1. 创建hadoop用户
CTRL+ALT+T打开终端
添加hadoop用户(用户名字可以自己起)
jec@ubuntu:~$ sudo useradd -m hadoop -s /bin/bash
ubuntu终端复制粘贴快捷键,就是平常windows上的快捷键再加上shift:CTRL+SHIFT+C(复制),CTRL+SHIFT+V(粘贴)
给hadoop用户设置密码,这里简单设置为:1,按照提示输入两次密码
jec@ubuntu:~$ sudo passwd hadoop
给hadoop用户增加管理员权限,方便部署,避免一些对新手来说比较棘手的权限问题
jec@ubuntu:~$ sudo adduser hadoop sudo
最后注销当前用户,使用hadoop账户登陆(注意别忘了)
使用hadoop用户登陆之后,需要先更新一下apt
hadoop@ubuntu:~$ sudo apt-get update
2. 安装、配置ssh无密码登陆
集群、单节点模式都需要用到ssh登陆,ubuntu默认安装了ssh client,所以我还需要装ssh server
hadoop@ubuntu:~$ sudo apt-get install openssh-server
安装之后,使用如下命令登陆本机(首次登陆会有提示,输入yes即可)
hadoop@ubuntu:~$ ssh localhost
The authenticity of host 'localhost (127.0.0.1)' can't be established.
ECDSA key fingerprint is ee:34:6e:5a:fd:57:f3:bc:68:98:56:10:5c:fd:24:af.
Are you sure you want to continue connecting (yes/no)? yes
由于每次ssh登陆都需要输入密码,所以我们需要配置成无密码登陆比较方便,先退出刚才的ssh,然后利用ssh-keygen生成密钥,并将密钥加入到授权中:
hadoop@ubuntu:~$ exit #退出刚才的ssh localhost
hadoop@ubuntu:~$ cd ~/.ssh/ #若没有该目录,请先执行一次ssh localhost
hadoop@ubuntu:~/.ssh$ ssh-keygen -t rsa #会有提示,都按回车就可以
Generating public/private rsa key pair.
Enter file in which to save the key (/home/hadoop/.ssh/id_rsa):
Enter passphrase (empty for no passphrase):
Enter same passphrase again:
Your identification has been saved in /home/hadoop/.ssh/id_rsa.
Your public key has been saved in /home/hadoop/.ssh/id_rsa.pub.
The key fingerprint is:
c9:ae:6f:5f:be:c0:2a:50:1b:0a:26:1b:3e:9d:11:31 hadoop@ubuntu
The key's randomart image is:
+--[ RSA 2048]----+
| E. |
| .. |
| . |
|o o. o. . |
|.=..oo oS |
|.o oo .. . |
| . . . o . |
| .... + |
| .+o.. o. |
+-----------------+
hadoop@ubuntu:~/.ssh$ cat ./id_rsa.pub >> ./authorized_keys #加入授权
再用ssh locahost命令,无需密码就能直接登陆了
hadoop@ubuntu:~/.ssh$ ssh localhost
Welcome to Ubuntu 14.04.4 LTS (GNU/Linux 4.2.0-27-generic x86_64)
* Documentation: https://help.ubuntu.com/
The programs included with the Ubuntu system are free software;
the exact distribution terms for each program are described in the
individual files in /usr/share/doc/*/copyright.
Ubuntu comes with ABSOLUTELY NO WARRANTY, to the extent permitted by
applicable law.
