如何搭建配置centos虚拟机请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（一）VMW安装四台CentOS，并实现本机与它们能交互，虚拟机内部实现可以上网。》

如何安装hadoop2.9.0请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二）安装hadoop2.9.0》

如何配置hadoop2.9.0 HA 请参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（十）安装hadoop2.9.0搭建HA》

安装spark的服务器：

192.168.0.120      master

192.168.0.121      slave1

192.168.0.122      slave2

192.168.0.123      slave3

从spark官网下载spark安装包：

官网地址：http://spark.apache.org/downloads.html

注意：上一篇文章中我们安装了hadoop2.9.0，但是这里没有发现待下载spark对应的hadoop版本可选项中发现hadoop2.9.0，因此也只能选择“Pre-built for Apache Hadoop 2.7 and later”。

这spark可选版本比较多，就选择“2.2.1(Dec 01 2017)”。

选中后，此时带下来的spark安装包版本信息为：

下载“spark-2.2.1-bin-hadoop2.7.tgz”，上传到master的/opt目录下，并解压：

[root@master opt]# tar -zxvf spark-2.2.-bin-hadoop2..tgz

[root@master opt]# ls

hadoop-2.9.  hadoop-2.9..tar.gz  jdk1..0_171  jdk-8u171-linux-x64.tar.gz  scala-2.11.  scala-2.11..tgz  spark-2.2.-bin-hadoop2.  spark-2.2.-bin-hadoop2..tgz

[root@master opt]#

配置Spark

[root@master opt]# ls

hadoop-2.9.  hadoop-2.9..tar.gz  jdk1..0_171  jdk-8u171-linux-x64.tar.gz  scala-2.11.  scala-2.11..tgz  spark-2.2.-bin-hadoop2.  spark-2.2.-bin-hadoop2..tgz

[root@master opt]# cd spark-2.2.-bin-hadoop2./conf/

[root@master conf]# ls

docker.properties.template  metrics.properties.template   spark-env.sh.template

fairscheduler.xml.template  slaves.template

log4j.properties.template   spark-defaults.conf.template

[root@master conf]# scp spark-env.sh.template spark-env.sh

[root@master conf]# ls

docker.properties.template  metrics.properties.template   spark-env.sh

fairscheduler.xml.template  slaves.template               spark-env.sh.template

log4j.properties.template   spark-defaults.conf.template

[root@master conf]# vi spark-env.sh

在spark-env.sh末尾添加以下内容（这是我的配置，你需要根据自己安装的环境情况自行修改）：

export SCALA_HOME=/opt/scala-2.11.0
export JAVA_HOME=/opt/jdk1.8.0_171
export HADOOP_HOME=/opt/hadoop-2.9.0
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
SPARK_MASTER_IP=master
SPARK_LOCAL_DIRS=/opt/spark-2.2.1-bin-hadoop2.7
SPARK_DRIVER_MEMORY=1G

注：在设置Worker进程的CPU个数和内存大小，要注意机器的实际硬件条件，如果配置的超过当前Worker节点的硬件条件，Worker进程会启动失败。

vi slaves在slaves文件下填上slave主机名：

[root@master conf]# scp slaves.template slaves

[root@master conf]# vi slaves

配置内容为：

#localhost

slave1

slave2

slave3

将配置好的spark-2.2.1-bin-hadoop2.7文件夹分发给所有slaves吧

scp -r /opt/spark-2.2.-bin-hadoop2. spark@slave1:/opt/

scp -r /opt/spark-2.2.-bin-hadoop2. spark@slave2:/opt/

scp -r /opt/spark-2.2.-bin-hadoop2. spark@slave3:/opt/

 注意：此时默认slave1,slave2,slave3上是没有/opt/spark-2.2.1-bin-hadoop2.7，因此直接拷贝可能会出现无权限操作 。

解决方案，分别在slave1,slave2,slave3的/opt下创建spark-2.2.1-bin-hadoop2.7，并分配777权限。

[root@slave1 opt]# mkdir spark-2.2.1-bin-hadoop2.7

[root@slave1 opt]# chmod 777 spark-2.2.1-bin-hadoop2.7

[root@slave1 opt]#

之后，再次操作拷贝就有权限操作了。

启动Spark

在spark安装目录下执行下面命令才行 , 目前的master安装目录在/opt/spark-2.2.1-bin-hadoop2.7

sbin/start-all.sh

此时，我使用非root账户（spark用户名的用户）启动spark，出现master上spark无权限写日志的问题：

[spark@master opt]$ cd /opt/spark-2.2.-bin-hadoop2.

