Spark学习笔记 （二）Spark2.3 HA集群的分布式安装图文详解

Spark学习笔记 （二）Spark2.3 HA集群的分布式安装图文详解

本文实例讲述了Spark2.3 HA集群的分布式安装。分享给大家供大家参考，具体如下：

一、-Spark安装包

1、从官网-

http://spark.apache.org/downloads.html

2、从微软的镜像站-

http://mirrors.hust.edu-/apache/

3、从清华的镜像站-

https://mirrors.tuna.tsinghua.edu-/apache/

二、安装基础

1、java8安装成功

2、zookeeper安装成功

3、hadoop2.7.5 HA安装成功

4、Scala安装成功（不安装进程也可以启动）

三、Spark安装过程

1、上传并解压缩

[hadoop@hadoop1 ~]$ ls

apps data exam inithive.conf movie spark-2.3.0-bin-hadoop2.7.tgz udf.jar

cookies data.txt executions json.txt projects student zookeeper.out

course emp hive.sql log sougou temp

[hadoop@hadoop1 ~]$ tar -zxvf spark-2.3.0-bin-hadoop2.7.tgz -C apps/

2、为安装包创建一个软连接

[hadoop@hadoop1 ~]$ cd apps/

[hadoop@hadoop1 apps]$ ls

hadoop-2.7.5 hbase-1.2.6 sparwIDqKk-2.3.0-bin-hadoop2.7 zookeeper-3.4.10 zookeeper.out

[hadoop@hadoop1 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark

[hadoop@hadoop1 apps]$ ll

总用量 36

drwxr-xr-x. 10 hadoop hadoop 4096 3月 23 20:29 hadoop-2.7.5

drwxrwxr-x. 7 hadoop hadoop 4096 3月 29 13:15 hbase-1.2.6

lrwxrwxrwx. 1 hadoop hadoop 26 4月 20 13:48 spark -> spark-2.3.0-bin-hadoop2.7/

drwxr-xr-x. 13 hadoop hadoop 4096 2月 23 03:42 spark-2.3.0-bin-hadoop2.7

drwxr-xr-x. 10 hadoop hadoop 4096 3月 23 2017 zookeeper-3.4.10

-rw-rw-r--. 1 hadoop hadoop 17559 3月 29 13:37 zookeeper.out

[hadoop@hadoop1 apps]$

3、进入spark/conf修改配置文件

（1）进入配置文件所在目录

[hadoop@hadoop1 ~]$ cd apps/spark/conf/

[hadoop@hadoop1 conf]$ ll

总用量 36

-rw-r--r--. 1 hadoop hadoop 996 2月 23 03:42 docker.properties.template

-rw-r--r--. 1 hadoop hadoop 1105 2月 23 03:42 fairscheduler.xml.template

-rw-r--r--. 1 hadoop hadoop 2025 2月 23 03:42 log4j.properties.template

-rw-r--r--. 1 hadoop hadoop 7801 2月 23 03:42 metrics.properties.template

-rw-r--r--. 1 hadoop hadoop 865 2月 23 03:42 slaves.template

-rw-r--r--. 1 hadoop hadoop 1292 2月 23 03:42 spark-defaults.conf.template

-rwxr-xr-x. 1 hadoop hadoop 4221 2月 23 03:42 spark-env.sh.template

[hadoop@hadoop1 conf]$

（2）复制spark-env.sh.template并重命名为spark-env.sh，并在文件最后添加配置内容

[hadoop@hadoop1 conf]$ cp spark-env.sh.template spark-env.sh

[hadoop@hadoop1 conf]$ vi spark-env.sh

export JAVA_HOME=/usr/local/jdk1.8.0_73

#export SCALA_HOMEhttp://=/usr/share/scala

export HADOOP_HOME=/home/hadoop/apps/hadoop-2.7.5

export HADOOP_CONF_DIR=/home/hadoop/apps/hadoop-2.7.5/etc/hadoop

export SPARK_WORKER_MEMORY=500m

export SPARK_WORKER_CORES=1

export SPARK_DAEMON_JAVA_OPTS="-Dspark.deploy.recoveryMode=ZOOKEEPER -Dspark.deploy.zookeeper.url=hadoop1:2181,hadoop2:2181,hadoop3:2181,hadoop4:2181 -Dspark.deploy.zookeeper.dir=/spark"

