Spark进阶之路-Spark HA配置

　　　　　　　　　　　　　　Spark进阶之路-Spark HA配置

　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　作者：尹正杰

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　　集群部署完了，但是有一个很大的问题，那就是Master节点存在单点故障，要解决此问题，就要借助zookeeper，并且启动至少两个Master节点来实现高可靠，配置方式比较简单。本篇博客的搭建环境是基于Standalone模式进行的（https://www.cnblogs.com/yinzhengjie/p/9458161.html）

1>.编辑spark-env.sh文件，去掉之前的master主机，并指定zookeeper集群的主机

[yinzhengjie@s101 ~]$ grep -v ^# /soft/spark/conf/spark-env.sh | grep -v ^$
export JAVA_HOME=/soft/jdk
SPARK_MASTER_PORT=
export SPARK_HISTORY_OPTS="-Dspark.history.ui.port=4000 -Dspark.history.retainedApplications=3 -Dspark.history.fs.logDirectory=hdfs://s105:8020/yinzhengjie/logs"
export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=s102:2181,s103:2181,s103:2181 -Dspark.deploy.zookeeper.dir=/spark"　　　　　　#指定zookeeper的集群地址以及spark在spark存放的路径。
[yinzhengjie@s101 ~]$ 

2>.分发配置

[yinzhengjie@s101 ~]$ more `which xrsync.sh`
#!/bin/bash
#@author :yinzhengjie
#blog:http://www.cnblogs.com/yinzhengjie
#EMAIL:[e-mail]

#判断用户是否传参
if [ $# -lt  ];then
        echo "请输入参数";
        exit
fi

#获取文件路径
file=$@

#获取子路径
filename=`basename $file`

#获取父路径
dirpath=`dirname $file`

#获取完整路径
cd $dirpath
fullpath=`pwd -P`

#同步文件到DataNode
for (( i=;i<=;i++ ))
do
        #使终端变绿色
        tput setaf
        echo =========== s$i %file ===========
        #使终端变回原来的颜色，即白灰色
        tput setaf
        #远程执行命令
        rsync -lr $filename `whoami`@s$i:$fullpath
        #判断命令是否执行成功
        if [ $? ==  ];then
                echo "命令执行成功"
        fi
done
[yinzhengjie@s101 ~]$ 

同步文件的脚本，需要配置无秘钥登录才能使用哟（[yinzhengjie@s101 ~]$ more `which xrsync.sh`）

[yinzhengjie@s101 ~]$ xrsync.sh /soft/spark
=========== s102 %file ===========
命令执行成功
=========== s103 %file ===========
命令执行成功
=========== s104 %file ===========
命令执行成功
=========== s105 %file ===========
命令执行成功
[yinzhengjie@s101 ~]$ xrsync.sh /soft/spark-2.1.-bin-hadoop2./
=========== s102 %file ===========
命令执行成功
=========== s103 %file ===========
命令执行成功
=========== s104 %file ===========
命令执行成功
=========== s105 %file ===========
命令执行成功
[yinzhengjie@s101 ~]$ 

3>.s101启动master集群

[yinzhengjie@s101 ~]$ /soft/spark/sbin/start-all.sh
starting org.apache.spark.deploy.master.Master, logging to /soft/spark/logs/spark-yinzhengjie-org.apache.spark.deploy.master.Master--s101.out
s103: starting org.apache.spark.deploy.worker.Worker, logging to /soft/spark/logs/spark-yinzhengjie-org.apache.spark.deploy.worker.Worker--s103.out
s104: starting org.apache.spark.deploy.worker.Worker, logging to /soft/spark/logs/spark-yinzhengjie-org.apache.spark.deploy.worker.Worker--s104.out
s102: starting org.apache.spark.deploy.worker.Worker, logging to /soft/spark/logs/spark-yinzhengjie-org.apache.spark.deploy.worker.Worker--s102.out
[yinzhengjie@s101 ~]$
[yinzhengjie@s101 ~]$ xcall.sh jps
============= s101 jps ============
 DFSZKFailoverController
 Jps
 Master
 NameNode
 HistoryServer
命令执行成功
============= s102 jps ============
 QuorumPeerMain
 DataNode
 Jps
 JournalNode
 Worker
命令执行成功
============= s103 jps ============
 JournalNode
 Worker
 QuorumPeerMain
 Jps
 DataNode
命令执行成功
============= s104 jps ============
 Worker
 QuorumPeerMain
 Jps
 DataNode
 JournalNode
命令执行成功
============= s105 jps ============
 DFSZKFailoverController
 NameNode
 Jps
命令执行成功
[yinzhengjie@s101 ~]$  

4>.s105手动启动另外一个master

[yinzhengjie@s105 ~]$ /soft/spark/sbin/start-master.sh
starting org.apache.spark.deploy.master.Master, logging to /soft/spark/logs/spark-yinzhengjie-org.apache.spark.deploy.master.Master--s105.out
[yinzhengjie@s105 ~]$ jps
 Master
 Jps
 DFSZKFailoverController
 NameNode
[yinzhengjie@s105 ~]$ 

5>.连接spark集群

6>.查看master节点的webUI信息

　　s105的master信息如下:（此时s105啥也没有，worker没有正确到，正在运行的任务也没有争取到）

 　　s101的master信息如下:(你会发现目前的正在工作的master是s101)

7>.手动杀死s101的master进程

8>.查看spark-shell命令行是否可以正常工作

9>.检查集群中是否还有正常的master存活(很显然，此时一定是s105接管了任务)

10>.再次查看s105的webUI界面

　　由于s101的master进程已经被我们手动杀死了，因此我们无法通过webUI的形式访问它了：