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2.hadoop基本配置,本地模式,伪分布式搭建

2. Hadoop三种集群方式

1. 三种集群方式

本地模式

    hdfs dfs -ls /
    不需要启动任何进程

伪分布式
```
    所有进程跑在一个机器上
```
完全分布式
```
    每个机器运行不同的进程
```

2. 服务器基本配置

2.1 服务器配置及系统版本

CPU: 2核
内存: 4G
系统版本: Centos7 1511

2.2 服务器IP及主机名设置

服务器数量: 五台机器

主机名	公网IP	内网IP
hadoop-1	192.168.10.145	172.16.1.207
hadoop-2	192.168.10.149	172.16.1.206
hadoop-3	192.168.10.152	172.16.1.204
hadoop-4	192.168.10.153	172.16.1.208
hadoop-5	192.168.10.156	172.16.1.205

根据以上表格修改hosts表和主机名

修改Hosts
#vim /etc/hosts
192.168.10.145  hadoop-1
192.168.10.149  hadoop-2
192.168.10.152  hadoop-3
192.168.10.153  hadoop-4
192.168.10.156  hadoop-5

#scp /etc/hosts hadoop-2:/etc/
#scp /etc/hosts hadoop-3:/etc/
#scp /etc/hosts hadoop-4:/etc/
#scp /etc/hosts hadoop-5:/etc/

设置主机名
#hostnamectl set-hostname hadoop-1
#hostnamectl set-hostname hadoop-2
#hostnamectl set-hostname hadoop-3
#hostnamectl set-hostname hadoop-4
#hostnamectl set-hostname hadoop-5

ssh认证

hadoop-1主机上执行
#ssh-keygen -t rsa -P ''
#ssh-copy-id 192.168.10.145
#scp -r .ssh 192.168.10.149:/root/
#scp -r .ssh 192.168.10.152:/root/
#scp -r .ssh 192.168.10.153:/root/
#scp -r .ssh 192.168.10.156:/root/

创建 /soft 存放jdk和Hadoop目录

#ssh hadoop-1 'mkdir /soft'
#ssh hadoop-2 'mkdir /soft'
#ssh hadoop-3 'mkdir /soft'
#ssh hadoop-4 'mkdir /soft'
#ssh hadoop-5 'mkdir /soft'

安装jdk

#cd /root/
#确保已经下载了相关jdk包
#scp jdk-8u131-linux-x64.tar.gz hadoop-1:/soft/
#scp jdk-8u131-linux-x64.tar.gz hadoop-2:/soft/
#scp jdk-8u131-linux-x64.tar.gz hadoop-3:/soft/
#scp jdk-8u131-linux-x64.tar.gz hadoop-4:/soft/
#scp jdk-8u131-linux-x64.tar.gz hadoop-5:/soft/

所有的服务器
#tar xf /soft/jdk-8u131-linux-x64.tar.gz -C /soft
#ln -s /soft/jdk1.8.0_131 /soft/jdk  创建软连接
配置环境变量
#vim /etc/profile
JAVA_HOME=/soft/jdk
PATH=$PATH:$JAVA_HOME/bin
#source /etc/profile

配置hadoop

#scp hadoop-2.7.3.tar.gz hadoop-1:/soft/
#scp hadoop-2.7.3.tar.gz hadoop-2:/soft/
#scp hadoop-2.7.3.tar.gz hadoop-3:/soft/
#scp hadoop-2.7.3.tar.gz hadoop-4:/soft/
#scp hadoop-2.7.3.tar.gz hadoop-5:/soft/
所有服务器
#tar xf /soft/hadoop-2.7.3.tar.gz -C /soft
#ln -s /soft/hadoop-2.7.3 /soft/hadoop  创建软连接
修改环境变量
#vim /etc/profile
HADOOP_HOME=/soft/hadoop
PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
#source /etc/profile
修改hadoop-env.sh
# vim /soft/hadoop/etc/hadoop/hadoop-env.sh
修改hadoop-env.sh 修改hadoop的jAVA_HOME
export JAVA_HOME=/soft/jdk

