您的位置：首页 > 运维架构

安装Spark+hadoop，spark、hadoop分布式集群搭建...(亲自搭建过！！)

2017-11-20 17:58 645 查看

首先说一下我所用的版本：

spark-2.1.1-bin-hadoop2.7.tgz
hadoop-2.7.3.tar.gz
jdk-8u131-linux-x64.rpm

我们实验室有4台服务器：每个节点硬盘：

300GB

，内存：

64GB

。四个节点的

hostname

分别是

master

，

slave01

，

slave02

，

slave03

。

我用的是Spark做并行计算，用HDFS作为数据的分布式存储，这样的话就得安装hadoop利用里面的HDFS。如果你不用hadoop的话可以直接跳到第7步，直接安装spark即可！

1。先装
java1.8
环境：给各个节点上传
jdk-8u131-linux-x64.rpm
到
/home
里面。用rpm安装。

[root@localhost home]# rpm -ivh jdk-8u131-linux-x64.rpm
Preparing...                          ################################# [100%]
Updating / installing...
1:jdk1.8.0_131-2000:1.8.0_131-fcs  ################################# [100%]
Unpacking JAR files...
tools.jar...
plugin.jar...
javaws.jar...
deploy.jar...
rt.jar...
jsse.jar...
charsets.jar...
localedata.jar...
[root@localhost home]# java -version
java version "1.8.0_131"
Java(TM) SE Runtime Environment (build 1.8.0_131-b11)
Java HotSpot(TM) 64-Bit Server VM (build 25.131-b11, mixed mode)

如上：java1.8 安装成功！！

2。集群核准时间：(如果集群时间一致的话，此步略过！)

时间必须同步，因为节点之间要发送心跳，如果时间不一致的话，会产生错误。

用date -s 命令也行！(下面是ntp服务器来同步时间)

##在每个节点上执行安装ntp服务
[hadoop@master ~]$ sudo yum install -y ntp
##在每个节点上同时执行`sudo ntpdate us.pool.ntp.org`
[hadoop@master ~]$ sudo ntpdate us.pool.ntp.org
5 Oct 18:19:41 ntpdate[2997]: step time server 138.68.46.177 offset -6.006070 sec

或者也可以在某个节点上启动一个ntp服务器：

##在每个节点上执行安装ntp服务
[hadoop@master ~]$ sudo yum install -y ntp
##在192.168.2.219节点上执行`sudo ntpdate us.pool.ntp.org`把这个节点作为ntp同步服务器
[hadoop@master ~]$ sudo ntpdate us.pool.ntp.org
5 Oct 18:19:41 ntpdate[2997]: step time server 138.68.46.177 offset -6.006070 sec
##在各个节点上开启ntp服务
[hadoop@master ~]$ sudo service ntpd start
Redirecting to /bin/systemctl start  ntpd.service
##在其他节点上同步192.168.2.219节点ntp服务器上的时间。
[hadoop@slave01 ~]$ sudo ntpdate 192.168.2.219
5 Oct 18:27:45 ntpdate[3014]: adjust time server 192.168.147.6 offset -0.001338 sec

3。添加用户hadoop：

[root@localhost etc]# useradd -m hadoop -s /bin/bash
useradd: user 'hadoop' already exists
[root@localhost etc]# passwd hadoop
Changing password for user hadoop.
New password:
BAD PASSWORD: The password fails the dictionary check - it is too simplistic/systematic
Retype new password:
passwd: all authentication tokens updated successfully.
[root@localhost etc]# su - hadoop
[hadoop@localhost ~]$

4。给hadoop用户增加管理员权限，方便部署

[root@localhost ~]#visudo

找到 root ALL=(ALL) ALL 这行（应该在第98行，可以先按一下键盘上的 ESC 键，然后输入 :98 (按一下冒号，接着输入98，再按回车键)，可以直接跳到第98行），然后在这行下面增加一行内容：hadoop ALL=(ALL) ALL （当中的间隔为tab），如下图所示：

