基于腾讯云CVM搭建Hadoop集群及数据迁移最佳实践

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Vicwan

修改于 2020-04-09 10:17:59

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修改于 2020-04-09 10:17:59

文章被收录于专栏：腾讯云迁云技术团队专栏腾讯云迁云技术团队专栏

一、需求和目标

本文主要介绍如何在腾讯云CVM上搭建Hadoop集群，以及如何通过distcp工具将友商云Hadoop中的数据迁移到腾讯云自建Hadoop集群。

二、环境说明

JDK版本：jdk1.8.0_171

Hadoop版本：hadoop-2.7.4

主机	角色	软件
腾讯云tx-namenode 172.16.2.234	NameNode/SecondaryNameNode/ ResourceManager	HDFS/YARN
腾讯云tx-datanode1 172.16.2.4	DataNode/NodeManager	HDFS/YARN
腾讯云tx-datanode2 172.16.2.8	DataNode/NodeManager	HDFS/YARN
腾讯云tx-datanode3 172.16.2.7	DataNode/NodeManager	HDFS/YARN
友商云ali-namenode 10.1.125.118	NameNode/SecondaryNameNode/ ResourceManager	HDFS/YARN
友商云ali-datanode1 10.1.125.119	DataNode/NodeManager	HDFS/YARN
友商云ali-datanode2 10.1.125.116	DataNode/NodeManager	HDFS/YARN
友商云ali-datanode3 10.1.125.117	DataNode/NodeManager	HDFS/YARN

三、腾讯云Hadoop集群搭建

1、系统环境配置

1.1 配置主机名（永久修改）

（1）在腾讯云tx-namenode节点配置：

[root@tx-namenode ~]# vim /etc/sysconfig/network

NETWORKING=yes  #使用网络
HOSTNAME=tx-namenode  #设置主机名

（2）腾讯云tx-datanode1节点配置：

[root@tx-datanode1 ~]# vim /etc/sysconfig/network

NETWORKING=yes  #使用网络
HOSTNAME=tx-datanode1  #设置主机名

（3）腾讯云tx-datanode2节点配置：

[root@tx-datanode2 ~]# vim /etc/sysconfig/network

NETWORKING=yes  #使用网络
HOSTNAME=tx-datanode2  #设置主机名

（4）腾讯云tx-datanode3节点配置：

[root@tx-datanode3 ~]# vim /etc/sysconfig/network

NETWORKING=yes  #使用网络
HOSTNAME=tx-datanode3  #设置主机名

1.2 安装JAVA运行环境

（1）在/usr下创建Java目录

mkdir -p /usr/java

（2）将JDK包解压到/usr/java下

tar xvf jdk-8u171-linux-x64.tar -C /usr/java

（3）设置环境变量

vim /etc/profile

#添加如下配置
export JAVA_HOME=/usr/java/jdk1.8.0_171
export PATH=$PATH:$JAVA_HOME/bin
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar

export HADOOP_HOME="/usr/hadoop-2.7.4"
export PATH=$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$PATH

#重新加载，使配置生效
source /etc/profile

1.3 配置hosts

#腾讯云侧每个节点都需要修改
vim /etc/hosts

172.16.2.234  tx-namenode
172.16.2.4  tx-datanode1
172.16.2.8  tx-datanode2
172.16.2.7  tx-datanode3

1.4 配置SSH免秘钥登录

#注意：每个节点都需要操作
1.ssh-keygen，生成公钥和秘钥，在/root/.ssh/中
2.ssh-copy-id 其他节点IP 将公钥拷贝到其他节点

2、Hadoop安装与配置

2.1 配置HDFS集群

有3个相关的配置文件，hadoop-env.sh、core-site.xml、hdfs-site.xml，每台节点上都需要配置。

（1）配置 hadoop-env.sh

export JAVA_HOME=/usr/java/jdk1.8.0_171

（2）配置 core-site.xml

mkdir –p /var/hadoop  #创建Hadoop临时目录

vim core-site.xml

#添加如下代码
#一般hdfs的rpc通信端口用9000和8020，这里配置9000
<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://tx-namenode:9000</value>
    </property>
    <property>
        <name>hadoop.tmp.dir</name>
        <value>/var/hadoop</value>
    </property>
</configuration>