注：

#export SPARK_MASTER_IP=hadoop1  这个配置要注释掉。 

集群搭建时配置的spark参数可能和现在的不一样，主要是考虑个人电脑配置问题，如果memory配置太大，机器运行很慢。 

说明： 

-Dspark.deploy.recoveryMode=ZOOKEEPER    #说明整个集群状态是通过zookeeper来维护的，整个集群状态的恢复也是通过zookeeper来维护的。就是说用zookeeper做了spark的HA配置，Master(Active)挂掉的话，Master(standby)要想变成Master（Active）的话，Master(Standby)就要像zookeeper读取整个集群状态信息，然后进行恢复所有Worker和Driver的状态信息，和所有的Application状态信息； 

-Dspark.deploy.zookeeper.url=hadoop1:2181,hadoop2:2181,hadoop3:2181,hadoop4:2181#将所有配置了zookeeper，并且在这台机器上有可能做master(Active)的机器都配置进来；（我用了4台，就配置了4台） 

-Dspark.deploy.zookeeper.dir=/spark 

这里的dir和zookeeper配置文件zoo.cfg中的dataDir的区别？？？ 

-Dspark.deploy.zookeeper.dir是保存spark的元数据，保存了spark的作业运行状态； 

zookeeper会保存spark集群的所有的状态信息，包括所有的Workers信息，所有的Applactions信息，所有的Driver信息,如果集群

（3）复制slaves.template成slaves

[hadoop@hadoop1 conf]$ cp slaves.template slaves

[hadoop@hadoop1 conf]$ vi slaves

添加如下内容

hadoop1

hadoop2

hadoop3

hadoop4

（4）将安装包分发给其他节点

[hadoop@hadoop1 ~]$ cd apps/

[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop2:$PWD

[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop3:$PWD

[hadoop@hadoop1 apps]$ scp -r spark-2.3.0-bin-hadoop2.7/ hadoop4:$PWD

创建软连接

[hadoop@hadoop2 ~]$ cd apps/

[hadoop@hadoop2 apps]$ ls

hadoop-2.7.5 hbase-1.2.6 spark-2.3.0-bin-hadoop2.7 zookeeper-3.4.10

[hadoop@hadoop2 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark

[hadoop@hadoop2 apps]$ ll

总用量 16

drwxr-xr-x 10 hadoop hadoop 4096 3月 23 20:29 hadoop-2.7.5

drwxrwxr-x 7 hadoop hadoop 4096 3月 29 13:15 hbase-1.2.6

lrwxrwxrwx 1 hadoop hadoop 26 4月 20 19:26 spark -> spark-2.3.0-bin-hadoop2.7/

drwxr-xr-x 13 hadoop hadoop 4096 4月 20 19:24 spark-2.3.0-bin-hadoop2.7

drwxr-xr-x 10 hadoop hadoop 4096 3月 21 19:31 zookeeper-3.4.10

[hadoop@hadoop2 apps]$

4、配置环境变量

所有节点均要配置

[hadoop@hadoop1 spark]$ vi ~/.bashrc

#Spark

export SPARK_HOME=/home/hadoop/apps/spark

export PATH=$PATH:$SPARK_HOME/bin

保存并使其立即生效

[hadoop@hadoop1 spark]$ source ~/.bashrc

四、启动

1、先启动zookeeper集群

所有节点均要执行

[hadoop@hadoop1 ~]$ zkServer.sh start

ZooKeeper JMX enabled by default

Using config: /home/hadoop/apps/zookeeper-3.4.10/bin/../conf/zoo.cfg

Starting zookeeper ... STARTED

[hadoop@hadoop1 ~]$ zkServer.sh status

ZooKeeper JMX enabled by default

Using config: /home/hadoop/apps/zookeeper-3.4.10/bin/../conf/zoo.cfg

Mode: follower

[hadoop@hadoop1 ~]$

2、在启动HDFS集群

任意一个节点执行即可

[hadoop@hadoop1 ~]$ start-dfs.sh

3、在启动Spark集群

在一个节点上执行

[hadoop@hadoop1 ~]$ cd apps/spark/sbin/

[hadoop@hadoop1 sbin]$ start-all.sh

4、查看进程

5、问题

查看进程发现spark集群只有hadoop1成功启动了Master进程，其他3个节点均没有启动成功，需要手动启动，进入到/home/hadoop/apps/spark/sbin目录下执行以下命令，3个节点都要执行

