centos7 yum安装java运行环境，初识hadoop

Posted 2020-10-31

安装java运行环境

1.实验机相关信息：
[[email protected] ~]# cat /etc/redhat-release
CentOS Linux release 7.2.1511 (Core)
[[email protected] ~]# uname -r
3.10.0-327.el7.x86_6
2.配置epel源，以yum方式安装openjdk
yum search java | grep -i JDK
yum install java-1.8.0-openjdk java-1.8.0-openjdk-devel
3.设置JAVA_HOME 环境变量
[[email protected] ~]# cat /etc/profile.d/java_home.sh
export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.161-0.b14.el7_4.x86_64
export PATH=$PATH:$JAVA_HOME/bin
使配置生效
source /etc/profile.d/java_home.sh 或 . /etc/profile.d/java_home.sh
4.测试java是否安装配置成功
[[email protected] ~]# java -version
openjdk version "1.8.0_161"
OpenJDK Runtime Environment (build 1.8.0_161-b14)
OpenJDK 64-Bit Server VM (build 25.161-b14, mixed mode)

5.创建java小程序，编译 打印hello world

[[email protected] ~]# cat helloworld.java
public class helloworld {
        public  static void  main(String[] args){
                System.out.println("hello wolrd!");
        }
}

[[email protected] ~]# javac helloworld.java #编译后会出现helloworld.class 这个类文件
[[email protected] ~]# java helloworld #运行
hello wolrd!

6. 如何运行 .jar .war 这些java应用？
   java -jar /path/to/*.jar [arg1] [arg2]

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接下来认识hadoop 官网：http://hadoop.apache.org/

什么是Apache Hadoop？
Apache?Hadoop?项目为可靠的，可扩展的分布式计算开发开源软件。
Apache Hadoop软件库是一个框架，它允许使用简单的编程模型跨计算机群集分布式处理大型数据集。
旨在从单个服务器扩展到数千台机器，每台机器提供本地计算和存储。
该库本身不是依靠硬件来提供高可用性，而是设计用于在应用层检测并处理故障，从而在一组计算机之上提供高可用性服务，每个计算机都可能出现故障。

hadoop单机模式运行

官网下载二进制包，解压到/usr/locl目录，创建软连接同目录下hadoop，配置PATH变量，使生效

[[email protected] ~]$ cat /etc/profile.d/hadoop.sh 
export PATH=$PATH:/usr/local/hadoop/bin:/usr/local/hadoop/sbin

[[email protected] ~]# hadoop
Usage: hadoop [OPTIONS] SUBCOMMAND [SUBCOMMAND OPTIONS]
 or    hadoop [OPTIONS] CLASSNAME [CLASSNAME OPTIONS]
  where CLASSNAME is a user-provided Java class

  OPTIONS is none or any of:

buildpaths                       attempt to add class files from build tree
--config dir                     Hadoop config directory
--debug                          turn on shell script debug mode
--help                           usage information
hostnames list[,of,host,names]   hosts to use in slave mode
hosts filename                   list of hosts to use in slave mode
loglevel level                   set the log4j level for this command
workers                          turn on worker mode

  SUBCOMMAND is one of:

    Admin Commands:

daemonlog     get/set the log level for each daemon

    Client Commands:

archive       create a Hadoop archive
checknative   check native Hadoop and compression libraries availability
classpath     prints the class path needed to get the Hadoop jar and the required libraries
conftest      validate configuration XML files
credential    interact with credential providers
distch        distributed metadata changer
distcp        copy file or directories recursively
dtutil        operations related to delegation tokens
envvars       display computed Hadoop environment variables
fs            run a generic filesystem user client
gridmix       submit a mix of synthetic job, modeling a profiled from production load
jar <jar>     run a jar file. NOTE: please use "yarn jar" to launch YARN applications, not this command.
jnipath       prints the java.library.path
kdiag         Diagnose Kerberos Problems
kerbname      show auth_to_local principal conversion
key           manage keys via the KeyProvider
rumenfolder   scale a rumen input trace
rumentrace    convert logs into a rumen trace
s3guard       manage metadata on S3
trace         view and modify Hadoop tracing settings
version       print the version

    Daemon Commands:

kms           run KMS, the Key Management Server

SUBCOMMAND may print help when invoked w/o parameters or with -h.

Hadoop 默认配置是以非分布式模式运行，即单 Java 进程，方便进行调试。可以执行附带的例子 WordCount 来感受下 Hadoop 的运行。将 input 文件夹中的文件作为输入，统计当中符合正则表达式 wo[a-z.]+ 的单词出现的次数，并输出结果到 output 文件夹中。

如果需要再次运行，需要删除output文件夹（因为Hadoop 默认不会覆盖结果文件）：

  # cd /usr/local/hadoop/
  # mkdir input
  # cp etc/hadoop/*.xml   input
  # bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.0.jar grep input output ‘dfs[a-z.]+‘
  # cat output/*
         1  work

[[email protected] /usr/local/hadoop]# hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.0.jar grep /etc/passwd output ‘root‘
[[email protected] /usr/local/hadoop]# cat output/*