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Docker搭建Hadoop集群

文章转自: https://blog.csdn.net/lizongti/article/details/102756472 

目录

环境准备

依赖

安装Docker

单例模式(Without Docker)

安装

安装JDK

安装Hadoop

配置

环境变量

设置免密登录

修改 hadoop-env.sh

HDFS

创建目录

修改core-site.xml

修改hdfs-site.xml

格式化HDFS

启动HDFS

HDFS Web

HDFS 测试

YARN

修改mapred-site.xml

修改yarn-site.xml

启动Yarn

Yarn Web

Yarn 测试

集群搭建(Without Docker)

准备

配置master

配置单例

修改hdfs-site.xml

修改masters

删除slaves

拷贝到slaves节点

修改slaves

HDFS

清空目录

格式化HDFS

启动HDFS

测试HDFS

YARN

启动YARN

测试YARN


环境准备

依赖

CentOS7.6

安装Docker

参照安装(点击)

单例模式(Without Docker)

安装

安装JDK

去官网上下载1.8版本的tar.gz ,如果使用yum安装或者下载rpm包安装,则会缺少Scala2.11需要的部分文件。

tar xf jdk-8u221-linux-x64.tar -C /usr/lib/jvm
rm -rf /usr/bin/java
ln -s /usr/lib/jvm/jdk1.8.0_221/bin/java /usr/bin/java

编辑文件

vim /etc/profile.d/java.sh

添加

export JAVA_HOME=/usr/lib/jvm/jdk1.8.0_221
export JRE_HOME=${JAVA_HOME}/jre
export CLASSPATH=${JAVA_HOME}/lib:${JRE_HOME}/lib:$CLASSPATH
export PATH=${JAVA_HOME}/bin:$PATH

然后使环境变量生效

source /etc/profile

执行以下命令检查环境变量

[root@vm1 bin]# echo $JAVA_HOME
/usr/lib/jvm/jdk1.8.0_221
[root@vm1 bin]# echo $JAVA_HOME
/usr/lib/jvm/jdk1.8.0_221

安装Hadoop

为了和另一篇的Spark达到版本兼容,使用官网hadoop2.7版本

wget https://archive.apache.org/dist/hadoop/common/hadoop-2.7.7/hadoop-2.7.7.tar.gz

解压

tar xf hadoop-2.7.7.tar.gz -C /opt/

配置

环境变量

编辑文件

vim /etc/profile.d/hadoop.sh

添加

export HADOOP_HOME=/opt/hadoop-2.7.7
export PATH=$PATH:$HADOOP_HOME/bin

然后使环境变量生效

source /etc/profile

设置免密登录

本机也需要配置免密登录
参照这里

修改 hadoop-env.sh

配置启动脚本内的JAVA_HOME

vi /opt/hadoop-2.7.7/etc/hadoop/hadoop-env.sh

使用

export JAVA_HOME=/usr/lib/jvm/jdk1.8.0_221

替换

export JAVA_HOME=${JAVA_HOME}

HDFS

创建目录

mkdir -p /opt/hadoop-2.7.7/hdfs/name
mkdir -p /opt/hadoop-2.7.7/hdfs/data

修改core-site.xml

配置访问节点

vi /opt/hadoop-2.7.7/etc/hadoop/core-site.xml

替换

<configuration>
</configuration>

为以下配置

<configuration>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>file:/opt/hadoop-2.7.7/tmp</value>
    </property>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://vm1:9000</value>
    </property>
</configuration>
  • 通过hadoop.tmp.dir指定hadoop数据存储的临时文件夹,如没有配置hadoop.tmp.dir参数,此时系统默认的临时目录为:/tmp/hadoop-root。而这个目录在每次重启后都会被删除。
  • 通过fs.defaultFS指定默认访问文件系统的地址,否则默认访问本地文件,而非HDFS上的文件

