> 增加 slave 节点

S1PA222

4.4、配置 core-site.xml 文件 –>> 增加 hadoop 核心配置（hdfs 文件端口是 9000、file:/home/spark/opt/hadoop-2.6.0/tmp、）

<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://S1PA11:9000</value>
</property>

<property>
<name>io.file.buffer.size</name>
<value>131072</value>
</property>
<property>
<name>hadoop.tmp.dir</name>
<value>file:/home/spark/opt/hadoop-2.6.0/tmp</value>
<description>Abasefor other temporary directories.</description>
</property>
<property>
<name>hadoop.proxyuser.spark.hosts</name>
<value>*</value>
</property>
<property>
<name>hadoop.proxyuser.spark.groups</name>
<value>*</value>
</property>
</configuration>

4.5、配置 hdfs-site.xml 文件 –>> 增加 hdfs 配置信息（namenode、datanode 端口和目录位置）

<configuration>
<property>
<name>dfs.namenode.secondary.http-address</name>
<value>S1PA11:9001</value>
</property>

<property>
<name>dfs.namenode.name.dir</name>
<value>file:/home/spark/opt/hadoop-2.6.0/dfs/name</value>
</property>

<property>
<name>dfs.datanode.data.dir</name>
<value>file:/home/spark/opt/hadoop-2.6.0/dfs/data</value>
</property>

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

<property>
<name>dfs.webhdfs.enabled</name>
<value>true</value>
</property>

</configuration>

4.6、配置 mapred-site.xml 文件 –>> 增加 mapreduce 配置（使用 yarn 框架、jobhistory 使用地址以及 web 地址）

<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
<property>
<name>mapreduce.jobhistory.address</name>
<value>S1PA11:10020</value>
</property>
<property>
<name>mapreduce.jobhistory.webapp.address</name>
<value>S1PA11:19888</value>
</property>
</configuration>

4.7、配置 yarn-site.xml 文件 –>> 增加 yarn 功能

<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
<property>
<name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
<value>org.apache.hadoop.mapred.ShuffleHandler</value>
</property>
<property>
<name>yarn.resourcemanager.address</name>
<value>S1PA11:8032</value>
</property>
<property>
<name>yarn.resourcemanager.scheduler.address</name>
<value>S1PA11:8030</value>
</property>
<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>S1PA11:8035</value>
</property>
<property>
<name>yarn.resourcemanager.admin.address</name>
<value>S1PA11:8033</value>
</property>
<property>
<name>yarn.resourcemanager.webapp.address</name>
<value>S1PA11:8088</value>
</property>

</configuration>

5、将配置好的 hadoop 文件 copy 到另一台 slave 机器上

[spark@S1PA11 opt]$ scp -r hadoop-2.6.0/ spark@10.126.34.43:~/opt/

四、验证

1、格式化 namenode:

[spark@S1PA11 opt]$ cd hadoop-2.6.0/
[spark@S1PA11 hadoop-2.6.0]$ ls
bin dfs etc include input lib libexec LICENSE.txt logs NOTICE.txt README.txt sbin share tmp
[spark@S1PA11 hadoop-2.6.0]$ ./bin/hdfs namenode -format

[spark@S1PA222 .ssh]$ cd ~/opt/hadoop-2.6.0
[spark@S1PA222 hadoop-2.6.0]$ ./bin/hdfs namenode -format

2、启动 hdfs:

[spark@S1PA11 hadoop-2.6.0]$ ./sbin/start-dfs.sh
15/01/05 16:41:04 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
Starting namenodes on [S1PA11]
S1PA11: starting namenode, logging to /home/spark/opt/hadoop-2.6.0/logs/hadoop-spark-namenode-S1PA11.out
S1PA222: starting datanode, logging to /home/spark/opt/hadoop-2.6.0/logs/hadoop-spark-datanode-S1PA222.out
Starting secondary namenodes [S1PA11]
S1PA11: starting secondarynamenode, logging to /home/spark/opt/hadoop-2.6.0/logs/hadoop-spark-secondarynamenode-S1PA11.out
15/01/05 16:41:21 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
[spark@S1PA11 hadoop-2.6.0]$ jps
22230 Master
30889 Jps
22478 Worker
30498 NameNode
30733 SecondaryNameNode
19781 ResourceManager

3、停止 hdfs:

[spark@S1PA11 hadoop-2.6.0]$./sbin/stop-dfs.sh
15/01/05 16:40:28 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
Stopping namenodes on [S1PA11]
S1PA11: stopping namenode
S1PA222: stopping datanode
Stopping secondary namenodes [S1PA11]
S1PA11: stopping secondarynamenode
15/01/05 16:40:48 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
[spark@S1PA11 hadoop-2.6.0]$ jps
30336 Jps
22230 Master
22478 Worker
19781 ResourceManager

4、启动 yarn:

[spark@S1PA11 hadoop-2.6.0]$./sbin/start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /home/spark/opt/hadoop-2.6.0/logs/yarn-spark-resourcemanager-S1PA11.out
S1PA222: starting nodemanager, logging to /home/spark/opt/hadoop-2.6.0/logs/yarn-spark-nodemanager-S1PA222.out
[spark@S1PA11 hadoop-2.6.0]$ jps
31233 ResourceManager
22230 Master
22478 Worker
30498 NameNode
30733 SecondaryNameNode
31503 Jps