5。SSH无密通信：

[root@master .ssh]#su - hadoop
[hadoop@master ~]$
[hadoop@master ~]$ ssh localhost    # 如果没有该目录，先执行一次ssh localhost
[hadoop@master ~]$ cd ~/.ssh
[hadoop@master ~]$ rm ./id_rsa*    # 删除之前生成的公匙（如果有）
[hadoop@master ~]$ ssh-keygen -t rsa  # 一直按回车就可以
让 Master 节点需能无密码 SSH 本机，在 Master 节点上执行：
[hadoop@master .ssh]$ cat ./id_rsa.pub >> ./authorized_keys
完成后可执行 ssh Master 验证一下（可能需要输入 yes，成功后执行 exit 返回原来的终端）。接着在 master 节点将上公匙传输到 slave01,slave02,slave03 节点：
[hadoop@master .ssh]$ scp ~/.ssh/id_rsa.pub hadoop@slave01:/home/hadoop/
[hadoop@master .ssh]$ scp ~/.ssh/id_rsa.pub hadoop@slave02:/home/hadoop/
[hadoop@master .ssh]$ scp ~/.ssh/id_rsa.pub hadoop@slave03:/home/hadoop/
接着在 slave01,slave02,slave03节点上，将 ssh 公匙加入授权：【分别在其他三个节点上执行以下命令：】
[hadoop@slave03 ~]$ mkdir ~/.ssh
[hadoop@slave03 ~]$ cat ~/id_rsa.pub >> ~/.ssh/authorized_keys
[hadoop@slave03 ~]$ rm ~/id_rsa.pub
然后在master上执行ssh slave01  但是还是不行解决方法：

常见免密码登录失败分析

配置问题

检查配置文件/etc/ssh/sshd_config是否开启了AuthorizedKeysFile选项

检查AuthorizedKeysFile选项指定的文件是否存在并内容正常

目录权限问题

~权限设置为700

~/.ssh权限设置为700

~/.ssh/authorized_keys的权限设置为600

sudo chmod 700 ~
sudo chmod 700 ~/.ssh
sudo chmod 600 ~/.ssh/authorized_keys

6。安装hadoop：

下面是hosts文件内容：

192.168.2.189 slave01

192.168.2.240 slave02

192.168.2.176 slave03

192.168.2.219 master

hadoop-2.7.3.tar.gz放在~下。【一般安装文件都放在~下面】

我们选择将 Hadoop 安装至 /usr/local/ 中：

[hadoop@master ~]$  sudo tar -zxf ~/hadoop-2.7.3.tar.gz -C /usr/local   # 解压到/usr/local中
[hadoop@master ~]$ cd /usr/local/
[hadoop@master ~]$ sudo mv ./hadoop-2.7.3/ ./hadoop         # 将文件夹名改为hadoop
[hadoop@master ~]$ sudo chown -R hadoop:hadoop ./hadoop           # 修改文件权限

Hadoop 解压后即可使用。输入如下命令来检查 Hadoop 是否可用，成功则会显示 Hadoop 版本信息：

[hadoop@master local]$ cd /usr/local/hadoop
[hadoop@master hadoop]$ ./bin/hadoop version
Hadoop 2.7.3
Subversion https://git-wip-us.apache.org/repos/asf/hadoop.git -r baa91f7c6bc9cb92be5982de4719c1c8af91ccff
Compiled by root on 2016-08-18T01:41Z
Compiled with protoc 2.5.0
From source with checksum 2e4ce5f957ea4db193bce3734ff29ff4
This command was run using /usr/local/hadoop/share/hadoop/common/hadoop-common-2.7.3.jar

