spark运行及开发环境搭建

一、Linux下spark运行环境搭建

http://wenku.baidu.com/link?url=V14fWw5C3vp2G7YhTApqknz_EKwowBGP8lL_TvSbXa8PN2vASVAHUSouK7p0Pu14h3IBf8zmdfPUNUT-2Hr-cnDUzivYJKupgWnEkbHTY8i

参考

http://wenku.baidu.com/link?url=-b2L9j7w2OSic3F7rA3LGPfhQpU45jBHAzVdmYesDDw4G6qGRi35-C7cFi8Oc3E-b1aqjn3agCDSjR4IzwEF2elJouLPSjZtcKdxYEIZQQK

1、scala安装

需改为安装2.10的版本才可以和spark1.3版本的匹配

tar-zxvfscala-2.10.5.tgz

vim/etc/profile

exportSCALA_HOME=/opt/scala-2.10.5

exportPATH=${SCALA_HOME}/bin:$PATH

source/etc/profile

2、下载spark1.3版本

http://apache.fayea.com/spark/spark-1.3.0/spark-1.3.0-bin-hadoop2.4.tgz

tar-zxvfspark-1.3.0-bin-hadoop2.4.tgz

mvspark-1.3.0-bin-hadoop2.4spark1.3

tty:[0]jobs:[0]cwd:[/opt/spark1.3/conf]

17:50[root@10.10.72.182]$cpspark-env.sh.templatespark-env.sh

exportSCALA_HOME=/opt/scala-2.10.5

#最大内存

exportSPARK_WORK_MEMORY=1g

exportSPARK_MASTER_IP=10.10.72.182

exportMASTER=spark://10.10.72.182:7077

#hadoop的配置信息路径，根据hadoop搭建过程实际目录

exportHADOOP_CONF_DIR=/opt/hadoop-2.4.0/etc/hadoop

exportJAVA_HOME=/opt/jdk

配置slaves文件

cpslaves.templateslaves

18:08[root@10.10.72.182]$vimslaves

#ASparkWorkerwillbestartedoneachofthemachineslistedbelow.

cloud01

cloud02

cloud03

scp-rspark1.3root@cloud02:/opt/

scp-rspark1.3root@cloud03:/opt/

分别在3台服务器上修改/etc/profile文件

添加

exportSPARK_HOME=/opt/spark1.3

exportPATH=$SPARK_HOME/bin;$SPARK_HOME/sbin;$PATH

仅需在182上启动spark即可183和184会自动启动

/opt/spark1.3

启动

./sbin/start-all.sh

关闭

./sbin/stop-all.sh

查看jps

15:03[root@10.10.72.182]$jps

12972NodeManager

4000Application

12587QuorumPeerMain

25042Jps

12879ResourceManager

12739DataNode

12648JournalNode

24790Master

24944Worker

15:09[root@10.10.72.184]$jps

11802DFSZKFailoverController

11505JournalNode

11906NodeManager

17757Jps

11417QuorumPeerMain

11600NameNode

11692DataNode

17104Worker

访问http://10.10.72.182:8080/

查看spark运行情况

--------------------------------------------------------------------

二、windowsIDEA开发环境搭建

参考

http://blog.csdn.net/ichsonx/article/details/44594567

http://ju.outofmemory.cn/entry/94851

1、安装scala

http://www.scala-lang.org/download/

下载最新版本然后安装

由于版本spark为1.3版本原因scala请使用2.10.5版本

C:\ProgramFiles(x86)\scala\bin

2、Intellij中安装Scala插件

Plugins-->Browserepositories中输入Scala插件

3、在spark官网下载spark包

下载预编译版本spark-1.3.0-bin-hadoop2.4

在intellijIDEA中创建scalaproject，并依次选择“File”–>“projectstructure”–>“Libraries”，选择“+”，将spark-hadoop对应的包导入，

比如导入spark-assembly-1.3.0-hadoop2.2.0.jar（只需导入该jar包，其他不需要），如果IDE没有识别scala库，则需要以同样方式将scala库导入。

之后开发scala程序即可。

E:\spark\spark-1.3.0-bin-hadoop2.4\spark-1.3.0-bin-hadoop2.4\lib\spark-assembly-1.3.0-hadoop2.4.0.jar

E:\spark\spark-1.3.0-bin-hadoop2.4\spark-1.3.0-bin-hadoop2.4\lib\spark-assembly-1.3.0-hadoop2.4.0.jar

3、创建Scala工程

参考http://www.aboutyun.com/thread-12496-1-4.html

NewProject

选择SBT新建SBT工程

然后新建module

选择ScalaSDK

在Run/DebugCongigurations中添加一个Application

新建class是选择object

valconf=newSparkConf().setAppName("SparkPi").setMaster("local")