[hadoop@hadoop2 ~]$ cd ~/apps/spark/sbin/

[hadoop@hadoop2 sbin]$ start-master.sh

6、执行之后再次查看进程

Master进程和Worker进程都以启动成功

五、验证

1、查看Web界面Master状态

hadoop1是ALIVE状态，hadoop2、hadoop3和hadoop4均是STANDBY状态

hadoop1节点

hadoop2节点

hadoop3

hadoop4

2、验证HA的高可用

手动干掉hadoop1上面的Master进程，观察是否会自动进行切换

干掉hadoop1上的Master进程之后，再次查看web界面

hadoo1节点，由于Master进程被干掉，所以界面无法访问

hadoop2节点，Master被干掉之后，hadoop2节点上的Master成功篡位成功，成为ALIVE状态

hadoop3节点

hadoop4节点

六、执行Spark程序on standalone

1、执行第一个Spark程序

[hadoop@hadoop3 ~]$ /home/hadoop/apps/spark/bin/spark-submit \

> --class org.apache.spark.examples.SparkPi \

> --master spark://hadoop1:7077 \

> --executor-memory 500m \

> --total-executor-cores 1 \

> /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \

> 100

其中的spark://hadoop1:7077是下图中的地址

运行结果

2、启动spark shell

[hadoop@hadoop1 ~]$ /home/hadoop/apps/spark/bin/spark-shell \

> --master spark://hadoop1:7077 \

> --executor-memory 500m \

> --total-executor-cores 1

参数说明：

--master spark://hadoop1:7077 指定Master的地址

--executor-memory 500m:指定每个worker可用内存为500m

--total-executor-cores 1: 指定整个集群使用的cup核数为1个

注意：

如果启动spark shell时没有指定master地址，但是也可以正常启动spark shell和执行spark shell中的程序，其实是启动了spark的local模式，该模式仅在本机启动一个进程，没有与集群建立联系。

Spark Shell中已经默认将SparkContext类初始化为对象sc。用户代码如果需要用到，则直接应用sc即可

Spark Shell中已经默认将SparkSQl类初始化为对象spark。用户代码如果需要用到，则直接应用spark即可

3、 在spark shell中编写WordCount程序

（1）编写一个hello.txt文件并上传到HDFS上的spark目录下

[hadoop@hadoop1 ~]$ vi hello.txt

[hadoop@hadoop1 ~]$ hadoop fs -mkdir -p /spark

[hadoop@hadoop1 ~]$ hadoop fs -put hello.txt /spark

hello.txt的内容如下

you,jump

i,jump

you,jump

i,jump

jump

（2）在spark shell中用scala语言编写spark程序

scala> sc.textFile("/spark/hello.txt").flatMap(_.split(",")).map((_,1)).reduceByKey(_+_).saveAsTextFile("/spark/out")

说明：

sc是SparkContext对象，该对象是提交spark程序的入口

textFile("/spark/hello.txt")是hdfs中读取数据

flatMap(_.split(" "))先map再压平

map((_,1))将单词和1构成元组

reduceByKey(_+_)按照key进行reduce，并将value累加

saveAsTextFile("/spark/out")将结果写入到hdfs中

（3）使用hdfs命令查看结果http://

[hadoop@hadoop2 ~]$ hadoop fs -cat /spark/out/p*

(jump,5)

(you,2)

(i,2)

[hadoop@hadoop2 ~]$

七、 执行Spark程序on YARN

1、前提

成功启动zookeeper集群、HDFS集群、YARN集群

2、启动Spark on YARN

[hadoop@hadoop1 bin]$ spark-shell --master yarn --deploy-mode client

报错如下：

报错原因：内存资源给的过小，yarn直接kill掉进程，则报rpc连接失败、ClosedChannelException等错误。

解决方法：

先停止YARN服务，然后修改yarn-site.xml，增加如下内容

yarn.nodemanager.vmem-check-enabled

false

Whether virtual memory limits will be enforced for containers

yarn.nodemanager.vmem-pmem-ratio

4

Ratio between virtual memory to physical memory when setting memory limits for containers

将新的yarn-site.xml文件分发到其他Hadoop节点对应的目录下，最后在重新启动YARN。

重新执行以下命令启动spark on yarn

[hadoop@hadoop1 hadoop]$ spark-shell --master yarn --deploy-mode client

启动成功

3、打开YARN的web界面

打开YARN WEB页面：http://hadoop4:8088

可以看到Spark shell应用程序正在运行

单击ID号链接，可以看到该应用程序的详细信息

单击“ApplicationMaster”链接

4、运行程序

scala> val array = Array(1,2,3,4,5)

array: Array[Int] = Array(1, 2, 3, 4, 5)

scala> val rdd = sc.makeRDD(array)

