Spark 2.2.0 高可用搭建

2019年8月24日 21:41:01Linux大全评论894 views阅读模式

一、概述

1.实验环境基于以前搭建的Haoop HA；

2.spark HA所需要的Zookeeper环境前文已经配置过，此处不再重复。

3.所需软件包为：scala-2.12.3.tgz、spark-2.2.0-bin-Hadoop2.7.tar

4.主机规划

bd1

bd2

bd3

Worker

bd4

bd5

Master、Worker

二、配置Scala

1.解压并拷贝

[root@bd1 ~]
# tar -zxf scala-2.12.3.tgz
[root@bd1 ~]
# cp -r scala-2.12.3 /usr/local/

2.配置环境变量

[root@bd1 ~]
# vim /etc/profile
export
SCALA_HOME=
/usr/local/scala
export
PATH=:$SCALA_HOME
/bin
:$PATH
[root@bd1 ~]
# source /etc/profile

3.验证

[root@bd1 ~]
# scala -version
Scala code runner version 2.12.3 -- Copyright 2002-2017, LAMP
/EPFL
and Lightbend, Inc.

三、配置Spark

1.解压并拷贝

[root@bd1 ~]
# tar -zxf spark-2.2.0-bin-hadoop2.7.tgz
[root@bd1 ~]
# cp spark-2.2.0-bin-hadoop2.7 /usr/local/spark

2.配置环境变量

[root@bd1 ~]
# vim /etc/profile
export
SCALA_HOME=
/usr/local/scala
export
PATH=:$SCALA_HOME
/bin
:$PATH
[root@bd1 ~]
# source /etc/profile

3.修改spark-env.sh #文件不存在需要拷贝模板

[root@bd1 conf]
# vim spark-env.sh
export
JAVA_HOME=
/usr/local/jdk
export
HADOOP_HOME=
/usr/local/hadoop
export
HADOOP_CONF_DIR=
/usr/local/hadoop/etc/hadoop
export
SCALA_HOME=
/usr/local/scala
export
SPARK_DAEMON_JAVA_OPTS=
"-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=bd4:2181,bd5:2181 -Dspark.deploy.zookeeper.dir=/spark"
export
SPARK_WORKER_MEMORY=1g
export
SPARK_WORKER_CORES=2
export
SPARK_WORKER_INSTANCES=1

4.修改spark-defaults.conf #文件不存在需要拷贝模板

[root@bd1 conf]
# vim spark-defaults.conf
spark.master spark:
//master
:7077
spark.eventLog.enabled
true
spark.eventLog.
dir
hdfs:
//master
:
/user/spark/history
spark.serializer org.apache.spark.serializer.KryoSerializer

5.在HDFS文件系统中新建日志文件目录

hdfs dfs -
mkdir
-p
/user/spark/history
hdfs dfs -
chmod
777
/user/spark/history

6.修改slaves

[root@bd1 conf]
# vim slaves
bd1
bd2
bd3
bd4
bd5

四、同步到其他主机

1.使用scp同步Scala到bd2-bd5

scp
-r
/usr/local/scala
root@bd2:
/usr/local/
scp
-r
/usr/local/scala
root@bd3:
/usr/local/
scp
-r
/usr/local/scala
root@bd4:
/usr/local/
scp
-r
/usr/local/scala
root@bd5:
/usr/local/

2.同步Spark到bd2-bd5

scp
-r
/usr/local/spark
root@bd2:
/usr/local/
scp
-r
/usr/local/spark
root@bd3:
/usr/local/
scp
-r
/usr/local/spark
root@bd4:
/usr/local/
scp
-r
/usr/local/spark
root@bd5:
/usr/local/

五、启动集群并测试HA

1.启动顺序为：zookeeper-->hadoop-->spark

2.启动spark

bd4：

[root@bd4 sbin]
# cd /usr/local/spark/sbin/
[root@bd4 sbin]
# ./start-all.sh
starting org.apache.spark.deploy.master.Master, logging to
/usr/local/spark/logs/spark-root-org
.apache.spark.deploy.master.Master-1-bd4.out
bd4: starting org.apache.spark.deploy.worker.Worker, logging to
/usr/local/spark/logs/spark-root-org
.apache.spark.deploy.worker.Worker-1-bd4.out
bd2: starting org.apache.spark.deploy.worker.Worker, logging to
/usr/local/spark/logs/spark-root-org
.apache.spark.deploy.worker.Worker-1-bd2.out
bd3: starting org.apache.spark.deploy.worker.Worker, logging to
/usr/local/spark/logs/spark-root-org
.apache.spark.deploy.worker.Worker-1-bd3.out
bd5: starting org.apache.spark.deploy.worker.Worker, logging to
/usr/local/spark/logs/spark-root-org
.apache.spark.deploy.worker.Worker-1-bd5.out
bd1: starting org.apache.spark.deploy.worker.Worker, logging to
/usr/local/spark/logs/spark-root-org
.apache.spark.deploy.worker.Worker-1-bd1.out

[root@bd4 sbin]
# jps
3153 DataNode
7235 Jps
3046 JournalNode
7017 Master
3290 NodeManager
7116 Worker
2958 QuorumPeerMain

bd5：

[root@bd5 sbin]
# ./start-master.sh
starting org.apache.spark.deploy.master.Master, logging to
/usr/local/spark/logs/spark-root-org
.apache.spark.deploy.master.Master-1-bd5.out

[root@bd5 sbin]
# jps
3584 NodeManager
5602 RunJar
3251 QuorumPeerMain
8564 Master
3447 DataNode
8649 Jps
8474 Worker
3340 JournalNode

3.停掉bd4的Master进程

[root@bd4 sbin]
# kill -9 7017
[root@bd4 sbin]
# jps
3153 DataNode
7282 Jps
3046 JournalNode
3290 NodeManager
7116 Worker
2958 QuorumPeerMain