6.1。 Hadoop单机配置(非分布式) ，注：先把一个节点的hadoop装好后，然后再依次拷贝到其他的节点上。

Hadoop 默认模式为非分布式模式，无需进行其他配置即可运行。非分布式即单 Java 进程，方便进行调试。

现在我们可以执行例子来感受下 Hadoop 的运行。Hadoop 附带了丰富的例子（运行

./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar

可以看到所有例子），包括

wordcount、terasort、join、grep

等。

在此我们选择运行 grep 例子，我们将 input 文件夹中的所有文件作为输入，筛选当中符合正则表达式

dfs[a-z.]+

的单词并统计出现的次数，最后输出结果到 output 文件夹中。

[hadoop@master hadoop]$ cd /usr/local/hadoop
[hadoop@master hadoop]$ mkdir ./input
[hadoop@master hadoop]$ cp ./etc/hadoop/*.xml ./input # 将配置文件作为输入文件
[hadoop@master hadoop]$./bin/hadoop jar ./share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep ./input ./output 'dfs[a-z.]+'
[hadoop@master hadoop]$ cat ./output/*       # 查看运行结果
1       dfsadmin

6.2。修改/usr/local/hadoop/etc/hadoop/slaves:这里配置的是三个运行节点，master节点只做master不作为运行节点。

[hadoop@master hadoop]$ vi slaves      #里面内容是：
slave01
slave02
slave03

6.3。文件 core-site.xml 改为下面的配置：

<configuration>
<property>
<name>hadoop.tmp.dir</name>
<value>/usr/local/hadoop/tmp</value>
<description>Abase for other temporary directories.</description>
</property>
<property>
<name>fs.defaultFS</name>
<value>hdfs://master:9000</value>
</property>
</configuration>

6.4。文件 hdfs-site.xml，dfs.replication 一般设为 3：

<configuration>
<property>
<name>dfs.replication</name>
<value>3</value>
</property>
</configuration>

6.5。文件 mapred-site.xml （可能需要先重命名，默认文件名为 mapred-site.xml.template），然后配置修改如下：

<configuration>
<property>
<name>mapreduce.framework.name</name>
<value>yarn</value>
</property>
</configuration>

6.6。文件 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>master</value>
</property>
<property>
<name>yarn.nodemanager.pmem-check-enabled</name>
<value>false</value>
</property>
<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
</configuration>

6.7。配置好后，将 master 上的 /usr/local/Hadoop 文件夹复制到各个节点上。在 master 节点上执行：

之前有跑过伪分布式模式，建议在切换到集群模式前先删除之前的临时文件。
[hadoop@master ~]$cd /usr/local
[hadoop@master ~]$sudo rm -r ./hadoop/tmp     # 删除 Hadoop 临时文件
[hadoop@master ~]$sudo rm -r ./hadoop/logs/*   # 删除日志文件
[hadoop@master ~]$tar -zcf ~/hadoop.master.tar.gz ./hadoop # 先压缩再复制
[hadoop@master ~]$cd ~
[hadoop@master ~]$scp ./hadoop.master.tar.gz slave01:/home/hadoop
[hadoop@master ~]$scp ./hadoop.master.tar.gz slave02:/home/hadoop
[hadoop@master ~]$scp ./hadoop.master.tar.gz slave03:/home/hadoop

6.8。在各个节点执行：

在 slave01 节点上执行：

[hadoop@slave01 ~]$ sudo rm -r /usr/local/hadoop       # 删掉旧的（如果存在）
[hadoop@slave01 ~]$ sudo tar -zxf ~/hadoop.master.tar.gz -C /usr/local
[hadoop@slave01 ~]$ sudo chown -R hadoop /usr/local/hadoop

在 slave02 节点上执行：

[hadoop@slave02 ~]$ sudo rm -r /usr/local/hadoop       # 删掉旧的（如果存在）
[hadoop@slave02 ~]$ sudo tar -zxf ~/hadoop.master.tar.gz -C /usr/local
[hadoop@slave02 ~]$ sudo chown -R hadoop /usr/local/hadoop

