Mac Hadoop2.6（CDH5.9.2）伪分布式集群安装

操作系统： MAC OS X

一、准备

1、 JDK 1.8

　　下载地址：http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html

2、Hadoop CDH

　　下载地址：https://archive.cloudera.com/cdh5/cdh/5/

　　本次安装版本：hadoop-2.6.0-cdh5.9.2.tar.gz

二、配置SSH（免密码登录）

1、打开iTerm2 终端，输入：ssh-keygen -t rsa ，回车，next -- 生成秘钥
2、cat id_rsa_xxx.pub >> authorized_keys -- 用于授权你的公钥到本地可以无密码登录
3、chmod 600 authorized_keys -- 赋权限
4、ssh localhost -- 免密码登录，如果显示最后一次登录时间，则登录成功

三、配置Hadoop&环境变量

1、创建hadoop目录&解压

　　mkdir -p work/install/hadoop-cdh5.9.2 -- hadoop 主目录
　　mkdir -p work/install/hadoop-cdh5.9.2/current/tmp work/install/hadoop-cdh5.9.2/current/nmnode work/install/hadoop-cdh5.9.2/current/dtnode -- hadoop 临时、名称节点、数据节点目录

　　tar -xvf hadoop-2.6.0-cdh5.9.2.tar.gz -- 解压包

2、配置 .bash_profile 环境变量

1 HADOOP_HOME="/Users/kimbo/work/install/hadoop-cdh5.9.2/hadoop-2.6.0-cdh5.9.2"
2 
3 JAVA_HOME="/Library/Java/JavaVirtualMachines/jdk1.8.0_152.jdk/Contents/Home"
4 HADOOP_HOME="/Users/kimbo/work/install/hadoop-cdh5.9.2/hadoop-2.6.0-cdh5.9.2"
5 
6 PATH="/usr/local/bin:~/cmd:$JAVA_HOME/bin:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH"
7 CLASSPATH=".:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar"
8 
9 export JAVA_HOME PATH CLASSPATH HADOOP_HOME

View Code

　　source .bash_profile -- 生效环境变量

3、修改配置文件（重点）

　　cd $HADOOP_HOME/etc/hadoop

core-site.xml

<configuration>
  <property>
    <name>hadoop.tmp.dir</name>
    <value>/Users/zhangshaosheng/work/install/hadoop-cdh5.9.2/current/tmp</value>
  </property>
  <property>
    <name>fs.defaultFS</name>
    <value>hdfs://localhost:8020</value>
  </property>
  <property>
    <name>fs.trash.interval</name>
    <value>4320</value>
    <description> 3 days = 60min*24h*3day </description>
  </property>
</configuration>

View Code

hdfs-site.xml

 1 <configuration>
 2   <property>
 3     <name>dfs.namenode.name.dir</name>
 4     <value>/Users/zhangshaosheng/work/install/hadoop-cdh5.9.2/current/nmnode</value>
 5   </property>
 6   <property>
 7     <name>dfs.datanode.data.dir</name>
 8     <value>/Users/zhangshaosheng/work/install/hadoop-cdh5.9.2/current/dtnode</value>
 9   </property>
10   <property>
11     <name>dfs.datanode.http.address</name>
12     <value>localhost:50075</value>
13   </property>
14   <property>
15     <name>dfs.replication</name>
16     <value>1</value>
17   </property>
18   <property>
19     <name>dfs.permissions.enabled</name>
20     <value>false</value>
21   </property>
22 </configuration>