修改hdfs-site.xml

配置副本个数

vi /opt/hadoop-2.7.7/etc/hadoop/hdfs-site.xml

替换

<configuration>
</configuration>

为以下配置

<configuration>
	<property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
    <property>
        <name>dfs.name.dir</name>
        <value>/opt/hadoop-2.7.7/hdfs/name</value>
    </property>
    <property>
        <name>dfs.data.dir</name>
        <value>/opt/hadoop-2.7.7/hdfs/data</value>
    </property>
</configuration>
  • 通过dfs.replication指定HDFS的备份因子为1
  • 通过dfs.name.dir指定namenode节点的文件存储目录,这个参数用于确定将HDFS文件系统的元信息保存在什么目录下。如果这个参数设置为多个目录,那么这些目录下都保存着元信息的多个备份。
  • 通过dfs.data.dir指定datanode节点的文件存储目录,这个参数用于确定将HDFS文件系统的数据保存在什么目录下。
    我们可以将这个参数设置为多个分区上目录,即可将HDFS建立在不同分区上

格式化HDFS

cd /opt/hadoop-2.7.7/bin
hdfs namenode -format

启动HDFS

cd /opt/hadoop-2.7.7/sbin
./start-dfs.sh

运行结果

Starting namenodes on [vm1]
vm1: starting namenode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-namenode-vm1.out
localhost: starting datanode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-datanode-vm1.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-secondarynamenode-vm1.out

HDFS Web

访问HDFS web界面 http://vm1:50070
在这里插入图片描述

HDFS 测试

生成测试数据

mkdir -p /tmp/input
vi /tmp/input/1

加入

a
b
a
hadoop fs -mkdir -p /tmp/input
hadoop fs -put /tmp/input/1 /tmp/input
hadoop fs -ls  /tmp/input
Found 1 items
-rw-r--r--   1 root supergroup          6 2019-10-28 11:34 /tmp/input/1

YARN

修改mapred-site.xml

设置调度器为yarn

cp /opt/hadoop-2.7.7/etc/hadoop/mapred-site.xml.template /opt/hadoop-2.7.7/etc/hadoop/mapred-site.xml
vi /opt/hadoop-2.7.7/etc/hadoop/mapred-site.xml

替换

<configuration>
</configuration>

为以下配置

<configuration>
    <property>
	    <name>mapreduce.framework.name</name>
	    <value>yarn</value>
    </property>
	<property>
      <name>mapred.job.tracker</name>
      <value>http://vm1:9001</value>
  </property>
</configuration>
  • 通过指定mapreduce.framework.name来设置map-reduce任务使用yarn的调度系统。如果设置为local表示本地运行,设置为classic表示经典mapreduce框架。
  • 通过指定mapred.job.tracker来设置map-reduce任务的job tracker的IP和Port。

修改yarn-site.xml

vi /opt/hadoop-2.7.7/etc/hadoop/yarn-site.xml

替换

<configuration>
</configuration>

为以下配置

<configuration>
	<property>
	    <name>yarn.nodemanager.aux-services</name>
	    <value>mapreduce_shuffle</value>
	</property>
	<property>
	    <name>yarn.resourcemanager.hostname</name>
	    <value>vm1</value>
	</property>
</configuration>
  • 通过指定yarn.nodemanager.aux-services为mapreduce_shuffle来避免 “The auxService:mapreduce_shuffle does not exist” 错误
  • 通过指定yarn.resourcemanager.hostname来设置rm所在的主机。

启动Yarn

./start-yarn.sh

显示

starting yarn daemons
starting resourcemanager, logging to /opt/hadoop-2.7.7/logs/yarn-root-resourcemanager-vm1.out
localhost: starting nodemanager, logging to /opt/hadoop-2.7.7/logs/yarn-root-nodemanager-vm1.out