（3）配置 hdfs-site.xml

vim hdfs-site.xml

#添加如下代码
<configuration>
<property>
        <name>dfs.namenode.http-address</name>
        <value>tx-namenode:50070</value>
    </property>
    <property>
        <name>dfs.namenode.secondary.http-address</name>
        <value>tx-namenode:50090</value>
    </property>
    <property>
        <name>dfs.replication</name>
        <value>2</value> #指定HDFS副本数量为2
    </property>
    <property>
 <name>dfs.client.use.datanode.hostname</name>
 <value>true</value> #设置为true，确保客户端访问datanode的时候是通过主机域名访问，就不会出现通过内网IP来访问了
 <description>only cofig in clients</description>
</property>
</configuration>

2.2 配置YARN集群

有2个相关的配置文件，mapred-site.xml、yarn-site.xml，在每个节点上都需要做配置。

（1）mapred-site.xml 配置

默认有mapred.xml.template文件，我们要复制该文件，并命名为mapred.xml，该文件用于指定MapReduce使用的框架

cp mapred-site.xml.template mapred-site.xml

vim mapred-site.xml

#添加如下代码
<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>

（2）yarn-site.xml 配置

vim yarn-site.xml

#添加如下代码
<configuration>

<!-- Site specific YARN configuration properties -->
<property>
        <name>yarn.resourcemanager.hostname</name>
        <value>tx-namenode</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
<property>
        <name>yarn.nodemanager.resource.cpu-vcores</name>
        <value>2</value>
</property>
<property>
    <name>yarn.nodemanager.resource.memory-mb</name>
    <value>4096</value>
    <discription>每个节点可用内存,单位MB</discription>
</property>
</configuration>