在 slave03 节点上执行：

[hadoop@slave03 ~]$ sudo rm -r /usr/local/hadoop       # 删掉旧的（如果存在）
[hadoop@slave03 ~]$ sudo tar -zxf ~/hadoop.master.tar.gz -C /usr/local
[hadoop@slave03 ~]$ sudo chown -R hadoop /usr/local/hadoop

6.9。首次启动需要先在 Master 节点执行 NameNode 的格式化：

[hadoop@master ~]$ hdfs namenode -format     #首次运行需要执行初始化，之后不需要

6.10。CentOS系统需要关闭防火墙

CentOS系统默认开启了防火墙，在开启 Hadoop 集群之前，需要关闭集群中每个节点的防火墙。有防火墙会导致 ping 得通但 telnet 端口不通，从而导致 DataNode 启动了，但 Live datanodes 为 0 的情况。

在 CentOS 中，可以通过如下命令关闭防火墙：

在 CentOS 6.x 中，可以通过如下命令关闭防火墙：
sudo service iptables stop   # 关闭防火墙服务
sudo chkconfig iptables off  # 禁止防火墙开机自启，就不用手动关闭了
Shell 命令
若用是 CentOS 7，需通过如下命令关闭（防火墙服务改成了 firewall）：
systemctl stop firewalld.service    # 关闭firewall
systemctl disable firewalld.service # 禁止firewall开机启动

6.11。接着可以启动 hadoop 了，启动需要在 master 节点上进行：

注意修改：

vi /usr/local/hadoop/etc/hadoop/hadoop-env.sh

，

把

export JAVA_HOME=${JAVA_HOME}改为：export JAVA_HOME=/usr/java/jdk1.8.0_131/

在/usr/local/hadoop/sbin   启动hadoop      ./start-all.sh
[hadoop@master sbin]$ ./start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [master]
master: starting namenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-namenode-master.out
slave03: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-slave03.out
slave01: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-slave01.out
slave02: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-slave02.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-secondarynamenode-master.out
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-resourcemanager-master.out
slave01: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-slave01.out
slave02: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-slave02.out
slave03: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-slave03.out

然后jps

[hadoop@master hadoop]$ jps
6194 ResourceManager
5717 NameNode
5960 SecondaryNameNode
6573 Jps
[hadoop@slave01 hadoop]$ jps
4888 Jps
4508 DataNode
4637 NodeManager
[hadoop@slave02 hadoop]$ jps
3841 DataNode
3970 NodeManager
4220 Jps
[hadoop@slave03 hadoop]$ jps
4032 NodeManager
4282 Jps
3903 DataNode

6.12. 打开hadoop WEBUI

在浏览器中输入http://192.168.2.219:50070 【注意浏览器要与192.168.2.219为局域网】

6.13。执行分布式实例

首先创建 HDFS 上的用户目录：
[hadoop@master hadoop]$ hdfs dfs -mkdir -p /user/hadoop
将 /usr/local/hadoop/etc/hadoop 中的配置文件作为输入文件复制到分布式文件系统中：
[hadoop@master hadoop]$ hdfs dfs -mkdir input
[hadoop@master hadoop]$ hdfs dfs -put /usr/local/hadoop/etc/hadoop/*.xml input