Yarn Web

访问http://vm1:8088
在这里插入图片描述

Yarn 测试

执行命令

```shell
hadoop jar /opt/hadoop-2.7.7/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar wordcount /tmp/input /tmp/result

执行日志

19/10/28 11:34:52 INFO client.RMProxy: Connecting to ResourceManager at vm1/192.168.1.101:8032
19/10/28 11:34:53 INFO input.FileInputFormat: Total input paths to process : 1
19/10/28 11:34:53 INFO mapreduce.JobSubmitter: number of splits:1
19/10/28 11:34:54 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1572232474055_0002
19/10/28 11:34:54 INFO impl.YarnClientImpl: Submitted application application_1572232474055_0002
19/10/28 11:34:54 INFO mapreduce.Job: The url to track the job: http://vm1:8088/proxy/application_1572232474055_0002/
19/10/28 11:34:54 INFO mapreduce.Job: Running job: job_1572232474055_0002
19/10/28 11:35:06 INFO mapreduce.Job: Job job_1572232474055_0002 running in uber mode : false
19/10/28 11:35:06 INFO mapreduce.Job:  map 0% reduce 0%
19/10/28 11:35:11 INFO mapreduce.Job:  map 100% reduce 0%
19/10/28 11:35:16 INFO mapreduce.Job:  map 100% reduce 100%
19/10/28 11:35:17 INFO mapreduce.Job: Job job_1572232474055_0002 completed successfully
19/10/28 11:35:18 INFO mapreduce.Job: Counters: 49
	File System Counters
		FILE: Number of bytes read=22
		FILE: Number of bytes written=245617
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=98
		HDFS: Number of bytes written=8
		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
		Data-local map tasks=1
		Total time spent by all maps in occupied slots (ms)=2576
		Total time spent by all reduces in occupied slots (ms)=3148
		Total time spent by all map tasks (ms)=2576
		Total time spent by all reduce tasks (ms)=3148
		Total vcore-milliseconds taken by all map tasks=2576
		Total vcore-milliseconds taken by all reduce tasks=3148
		Total megabyte-milliseconds taken by all map tasks=2637824
		Total megabyte-milliseconds taken by all reduce tasks=3223552
	Map-Reduce Framework
		Map input records=3
		Map output records=3
		Map output bytes=18
		Map output materialized bytes=22
		Input split bytes=92
		Combine input records=3
		Combine output records=2
		Reduce input groups=2
		Reduce shuffle bytes=22
		Reduce input records=2
		Reduce output records=2
		Spilled Records=4
		Shuffled Maps =1
		Failed Shuffles=0
		Merged Map outputs=1
		GC time elapsed (ms)=425
		CPU time spent (ms)=1400
		Physical memory (bytes) snapshot=432537600
		Virtual memory (bytes) snapshot=4235526144
		Total committed heap usage (bytes)=304087040
	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=6
	File Output Format Counters 
		Bytes Written=8

查看运行结果

hadoop fs -cat /tmp/result/part-r-00000

显示

a       2
b       1

集群搭建(Without Docker)

准备

  • 部署三台机器vm1, vm2,vm3在一个子网当中。

配置master

配置单例

先在vm1上执行与单例配置完全一样的配置过程

修改hdfs-site.xml

vi /opt/hadoop-2.7.7/etc/hadoop/hdfs-site.xml

替换

	<property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>

为以下配置

	<property>
        <name>dfs.replication</name>
        <value>2</value>
    </property>

这里的副本数dfs.replication配置成2

修改masters

echo "vm1" > /opt/hadoop-2.7.7/etc/hadoop/masters

删除slaves

rm /opt/hadoop-2.7.7/etc/hadoop/slaves

拷贝到slaves节点

scp -r /opt/hadoop-2.7.7 root@vm2:/opt/
scp -r /opt/hadoop-2.7.7 root@vm3:/opt/

修改slaves

cat > /opt/hadoop-2.7.7/etc/hadoop/slaves <<EOF
vm1
vm2
vm3
EOF

把vm2和vm3写入到slaves里面去

HDFS

清空目录

rm -rf /opt/hadoop-2.7.7/hdfs/data/*
rm -rf /opt/hadoop-2.7.7/hdfs/name/*

格式化HDFS

重新格式化dfs

hadoop namenode -format

启动HDFS

/opt/hadoop-2.7.7/sbin