3. 本地模式

本地就是单机模式，hadoop默认安装完就是单机模式
hdfs默认使用本地的文件系统
hdfs dfs -ls / 查看本地文件系统和linux的ls /一样

3.1 测试单词统计

#hdfs dfs -mkdir /input
#cd /input/
#echo “hello word” > file1.txt
#echo “hello hadoop” > file2.txt
#echo “hello mapreduce” >> file2.txt
#cd /soft/hadoop/
#hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /input /output
#hdfs dfs  -ls /output/
Found 2 items
-rw-r--r--   1 root root          0 2017-06-21 11:56 /output/_SUCCESS
-rw-r--r--   1 root root         48 2017-06-21 11:56 /output/part-r-00000

4. 伪分布式

4.1 伪分布式介绍

Hadoop 可以在单节点上以伪分布式的方式运行，Hadoop 进程以分离的 Java 进程来运行，节点既作为 NameNode 也作为 DataNode，同时，读取的是 HDFS 中的文件。
Hadoop 的配置文件位于 /soft/hadoop/etc/hadoop/ 中，伪分布式需要修改2个配置文件 core-site.xml 和 hdfs-site.xml 。Hadoop的配置文件是 xml 格式，每个配置以声明 property 的 name 和 value 的方式来实现。

4.2 伪分布式搭建

配置伪分布式

#mkdir /data/hadoop
#cd /soft/hadoop/etc/
#mv hadoop local
#cp -r local pseudo
#ln -s pseudo hadoop
#cd hadoop

修改core-site.xml配置文件

#vim core-site.xml 
[core-site.xml配置如下]

<?xml version="1.0"?>
<configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>file:/data/hadoop/tmp</value>
        <description>Abase for other temporary directories.</description>
    </property>
    <property>
        <name>fs.defaultFS</name>
		    <value>hdfs://localhost/</value>
	   </property>
</configuration>

修改hdfs-site.xml配置文件

#vim hdfs-site.xml 
[hdfs-site.xml配置如下]
<?xml version="1.0"?>
<configuration>
	<property>
		<name>dfs.replication</name>
		<value>1</value>
	</property>
	  <property>
        <name>dfs.namenode.name.dir</name>
        <value>file:/data/hadoop/tmp/dfs/name</value>
   </property>
   <property>
        <name>dfs.datanode.data.dir</name>
        <value>file:/data/hadoop/tmp/dfs/data</value>
   </property>
</configuration>

hadoop 的运行方式是由配置文件决定的（运行 Hadoop 时会读取配置文件）
因此如果需要从伪分布式模式切换回非分布式模式，需要删除 core-site.xml 中的配置项。
此外，伪分布式虽然只需要配置 fs.defaultFS 和 dfs.replication 就可以运行（官方教程如此）
不过若没有配置 hadoop.tmp.dir 参数，则默认使用的临时目录为 /tmp/hadoo-hadoop，而这个目录在重启时有可能被系统清理掉，导致必须重新执行 format 才行。所以我们进行了设置，同时也指定 dfs.namenode.name.dir 和 dfs.datanode.data.dir，否则在接下来的步骤中可能会出错。
YARN 是从 MapReduce 中分离出来的，负责资源管理与任务调度。YARN 运行于 MapReduce 之上，提供了高可用性、高扩展性，YARN 的更多介绍在此不展开，有兴趣的可查阅相关资料

修改mapred-site.xml配置文件

#cp mapred-site.xml.template mapred-site.xml
#vim mapred-site.xml
[mapred-site.xml配置如下]
<?xml version="1.0"?>
<configuration>
    	<property>
    		<name>mapreduce.framework.name</name>
    		<value>yarn</value>
    	</property>
</configuration>