2.3 集群启动与测试

（1）格式化namenode，只需要在腾讯云tx-namenode上执行一次

#格式化
./bin/hdfs namenode -format

（2）启动和停止HDFS集群，在每个节点上执行

#启动
./sbin/start-dfs.sh

#停止
./sbin/stop-dfs.sh

（3）验证HDFS集群是否组建成功

./bin/hdfs dfsadmin -report

看到4个节点，说明HDFS集群正常

HDFS的web管理控制台，端口是50070

（4）验证角色

jps

（5）启动和停止yarn集群，在每个节点上执行

#启动
./sbin/start-yarn.sh

#停止
./sbin/stop-yarn.sh

（6）验证yarn集群状态

./bin/yarn node -list

看到4个节点，说明yarn集群正常

可以访问YARN的管理界面，端口是8088，验证YARN，如下图所示：

2.4 运行一个MR任务

Hadoop安装包里提供了现成的例子，在Hadoop的share/hadoop/mapreduce目录下。运行例子：

[root@tx-namenode hadoop-2.7.4]# ./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.4.jar pi 5 10
Number of Maps  = 5
Samples per Map = 10
Wrote input for Map #0
Wrote input for Map #1
Wrote input for Map #2
Wrote input for Map #3
Wrote input for Map #4
Starting Job
19/03/02 21:17:36 INFO client.RMProxy: Connecting to ResourceManager at ali-namenode/10.10.2.12:8032
19/03/02 21:17:37 INFO input.FileInputFormat: Total input paths to process : 5
19/03/02 21:17:37 INFO mapreduce.JobSubmitter: number of splits:5
19/03/02 21:17:37 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1551530990772_0001
19/03/02 21:17:37 INFO impl.YarnClientImpl: Submitted application application_1551530990772_0001
19/03/02 21:17:37 INFO mapreduce.Job: The url to track the job: http://ali-namenode:8088/proxy/application_1551530990772_0001/
19/03/02 21:17:37 INFO mapreduce.Job: Running job: job_1551530990772_0001
19/03/02 21:17:43 INFO mapreduce.Job: Job job_1551530990772_0001 running in uber mode : false
19/03/02 21:17:43 INFO mapreduce.Job:  map 0% reduce 0%
19/03/02 21:17:48 INFO mapreduce.Job:  map 20% reduce 0%
19/03/02 21:17:49 INFO mapreduce.Job:  map 100% reduce 0%
19/03/02 21:17:53 INFO mapreduce.Job:  map 100% reduce 100%
19/03/02 21:17:53 INFO mapreduce.Job: Job job_1551530990772_0001 completed successfully
19/03/02 21:17:53 INFO mapreduce.Job: Counters: 49
	File System Counters
		FILE: Number of bytes read=116
		FILE: Number of bytes written=852369
		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=215
		HDFS: Number of read operations=23
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=3
	Job Counters
		Launched map tasks=5
		Launched reduce tasks=1
		Data-local map tasks=5
		Total time spent by all maps in occupied slots (ms)=17114
		Total time spent by all reduces in occupied slots (ms)=2057
		Total time spent by all map tasks (ms)=17114
		Total time spent by all reduce tasks (ms)=2057
		Total vcore-milliseconds taken by all map tasks=17114
		Total vcore-milliseconds taken by all reduce tasks=2057
		Total megabyte-milliseconds taken by all map tasks=17524736
		Total megabyte-milliseconds taken by all reduce tasks=2106368
	Map-Reduce Framework
		Map input records=5
		Map output records=10
		Map output bytes=90
		Map output materialized bytes=140
		Input split bytes=745
		Combine input records=0
		Combine output records=0
		Reduce input groups=2
		Reduce shuffle bytes=140
		Reduce input records=10
		Reduce output records=0
		Spilled Records=20
		Shuffled Maps =5
		Failed Shuffles=0
		Merged Map outputs=5
		GC time elapsed (ms)=426
		CPU time spent (ms)=2190
		Physical memory (bytes) snapshot=1516769280
		Virtual memory (bytes) snapshot=12634689536
		Total committed heap usage (bytes)=1082130432
	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=590
	File Output Format Counters
		Bytes Written=97
Job Finished in 17.683 seconds
Estimated value of Pi is 3.28000000000000000000

四、Hadoop集群间的数据迁移

目的：用Hadoop自带的distcp工具，将友商云HDFS的数据迁移到腾讯云

1、配置注意事项

（1）确保友商云和腾讯云侧的主机名不一样；

（2）友商云和腾讯云侧所有节点配置公网IP；

（3）hosts配置：所有节点上都配置本地集群内的内网IP与主机名映射 + 对方集群的外网IP与主机名映射；

在友商云上hosts配置如下，因为要将友商云HDFS数据拷贝到腾讯云，所以在友商云每个节点需要添加腾讯云节点外网IP：

（4）安全组放行流量，确保友商云所有节点与腾讯云所有节点互相能够连通。

2、在友商云Hadoop集群上执行distcp进行拷贝

[root@ali-namenode hadoop-2.7.4]#./bin/hadoop distcp hdfs://ali-namenode:9000/ali4 hdfs://tx-namenode:9000/

