使用docker部署skywalking集群 docker搭建spark集群

docker6_搭建spark集群

• 1、安装spark
• 2、配置spark
• 3、Standalone-HA
• 4、Spark-On-Yarn

链接: 基于CentOS 8镜像的spark集群搭建连接进入node1容器

docker exec -it gpb_hdp_node1 bash
或
ssh -p 10122 root@localhost

1、安装spark

退出到实体机

上传安装包到/root目录

siriyang@siriyangs-MacBook-Pro 资料 % scp -P 10022 spark-3.0.1-bin-hadoop2.7.tgz root@localhost:/root/
root@localhost's password:
spark-3.0.1-bin-hadoop2.7.tgz                                                                                                                    100%  210MB  44.3MB/s   00:04

在实体机使用ssh远程连接到node1

ssh root@localhost -p 10022

解压安装包到/export/server/目录

tar -zxvf spark-3.0.1-bin-hadoop2.7.tgz -C /export/server/

修改权限

chown -R root /export/server/spark-2.4.0-bin-hadoop2.7
chgrp -R root /export/server/spark-2.4.0-bin-hadoop2.7

创建软连接

ln -s /export/server/spark-2.4.0-bin-hadoop2.7 /export/server/spark

启动spark交互式窗口

/export/server/spark/bin/spark-shell

查看http://172.23.27.197/:4040

测试
准备文件

vim /root/words.txt

hello me you her
hello me you
hello me
hello

执行WordCount

val textFile = sc.textFile("file:///root/words.txt")
val counts = textFile.flatMap(_.split(" ")).map((_,1)).reduceByKey(_ + _)
counts.collect

2、配置spark

配置slaves/workers

进入配置目录

cd /export/server/spark/conf

修改配置文件名称

cp slaves.template slaves
vim slaves

内容如下：

node2
node3

配置master

进入配置目录

cd /export/server/spark/conf

修改配置文件名称

cp spark-env.sh.template spark-env.sh

修改配置文件

vim spark-env.sh

增加如下内容：

## 设置JAVA安装目录
JAVA_HOME=/export/server/jdk1.8.0_231

## HADOOP软件配置文件目录，读取HDFS上文件和运行Spark在YARN集群时需要,先提前配上
HADOOP_CONF_DIR=/export/server/hadoop/etc/hadoop
YARN_CONF_DIR=/export/server/hadoop/etc/hadoop

## 指定spark老大Master的IP和提交任务的通信端口
SPARK_MASTER_HOST=node1
SPARK_MASTER_PORT=7077

SPARK_MASTER_WEBUI_PORT=8080

SPARK_WORKER_CORES=1
SPARK_WORKER_MEMORY=1g

分发

将配置好的将 Spark 安装包分发给集群中其它机器，命令如下：

cd /export/server/
scp -r spark node3:$PWD
scp -r spark node2:$PWD

测试

1.集群启动和停止

在主节点上启动spark集群

/export/server/spark/sbin/start-all.sh

在主节点上停止spark集群

/export/server/spark/sbin/stop-all.sh

/export/server/spark/bin/spark-shell --master spark://node1:7077

http://172.23.27.197/:8080/ 链接: Linux 查看端口占用情况

网上说是zookeeper的小部件占用了8080这个端口，
导致http://172.23.27.197/:8080/无法访问。
需要解决端口占用情况

[root@69432059c763 server]#  netstat -anp | grep 8080
tcp        0      0 0.0.0.0:18080           0.0.0.0:*               LISTEN      20762/java
tcp        0      0 0.0.0.0:8080            0.0.0.0:*               LISTEN      1770/java
[root@69432059c763 server]# kill -9 1770
[root@69432059c763 server]#  netstat -anp | grep 8080
tcp        0      0 0.0.0.0:18080           0.0.0.0:*               LISTEN      20762/java
[root@69432059c763 server]# kill -9 20762
[root@69432059c763 server]#  netstat -anp | grep 8080
[root@69432059c763 server]# /export/server/spark/sbin/stop-all.sh
node3: stopping org.apache.spark.deploy.worker.Worker
node2: stopping org.apache.spark.deploy.worker.Worker
stopping org.apache.spark.deploy.master.Master
[root@69432059c763 server]# /export/server/spark/sbin/start-all.sh
starting org.apache.spark.deploy.master.Master, logging to /export/server/spark/logs/spark-root-org.apache.spark.deploy.master.Master-1-69432059c763.out
node2: starting org.apache.spark.deploy.worker.Worker, logging to /export/server/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-7797b90e0ccd.out
node3: starting org.apache.spark.deploy.worker.Worker, logging to /export/server/spark/logs/spark-root-org.apache.spark.deploy.worker.Worker-1-068874a8c481.out
[root@69432059c763 server]#  netstat -anp | grep 8080
tcp        0      0 0.0.0.0:8080            0.0.0.0:*               LISTEN      24491/java

http://172.23.27.197/:8080/

3、Standalone-HA

1、配置
1.启动zk

/export/server/zookeeper/bin/zkServer.sh status
/export/server/zookeeper/bin/zkServer.sh stop
/export/server/zookeeper/bin/zkServer.sh start

2.修改配置

vim /export/server/spark/conf/spark-env.sh

注释

#SPARK_MASTER_HOST=node1

增加

SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=node1:2181,node2:2181,node3:2181 -Dspark.deploy.zookeeper.dir=/spark-ha"