5、停止 yarn:

[spark@S1PA11 hadoop-2.6.0]$ ./sbin/stop-yarn.sh
stopping yarn daemons
stopping resourcemanager
S1PA222: stopping nodemanager
no proxyserver to stop
[spark@S1PA11 hadoop-2.6.0]$ jps
31167 Jps
22230 Master
22478 Worker
30498 NameNode
30733 SecondaryNameNode

6、查看集群状态：

[spark@S1PA11 hadoop-2.6.0]$ ./bin/hdfs dfsadmin -report
15/01/05 16:44:50 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
Configured Capacity: 52101857280 (48.52 GB)
Present Capacity: 45749510144 (42.61 GB)
DFS Remaining: 45748686848 (42.61 GB)
DFS Used: 823296 (804 KB)
DFS Used%: 0.00%
Under replicated blocks: 10
Blocks with corrupt replicas: 0
Missing blocks: 0

————————————————-
Live datanodes (1):

Name: 10.126.45.56:50010 (S1PA222)
Hostname: S1PA209
Decommission Status : Normal
Configured Capacity: 52101857280 (48.52 GB)
DFS Used: 823296 (804 KB)
Non DFS Used: 6352347136 (5.92 GB)
DFS Remaining: 45748686848 (42.61 GB)
DFS Used%: 0.00%
DFS Remaining%: 87.81%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Mon Jan 05 16:44:50 CST 2015

7、查看 hdfs：http://10.58.44.47:50070/

Hadoop2.6 版本集群环境搭建

8、查看 RM：http://10.58.44.47:8088/

Hadoop2.6 版本集群环境搭建

9、运行 wordcount 程序

9.1、创建 input 目录：[spark@S1PA11 hadoop-2.6.0]$ mkdir input

9.2、在 input 创建 f1、f2 并写内容

[spark@S1PA11 hadoop-2.6.0]$ cat input/f1
Hello world bye jj
[spark@S1PA11 hadoop-2.6.0]$ cat input/f2
Hello Hadoop bye Hadoop

9.3、在 hdfs 创建 /tmp/input 目录

[spark@S1PA11 hadoop-2.6.0]$ ./bin/hadoop fs -mkdir /tmp
15/01/05 16:53:57 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable

[spark@S1PA11 hadoop-2.6.0]$ ./bin/hadoop fs -mkdir /tmp/input
15/01/05 16:54:16 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable

9.4、将 f1、f2 文件 copy 到 hdfs /tmp/input 目录

[spark@S1PA11 hadoop-2.6.0]$ ./bin/hadoop fs -put input/ /tmp
15/01/05 16:56:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable

9.5、查看 hdfs 上是否有 f1、f2 文件

[spark@S1PA11 hadoop-2.6.0]$ ./bin/hadoop fs -ls /tmp/input/
15/01/05 16:57:42 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
Found 2 items
-rw-r–r– 3 spark supergroup 20 2015-01-04 19:09 /tmp/input/f1
-rw-r–r– 3 spark supergroup 25 2015-01-04 19:09 /tmp/input/f2

9.6、执行 wordcount 程序

[spark@S1PA11 hadoop-2.6.0]$ ./bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.0.jar wordcount /tmp/input /output
15/01/05 17:00:09 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable
15/01/05 17:00:09 INFO client.RMProxy: Connecting to ResourceManager at S1PA11/10.58.44.47:8032
15/01/05 17:00:11 INFO input.FileInputFormat: Total input paths to process : 2
15/01/05 17:00:11 INFO mapreduce.JobSubmitter: number of splits:2
15/01/05 17:00:11 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1420447392452_0001
15/01/05 17:00:12 INFO impl.YarnClientImpl: Submitted application application_1420447392452_0001
15/01/05 17:00:12 INFO mapreduce.Job: The url to track the job: http://S1PA11:8088/proxy/application_1420447392452_0001/
15/01/05 17:00:12 INFO mapreduce.Job: Running job: job_1420447392452_0001

9.7、查看执行结果

[spark@S1PA11 hadoop-2.6.0]$ ./bin/hadoop fs -cat /output/part-r-0000
15/01/05 17:06:10 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable

CentOS 安装和配置 Hadoop2.2.0 http://www.linuxidc.com/Linux/2014-01/94685.htm

Ubuntu 13.04 上搭建 Hadoop 环境 http://www.linuxidc.com/Linux/2013-06/86106.htm

Ubuntu 12.10 +Hadoop 1.2.1 版本集群配置 http://www.linuxidc.com/Linux/2013-09/90600.htm

Ubuntu 上搭建 Hadoop 环境（单机模式 + 伪分布模式）http://www.linuxidc.com/Linux/2013-01/77681.htm

Ubuntu 下 Hadoop 环境的配置 http://www.linuxidc.com/Linux/2012-11/74539.htm

单机版搭建 Hadoop 环境图文教程详解 http://www.linuxidc.com/Linux/2012-02/53927.htm

搭建 Hadoop 环境（在 Winodws 环境下用虚拟机虚拟两个 Ubuntu 系统进行搭建）http://www.linuxidc.com/Linux/2011-12/48894.htm

更多 Hadoop 相关信息见 Hadoop 专题页面 http://www.linuxidc.com/topicnews.aspx?tid=13

正文完

星哥说事-微信公众号