通过查看 DataNode 的状态（占用大小有改变），输入文件确实复制到了 DataNode 中，如下图所示：

接着就可以运行 MapReduce 作业了：【注意运行前要保证节点时间一致】

####命令：
hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep input output 'dfs[a-z.]+'
----------
####执行过程运行的log如下：
[hadoop@master hadoop]$ hadoop jar /usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-*.jar grep input output 'dfs[a-z.]+'
17/11/13 22:26:21 INFO client.RMProxy: Connecting to ResourceManager at master/192.168.2.219:8032
17/11/13 22:26:21 INFO input.FileInputFormat: Total input paths to process : 9
17/11/13 22:26:21 INFO mapreduce.JobSubmitter: number of splits:9
17/11/13 22:26:21 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1510581226826_0004
17/11/13 22:26:22 INFO impl.YarnClientImpl: Submitted application application_1510581226826_0004
17/11/13 22:26:22 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1510581226826_0004/ 17/11/13 22:26:22 INFO mapreduce.Job: Running job: job_1510581226826_0004
17/11/13 22:26:28 INFO mapreduce.Job: Job job_1510581226826_0004 running in uber mode : false
17/11/13 22:26:28 INFO mapreduce.Job:  map 0% reduce 0%
17/11/13 22:26:32 INFO mapreduce.Job:  map 33% reduce 0%
17/11/13 22:26:33 INFO mapreduce.Job:  map 100% reduce 0%
17/11/13 22:26:37 INFO mapreduce.Job:  map 100% reduce 100%
17/11/13 22:26:37 INFO mapreduce.Job: Job job_1510581226826_0004 completed successfully
17/11/13 22:26:37 INFO mapreduce.Job: Counters: 50
File System Counters
FILE: Number of bytes read=51
FILE: Number of bytes written=1190205
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=28817
HDFS: Number of bytes written=143
HDFS: Number of read operations=30
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Killed map tasks=1
Launched map tasks=9
Launched reduce tasks=1
Data-local map tasks=9
Total time spent by all maps in occupied slots (ms)=26894
Total time spent by all reduces in occupied slots (ms)=2536
Total time spent by all map tasks (ms)=26894
Total time spent by all reduce tasks (ms)=2536
Total vcore-milliseconds taken by all map tasks=26894
Total vcore-milliseconds taken by all reduce tasks=2536
Total megabyte-milliseconds taken by all map tasks=27539456
Total megabyte-milliseconds taken by all reduce tasks=2596864
Map-Reduce Framework
Map input records=796
Map output records=2
Map output bytes=41
Map output materialized bytes=99
Input split bytes=1050
Combine input records=2
Combine output records=2
Reduce input groups=2
Reduce shuffle bytes=99
Reduce input records=2
Reduce output records=2
Spilled Records=4
Shuffled Maps =9
Failed Shuffles=0
Merged Map outputs=9
GC time elapsed (ms)=762
CPU time spent (ms)=7040
Physical memory (bytes) snapshot=2680807424
Virtual memory (bytes) snapshot=19690971136
Total committed heap usage (bytes)=1957691392
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=27767
File Output Format Counters
Bytes Written=143
17/11/13 22:26:37 INFO client.RMProxy: Connecting to ResourceManager at master/192.168.2.219:8032
17/11/13 22:26:37 INFO input.FileInputFormat: Total input paths to process : 1
17/11/13 22:26:37 INFO mapreduce.JobSubmitter: number of splits:1
17/11/13 22:26:37 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1510581226826_0005
17/11/13 22:26:37 INFO impl.YarnClientImpl: Submitted application application_1510581226826_0005
17/11/13 22:26:37 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1510581226826_0005/ 17/11/13 22:26:37 INFO mapreduce.Job: Running job: job_1510581226826_0005
17/11/13 22:26:48 INFO mapreduce.Job: Job job_1510581226826_0005 running in uber mode : false
17/11/13 22:26:48 INFO mapreduce.Job:  map 0% reduce 0%
17/11/13 22:26:52 INFO mapreduce.Job:  map 100% reduce 0%
17/11/13 22:26:57 INFO mapreduce.Job:  map 100% reduce 100%
17/11/13 22:26:58 INFO mapreduce.Job: Job job_1510581226826_0005 completed successfully
17/11/13 22:26:58 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=51
FILE: Number of bytes written=237047
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=271
HDFS: Number of bytes written=29
HDFS: Number of read operations=7
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)=2331
Total time spent by all reduces in occupied slots (ms)=2600
Total time spent by all map tasks (ms)=2331
Total time spent by all reduce tasks (ms)=2600
Total vcore-milliseconds taken by all map tasks=2331
Total vcore-milliseconds taken by all reduce tasks=2600
Total megabyte-milliseconds taken by all map tasks=2386944
Total megabyte-milliseconds taken by all reduce tasks=2662400
Map-Reduce Framework
Map input records=2
Map output records=2
Map output bytes=41
Map output materialized bytes=51
Input split bytes=128
Combine input records=0
Combine output records=0
Reduce input groups=1
Reduce shuffle bytes=51
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)=110
CPU time spent (ms)=1740
Physical memory (bytes) snapshot=454008832
Virtual memory (bytes) snapshot=3945603072
Total committed heap usage (bytes)=347078656
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=143
File Output Format Counters
Bytes Written=29
----------
查看运行结果：
[hadoop@master hadoop]$ hdfs dfs -cat output/*
1       dfsadmin
1       dfs.replication