[spark@master spark-2.2.-bin-hadoop2.]$ sbin/start-all.sh

mkdir: cannot create directory ‘/opt/spark-2.2.-bin-hadoop2./logs’: Permission denied

chown: cannot access ‘/opt/spark-2.2.-bin-hadoop2./logs’: No such file or directory

starting org.apache.spark.deploy.master.Master, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.master.Master--master.out

/opt/spark-2.2.-bin-hadoop2./sbin/spark-daemon.sh: line : /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.master.Master--master.out: No such file or directory

failed to launch: nice -n  /opt/spark-2.2.-bin-hadoop2./bin/spark-class org.apache.spark.deploy.master.Master --host master --port  --webui-port

tail: cannot open ‘/opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.master.Master--master.out’ for reading: No such file or directory

full log in /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.master.Master--master.out

slave1: starting org.apache.spark.deploy.worker.Worker, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.worker.Worker--slave1.out

slave3: starting org.apache.spark.deploy.worker.Worker, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.worker.Worker--slave3.out

slave2: starting org.apache.spark.deploy.worker.Worker, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.worker.Worker--slave2.out

[spark@master spark-2.2.-bin-hadoop2.]$ cd ..

[spark@master opt]$ su root

Password:

[root@master opt]# chmod  spark-2.2.-bin-hadoop2.

[root@master opt]# su spark

[spark@master opt]$ cd spark-2.2.-bin-hadoop2.

[spark@master spark-2.2.-bin-hadoop2.]$ sbin/start-all.sh

starting org.apache.spark.deploy.master.Master, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.master.Master--master.out

slave2: org.apache.spark.deploy.worker.Worker running as process .  Stop it first.

slave3: org.apache.spark.deploy.worker.Worker running as process .  Stop it first.

slave1: org.apache.spark.deploy.worker.Worker running as process .  Stop it first.

[spark@master spark-2.2.-bin-hadoop2.]$ sbin/stop-all.sh

slave1: stopping org.apache.spark.deploy.worker.Worker

slave3: stopping org.apache.spark.deploy.worker.Worker

slave2: stopping org.apache.spark.deploy.worker.Worker

stopping org.apache.spark.deploy.master.Master

[spark@master spark-2.2.-bin-hadoop2.]$ sbin/start-all.sh

starting org.apache.spark.deploy.master.Master, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.master.Master--master.out

slave1: starting org.apache.spark.deploy.worker.Worker, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.worker.Worker--slave1.out

slave3: starting org.apache.spark.deploy.worker.Worker, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.worker.Worker--slave3.out

slave2: starting org.apache.spark.deploy.worker.Worker, logging to /opt/spark-2.2.-bin-hadoop2./logs/spark-spark-org.apache.spark.deploy.worker.Worker--slave2.out

解决方案：给master的spark安装目录也分配777操作权限。

验证 Spark 是否安装成功

启动过程发现问题：

1）以spark on yarn方式运行spark-shell抛出异常：ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map(),Set()) to AM was unsuccessful：解决方案参考《Kafka：ZK+Kafka+Spark Streaming集群环境搭建（六）针对spark2.2.1以yarn方式启动spark-shell抛出异常：ERROR cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Sending RequestExecutors(0,0,Map(),Set()) to AM was unsuccessful》

用jps检查，在 master 上正常启动包含以下几个进程：

$ jps

 Jps

 SecondaryNameNode

 Master

 NameNode

 ResourceManager

在 slave 上正常启动包含以下几个进程：

$jps

 DataNode

 Worker

 Jps

 NodeManager

进入Spark的Web管理页面： http://192.168.0.120:8080

运行示例

本地方式两线程运行测试：

[spark@master spark-2.2.-bin-hadoop2.]$ cd /opt/spark-2.2.-bin-hadoop2.

[spark@master spark-2.2.-bin-hadoop2.]$ ./bin/run-example SparkPi  --master local[]

Spark Standalone 集群模式运行

[spark@master spark-2.2.-bin-hadoop2.]$ cd /opt/spark-2.2.-bin-hadoop2.

[spark@master spark-2.2.-bin-hadoop2.]$ ./bin/spark-submit \

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

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

> examples/jars/spark-examples_2.-2.2..jar \

>

此时是可以从spark监控界面查看到运行状况：

Spark on YARN 集群上 yarn-cluster 模式运行

[spark@master spark-2.2.-bin-hadoop2.]$ cd /opt/spark-2.2.-bin-hadoop2.