3. 安装jdk
jdk8下载地址,我这里下载是:jdk-8u91-linux-x64.tar.gz
下载之后进行解压
hadoop@ubuntu:~$ cd ~/Downloads/
hadoop@ubuntu:~/Downloads$ tar zxvf jdk-8u91-linux-x64.tar.gz
将解压的文件夹移动到/usr/local目录下
hadoop@ubuntu:~/Downloads$ sudo mv jdk1.8.0_91 /usr/local
配置jdk环境变量,使用 gedit打开 /etc/profile 文件
hadoop@ubuntu:~/Downloads$ sudo gedit /etc/profile
在文件最后一行复制以下几行,并保存
export JAVA_HOME=/usr/local/jdk1.8.0_91
export JRE_HOME=/usr/local/jdk1.8.0_91/jre
export CLASSPATH=.:$JAVA_HOME/lib:$JRE_HOME/lib:$CLASSPATH
export PATH=$JAVA_HOME/bin:$JRE_HOME/bin:$PATH
执行: source /etc/profile使修改立即生效
hadoop@ubuntu:~/Downloads$ source /etc/profile
使用:java -version查看安装是否成功
hadoop@ubuntu:~/Downloads$ java -version
java version "1.8.0_91"
Java(TM) SE Runtime Environment (build 1.8.0_91-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.91-b14, mixed mode)
4. 安装hadoop2
hadoop2下载地址,这里下载的是:hadoop-2.7.2.tar.gz
解压下载好的hadoop
hadoop@ubuntu:~/Downloads$ cd ~/Downloads/ #进入到下载目录
hadoop@ubuntu:~/Downloads$ tar zxvf hadoop-2.7.2.tar.gz
将解压后的hadoop移动到/usr/local目录下
hadoop@ubuntu:~/Downloads$ sudo mv hadoop-2.7.2 /usr/local
将hadoop文件夹及子目录的所有者更改为hadoop用户
hadoop@ubuntu:~/Downloads$ cd /usr/local
hadoop@ubuntu:/usr/local$ sudo chown -R hadoop hadoop-2.7.2
查看hadoop版本,是否可以成功使用
hadoop@ubuntu:/usr/local$ hadoop-2.7.2/bin/hadoop version
Hadoop 2.7.2
Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r b165c4fe8a74265c792ce23f546c64604acf0e41
Compiled by jenkins on 2016-01-26T00:08Z
Compiled with protoc 2.5.0
From source with checksum d0fda26633fa762bff87ec759ebe689c
This command was run using /usr/local/hadoop-2.7.2/share/hadoop/common/hadoop-common-2.7.2.jar
5. hadoop单机配置(非分布式)
hadoop默认模式为非分布式模式,无需进行其他配置即可运行。非分布式即单java进程,方便进行调试。
现在来执行例子来感受hadoop的运行。hadoop附带了丰富的例子(可以通过运行 ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar来查看所有的例子)
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar
An example program must be given as the first argument.
Valid program names are:
aggregatewordcount: An Aggregate based map/reduce program that counts the words in the input files.
aggregatewordhist: An Aggregate based map/reduce program that computes the histogram of the words in the input files.
bbp: A map/reduce program that uses Bailey-Borwein-Plouffe to compute exact digits of Pi.
dbcount: An example job that count the pageview counts from a database.
distbbp: A map/reduce program that uses a BBP-type formula to compute exact bits of Pi.
grep: A map/reduce program that counts the matches of a regex in the input.
join: A job that effects a join over sorted, equally partitioned datasets
multifilewc: A job that counts words from several files.
pentomino: A map/reduce tile laying program to find solutions to pentomino problems.
pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.
randomtextwriter: A map/reduce program that writes 10GB of random textual data per node.
randomwriter: A map/reduce program that writes 10GB of random data per node.
secondarysort: An example defining a secondary sort to the reduce.
sort: A map/reduce program that sorts the data written by the random writer.
sudoku: A sudoku solver.
teragen: Generate data for the terasort
terasort: Run the terasort
teravalidate: Checking results of terasort
wordcount: A map/reduce program that counts the words in the input files.
wordmean: A map/reduce program that counts the average length of the words in the input files.
wordmedian: A map/reduce program that counts the median length of the words in the input files.
wordstandarddeviation: A map/reduce program that counts the standard deviation of the length of the words in the input files.
