hadoop分布式环境的搭建过程

本篇内容介绍了“hadoop分布式环境的搭建过程”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！

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1. Java安装与环境配置

Hadoop是基于Java的，所以首先需要安装配置好java环境。从官网下载JDK，我用的是1.8版本。在Mac下可以在终端下使用scp命令远程拷贝到虚拟机linux中。

danieldu@daniels-MacBook-Pro-857 ~/Downloads scp jdk-8u121-linux-x64.tar.gz root@hadoop100:/opt/softwareroot@hadoop100's password:danieldu@daniels-MacBook-Pro-857 ~/Downloads

其实我在Mac上装了一个神器-Forklift。可以通过SFTP的方式连接到远程linux。然后在操作本地电脑一样，直接把文件拖过去就行了。而且好像配置文件的编辑，也可以不用在linux下用vi，直接在Mac下用sublime远程打开就可以编辑了：）

然后在linux虚拟机中（ssh 登录上去）解压缩到/opt/modules目录下

[root@hadoop100 include]# tar -zxvf /opt/software/jdk-8u121-linux-x64.tar.gz -C /opt/modules/

然后需要设置一下环境变量，打开 /etc/profile, 添加JAVA_HOME并设置PATH用vi打开也行，或者如果你也安装了类似forklift这样的可以远程编辑文件的工具那更方便。

vi /etc/profile

按shift + G 跳到文件最后，按i切换到编辑模式，添加下面的内容，主要路径要搞对。

#JAVA_HOMEexport JAVA_HOME=/opt/modules/jdk1.8.0_121export PATH=$PATH:$JAVA_HOME/bin

按ESC ，然后：wq存盘退出。

执行下面的语句使更改生效

[root@hadoop100 include]# source /etc/profile

检查java是否安装成功。如果能看到版本信息就说明安装成功了。

[root@hadoop100 include]# java -versionjava version "1.8.0_121"Java(TM) SE Runtime Environment (build 1.8.0_121-b13)Java HotSpot(TM) 64-Bit Server VM (build 25.121-b13, mixed mode)[root@hadoop100 include]#

2. Hadoop安装与环境配置

Hadoop的安装也是只需要把hadoop的tar包拷贝到linux，解压，设置环境变量.然后用之前做好的xsync脚本，把更新同步到集群中的其他机器。如果你不知道xcall、xsync怎么写的。可以翻一下之前的文章。这样集群里的所有机器就都设置好了。

[root@hadoop100 include]# tar -zxvf /opt/software/hadoop-2.7.3.tar.gz -C /opt/modules/[root@hadoop100 include]# vi /etc/profile 继续添加HADOOP_HOME#JAVA_HOMEexport JAVA_HOME=/opt/modules/jdk1.8.0_121export PATH=$PATH:$JAVA_HOME/bin#HADOOP_HOMEexport HADOOP_HOME=/opt/modules/hadoop-2.7.3export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin [root@hadoop100 include]# source /etc/profile把更改同步到集群中的其他机器[root@hadoop100 include]# xsync /etc/profile[root@hadoop100 include]# xcall source /etc/profile[root@hadoop100 include]# xsync hadoop-2.7.3/

3. Hadoop分布式配置

然后需要对Hadoop集群环境进行配置。对于集群的资源配置是这样安排的，当然hadoop100显得任务重了一点 hadoop分布式环境的搭建过程

编辑0/opt/modules/hadoop-2.7.3/etc/hadoop/mapred-env.sh、yarn-env.sh、hadoop-env.sh 这几个shell文件中的JAVA_HOME，设置为真实的绝对路径。

export JAVA_HOME=/opt/modules/jdk1.8.0_121

打开编辑 /opt/modules/hadoop-2.7.3/etc/hadoop/core-site.xml, 内容如下

fs.defaultFS hdfs://hadoop100:9000 hadoop.tmp.dir /opt/modules/hadoop-2.7.3/data/tmp

编辑/opt/modules/hadoop-2.7.3/etc/hadoop/hdfs-site.xml，指定让dfs复制5份，因为我这里有5台虚拟机组成的集群。每台机器都担当DataNode的角色。暂时也把secondary name node也放在hadoop100上，其实这里不太好，最好能和主namenode分开在不同机器上。

dfs.replication 5 dfs.namenode.secondary.http-address hadoop100:50090 dfs.permissions false

