Spark学习之路 （二）Spark2.3 HA集群的分布式安装

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目录

一、下载Spark安装包
1、从官网下载
2、从微软的镜像站下载
3、从清华的镜像站下载
二、安装基础
三、Spark安装过程
 1、上传并解压缩
2、为安装包创建一个软连接
3、进入spark/conf修改配置文件
4、配置环境变量
四、启动
1、先启动zookeeper集群
2、在启动HDFS集群
3、在启动Spark集群
4、查看进程
5、问题
6、执行之后再次查看进程
五、验证
1、查看Web界面Master状态
2、验证HA的高可用
六、执行Spark程序on standalone
1、执行第一个Spark程序
2、启动spark shell
3、 在spark shell中编写WordCount程序
七、 执行Spark程序on YARN
1、前提
2、启动Spark on YARN
3、打开YARN的web界面
4、运行程序
5、执行Spark自带的示例程序PI

一、下载Spark安装包

1、从官网下载

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

2、从微软的镜像站下载

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

3、从清华的镜像站下载

https://mirrors.tuna.tsinghua.edu.cn/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  spark-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_HOME=/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

[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 ~]$