修改yarn-site.xml配置文件

#vim yarn-site.xml 
[yarn-site.xml配置如下]
	<?xml version="1.0"?>
<configuration>
<property>
	<name>yarn.resourcemanager.hostname</name>
	<value>localhost</value>
</property>
<property>
	<name>yarn.nodemanager.aux-services</name>
	<value>mapreduce_shuffle</value>
</property>
</configuration>

修改slaves配置文件

#vim slaves
[slaves配置如下]
  localhost

格式化hdfs分布式文件系统

#hadoop namenode -format
[root@hadoop-1 hadoop]# hadoop namenode -format

省略--------
17/05/15 09:29:01 INFO util.ExitUtil: Exiting with status 0
17/05/15 09:29:01 INFO namenode.NameNode: SHUTDOWN_MSG: 
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at hadoop-1/172.16.1.207
************************************************************/

启动hadoop服务

#start-all.sh
[root@hadoop-1 hadoop]# start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [localhost]
The authenticity of host 'localhost (::1)' can't be established.
ECDSA key fingerprint is da:38:db:62:7e:97:52:6e:11:1b:81:93:1b:a4:b4:e6.
Are you sure you want to continue connecting (yes/no)? yes
localhost: Warning: Permanently added 'localhost' (ECDSA) to the list of known hosts.
localhost: starting namenode, logging to /soft/hadoop-2.7.3/logs/hadoop-root-namenode-hadoop-1.out
localhost: starting datanode, logging to /soft/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop-1.out
Starting secondary namenodes [0.0.0.0]
The authenticity of host '0.0.0.0 (0.0.0.0)' can't be established.
ECDSA key fingerprint is da:38:db:62:7e:97:52:6e:11:1b:81:93:1b:a4:b4:e6.
Are you sure you want to continue connecting (yes/no)? yes
0.0.0.0: Warning: Permanently added '0.0.0.0' (ECDSA) to the list of known hosts.
0.0.0.0: starting secondarynamenode, logging to /soft/hadoop-2.7.3/logs/hadoop-root-secondarynamenode-hadoop-1.out
starting yarn daemons
starting resourcemanager, logging to /soft/hadoop-2.7.3/logs/yarn-root-resourcemanager-hadoop-1.out
localhost: starting nodemanager, logging to /soft/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop-1.out


9. **判断是否启动成功**

    ```
    启动完成后，可以通过命令 jps 来判断是否成功启动，若成功启动则会列出如下进程:   
    [root@hadoop-1 hadoop]# jps
    14784 NameNode
    15060 SecondaryNameNode
    14904 DataNode
    15211 ResourceManager
    15628 Jps
    15374 NodeManager
    如果 SecondaryNameNode 没有启动，请运行 sbin/stop-dfs.sh 关闭进程，
    然后再次尝试启动尝试）。如果没有 NameNode 或 DataNode ，那就是配置不成功，
    请仔细检查之前步骤，或通过查看启动日志排查原因
    ```
10. **登陆WEB查看**

    打开http://192.168.10.145:50070
    ![](http://img.liuyao.me/14981417208845.png)


##4.2 伪分布式单词统计

1. **在本地创建目录和分析的log**

    ```
    #mkdir  /input
    #cd /input
    #echo "hello world" > file1.log
    #echo "hello world" > file2.log
    #echo "hello hadoop" > file3.log
    #echo "hello hadoop" > file4.log
    #echo "map" > file5.log
    ```
2. **在hdfs创建目录和上传本地log**
    
    ```
    #hdfs dfs -mkdir -p /input/
    #hdfs dfs -ls /
    Found 1 items
    drwxr-xr-x   - root supergroup          0 2017-06-22 10:29 /input
    #hdfs dfs -put file* /input/
    # hdfs dfs -ls /input/
    Found 5 items
    -rw-r--r--   1 root supergroup         12 2017-06-22 10:32 /input/file1.log
    -rw-r--r--   1 root supergroup         12 2017-06-22 10:32 /input/file2.log
    -rw-r--r--   1 root supergroup         13 2017-06-22 10:32 /input/file3.log
    -rw-r--r--   1 root supergroup         13 2017-06-22 10:32 /input/file4.log
    -rw-r--r--   1 root supergroup          4 2017-06-22 10:32 /input/file5.log
    ```