执行成功信息如下：

[root@ali-namenode hadoop-2.7.4]# ./bin/hadoop distcp hdfs://ali-namenode:9000/ali4 hdfs://tx-namenode:9000/
19/03/03 17:22:52 INFO tools.DistCp: Input Options: DistCpOptions{atomicCommit=false, syncFolder=false, deleteMissing=false, ignoreFailures=false, maxMaps=20, sslConfigurationFile='null', copyStrategy='uniformsize', sourceFileListing=null, sourcePaths=[hdfs://ali-namenode:9000/ali4], targetPath=hdfs://tx-namenode:9000/, targetPathExists=true, preserveRawXattrs=false}
19/03/03 17:22:52 INFO client.RMProxy: Connecting to ResourceManager at ali-namenode/10.1.125.118:8032
19/03/03 17:22:52 INFO Configuration.deprecation: io.sort.mb is deprecated. Instead, use mapreduce.task.io.sort.mb
19/03/03 17:22:52 INFO Configuration.deprecation: io.sort.factor is deprecated. Instead, use mapreduce.task.io.sort.factor
19/03/03 17:22:53 INFO client.RMProxy: Connecting to ResourceManager at ali-namenode/10.1.125.118:8032
19/03/03 17:22:53 INFO mapreduce.JobSubmitter: number of splits:1
19/03/03 17:22:53 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1551594039839_0008
19/03/03 17:22:54 INFO impl.YarnClientImpl: Submitted application application_1551594039839_0008
19/03/03 17:22:54 INFO mapreduce.Job: The url to track the job: http://ali-namenode:8088/proxy/application_1551594039839_0008/
19/03/03 17:22:54 INFO tools.DistCp: DistCp job-id: job_1551594039839_0008
19/03/03 17:22:54 INFO mapreduce.Job: Running job: job_1551594039839_0008
19/03/03 17:23:00 INFO mapreduce.Job: Job job_1551594039839_0008 running in uber mode : false
19/03/03 17:23:00 INFO mapreduce.Job:  map 0% reduce 0%
19/03/03 17:23:06 INFO mapreduce.Job:  map 100% reduce 0%
19/03/03 17:23:06 INFO mapreduce.Job: Job job_1551594039839_0008 completed successfully
19/03/03 17:23:06 INFO mapreduce.Job: Counters: 32
	File System Counters
		FILE: Number of bytes read=0
		FILE: Number of bytes written=144218
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=355
		HDFS: Number of bytes written=36
		HDFS: Number of read operations=12
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
	Job Counters
		Launched map tasks=1
		Other local map tasks=1
		Total time spent by all maps in occupied slots (ms)=3353
		Total time spent by all reduces in occupied slots (ms)=0
		Total time spent by all map tasks (ms)=3353
		Total vcore-milliseconds taken by all map tasks=3353
		Total megabyte-milliseconds taken by all map tasks=3433472
	Map-Reduce Framework
		Map input records=1
		Map output records=1
		Input split bytes=134
		Spilled Records=0
		Failed Shuffles=0
		Merged Map outputs=0
		GC time elapsed (ms)=59
		CPU time spent (ms)=470
		Physical memory (bytes) snapshot=170491904
		Virtual memory (bytes) snapshot=2106613760
		Total committed heap usage (bytes)=93323264
	File Input Format Counters
		Bytes Read=221
	File Output Format Counters
		Bytes Written=36
	DistCp Counters
		Bytes Skipped=5
		Files Skipped=1

在腾讯云HDFS上也可以查到这个文件，说明拷贝成功。

五、通过外网distcp失败案例分析

1、问题现象

通过外网disctp工具拷贝文件失败，从图中报错信息中可以看到remote IP是一个内网IP，因为两个Hadoop集群内网不通，连接肯定失败。

2、问题分析解决

注意：distcp工具可以理解为Hadoop的client，可以在源端执行（push），也可以在目的端（pull）执行，但是在外网拷贝的情况下，一定要在hdfs-site.xml文件中增加以下配置：

<property>
 <name>dfs.client.use.datanode.hostname</name>
 <value>true</value>
</property>

结论：distcp作为client去NN请求DN的时候，确保返回的是DN的域名（因为在云内部如果返回IP的话就是内网IP，很显然就会连接失败），因为本地有做对端DN与外网IP的hosts绑定，这时候连外网IP就能成功！！

六、总结

Hadoop集群间迁移一般采用distcp工具，这里介绍的是通过在外网如果实现数据的迁移。在企业实际的生产环境中，如果数据量比较大，可以用专线将两边内网打通，基于内网来做数据迁移。

另外如果是冷数据，可以将冷数据迁移到腾讯云COS存储，可以降低成本，具体见：https://cloud.tencent.com/document/product/436/7212

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

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原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

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