[spark@master spark-2.2.-bin-hadoop2.]$ ./bin/spark-submit \

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

> --master yarn-cluster \

> /opt/spark-2.2.-bin-hadoop2./examples/jars/spark-examples_2.-2.2..jar \

>

执行日志信息：

[spark@master hadoop-2.9.]$ cd /opt/spark-2.2.-bin-hadoop2.

[spark@master spark-2.2.-bin-hadoop2.]$ ./bin/spark-submit \

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

> --master yarn-cluster \

> /opt/spark-2.2.-bin-hadoop2./examples/jars/spark-examples_2.-2.2..jar \

>

Warning: Master yarn-cluster is deprecated since 2.0. Please use master "yarn" with specified deploy mode instead.

// :: WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

// :: INFO client.RMProxy: Connecting to ResourceManager at master/192.168.0.120:

// :: INFO yarn.Client: Requesting a new application from cluster with  NodeManagers

// :: INFO yarn.Client: Verifying our application has not requested more than the maximum memory capability of the cluster ( MB per container)

// :: INFO yarn.Client: Will allocate AM container, with  MB memory including  MB overhead

// :: INFO yarn.Client: Setting up container launch context for our AM

// :: INFO yarn.Client: Setting up the launch environment for our AM container

// :: INFO yarn.Client: Preparing resources for our AM container

// :: WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.

// :: INFO yarn.Client: Uploading resource file:/opt/spark-2.2.-bin-hadoop2./spark-f46b4dc7--4bb3-babd-c3124d1a7e07/__spark_libs__1523582418834894726.zip -> hdfs://master:9000/user/spark/.sparkStaging/application_1530369937777_0001/__spark_libs__1523582418834894726.zip

// :: INFO yarn.Client: Uploading resource file:/opt/spark-2.2.-bin-hadoop2./examples/jars/spark-examples_2.-2.2..jar -> hdfs://master:9000/user/spark/.sparkStaging/application_1530369937777_0001/spark-examples_2.11-2.2.1.jar

// :: INFO yarn.Client: Uploading resource file:/opt/spark-2.2.-bin-hadoop2./spark-f46b4dc7--4bb3-babd-c3124d1a7e07/__spark_conf__4967231916988729566.zip -> hdfs://master:9000/user/spark/.sparkStaging/application_1530369937777_0001/__spark_conf__.zip

// :: INFO spark.SecurityManager: Changing view acls to: spark

// :: INFO spark.SecurityManager: Changing modify acls to: spark

// :: INFO spark.SecurityManager: Changing view acls groups to:

// :: INFO spark.SecurityManager: Changing modify acls groups to:

// :: INFO spark.SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(spark); groups with view permissions: Set(); users  with modify permissions: Set(spark); groups with modify permissions: Set()

// :: INFO yarn.Client: Submitting application application_1530369937777_0001 to ResourceManager

// :: INFO impl.YarnClientImpl: Submitted application application_1530369937777_0001

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client:

         client token: N/A

         diagnostics: AM container is launched, waiting for AM container to Register with RM

         ApplicationMaster host: N/A

         ApplicationMaster RPC port: -

         queue: default

         start time:

         final status: UNDEFINED

         tracking URL: http://master:8088/proxy/application_1530369937777_0001/

         user: spark

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: ACCEPTED)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client:

         client token: N/A

         diagnostics: N/A

         ApplicationMaster host: 192.168.0.121

         ApplicationMaster RPC port:

         queue: default

         start time:

         final status: UNDEFINED

         tracking URL: http://master:8088/proxy/application_1530369937777_0001/

         user: spark

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: RUNNING)

// :: INFO yarn.Client: Application report for application_1530369937777_0001 (state: FINISHED)

// :: INFO yarn.Client:

         client token: N/A

         diagnostics: N/A

         ApplicationMaster host: 192.168.0.121

         ApplicationMaster RPC port:

         queue: default

         start time:

         final status: SUCCEEDED

         tracking URL: http://master:8088/proxy/application_1530369937777_0001/

         user: spark

// :: INFO util.ShutdownHookManager: Shutdown hook called

// :: INFO util.ShutdownHookManager: Deleting directory /opt/spark-2.2.-bin-hadoop2./spark-f46b4dc7--4bb3-babd-c3124d1a7e07

从hadoop yarn监控界面查看执行任务：

另外也可以进入http://slave1:8042查看slave1的信息：

注意：Spark on YARN 支持两种运行模式，分别为yarn-cluster和yarn-client，具体的区别可以看这篇博文，从广义上讲，yarn-cluster适用于生产环境；而yarn-client适用于交互和调试，也就是希望快速地看到application的输出。

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