3. **使用自带jar包进行单词统计**

    ```
    # hadoop jar /soft/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /input /output
    17/05/15 09:48:38 INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:8032
    17/05/15 09:48:39 INFO input.FileInputFormat: Total input paths to process : 5
    17/05/15 09:48:40 INFO mapreduce.JobSubmitter: number of splits:5
    17/05/15 09:48:40 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1494855010567_0001
    17/05/15 09:48:40 INFO impl.YarnClientImpl: Submitted application application_1494855010567_0001
    17/05/15 09:48:40 INFO mapreduce.Job: The url to track the job: http://hadoop-1:8088/proxy/application_1494855010567_0001/
    17/05/15 09:48:40 INFO mapreduce.Job: Running job: job_1494855010567_0001
    17/05/15 09:48:48 INFO mapreduce.Job: Job job_1494855010567_0001 running in uber mode : false
    17/05/15 09:48:48 INFO mapreduce.Job:  map 0% reduce 0%
    17/05/15 09:48:59 INFO mapreduce.Job:  map 20% reduce 0%
    17/05/15 09:49:00 INFO mapreduce.Job:  map 80% reduce 0%
    17/05/15 09:49:01 INFO mapreduce.Job:  map 100% reduce 0%
    17/05/15 09:49:06 INFO mapreduce.Job:  map 100% reduce 100%
    17/05/15 09:49:06 INFO mapreduce.Job: Job job_1494855010567_0001 completed successfully
    17/05/15 09:49:06 INFO mapreduce.Job: Counters: 50
                File System Counters
                FILE: Number of bytes read=114
                FILE: Number of bytes written=711875
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=539
                HDFS: Number of bytes written=31
                HDFS: Number of read operations=18
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        Job Counters 
                Killed map tasks=1
                Launched map tasks=5
                Launched reduce tasks=1
                Data-local map tasks=5
                Total time spent by all maps in occupied slots (ms)=48562
                Total time spent by all reduces in occupied slots (ms)=4413
                Total time spent by all map tasks (ms)=48562
                Total time spent by all reduce tasks (ms)=4413
                Total vcore-milliseconds taken by all map tasks=48562
                Total vcore-milliseconds taken by all reduce tasks=4413
                Total megabyte-milliseconds taken by all map tasks=49727488
                Total megabyte-milliseconds taken by all reduce tasks=4518912
        Map-Reduce Framework
                Map input records=5
                Map output records=9
                Map output bytes=90
                Map output materialized bytes=138
                Input split bytes=485
                Combine input records=9
                Combine output records=9
                Reduce input groups=4
                Reduce shuffle bytes=138
                Reduce input records=9
                Reduce output records=4
                Spilled Records=18
                Shuffled Maps =5
                Failed Shuffles=0
                Merged Map outputs=5
                GC time elapsed (ms)=1662
                CPU time spent (ms)=2740
                Physical memory (bytes) snapshot=1523605504
                Virtual memory (bytes) snapshot=12609187840
                Total committed heap usage (bytes)=1084227584
        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=54
        File Output Format Counters 
                Bytes Written=31
    ```
 
4. ** 查看结果**

    ```
    [root@hadoop-1 ~]# hdfs dfs -cat /output/*
    hadoop  2
    hello   4
    map     1
    world   
    [root@hadoop-1 ~]# hdfs dfs -get /output/*  .
    [root@hadoop-1 ~]# ls
    file1.log  file2.log  file3.log  file4.log  file5.log  part-r-00000  _SUCCESS
    [root@hadoop-1 ~]# cat part-r-00000 
    hadoop  2
    hello   4
    map     1

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原文地址：https://www.cnblogs.com/liu-yao/p/7067615.html