我们选择运行grep例子,我们讲input文件夹中的所有文件作为输入,筛选当中符合正则表达式dfs[a-z.]+的单词病统计出现的次数,最后输出结果到output文件夹中。
hadoop@ubuntu:~$ cd /usr/local/hadoop-2.7.2/
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ mkdir ./input
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ cp ./etc/hadoop/*.xml ./input/ #将配置文件作为输入文件
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar grep ./input ./output 'dfs[a-z.]+'
... #以下是程序执行成功的输出信息
16/05/22 16:55:31 INFO mapreduce.Job: Job job_local1230263548_0002 completed successfully
16/05/22 16:55:31 INFO mapreduce.Job: Counters: 30
File System Counters
FILE: Number of bytes read=1159418
FILE: Number of bytes written=2225516
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
Map-Reduce Framework
Map input records=1
Map output records=1
Map output bytes=17
Map output materialized bytes=25
Input split bytes=127
Combine input records=0
Combine output records=0
Reduce input groups=1
Reduce shuffle bytes=25
Reduce input records=1
Reduce output records=1
Spilled Records=2
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=74
Total committed heap usage (bytes)=265191424
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=123
File Output Format Counters
Bytes Written=23
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ cat ./output/* #查看程序的执行结果
1 dfsadmin
注意:hadoop默认不会覆盖结果文件,如果再次运行上面实例,需要先将output文件夹删除,否则会提示出错。
6. hadoop伪分布式配置
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 /usr/local/hadoop-2.7.2/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。
修改配置文件 core-site.xml
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ sudo gedit ./etc/hadoop/core-site.xml
修改为下面配置:
<configuration>
<property>
<name>hadoop.tmp.dir</name>
<value>file:/usr/local/hadoop-2.7.2/tmp</value>
<description>Abase for other temporary directories.</description>
</property>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>
</configuration>
同样的,修改配置文件 hdfs-site.xml,如下:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ sudo gedit ./etc/hadoop/hdfs-site.xml
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>
<property>
<name>dfs.namenode.name.dir</name>
<value>file:/usr/local/hadoop-2.7.2/tmp/dfs/name</value>
</property>
<property>
<name>dfs.datanode.data.dir</name>
<value>file:/usr/local/hadoop-2.7.2/tmp/dfs/data</value>
</property>
</configuration>
Hadoop配置文件说明
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。
配置完成后,执行 NameNode 的格式化:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs namenode -format
#以下消息表明格式化成功
16/05/22 18:58:11 INFO common.Storage: Storage directory /usr/local/hadoop-2.7.2/tmp/dfs/name has been successfully formatted.
16/05/22 18:58:12 INFO namenode.NNStorageRetentionManager: Going to retain 1 images with txid >= 0
16/05/22 18:58:12 INFO util.ExitUtil: Exiting with status 0
16/05/22 18:58:12 INFO namenode.NameNode: SHUTDOWN_MSG:
/************************************************************
SHUTDOWN_MSG: Shutting down NameNode at ubuntu/127.0.1.1
************************************************************/
接着开启 NameNode 和 DataNode 守护进程。
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./sbin/start-dfs.sh
如果报以下错误
Starting namenodes on [localhost]
localhost: Error: JAVA_HOME is not set and could not be found.
localhost: Error: JAVA_HOME is not set and could not be found.
Starting secondary namenodes [0.0.0.0]
0.0.0.0: Error: JAVA_HOME is not set and could not be found.