YARN 是hadoop的集中资源管理服务，放在hadoop100上。编辑/opt/modules/hadoop-2.7.3/etc/hadoop/yarn-site.xml

yarn.nodemanager.aux-services mapreduce_shuffle yarn.resourcemanager.hostname hadoop100 yarn.log-aggregation-enbale true yarn.log-aggregation.retain-seconds 604800

为了让集群能一次启动，编辑slaves文件（/opt/modules/hadoop-2.7.3/etc/hadoop/slaves），把集群中的几台机器都加入到slave文件中，一台占一行。

hadoop100hadoop101hadoop102hadoop103hadoop104

最后，在hadoop100上全部做完相关配置更改后，把相关的修改同步到集群中的其他机器

xsync hadoop-2.7.3/

在启动Hadoop之前需要format一下hadoop设置。

hdfs namenode -format

然后就可以启动hadoop了。从下面的输出过程可以看到整个集群从100到104的5台机器都已经启动起来了。通过jps可以查看当前进程。

[root@hadoop100 sbin]# ./start-dfs.shStarting namenodes on [hadoop100]hadoop100: starting namenode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-namenode-hadoop100.outhadoop101: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop101.outhadoop102: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop102.outhadoop100: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop100.outhadoop103: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop103.outhadoop104: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop104.outStarting secondary namenodes [hadoop100]hadoop100: starting secondarynamenode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-secondarynamenode-hadoop100.out[root@hadoop100 sbin]# jps2945 NameNode3187 SecondaryNameNode3047 DataNode3351 Jps[root@hadoop100 sbin]# ./start-yarn.shstarting yarn daemonsstarting resourcemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-resourcemanager-hadoop100.outhadoop103: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop103.outhadoop102: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop102.outhadoop104: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop104.outhadoop101: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop101.outhadoop100: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop100.out[root@hadoop100 sbin]# jps3408 ResourceManager2945 NameNode3187 SecondaryNameNode3669 Jps3047 DataNode3519 NodeManager[root@hadoop100 sbin]#

4. Hadoop的使用

使用hadoop可以通过API调用，这里先看看使用命令调用，确保hadoop环境已经正常运行了。

这中间有个小插曲，我通过下面的命令查看hdfs上面的文件时，发现连接不上。

[root@hadoop100 ~]# hadoop fs -lsls: Call From hadoop100/192.168.56.100 to hadoop100:9000 failed on connection exception: java.net.ConnectException: Connection refused; For more details see: http://wiki.apache.org/hadoop/ConnectionRefused

后来发现，是我中间更改过前面提到的xml配置文件，忘记format了。修改配置后记得要format。

hdfs namenode -format

hdfs 文件操作

[root@hadoop100 sbin]# hadoop fs -ls /[root@hadoop100 sbin]# hadoop fs -put ~/anaconda-ks.cfg /[root@hadoop100 sbin]# hadoop fs -ls /Found 1 items-rw-r--r-- 5 root supergroup 1233 2019-09-16 16:31 /anaconda-ks.cfg[root@hadoop100 sbin]# hadoop fs -cat /anaconda-ks.cfg

文件内容

[root@hadoop100 ~]# mkdir tmp[root@hadoop100 ~]# hadoop fs -get /anaconda-ks.cfg ./tmp/[root@hadoop100 ~]# ll tmp/total 4-rw-r--r--. 1 root root 1233 Sep 16 16:34 anaconda-ks.cfg