3.分发配置

[root@69432059c763 conf]# scp -r spark-env.sh node2:$PWD
spark-env.sh                                       100% 4842     4.7MB/s   00:00
[root@69432059c763 conf]# scp -r spark-env.sh node3:$PWD
spark-env.sh                                       100% 4842     4.8MB/s   00:00

2、测试
0.启动zk服务

/export/server/zookeeper/bin/zkServer.sh status
/export/server/zookeeper/bin/zkServer.sh stop
/export/server/zookeeper/bin/zkServer.sh start

1.node1上启动Spark集群执行

/export/server/spark/sbin/stop-all.sh
/export/server/spark/sbin/start-all.sh

2.在node2上再单独只起个master:

/export/server/spark/sbin/start-master.sh

3.查看WebUI

http://node1:8080/
http://node2:8080/

4.模拟node1宕机

jps
kill -9 进程id

5.再次查看web-ui

4、Spark-On-Yarn

0.关闭之前的Spark-Standalone集群

/export/server/spark/sbin/stop-all.sh

1.配置Yarn历史服务器并关闭资源检查

vim /export/server/hadoop/etc/hadoop/yarn-site.xml

<configuration><!-- 配置yarn主节点的位置 -->
    <property><name>yarn.resourcemanager.hostname</name><value>node1</value></property><property><name>yarn.nodemanager.aux-services</name><value>mapreduce_shuffle</value></property><!-- 设置yarn集群的内存分配方案 -->
    <property><name>yarn.nodemanager.resource.memory-mb</name><value>20480</value></property><property><name>yarn.scheduler.minimum-allocation-mb</name><value>2048</value></property><property><name>yarn.nodemanager.vmem-pmem-ratio</name><value>2.1</value></property><!-- 开启日志聚合功能 -->
    <property><name>yarn.log-aggregation-enable</name><value>true</value></property><!-- 设置聚合日志在hdfs上的保存时间 -->
    <property><name>yarn.log-aggregation.retain-seconds</name><value>604800</value></property><!-- 设置yarn历史服务器地址 -->
    <property><name>yarn.log.server.url</name><value>http://node1:19888/jobhistory/logs</value></property><!-- 关闭yarn内存检查 -->
    <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>

注意:如果之前没有配置,现在配置了需要分发并重启yarn

cd /export/server/hadoop/etc/hadoop
scp yarn-site.xml node2:$PWD
scp yarn-site.xml node3:$PWD
/export/server/hadoop/sbin/stop-yarn.sh
/export/server/hadoop/sbin/start-yarn.sh

2.配置Spark的历史服务器和Yarn的整合

• 修改spark-defaults.conf
  进入配置目录

cd /export/server/spark/conf

修改配置文件名称

mv spark-defaults.conf.template spark-defaults.conf
vim spark-defaults.conf

添加内容：

spark.eventLog.enabled                  true
spark.eventLog.dir                      hdfs://node1:8020/sparklog/
spark.eventLog.compress                 true
spark.yarn.historyServer.address        node1:18080

• 修改spark-env.sh

修改配置文件

vim /export/server/spark/conf/spark-env.sh

增加如下内容：

## 配置spark历史日志存储地址
SPARK_HISTORY_OPTS="-Dspark.history.fs.logDirectory=hdfs://node1:8020/sparklog/ -Dspark.history.fs.cleaner.enabled=true"

手动创建

hadoop fs -mkdir -p /sparklog

• 修改日志级别

进入目录

cd /export/server/spark/conf

修改日志属性配置文件名称

mv log4j.properties.template log4j.properties

改变日志级别

vim log4j.properties

修改内容如下：

• 分发-可选,如果只在node1上提交spark任务到yarn,那么不需要分发

cd /export/server/spark/conf
scp spark-env.sh node2:$PWD
scp spark-env.sh node3:$PWD
scp spark-defaults.conf node2:$PWD
scp spark-defaults.conf node3:$PWD
scp log4j.properties node2:$PWD
scp log4j.properties node3:$PWD

3.配置依赖的Spark 的jar包

1.在HDFS上创建存储spark相关jar包的目录

hadoop fs -mkdir -p /spark/jars/

2.上传$SPARK_HOME/jars所有jar包到HDFS

hadoop fs -put /export/server/spark/jars/* /spark/jars/

3.在node1上修改spark-defaults.conf

vim /export/server/spark/conf/spark-defaults.conf

添加内容

spark.yarn.jars hdfs://node1:8020/spark/jars/*

分发同步-可选

cd /export/server/spark/conf
scp spark-defaults.conf root@node2:$PWD
scp spark-defaults.conf root@node3:$PWD

4.启动服务

• 启动HDFS和YARN服务，在node1执行命令

/export/server/hadoop/sbin/start-dfs.sh
/export/server/hadoop/sbin/start-yarn.sh

或

/export/server/hadoop/sbin/start-all.sh

• 启动MRHistoryServer服务，在node1执行命令

/export/server/hadoop/sbin/mr-jobhistory-daemon.sh start historyserver

• 启动Spark HistoryServer服务，，在node1执行命令

/export/server/spark/sbin/start-history-server.sh

• MRHistoryServer服务WEB UI页面：
  http://node1:19888/
• Spark HistoryServer服务WEB UI页面：
• http://node1:18080/