则需要修改./etc/hadoop/hadoop-env.sh
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ gedit ./etc/hadoop/hadoop-env.sh #用gedit打开
# 找到下面这行
export JAVA_HOME=${JAVA_HOME}
# 修改成下面,使用jdk的绝对路径
export JAVA_HOME=/usr/local/jdk1.8.0_91
再重新执行./sbin/start-dfs.sh即可
启动完成后,使用jps来判断是否成功启动。若成功启动则会列出如下进程: “NameNode”、”DataNode” 和 “SecondaryNameNode”(如果 SecondaryNameNode 没有启动,请运行 sbin/stop-dfs.sh 关闭进程,然后再次尝试启动尝试)。如果没有 NameNode 或 DataNode ,那就是配置不成功,请仔细检查之前步骤,或通过查看启动日志排查原因。
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ jps
7026 NameNode
7334 SecondaryNameNode
7145 DataNode
7612 Jps
成功启动后,可以访问 Web 界面 http://localhost:50070 查看 NameNode 和 Datanode 信息,还可以在线查看 HDFS 中的文件。
7. 运行hadoop伪分布式实例
上面的单机模式,grep 例子读取的是本地数据,伪分布式读取的则是 HDFS 上的数据。要使用 HDFS,首先需要在 HDFS 中创建用户目录:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs dfs -mkdir -p /user/hadoop
#-p表示父目录
接着将 ./etc/hadoop 中的 xml 文件作为输入文件复制到分布式文件系统中,即将 /usr/local/hadoop/etc/hadoop 复制到分布式文件系统中的 /user/hadoop/input 中。我们使用的是 hadoop 用户,并且已创建相应的用户目录 /user/hadoop ,因此在命令中就可以使用相对路径如 input,其对应的绝对路径就是 /user/hadoop/input:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs dfs -mkdir input
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs dfs -put ./etc/hadoop/*.xml input
复制完成后,可以通过如下命令查看文件列表:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs dfs -ls input
伪分布式运行 MapReduce 作业的方式跟单机模式相同,区别在于伪分布式读取的是HDFS中的文件(可以将单机步骤中创建的本地 input 文件夹,输出结果 output 文件夹都删掉来验证这一点)。
先删除单机步骤中创建的input文件夹,还有输出结果output文件夹
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ rm -rf input
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ rm -rf output
执行grep例子
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.2.jar grep input output 'dfs[a-z.]+'
结果如下,注意到刚才我们已经更改了配置文件,所以运行结果不同。
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs dfs -cat output/*
1 dfsadmin
1 dfs.replication
1 dfs.namenode.name.dir
1 dfs.datanode.data.dir
我们也可以将运行结果取回到本地:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ rm -rf ./output #需要先删除本地output文件夹
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs dfs -get output ./output
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ cat ./output/*
Hadoop 运行程序时,输出目录不能存在,否则会提示错误 “org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory hdfs://localhost:9000/user/hadoop/output already exists” ,因此若要再次执行,需要执行如下命令删除 output 文件夹:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./bin/hdfs dfs -rm -r output
16/05/23 09:53:13 INFO fs.TrashPolicyDefault: Namenode trash configuration: Deletion interval = 0 minutes, Emptier interval = 0 minutes.
Deleted output
8. 启动yarn
修改配置文件 mapred-site.xml
#先进行重命名:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ mv ./etc/hadoop/mapred-site.xml.template ./etc/hadoop/mapred-site.xml
#使用gedit修改
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ sudo gedit ./etc/hadoop/mapred-site.xml
#用以下文本替换
<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>
接着修改配置文件 yarn-site.xml:
<configuration>
<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>
</configuration>
启动 YARN 了(需要先执行过 ./sbin/start-dfs.sh):
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./sbin/start-yarn.sh
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./sbin/mr-jobhistory-daemon.sh start historyserver #开启历史服务器,才能在Web中查看任务运行情况
开启后通过 jps 查看,可以看到多了 NodeManager 和 ResourceManager 两个后台进程
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ jps
2849 NameNode
5090 NodeManager
5525 Jps
5433 JobHistoryServer
2971 DataNode
4957 ResourceManager
3151 SecondaryNameNode
启动 YARN 之后,运行实例的方法还是一样的,仅仅是资源管理方式、任务调度不同。观察日志信息可以发现,不启用 YARN 时,是 “mapred.LocalJobRunner” 在跑任务,启用 YARN 之后,是 “mapred.YARNRunner” 在跑任务。启动 YARN 有个好处是可以通过 Web 界面查看任务的运行情况:http://localhost:8088/cluster,如下图所示。
注意:不启动 YARN 需重命名 mapred-site.xml
如果不想启动 YARN,务必把配置文件 mapred-site.xml 重命名,改成 mapred-site.xml.template,需要用时改回来就行。否则在该配置文件存在,而未开启 YARN 的情况下,运行程序会提示 “Retrying connect to server: 0.0.0.0/0.0.0.0:8032” 的错误,这也是为何该配置文件初始文件名为 mapred-site.xml.template。
关闭 YARN 的脚本如下:
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./sbin/stop-yarn.sh
hadoop@ubuntu:/usr/local/hadoop-2.7.2$ ./sbin/mr-jobhistory-daemon.sh stop historyserver
到此,Hadoop单机和伪分布式的配置就好了。