运行时的输出信息与伪分布式类似，会显示 Job 的进度。

可能会有点慢，但如果迟迟没有进度，比如 5 分钟都没看到进度，那不妨重启 Hadoop 再试试。

6.14。关闭 Hadoop 集群也是在 Master 节点上执行的：./sbin/stop-all.sh 即可

[hadoop@master sbin]$ stop-all.sh
This script is Deprecated. Instead use stop-dfs.sh and stop-yarn.sh
Stopping namenodes on [master]
master: stopping namenode
slave01: stopping datanode
slave03: stopping datanode
slave02: stopping datanode
Stopping secondary namenodes [0.0.0.0]
0.0.0.0: stopping secondarynamenode
stopping yarn daemons
stopping resourcemanager
slave01: stopping nodemanager
slave02: stopping nodemanager
slave03: stopping nodemanager
slave01: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
slave02: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
slave03: nodemanager did not stop gracefully after 5 seconds: killing with kill -9
no proxyserver to stop

附录：增加一个master节点作为slaves ，这样运行节点就变成4个了，进入

/usr/local/hadoop/etc/hadoop/

修改slaves为：

master

slave01

slave02

slave03

$PWD就是当前目录，把此目录的slaves拷贝到其他三个节点上进行覆盖。

[hadoop@master hadoop]$ scp slaves hadoop@slave01:$PWD
[hadoop@master hadoop]$ scp slaves hadoop@slave02:$PWD
[hadoop@master hadoop]$ scp slaves hadoop@slave03:$PWD

再启动：

[hadoop@master hadoop]$ ./sbin/start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh
Starting namenodes on [master]
master: starting namenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-namenode-master.out
master: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-master.out
slave02: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-slave02.out
slave01: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-slave01.out
slave03: starting datanode, logging to /usr/local/hadoop/logs/hadoop-hadoop-datanode-slave03.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-hadoop-secondarynamenode-master.out
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-resourcemanager-master.out
slave02: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-slave02.out
slave01: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-slave01.out
master: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-master.out
slave03: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-hadoop-nodemanager-slave03.out

至此hadoop安装完成！！！！

7。安装spark

先把spark-2.1.1-bin-hadoop2.7.tgz传到~里。

执行命令：

[hadoop@master ~]$ sudo tar -zxf ~/spark-2.1.1-bin-hadoop2.7.tgz -C /usr/local/
[hadoop@master ~]$ cd /usr/local
[hadoop@master ~]$ sudo cp ./spark-2.1.1-bin-hadoop2.7.tgz/ ./spark
[hadoop@master ~]$ sudo chown -R hadoop:hadoop ./spark

7.1。在/usr/local/spark/conf里，修改spark-env.sh添加：

export JAVA_HOME=/usr/java/jdk1.8.0_131
export SPARK_MASTER_IP=192.168.2.219
export SPARK_MASTER_PORT=7077

7.2。在/usr/local/spark/conf里，添加内容到slaves，这里有4个运行节点把master也算进去了，master既做管理又做计算

[hadoop@master conf]$ cat slaves
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0 #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# A Spark Worker will be started on each of the machines listed below.
master
slave01
slave02
slave03