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at makeRDD at :26

scala> rdd.count

res0: Long = 5

scala>

再次查看YARN的web界面

查看executors

5、执行Spark自带的示例程序PI

[hadoop@hadoop1 ~]$ spark-submit --class org.apache.spark.examples.SparkPi \

> --master yarn \

> --deploy-mode cluster \

> --driver-memory 500m \

> --executor-memory 500m \

> --executor-cores 1 \

> /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \

> 10

执行过程

[hadoop@hadoop1 ~]$ spark-submit --class org.apache.spark.examples.SparkPi \

> --master yarn \

> --deploy-mode cluster \

> --driver-memory 500m \

> --executor-memory 500m \

> --executor-cores 1 \

> /home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar \

> 10

2018-04-21 17:57:32 WARN NativeCodeLoader:62 - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

2018-04-21 17:57:34 INFO ConfiguredRMFailoverProxyProvider:100 - Failing over to rm2

2018-04-21 17:57:34 INFO Client:54 - Requesting a new application from cluster with 4 NodeManagers

2018-04-21 17:57:34 INFO Client:54 - Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container)

2018-04-21 17:57:34 INFO Client:54 - Will allocate AM container, with 884 MB memory including 384 MB overhead

2018-04-21 17:57:34 INFO Client:54 - Setting up container launch context for our AM

2018-04-21 17:57:34 INFO Client:54 - Setting up the launch environment for our AM container

2018-04-21 17:57:34 INFO Client:54 - Preparing resources for our AM container

2018-04-21 17:57:36 WARN Client:66 - Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.

2018-04-21 17:57:39 INFO Client:54 - Uploading resource file:/tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720/__spark_libs__8262081479435245591.zip -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/__spark_libs__8262081479435245591.zip

2018-04-21 17:57:44 INFO Client:54 - Uploading resource file:/home/hadoop/apps/spark/examples/jars/spark-examples_2.11-2.3.0.jar -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/spark-examples_2.11-2.3.0.jar

2018-04-21 17:57:44 INFO Client:54 - Uploading resource file:/tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720/__spark_conf__2498510663663992254.zip -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/__spark_conf__.zip

2018-04-21 17:57:44 INFO SecurityManager:54 - Changing view acls to: hadoop

2018-04-21 17:57:44 INFO SecurityManager:54 - Changing modify acls to: hadoop

2018-04-21 17:57:44 INFO SecurityManager:54 - Changing view acls groups to:

2018-04-21 17:57:44 INFO SecurityManager:54 - Changing modify acls groups to:

2018-04-21 17:57:44 INFO SecurityManager:54 - SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(hadoop); groups with view permissions: Set(); users with modify permissions: Set(hadoop); groups with modify permissions: Set()

2018-04-21 17:57:44 INFO Client:54 - Submitting application application_1524303370510_0005 to ResourceManager

2018-04-21 17:57:44 INFO YarnClientImpl:273 - Submitted application application_1524303370510_0005

2018-04-21 17:57:45 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:45 INFO Client:54 -

client token: N/A

diagnostics: N/A

ApplicationMaster host: N/A

ApplicationMaster RPC port: -1

queue: default

start time: 1524304664749

final status: UNDEFINED

tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/

user: hadoop

2018-04-21 17:57:46 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:47 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:48 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:49 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:50 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:51 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:52 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:53 INFO Client:54 - Application report for application_1524303370510_0005 (state: ACCEPTED)

2018-04-21 17:57:54 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:57:54 INFO Client:54 -

client token: N/A

diagnostics: N/A

ApplicationMaster host: 192.168.123.104

ApplicationMaster RPC port: 0

queue: default

start time: 1524304664749

final status: UNDEFINED

tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/

user: hadoop

2018-04-21 17:57:55 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:57:56 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:57:57 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:57:58 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:57:59 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:00 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:01 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:02 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:03 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:04 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:05 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:06 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:07 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:08 INFO Client:54 - Application report for application_1524303370510_0005 (state: RUNNING)

2018-04-21 17:58:09 INFO Client:54 - Application report for application_1524303370510_0005 (state: FINISHED)

2018-04-21 17:58:09 INFO Client:54 -

client token: N/A

diagnostics: N/A

ApplicationMaster host: 192.168.123.104

ApplicationMaster RPC port: 0

queue: default

start time: 1524304664749

final status: SUCCEEDED

tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/

user: hadoop

2018-04-21 17:58:09 INFO Client:54 - Deleted staging directory hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005

2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Shutdown hook called

2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Deleting directory /tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720

2018-04-21 17:58:09 INFO ShutdownHookManager:54 - Deleting directory /tmp/spark-06de6905-8067-4f1e-a0a0-bc8a51daf535

[hadoop@hadoop1 ~]$

希望本文所述对大家spark程序设计有所帮助。

版权声明：本文内容由网络用户投稿，版权归原作者所有，本站不拥有其著作权，亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容，请联系我们jiasou666@gmail.com 处理，核实后本网站将在24小时内删除侵权内容。