View Code

yarn-site.xml

 1 <configuration> 
 2  <property>
 3     <name>yarn.nodemanager.aux-services</name>
 4     <value>mapreduce_shuffle</value>
 5   </property>
 6   <property>
 7     <name>yarn.log-aggregation-enable</name>
 8     <value>true</value>
 9     <description>Whether to enable log aggregation</description>
10   </property>
11   <property>
12     <name>yarn.nodemanager.remote-app-log-dir</name>
13     <value>/Users/zhangshaosheng/work/install/hadoop-cdh5.9.2/current/tmp/yarn-logs</value>
14     <description>Where to aggregate logs to.</description>
15   </property>
16   <property>
17     <name>yarn.nodemanager.resource.memory-mb</name>
18     <value>8192</value>
19     <description>Amount of physical memory, in MB, that can be allocated
20       for containers.</description>
21   </property>
22   <property>
23     <name>yarn.nodemanager.resource.cpu-vcores</name>
24     <value>2</value>
25     <description>Number of CPU cores that can be allocated
26       for containers.</description>
27   </property>
28   <property>
29     <name>yarn.scheduler.minimum-allocation-mb</name>
30     <value>1024</value>
31     <description>The minimum allocation for every container request at the RM,
32       in MBs. Memory requests lower than this won't take effect,
33       and the specified value will get allocated at minimum.</description>
34   </property>
35   <property>
36     <name>yarn.scheduler.maximum-allocation-mb</name>
37     <value>2048</value>
38     <description>The maximum allocation for every container request at the RM,
39       in MBs. Memory requests higher than this won't take effect,
40       and will get capped to this value.</description>
41   </property>
42   <property>
43     <name>yarn.scheduler.minimum-allocation-vcores</name>
44     <value>1</value>
45     <description>The minimum allocation for every container request at the RM,
46       in terms of virtual CPU cores. Requests lower than this won't take effect,
47       and the specified value will get allocated the minimum.</description>
48   </property>
49   <property>
50     <name>yarn.scheduler.maximum-allocation-vcores</name>
51     <value>2</value>
52     <description>The maximum allocation for every container request at the RM,
53       in terms of virtual CPU cores. Requests higher than this won't take effect,
54       and will get capped to this value.</description>
55   </property>
56 </configuration>

View Code

mapred-site.xml

 1  <property>
 2     <name>mapreduce.jobtracker.address</name>
 3     <value>localhost:8021</value>
 4   </property>
 5   <property>
 6     <name>mapreduce.jobhistory.done-dir</name>
 7     <value>/Users/zhangshaosheng/work/install/hadoop-cdh5.9.2/current/tmp/job-history/</value>
 8     <description></description>
 9   </property>
10   <property>
11     <name>mapreduce.framework.name</name>
12     <value>yarn</value>
13     <description>The runtime framework for executing MapReduce jobs.
14     Can be one of local, classic or yarn.
15     </description>
16   </property>
17 
18   <property>
19     <name>mapreduce.map.cpu.vcores</name>
20     <value>1</value>
21     <description>
22         The number of virtual cores required for each map task.
23     </description>
24   </property>
25   <property>
26     <name>mapreduce.reduce.cpu.vcores</name>
27     <value>1</value>
28     <description>
29         The number of virtual cores required for each reduce task.
30     </description>
31   </property>
32 
33   <property>
34     <name>mapreduce.map.memory.mb</name>
35     <value>1024</value>
36     <description>Larger resource limit for maps.</description>
37   </property>
38   <property>
39     <name>mapreduce.reduce.memory.mb</name>
40     <value>1024</value>
41     <description>Larger resource limit for reduces.</description>
42   </property>
43 <configuration>
44   <property>
45     <name>mapreduce.map.java.opts</name>
46     <value>-Xmx768m</value>
47     <description>Heap-size for child jvms of maps.</description>
48   </property>
49   <property>
50     <name>mapreduce.reduce.java.opts</name>
51     <value>-Xmx768m</value>
52     <description>Heap-size for child jvms of reduces.</description>
53   </property>
54 
55   <property>
56     <name>yarn.app.mapreduce.am.resource.mb</name>
57     <value>1024</value>
58     <description>The amount of memory the MR AppMaster needs.</description>
59   </property>
60 </configuration>

View Code

hadoop-env.sh

export JAVA_HOME=${JAVA_HOME} -- 添加 java环境变量

四、启动

　　1、格式化

　　　　hdfs namenode -format

　　如果hdfs命令识别不了，检查环境变量，是否配置正确了。

　　2、启动

　　　　cd $HADOOP_HOME/sbin

　　　　执行命名：start-all.sh ,按照提示，输入密码

五、验证

　　1、在终端输入： jps　

　　　　出现如下截图，说明ok了

　　2、登录web页面

　　　　a）HDFS : http://localhost:50070/dfshealth.html#tab-overview

　　　　b）YARN Cluster: http://localhost:8088/cluster

　　　　c）YARN ResourceManager/NodeManager: http://localhost:8042/node

相关阅读:
线程应用：（九）阻塞队列的应用
 线程应用：（八）Semaphere信号灯、CyclicBarrier汇合、CountDownLatch计数器、Exchanger
线程应用：（七）Lock & Condition同步通信
 线程应用：（六）线程池、Callable与Future
线程应用：（五）传统线程使用实例
 记一次linux安装mysql
Pycharm断点调试入门
 django admin后台插件：django-suit入门
 在django中使用django_debug_toolbar
pyquery详细用法
原文地址：https://www.cnblogs.com/kimbo/p/8724062.html