Windows平台安装配置Hadoop

Windows平台安装配置Hadoop

步骤： 
1. JDK安装（不会的戳这） 
2. 下载hadoop2.5.2.tar.gz，或者自行去百度下载。 
3. 下载hadooponwindows-master.zip【**能支持在windows运行hadoop的工具】

1、 安装hadoop2.5.2

下载hadoop2.5.2.tar.gz ，并解压到你想要的目录下，我放在D:\dev\hadoop-2.5.2 

2、配置hadoop环境变量

1.windows环境变量配置

右键单击个人电脑 –>属性 –>高级环境变量配置 –>高级选项卡 –>环境变量 –> 单击新建HADOOP_HOME，以下图 

2.接着编辑环境变量path，将hadoop的bin目录加入到后面；

3、修改hadoop配置文件

1. 编辑“D:\dev\hadoop-2.5.2\etc\hadoop”下的core-site.xml文件，将下列文本粘贴进去，并保存；

<configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/D:/dev/hadoop-2.5.2/workplace/tmp</value>
    </property>
    <property>
        <name>dfs.name.dir</name>
        <value>/D:/dev/hadoop-2.5.2/workplace/name</value>
    </property>
    <property>
        <name>fs.default.name</name>
        <value>hdfs://localhost:9000</value>
    </property>
</configuration>

 

2.编辑“D:\dev\hadoop-2.5.2\etc\hadoop”目录下的mapred-site.xml(没有就将mapred-site.xml.template重命名为mapred-site.xml)文件，粘贴一下内容并保存；

<configuration>
    <property>
       <name>mapreduce.framework.name</name>
       <value>yarn</value>
    </property>
    <property>
       <name>mapred.job.tracker</name>
       <value>hdfs://localhost:9001</value>
    </property>
</configuration>

3.编辑“D:\dev\hadoop-2.5.2\etc\hadoop”目录下的hdfs-site.xml文件，粘贴如下内容并保存。请自行建立data目录，在这里我是在HADOOP_HOME目录下建立了workplace/data目录；

<configuration>
    <!-- 这个参数设置为1，由于是单机版hadoop -->
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
    <property>
        <name>dfs.data.dir</name>
        <value>/D:/dev/hadoop-2.5.2/workplace/data</value>
    </property>
</configuration>

4.编辑“D:\dev\hadoop-2.5.2\etc\hadoop”目录下的yarn-site.xml文件，粘贴如下内容并保存；

<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>
</configuration>

5.编辑“D:\dev\hadoop-2.5.2\etc\hadoop”目录下的hadoop-env.cmd文件，将JAVA_HOME用 @rem注释掉，编辑为JAVA_HOME的路径，而后保存；

@rem set JAVA_HOME=%JAVA_HOME%

set JAVA_HOME=D:\java\jdk --jdk安装路径

4、替换文件

下载到的hadooponwindows-master.zip，解压，将bin目录(包含如下.dll和.exe文件)文件替换原来hadoop目录下的bin目录；

5、运行环境

1.运行cmd窗口，执行“hdfs namenode -format”； 
2.运行cmd窗口，切换到hadoop的sbin目录，执行“start-all.cmd”，它将会启动如下进程。

成功后，如图： 

至此，hadoop服务已经搭建完毕。

接下来上传测试，操做HDFS

根据你core-site.xml的配置，接下来你就能够经过：hdfs://localhost:9000来对hdfs进行操做了。

1.建立输入目录

C:\WINDOWS\system32>hadoop fs -mkdir hdfs://localhost:9000/user/

C:\WINDOWS\system32>hadoop fs -mkdir hdfs://localhost:9000/user/wcinput

2.上传数据到目录

C:\WINDOWS\system32>hadoop fs -put D:\file1.txt hdfs://localhost:9000/user/wcinput

C:\WINDOWS\system32>hadoop fs -put D:\file2.txt hdfs://localhost:9000/user/wcinput

