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. hbase-1.2. spark-2.3.0-bin-hadoop2.7 zookeeper-3.4. zookeeper.out
[hadoop@hadoop1 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark
[hadoop@hadoop1 apps]$ ll
总用量
drwxr-xr-x. hadoop hadoop 3月 : hadoop-2.7.
drwxrwxr-x. hadoop hadoop 3月 : hbase-1.2.
lrwxrwxrwx. hadoop hadoop 4月 : spark -> spark-2.3.0-bin-hadoop2.7/
drwxr-xr-x. hadoop hadoop 2月 : spark-2.3.-bin-hadoop2.
drwxr-xr-x. hadoop hadoop 3月 zookeeper-3.4.
-rw-rw-r--. hadoop hadoop 3月 : 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
-rw-r--r--. hadoop hadoop 2月 : docker.properties.template
-rw-r--r--. hadoop hadoop 2月 : fairscheduler.xml.template
-rw-r--r--. hadoop hadoop 2月 : log4j.properties.template
-rw-r--r--. hadoop hadoop 2月 : metrics.properties.template
-rw-r--r--. hadoop hadoop 2月 : slaves.template
-rw-r--r--. hadoop hadoop 2月 : spark-defaults.conf.template
-rwxr-xr-x. hadoop hadoop 2月 : 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. hbase-1.2. spark-2.3.0-bin-hadoop2.7 zookeeper-3.4.
[hadoop@hadoop2 apps]$ ln -s spark-2.3.0-bin-hadoop2.7/ spark
[hadoop@hadoop2 apps]$ ll
总用量
drwxr-xr-x hadoop hadoop 3月 : hadoop-2.7.
drwxrwxr-x hadoop hadoop 3月 : hbase-1.2.
lrwxrwxrwx hadoop hadoop 4月 : spark -> spark-2.3.0-bin-hadoop2.7/
drwxr-xr-x hadoop hadoop 4月 : spark-2.3.0-bin-hadoop2.7
drwxr-xr-x hadoop hadoop 3月 : zookeeper-3.4.
[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./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./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 \
> /home/hadoop/apps/spark/examples/jars/spark-examples_2.-2.3..jar \
>

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((_,)).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,)
(you,)
(i,)
[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

        <property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
<description>Whether virtual memory limits will be enforced for containers</description>
</property>
<property>
<name>yarn.nodemanager.vmem-pmem-ratio</name>
<value>4</value>
<description>Ratio between virtual memory to physical memory when setting memory limits for containers</description>
</property>

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(,,,,)
array: Array[Int] = Array(, , , , ) scala> val rdd = sc.makeRDD(array)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[] at makeRDD at <console>: scala> rdd.count
res0: Long = 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 \
> /home/hadoop/apps/spark/examples/jars/spark-examples_2.-2.3..jar \
>

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 \
> /home/hadoop/apps/spark/examples/jars/spark-examples_2.-2.3..jar \
>
-- :: WARN NativeCodeLoader: - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
-- :: INFO ConfiguredRMFailoverProxyProvider: - Failing over to rm2
-- :: INFO Client: - Requesting a new application from cluster with NodeManagers
-- :: INFO Client: - Verifying our application has not requested more than the maximum memory capability of the cluster ( MB per container)
-- :: INFO Client: - Will allocate AM container, with MB memory including MB overhead
-- :: INFO Client: - Setting up container launch context for our AM
-- :: INFO Client: - Setting up the launch environment for our AM container
-- :: INFO Client: - Preparing resources for our AM container
-- :: WARN Client: - Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
-- :: INFO Client: - 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
-- :: INFO Client: - Uploading resource file:/home/hadoop/apps/spark/examples/jars/spark-examples_2.-2.3..jar -> hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005/spark-examples_2.11-2.3.0.jar
-- :: INFO Client: - 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
-- :: INFO SecurityManager: - Changing view acls to: hadoop
-- :: INFO SecurityManager: - Changing modify acls to: hadoop
-- :: INFO SecurityManager: - Changing view acls groups to:
-- :: INFO SecurityManager: - Changing modify acls groups to:
-- :: INFO SecurityManager: - 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()
-- :: INFO Client: - Submitting application application_1524303370510_0005 to ResourceManager
-- :: INFO YarnClientImpl: - Submitted application application_1524303370510_0005
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: -
client token: N/A
diagnostics: N/A
ApplicationMaster host: N/A
ApplicationMaster RPC port: -
queue: default
start time:
final status: UNDEFINED
tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/
user: hadoop
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: ACCEPTED)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: -
client token: N/A
diagnostics: N/A
ApplicationMaster host: 192.168.123.104
ApplicationMaster RPC port:
queue: default
start time:
final status: UNDEFINED
tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/
user: hadoop
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: RUNNING)
-- :: INFO Client: - Application report for application_1524303370510_0005 (state: FINISHED)
-- :: INFO Client: -
client token: N/A
diagnostics: N/A
ApplicationMaster host: 192.168.123.104
ApplicationMaster RPC port:
queue: default
start time:
final status: SUCCEEDED
tracking URL: http://hadoop4:8088/proxy/application_1524303370510_0005/
user: hadoop
-- :: INFO Client: - Deleted staging directory hdfs://myha01/user/hadoop/.sparkStaging/application_1524303370510_0005
-- :: INFO ShutdownHookManager: - Shutdown hook called
-- :: INFO ShutdownHookManager: - Deleting directory /tmp/spark-93bd68c9-85de-482e-bbd7-cd2cee60e720
-- :: INFO ShutdownHookManager: - Deleting directory /tmp/spark-06de6905--4f1e-a0a0-bc8a51daf535
[hadoop@hadoop1 ~]$

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