本地运行

http://www.beanmoon.com/2014/10/11/%E5%A6%82%E4%BD%95%E4%BD%BF%E7%94%A8intellij%E6%90%AD%E5%BB%BAspark%E5%BC%80%E5%8F%91%E7%8E%AF%E5%A2%83%EF%BC%88%E4%B8%8B%EF%BC%89/

目前为止，我还没有找到在intellij中让spark直接在集群中运行的方法，通常的做法是先用intellij把已经编写好的spark程序打包，然后通过命令spark-submit的方式把jar包上传到集群中运行。

4、打包上传到Linux

http://www.open-open.com/doc/view/ebf1c03582804927877b08597dc14c66

参考

http://blog.csdn.net/javastart/article/details/43372977

依次选择“File”–>“ProjectStructure”–>“Artifact”，选择“+”–>“Jar”–>“FromModuleswithdependencies”，选择main函数，并在弹出框中选择输出jar位置，

并选择“OK”。

勾选Buildonmake项目make时会自动打包

D:\\IDEA\\idea_project_new\\sparkTest\\out\\artifacts\\sparkTest_jar代码中指定的目录

最后依次选择IDEA菜单的“Build”–>“BuildArtifact”编译生成jar包。具体如下图所示。

去掉scala和hadoop的依赖包

上传至10.10.72.182的/home/sparkTest/

spark1.3需要改为scala2.10

错误：

http://blog.csdn.net/u012432611/article/details/47274249

4、验证安装情况

参考

http://blog.csdn.net/jediael_lu/article/details/45310321

（1）运行自带示例

$bin/run-exampleorg.apache.spark.examples.SparkPi

（2）查看集群环境

http://master:8080/

（3）进入spark-shell

$spark-shell

（4）查看jobs等信息

http://master:4040/jobs/

提交到集群后通过spark-submit进行提交

tty:[0]jobs:[0]cwd:[/opt/spark1.3/bin]

16:48[root@10.10.72.182]$spark-submit--classmain.java.com.laifeng.SparkPi--masterspark://10.10.72.182:7077/home/sparkTest/sparkTest.jar

去掉scala-sdk-2.11和spark-assembly-1.1.0-hadoop相关依赖包

其中–class参数制定了我们刚才已打jar包的主类，–master参数制定了我们spark集群中master实例的身份。关于spark-submit参数的更多用法，可以通过spark-submit–help命令查看。

运行成功

spark-submit--classmain.java.com.laifeng.SparkPi--masterspark://10.10.72.182:7077/home/sparkTest/sparkTest.jar

4、找不到winutil.exe的问题

http://www.tuicool.com/articles/iABZJj

配置好后重启IDEA后运行成功。

5、local时遇到的问题

https://archive.apache.org/dist/hadoop/common/hadoop-2.4.0/

--------------------------------------

其他补充：

zookeeper命令

http://blog.csdn.net/xiaolang85/article/details/13021339

查看是否为leader

14:12[root@10.10.72.184]$echostat|nc127.0.0.12181

Zookeeperversion:3.4.6-1569965,builton02/20/201409:09GMT

Clients:

/127.0.0.1:5979[0](queued=0,recved=1,sent=0)

Latencymin/avg/max:0/0/8

Received:16

Sent:15

Connections:1

Outstanding:0

Zxid:0xb00000011

Mode:follower

Nodecount:10

tty:[0]jobs:[0]cwd:[/opt/zookeeper-3.4.6/bin]

14:18[root@10.10.72.182]$./zkServer.shstatus

JMXenabledbydefault

Usingconfig:/opt/zookeeper-3.4.6/bin/../conf/zoo.cfg

Mode:leader

spark-shell

tty:[0]jobs:[0]cwd:[/opt/spark1.3/bin]

17:16[root@10.10.72.182]$spark-shell

scala>sc.version

res0:String=1.3.0

scala>sc.appName

res1:String=Sparkshell

scala>:quit

---------------------------------------------------

远程debug方式

/opt/spark1.3/bin/spark-submit--classmain.scala.com.laifeng.SparkWorldCount--masterspark://10.10.72.182:7077/home/sparkTest/laifeng-spark.jar

参考

http://blog.csdn.net/happyanger6/article/details/47065423

方式1

命令方式

参考

bin/spark-submit--classsparksql.HiveOnSQLscalastudy.jar--driver-java-options-agentlib:jdwp=transport=dt_socket,address=9904,server=y,suspend=y

hadoopfs-rm-r/wuzhanwei/test/output1/

/opt/spark1.3/bin/spark-submit--classmain.scala.com.laifeng.SparkWorldCount--masterspark://10.10.72.182:7077/home/sparkTest/laifeng-spark.jar--driver-java-options-agentlib:jdwp=transport=dt_socket,address=8888,server=y,suspend=y