3.查看文件 

大功告成。

附录：hadoop自带的web控制台GUI

1.资源管理GUI:http://localhost:8088/； 

2.节点管理GUI:http://localhost:50070/； 

使用Hadoop自带的例子pi计算圆周率

D:\HADOOP\hadoop>hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.9.0.jar pi 10 10
Number of Maps  = 10
Samples per Map = 10
Wrote input for Map #0
Wrote input for Map #1
Wrote input for Map #2
Wrote input for Map #3
Wrote input for Map #4
Wrote input for Map #5
Wrote input for Map #6
Wrote input for Map #7
Wrote input for Map #8
Wrote input for Map #9
Starting Job
18/11/09 13:31:06 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/11/09 13:31:07 INFO input.FileInputFormat: Total input files to process : 10
18/11/09 13:31:07 INFO mapreduce.JobSubmitter: number of splits:10
18/11/09 13:31:07 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled
18/11/09 13:31:07 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1541741344890_0001
18/11/09 13:31:08 INFO impl.YarnClientImpl: Submitted application application_1541741344890_0001
18/11/09 13:31:08 INFO mapreduce.Job: The url to track the job: http://DESKTOP-S0J61R2:8088/proxy/application_1541741344890_0001/
18/11/09 13:31:08 INFO mapreduce.Job: Running job: job_1541741344890_0001
18/11/09 13:31:29 INFO mapreduce.Job: Job job_1541741344890_0001 running in uber mode : false
18/11/09 13:31:29 INFO mapreduce.Job:  map 0% reduce 0%
18/11/09 13:31:43 INFO mapreduce.Job:  map 50% reduce 0%
18/11/09 13:31:44 INFO mapreduce.Job:  map 60% reduce 0%
18/11/09 13:31:52 INFO mapreduce.Job:  map 90% reduce 0%
18/11/09 13:31:53 INFO mapreduce.Job:  map 100% reduce 0%
18/11/09 13:31:54 INFO mapreduce.Job:  map 100% reduce 100%
18/11/09 13:32:04 INFO mapreduce.Job: Job job_1541741344890_0001 completed successfully
18/11/09 13:32:04 INFO mapreduce.Job: Counters: 49
        File System Counters
                FILE: Number of bytes read=226
                FILE: Number of bytes written=2238841
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=2680
                HDFS: Number of bytes written=215
                HDFS: Number of read operations=43
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=3
        Job Counters
                Launched map tasks=10
                Launched reduce tasks=1
                Data-local map tasks=10
                Total time spent by all maps in occupied slots (ms)=99705
                Total time spent by all reduces in occupied slots (ms)=8623
                Total time spent by all map tasks (ms)=99705
                Total time spent by all reduce tasks (ms)=8623
                Total vcore-milliseconds taken by all map tasks=99705
                Total vcore-milliseconds taken by all reduce tasks=8623
                Total megabyte-milliseconds taken by all map tasks=102097920
                Total megabyte-milliseconds taken by all reduce tasks=8829952
        Map-Reduce Framework
                Map input records=10
                Map output records=20
                Map output bytes=180
                Map output materialized bytes=280
                Input split bytes=1500
                Combine input records=0
                Combine output records=0
                Reduce input groups=2
                Reduce shuffle bytes=280
                Reduce input records=20
                Reduce output records=0
                Spilled Records=40
                Shuffled Maps =10
                Failed Shuffles=0
                Merged Map outputs=10
                GC time elapsed (ms)=1144
                CPU time spent (ms)=4669
                Physical memory (bytes) snapshot=3203293184
                Virtual memory (bytes) snapshot=3625623552
                Total committed heap usage (bytes)=2142240768
        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=1180
        File Output Format Counters
                Bytes Written=97
Job Finished in 58.577 seconds
Estimated value of Pi is 3.20000000000000000000

 

Windows平台Hadoop出现 Exception message: CreateSymbolicLink error (1314): ???????????

平台：

   hadoop 2.7.1 

   windows 2008 server R2

问题描述：

  在使用kettel执行ELT任务到hive时 hadoop出现Exception message: CreateSymbolicLink error (1314): ???????????（建立符号表异常），通过分析发现为windows帐户不具有建立符号表的权限

 

解决方法：

1. 默认管理员能够建立符号表，可使用管理员命令行启动 hadoop的应用；

2. 经过：

   2.1.win+R gpedit.msc 
   2.2. 计算机配置->windows设置->安全设置->本地策略->用户权限分配->建立符号连接。 
   2.3. 把用户添加进去，重启或者注销

的方式来添加帐户的建立符号表权限信息。

参考文献：

https://stackoverflow.com/questions/28958999/hdfs-write-resulting-in-createsymboliclink-error-1314-a-required-privilege