7.3。配置好后，将Master主机上的/usr/local/spark文件夹复制到各个节点上。在Master主机上执行如下命令：

[hadoop@master local]$ cd /usr/local/
压缩spark包
[hadoop@master local]$ tar -zcf ~/spark.master.tar.gz ./spark
[hadoop@master local]$ cd ~
把spark压缩包远程传到其他节点
[hadoop@master local]$ scp ./spark.master.tar.gz slave01:/home/hadoop
[hadoop@master local]$ scp ./spark.master.tar.gz slave02:/home/hadoop
[hadoop@master local]$ scp ./spark.master.tar.gz slave03:/home/hadoop

在slave01,slave02，slave03节点上分别执行下面同样的操作：

[hadoop@slave01 spark]$ sudo tar -zxf ~/spark.master.tar.gz -C /usr/local
[hadoop@slave01 spark]$ sudo chown -R hadoop:hadoop /usr/local/spark

7.4。启动Spark集群

启动Hadoop集群

启动Spark集群前，要先启动Hadoop集群。在Master节点主机上运行如下命令：

[hadoop@master ~]$ cd /usr/local/hadoop/
[hadoop@master ~]$ ./sbin/start-all.sh

启动Spark集群

[hadoop@master spark]$ ./sbin/start-all.sh
starting org.apache.spark.deploy.master.Master, logging to /usr/local/spark/logs/spark-hadoop-org.apache.spark.deploy.master.Master-1-master.out
slave02: starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-slave02.out
master: starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-master.out
slave03: starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-slave03.out
slave01: starting org.apache.spark.deploy.worker.Worker, logging to /usr/local/spark/logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-slave01.out
[hadoop@master spark]$ jps
4641 Master
4068 ResourceManager
3447 NameNode
4807 Worker
3608 DataNode
3832 SecondaryNameNode
4938 Jps
4207 NodeManager

在浏览器上查看Spark独立集群管理器的集群信息，在192.168.2.219主机上打开浏览器，访问http://192.168.2.219:8080,如下图：

安装成功！！！

7.5。关闭Spark集群

关闭spark：

./stop-all.sh

关闭hadoop：

./sbin/stop-all.sh

[hadoop@master spark]$ ./sbin/stop-all.sh
master: stopping org.apache.spark.deploy.worker.Worker
slave03: stopping org.apache.spark.deploy.worker.Worker
slave02: stopping org.apache.spark.deploy.worker.Worker
slave01: stopping org.apache.spark.deploy.worker.Worker
stopping org.apache.spark.deploy.master.Master
[hadoop@master spark]$ cd ..
[hadoop@master local]$ cd hadoop
[hadoop@master spark]$ ./sbin/stop-all.sh
master: stopping org.apache.spark.deploy.worker.Worker
slave03: stopping org.apache.spark.deploy.worker.Worker
slave02: stopping org.apache.spark.deploy.worker.Worker
slave01: stopping org.apache.spark.deploy.worker.Worker
stopping org.apache.spark.deploy.master.Master
[hadoop@master hadoop]$ ./sbin/stop-all.sh
This script is Deprecated. Instead use stop-dfs.sh and stop-yarn.sh
Stopping namenodes on [master]
master: stopping namenode
master: stopping datanode
slave02: stopping datanode
slave03: stopping datanode
slave01: stopping datanode
Stopping secondary namenodes [0.0.0.0]
0.0.0.0: stopping secondarynamenode
stopping yarn daemons
stopping resourcemanager
slave03: stopping nodemanager
slave02: stopping nodemanager
slave01: stopping nodemanager
master: stopping nodemanager
no proxyserver to stop

集群已经停止！！

完成！有什么意见和问题请站内评价和提问。谢谢！

内容来自用户分享和网络整理，不保证内容的准确性，如有侵权内容，可联系管理员处理

标签： hadoop spark 服务器大数据环境分布式

相关文章推荐

新的分享

章节导航