./start-dfs.sh

显示

Starting namenodes on [vm1]
vm1: starting namenode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-namenode-vm1.out
vm3: starting datanode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-datanode-vm3.out
vm2: starting datanode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-datanode-vm2.out
vm1: starting datanode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-datanode-vm1.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /opt/hadoop-2.7.7/logs/hadoop-root-secondarynamenode-vm1.out

比如namenode的日志就在/opt/hadoop-2.7.7/logs/hadoop-root-namenode-vm1.log中

检查master进程

$ jps
75991 DataNode
76408 Jps
76270 SecondaryNameNode

检查slave进程

$ jps
29379 DataNode
29494 Jps

查看集群状态

hdfs dfsadmin -report -safemode

显示

[root@vm1 sbin]# 
Configured Capacity: 160982630400 (149.93 GB)
Present Capacity: 101929107456 (94.93 GB)
DFS Remaining: 101929095168 (94.93 GB)
DFS Used: 12288 (12 KB)
DFS Used%: 0.00%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0
Missing blocks (with replication factor 1): 0

-------------------------------------------------
Live datanodes (3):

Name: 192.168.1.103:50010 (vm3)
Hostname: vm3
Decommission Status : Normal
Configured Capacity: 53660876800 (49.98 GB)
DFS Used: 4096 (4 KB)
Non DFS Used: 10321448960 (9.61 GB)
DFS Remaining: 43339423744 (40.36 GB)
DFS Used%: 0.00%
DFS Remaining%: 80.77%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Mon Oct 28 13:42:20 CST 2019


Name: 192.168.1.102:50010 (vm2)
Hostname: vm2
Decommission Status : Normal
Configured Capacity: 53660876800 (49.98 GB)
DFS Used: 4096 (4 KB)
Non DFS Used: 13661077504 (12.72 GB)
DFS Remaining: 39999795200 (37.25 GB)
DFS Used%: 0.00%
DFS Remaining%: 74.54%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Mon Oct 28 13:42:21 CST 2019


Name: 192.168.1.101:50010 (vm1)
Hostname: vm1
Decommission Status : Normal
Configured Capacity: 53660876800 (49.98 GB)
DFS Used: 4096 (4 KB)
Non DFS Used: 35070996480 (32.66 GB)
DFS Remaining: 18589876224 (17.31 GB)
DFS Used%: 0.00%
DFS Remaining%: 34.64%
Configured Cache Capacity: 0 (0 B)
Cache Used: 0 (0 B)
Cache Remaining: 0 (0 B)
Cache Used%: 100.00%
Cache Remaining%: 0.00%
Xceivers: 1
Last contact: Mon Oct 28 13:42:21 CST 2019

测试HDFS

hadoop fs -mkdir -p /tmp/input
hadoop fs -put /tmp/input/1 /tmp/input
hadoop fs -ls  /tmp/input
Found 1 items
-rw-r--r--   1 root supergroup          6 2019-10-28 11:34 /tmp/input/1

YARN

启动YARN

/opt/hadoop-2.7.7/sbin
./start-yarn.sh

显示

starting yarn daemons
starting resourcemanager, logging to /opt/hadoop-2.7.7/logs/yarn-root-resourcemanager-vm1.out
vm2: starting nodemanager, logging to /opt/hadoop-2.7.7/logs/yarn-root-nodemanager-vm2.out
vm3: starting nodemanager, logging to /opt/hadoop-2.7.7/logs/yarn-root-nodemanager-vm3.out
vm1: starting nodemanager, logging to /opt/hadoop-2.7.7/logs/yarn-root-nodemanager-vm1.out

比如resourcemanager的日志就在/opt/hadoop-2.7.7/logs/yarn-root-resourcemanager-vm1.log中

检查master进程

$ jps
100464 ResourceManager
101746 Jps
53786 QuorumPeerMain

检查slave进程

$jps
36893 NodeManager
37181 Jps

测试YARN

hadoop jar /opt/hadoop-2.7.7/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.7.jar wordcount /tmp/input /tmp/result

结果同上。

更多参考:

https://cloud.tencent.com/developer/article/1084166

https://www.jianshu.com/p/7ab2b6168cc9

https://www.cnblogs.com/onetwo/p/6419925.html

转载文章,未经验证,一经验证如有问题将进行不当更正。

版权声明
本文为[boonya]所创,转载请带上原文链接,感谢
https://blog.csdn.net/boonya/article/details/108335318

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