hadoop分布式環(huán)境搭建過程

更新時間：2019年09月17日 11:22:07 作者：峻祁連

這篇文章主要介紹了hadoop分布式環(huán)境搭建過程，本文圖文并茂給大家介紹的非常詳細，具有一定的參考借鑒價值,需要的朋友可以參考下

1. Java安裝與環(huán)境配置

Hadoop是基于Java的，所以首先需要安裝配置好java環(huán)境。從官網(wǎng)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/software
root@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(huán)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(nèi)容，主要路徑要搞對。

#JAVA_HOME
export JAVA_HOME=/opt/modules/jdk1.8.0_121
export PATH=$PATH:$JAVA_HOME/bin

按ESC ，然后：wq存盤退出。

執(zhí)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 -version
java 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(huán)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(huán)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?？梢苑幌轮暗奈恼隆＿@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(xù)添加HADOOP_HOME
#JAVA_HOME
export JAVA_HOME=/opt/modules/jdk1.8.0_121
export PATH=$PATH:$JAVA_HOME/bin
#HADOOP_HOME
export HADOOP_HOME=/opt/modules/hadoop-2.7.3
export 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(huán)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(nèi)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。

hadoop100
hadoop101
hadoop102
hadoop103
hadoop104

最后，在hadoop100上全部做完相關(guān)配置更改后，把相關(guān)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(jīng)啟動起來了。通過jps可以查看當前進程。

[root@hadoop100 sbin]# ./start-dfs.sh
Starting namenodes on [hadoop100]
hadoop100: starting namenode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-namenode-hadoop100.out
hadoop101: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop101.out
hadoop102: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop102.out
hadoop100: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop100.out
hadoop103: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop103.out
hadoop104: starting datanode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-datanode-hadoop104.out
Starting secondary namenodes [hadoop100]
hadoop100: starting secondarynamenode, logging to /opt/modules/hadoop-2.7.3/logs/hadoop-root-secondarynamenode-hadoop100.out
[root@hadoop100 sbin]# jps
2945 NameNode
3187 SecondaryNameNode
3047 DataNode
3351 Jps
[root@hadoop100 sbin]# ./start-yarn.sh
starting yarn daemons
starting resourcemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-resourcemanager-hadoop100.out
hadoop103: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop103.out
hadoop102: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop102.out
hadoop104: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop104.out
hadoop101: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop101.out
hadoop100: starting nodemanager, logging to /opt/modules/hadoop-2.7.3/logs/yarn-root-nodemanager-hadoop100.out
[root@hadoop100 sbin]# jps
3408 ResourceManager
2945 NameNode
3187 SecondaryNameNode
3669 Jps
3047 DataNode
3519 NodeManager
[root@hadoop100 sbin]#

4. Hadoop的使用

使用hadoop可以通過API調(diào)用，這里先看看使用命令調(diào)用，確保hadoop環(huán)境已經(jīng)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(fā)現(xiàn)連接不上。

[root@hadoop100 ~]# hadoop fs -ls
ls: 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(fā)現(xiàn)，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(nèi)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(zhí)行MapReduce程序

hadoop中指向示例的MapReduce程序，wordcount，數(shù)數(shù)在一個文件中出現(xiàn)的詞的次數(shù)，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 ~/tmp
19/09/16 16:43:28 INFO client.RMProxy: Connecting to ResourceManager at hadoop100/192.168.56.100:8032
19/09/16 16:43:29 INFO input.FileInputFormat: Total input paths to process : 1
19/09/16 16:43:29 INFO mapreduce.JobSubmitter: number of splits:1
19/09/16 16:43:30 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1568622576365_0001
19/09/16 16:43:30 INFO impl.YarnClientImpl: Submitted application application_1568622576365_0001
19/09/16 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_0001
19/09/16 16:43:49 INFO mapreduce.Job: Job job_1568622576365_0001 running in uber mode : false
19/09/16 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 successfully
19/09/16 16:44:12 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=1470
FILE: Number of bytes written=240535
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=1335
HDFS: Number of bytes written=1129
HDFS: Number of read operations=6
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Rack-local map tasks=1
Total time spent by all maps in occupied slots (ms)=6932
Total time spent by all reduces in occupied slots (ms)=7991
Total time spent by all map tasks (ms)=6932
Total time spent by all reduce tasks (ms)=7991
Total vcore-milliseconds taken by all map tasks=6932
Total vcore-milliseconds taken by all reduce tasks=7991
Total megabyte-milliseconds taken by all map tasks=7098368
Total megabyte-milliseconds taken by all reduce tasks=8182784
Map-Reduce Framework
Map input records=46
Map output records=120
Map output bytes=1704
Map output materialized bytes=1470
Input split bytes=102
Combine input records=120
Combine output records=84
Reduce input groups=84
Reduce shuffle bytes=1470
Reduce input records=84
Reduce output records=84
Spilled Records=168
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=169
CPU time spent (ms)=1440
Physical memory (bytes) snapshot=300003328
Virtual memory (bytes) snapshot=4159303680
Total committed heap usage (bytes)=141471744
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=1233
File Output Format Counters
Bytes Written=1129
[root@hadoop100 ~]#

在web端管理界面中可以看到對應的application：

執(zhí)行的結(jié)果，看到就是“#” 出現(xiàn)的最多，出現(xiàn)了12次，這也難怪，里面好多都是注釋嘛。

[root@hadoop100 tmp]# hadoop fs -ls /root/tmp
Found 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-0000
cat: `/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#