hadoop分布式環境的搭建過程

本篇內容介紹了“hadoop分布式環境的搭建過程”的有關知識，在實際案例的操作過程中，不少人都會遇到這樣的困境，接下來就讓小編帶領大家學習一下如何處理這些情況吧！希望大家仔細閱讀，能夠學有所成！

1. Java安裝與環境配置

Hadoop是基于Java的，所以首先需要安裝配置好java環境。從官網下載JDK，我用的是1.8版本。 在Mac下可以在終端下使用scp命令遠程拷貝到虛擬機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

其實我在Mac上裝了一個神器-Forklift。 可以通過SFTP的方式連接到遠程linux。然后在操作本地電腦一樣，直接把文件拖過去就行了。而且好像配置文件的編輯，也可以不用在linux下用vi，直接在Mac下用sublime遠程打開就可以編輯了 ：）

然后在linux虛擬機中（ssh 登錄上去）解壓縮到/opt/modules目錄下

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

然后需要設置一下環境變量， 打開 /etc/profile, 添加JAVA_HOME并設置PATH用vi打開也行，或者如果你也安裝了類似forklift這樣的可以遠程編輯文件的工具那更方便。

vi /etc/profile

按shift + G 跳到文件最后，按i切換到編輯模式，添加下面的內容，主要路徑要搞對。

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

按ESC ， 然后 ：wq存盤退出。

執行下面的語句使更改生效

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

檢查java是否安裝成功。如果能看到版本信息就說明安裝成功了。

[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安裝與環境配置

Hadoop的安裝也是只需要把hadoop的tar包拷貝到linux，解壓，設置環境變量.然后用之前做好的xsync腳本，把更新同步到集群中的其他機器。如果你不知道xcall、xsync怎么寫的。可以翻一下之前的文章。這樣集群里的所有機器就都設置好了。

[root@hadoop100 include]# tar -zxvf /opt/software/hadoop-2.7.3.tar.gz -C /opt/modules/[root@hadoop100 include]# vi /etc/profile 繼續添加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機器[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

然后需要對Hadoop集群環境進行配置。對于集群的資源配置是這樣安排的，當然hadoop100顯得任務重了一點

編輯0/opt/modules/hadoop-2.7.3/etc/hadoop/mapred-env.sh、yarn-env.sh、hadoop-env.sh 這幾個shell文件中的JAVA_HOME，設置為真實的絕對路徑。

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

打開編輯 /opt/modules/hadoop-2.7.3/etc/hadoop/core-site.xml, 內容如下

<configuration> <property>  <name>fs.defaultFS</name>  <value>hdfs://hadoop100:9000</value> </property> <property>  <name>hadoop.tmp.dir</name>  <value>/opt/modules/hadoop-2.7.3/data/tmp</value> </property> </configuration

編輯/opt/modules/hadoop-2.7.3/etc/hadoop/hdfs-site.xml， 指定讓dfs復制5份，因為我這里有5臺虛擬機組成的集群。每臺機器都擔當DataNode的角色。暫時也把secondary name node也放在hadoop100上，其實這里不太好，最好能和主namenode分開在不同機器上。

<configuration> <property>  <name>dfs.replication</name>  <value>5</value> </property> <property>  <name>dfs.namenode.secondary.http-address</name>  <value>hadoop100:50090</value> </property> <property>  <name>dfs.permissions</name>  <value>false</value> </property></configuration>

YARN 是hadoop的集中資源管理服務，放在hadoop100上。 編輯/opt/modules/hadoop-2.7.3/etc/hadoop/yarn-site.xml

<configuration><!-- Site specific YARN configuration properties --> <property>  <name>yarn.nodemanager.aux-services</name>  <value>mapreduce_shuffle</value> </property> <property>  <name>yarn.resourcemanager.hostname</name>  <value>hadoop100</value> </property> <property>  <name>yarn.log-aggregation-enbale</name>  <value>true</value> </property> <property>  <name>yarn.log-aggregation.retain-seconds</name>  <value>604800</value> </property></configuration>

為了讓集群能一次啟動，編輯slaves文件（/opt/modules/hadoop-2.7.3/etc/hadoop/slaves），把集群中的幾臺機器都加入到slave文件中，一臺占一行。

hadoop100hadoop101hadoop102hadoop103hadoop104

最后，在hadoop100上全部做完相關配置更改后，把相關的修改同步到集群中的其他機器

xsync hadoop-2.7.3/

在啟動Hadoop之前需要format一下hadoop設置。

hdfs namenode -format

然后就可以啟動hadoop了。從下面的輸出過程可以看到整個集群從100到104的5臺機器都已經啟動起來了。通過jps可以查看當前進程。

[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可以通過API調用，這里先看看使用命令調用，確保hadoop環境已經正常運行了。

這中間有個小插曲，我通過下面的命令查看hdfs上面的文件時，發現連接不上。

[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

后來發現，是我中間更改過前面提到的xml配置文件，忘記format了。修改配置后記得要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

文件內容

[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

執行MapReduce程序

hadoop中指向示例的MapReduce程序，wordcount，數數在一個文件中出現的詞的次數，我隨便找了個anaconda-ks.cfg試了一下：

[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對應的application：

執行的結果，看到就是“#” 出現的最多，出現了12次，這也難怪，里面好多都是注釋嘛。

[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。。。

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

“hadoop分布式環境的搭建過程”的內容就介紹到這里了，感謝大家的閱讀。如果想了解更多行業相關的知識可以關注億速云網站，小編將為大家輸出更多高質量的實用文章！