17:09[root@10.10.72.182]$/opt/spark1.3/bin/spark-submit--classmain.scala.com.laifeng.SparkWorldCount--masterspark://10.10.72.182:7077/home/sparkTest/laifeng-spark.jar--driver-java-options-agentlib:jdwp=transport=dt_socket,address=8888,server=y,suspend=y

SparkassemblyhasbeenbuiltwithHive,includingDatanucleusjarsonclasspath

Listeningfortransportdt_socketataddress:8888

现在调试：自己修改的工程：

/opt/spark1.3/bin/spark-submit--classjava.com.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masterspark://10.10.72.182:7077/home/sparkTest/laifeng-spark.jarhdfs://ns1/input/clientupload20151027.csvhdfs://ns1/output2/output.csv--driver-java-options-agentlib:jdwp=transport=dt_socket,address=8888,server=y,suspend=y

hdfs://ns1/input/clientupload20151027.csv

hdfs://ns1/output2/output.csv

方式2

重点：

http://blog.csdn.net/javastart/article/details/43372977

http://blog.csdn.net/happyanger6/article/details/47065423

需停掉spark暂时未采用建议完成调试后在实验该方式

tty:[0]jobs:[0]cwd:[/opt/spark1.3/bin]

16:56[root@10.10.72.182]$vimspark-clas

------------------------------------------------------------------

/opt/spark1.3/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masterspark://10.10.72.182:7077/home/sparkTest/laifeng-spark.jarhdfs://ns1/input/clientupload20151106.csvhdfs://ns1/output6

/opt/spark1.3/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masterspark://10.10.72.182:7077/home/sparkTest/laifeng-spark.jarhdfs://ns1/input/clientupload20151106.csvhdfs://ns1/output6--driver-java-options-agentlib:jdwp=transport=dt_socket,address=8888,server=y,suspend=y

测试好使：

/opt/spark1.3/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masteryarn-cluster/home/sparkTest/laifeng-spark.jarhdfs://ns1/input/clientupload20151106.csvhdfs://ns1/output11yarn-cluster--num-executors3--driver-memory1g--executor-memory2g

已跑出数据

demo程序

/opt/spark-onyarn/spark/default/bin/spark-submit--classorg.apache.spark.examples.SparkPi--masteryarn-cluster--num-executors3--driver-memory2g--executor-memory4g/opt/spark-onyarn/spark/default/lib/spark-examples*.jar

采用yarn方式跑数据

参考：

/opt/spark-onyarn/spark/default/bin/spark-submit--classorg.apache.spark.examples.SparkPi--masteryarn-cluster--num-executors3--driver-memory1g--executor-memory2g/opt/spark-onyarn/spark/default/lib/spark-examples*.jar

线上

/opt/spark-onyarn/spark/default/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masteryarn-cluster/work/yule/linshi/spark/laifeng-spark.jar/workspace/yule/test/spark/clientupload20151105.csv/workspace/yule/test/spark/output4yarn-cluster--num-executors3--driver-memory2g--executor-memory4g

/opt/spark-onyarn/spark/default/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masteryarn-cluster/work/yule/linshi/spark/laifeng-spark.jar/workspace/yule/test/spark/clientupload20151105.csv/workspace/yule/test/spark/output4yarn-cluster--num-executors3--driver-memory2g--executor-memory4g

目录方式

/opt/spark-onyarn/spark/default/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masteryarn-cluster/work/yule/linshi/spark/laifeng-spark.jar/workspace/yule/test/spark//workspace/yule/test/sparkoutputyarn-cluster--num-executors3--driver-memory3g--executor-memory6g

yarn-client相当于是命令行会将你输入的代码提交到yarn上面执行yarn-cluster是将你写好的程序打成jar包然后提交到yarn上面去执行然后yarn会将jar包分发到各个节点并负责资源分配和任务管理

hdfs://youkuDfs/workspace/yule/test/spark/clientupload20151105.csv

runing任务界面http://a01.master.spark.hadoop.qingdao.youku:8088

historyserver界面：http://a01.master.spark.hadoop.qingdao.youku:18080/

实际解析

----------------------------------------------

laifeng-spark-clientup.jar

/opt/spark-onyarn/spark/default/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masteryarn-cluster/work/yule/online/spark/laifeng-spark-clientup.jar/workspace/yule/test/spark//workspace/yule/test/sparkoutputyarn-cluster--num-executors3--driver-memory3g--executor-memory6g

/opt/spark-onyarn/spark/default/bin/spark-submit--classcom.laifeng.ddshow.clientup.LaifengClientUpInfoAccessStat--masteryarn-cluster/work/yule/online/spark/laifeng-spark-clientup.jar/source/ent/laifeng/clientupload/20151108//workspace/yule/test/sparkclientInfo/20151108/yarn-cluster--num-executors3--driver-memory3g--executor-memory6g