执行MapReduce程序

hadoop中指向示例的MapReduce程序，wordcount，数数在一个文件中出现的词的次数，我随便找了个anaconda-ks.cfg试了一下：

[root@hadoop100 ~]# hadoop jar /opt/modules/hadoop-2.7.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /anaconda-ks.cfg ~/tmp19/09/16 16:43:28 INFO client.RMProxy: Connecting to ResourceManager at hadoop100/192.168.56.100:803219/09/16 16:43:29 INFO input.FileInputFormat: Total input paths to process : 119/09/16 16:43:29 INFO mapreduce.JobSubmitter: number of splits:119/09/16 16:43:30 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1568622576365_000119/09/16 16:43:30 INFO impl.YarnClientImpl: Submitted application application_1568622576365_000119/09/16 16:43:31 INFO mapreduce.Job: The url to track the job: http://hadoop100:8088/proxy/application_1568622576365_0001/19/09/16 16:43:31 INFO mapreduce.Job: Running job: job_1568622576365_000119/09/16 16:43:49 INFO mapreduce.Job: Job job_1568622576365_0001 running in uber mode : false19/09/16 16:43:49 INFO mapreduce.Job: map 0% reduce 0%19/09/16 16:43:58 INFO mapreduce.Job: map 100% reduce 0%19/09/16 16:44:10 INFO mapreduce.Job: map 100% reduce 100%19/09/16 16:44:11 INFO mapreduce.Job: Job job_1568622576365_0001 completed successfully19/09/16 16:44:12 INFO mapreduce.Job: Counters: 49File System CountersFILE: Number of bytes read=1470FILE: Number of bytes written=240535FILE: Number of read operations=0FILE: Number of large read operations=0FILE: Number of write operations=0HDFS: Number of bytes read=1335HDFS: Number of bytes written=1129HDFS: Number of read operations=6HDFS: Number of large read operations=0HDFS: Number of write operations=2Job CountersLaunched map tasks=1Launched reduce tasks=1Rack-local map tasks=1Total time spent by all maps in occupied slots (ms)=6932Total time spent by all reduces in occupied slots (ms)=7991Total time spent by all map tasks (ms)=6932Total time spent by all reduce tasks (ms)=7991Total vcore-milliseconds taken by all map tasks=6932Total vcore-milliseconds taken by all reduce tasks=7991Total megabyte-milliseconds taken by all map tasks=7098368Total megabyte-milliseconds taken by all reduce tasks=8182784Map-Reduce FrameworkMap input records=46Map output records=120Map output bytes=1704Map output materialized bytes=1470Input split bytes=102Combine input records=120Combine output records=84Reduce input groups=84Reduce shuffle bytes=1470Reduce input records=84Reduce output records=84Spilled Records=168Shuffled Maps =1Failed Shuffles=0Merged Map outputs=1GC time elapsed (ms)=169CPU time spent (ms)=1440Physical memory (bytes) snapshot=300003328Virtual memory (bytes) snapshot=4159303680Total committed heap usage (bytes)=141471744Shuffle ErrorsBAD_ID=0CONNECTION=0IO_ERROR=0WRONG_LENGTH=0WRONG_MAP=0WRONG_REDUCE=0File Input Format CountersBytes Read=1233File Output Format CountersBytes Written=1129[root@hadoop100 ~]#

在web端管理界面中可以看到对应的application：

执行的结果，看到就是“#” 出现的最多，出现了12次，这也难怪，里面好多都是注释嘛。

[root@hadoop100 tmp]# hadoop fs -ls /root/tmpFound 2 items-rw-r--r-- 5 root supergroup 0 2019-09-16 16:44 /root/tmp/_SUCCESS-rw-r--r-- 5 root supergroup 1129 2019-09-16 16:44 /root/tmp/part-r-00000[root@hadoop100 tmp]# hadoop fs -cat /root/tmp/part-r-0000cat: `/root/tmp/part-r-0000': No such file or directory[root@hadoop100 tmp]# hadoop fs -cat /root/tmp/part-r-00000# 12#version=DEVEL 1$6$JBLRSbsT070BPmiq$Of51A9N3Zjn/gZ23mLMlVs8vSEFL6ybkfJ1K1uJLAwumtkt1PaLcko1SSszN87FLlCRZsk143gLSV22Rv0zDr/ 1%addon 1%anaconda 1%end 3%packages 1--addsupport=zh_CN.UTF-8 1--boot-drive=sda 1--bootproto=dhcp 1--device=enp0s3 1--disable 1--disabled="chronyd" 1--emptyok 1。。。

通过web 界面可以查看hdfs中的文件列表 http://192.168.56.100:50070/explorer.html#

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