JAVA高级面试进阶训练营视频教程

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1.部署环境

• OS:Red Hat Enterprise Linux Server release 6.4 (Santiago)
• Hadoop:Hadoop 2.4.1
• Hive：0.11.0
• JDK:1.7.0_60
• Python:2.6.6(spark集群需要python2.6以上，否则无法在spark集群上运行py)
• Spark:0.9.1（最新版是1.1.0）
• Shark:0.9.1（目前最新的版本，但是只能够兼容到spark-0.9.1，见shark 0.9.1 release）
• Zookeeper：2.3.5（配置HA时使用，Spark HA配置参见我的博文：Spark:Master High Availability（HA）高可用配置的2种实现）
• Scala:2.11.2

2.Spark集群规划

• 账户:ebupt
• master:eb174
• slaves:eb174、eb175、eb176

3.建立ssh

cd ~
#生成公钥和私钥
ssh-keygen -q -t rsa -N "" -f /home/ebupt/.ssh/id_rsa
cd .ssh
cat id_rsa.pub > authorized_keys
chmod go-wx authorized_keys
#把文件authorized_keys复制到所有子节点的/home/ebupt/.ssh目录下
scp ~/.ssh/authorized_keys ebupt@eb175:~/.ssh/
scp ~/.ssh/authorized_keys ebupt@eb176:~/.ssh/

另一个简单的方法：

由于实验室集群eb170可以ssh到所有的机器，因此直接拷贝eb170的~/.ssh/所有文件到eb174的~/.ssh/中。这样做的好处是不破坏原有的eb170的ssh免登陆。

[ebupt@eb174 ~]$rm ~/.ssh/*
[ebupt@eb170 ~]$scp -r ~/.ssh/ ebupt@eb174:~/.ssh/

4.部署scala，完全拷贝到所有节点

tar zxvf scala-2.11.2.tgz

ln -s /home/ebupt/eb/scala-2.11.2 ~/scala

vi ~/.bash_profile

#添加环境变量
export SCALA_HOME=$HOME/scala
export PATH=$PATH:$SCALA_HOME/bin

通过scala –version便可以查看到当前的scala版本，说明scala安装成功。

[ebupt@eb174 ~]$ scala -version
Scala code runner version 2.11.2 -- Copyright 2002-2013, LAMP/EPFL

5.安装spark，完全拷贝到所有节点

解压建立软连接，配置环境变量，略。

[ebupt@eb174 ~]$ vi spark/conf/slaves 

#add the slaves
eb174
eb175
eb176

[ebupt@eb174 ~]$ vi spark/conf/spark-env.sh 

export SCALA_HOME=/home/ebupt/scala
export JAVA_HOME=/home/ebupt/eb/jdk1.7.0_60
export SPARK_MASTER_IP=eb174
export SPARK_WORKER_MEMORY=4000m

6.安装shark，完全拷贝到所有节点

解压建立软连接，配置环境变量，略。

[ebupt@eb174 ~]$ vi shark/conf/shark-env.sh 

export SPARK_MEM=1g

# (Required) Set the master program's memory
export SHARK_MASTER_MEM=1g

# (Optional) Specify the location of Hive's configuration directory. By default,
# Shark run scripts will point it to $SHARK_HOME/conf
export HIVE_HOME=/home/ebupt/hive
export HIVE_CONF_DIR="$HIVE_HOME/conf"

# For running Shark in distributed mode, set the following:
export HADOOP_HOME=/home/ebupt/hadoop
export SPARK_HOME=/home/ebupt/spark
export MASTER=spark://eb174:7077
# Only required if using Mesos:
#export MESOS_NATIVE_LIBRARY=/usr/local/lib/libmesos.so
source $SPARK_HOME/conf/spark-env.sh

#LZO compression native lib
export LD_LIBRARY_PATH=/home/ebupt/hadoop/share/hadoop/common

# (Optional) Extra classpath

export SPARK_LIBRARY_PATH=/home/ebupt/hadoop/lib/native

# Java options
# On EC2, change the local.dir to /mnt/tmp
SPARK_JAVA_OPTS=" -Dspark.local.dir=/tmp "
SPARK_JAVA_OPTS+="-Dspark.kryoserializer.buffer.mb=10 "
SPARK_JAVA_OPTS+="-verbose:gc -XX:-PrintGCDetails -XX:+PrintGCTimeStamps "
SPARK_JAVA_OPTS+="-XX:MaxPermSize=256m "
SPARK_JAVA_OPTS+="-Dspark.cores.max=12 "
export SPARK_JAVA_OPTS

# (Optional) Tachyon Related Configuration
#export TACHYON_MASTER="" # e.g. "localhost:19998"
#export TACHYON_WAREHOUSE_PATH=/sharktables # Could be any valid path name
export SCALA_HOME=/home/ebupt/scala
export JAVA_HOME=/home/ebupt/eb/jdk1.7.0_60

7.同步到slaves的脚本

7.1 master(eb174)的~/.bash_profile

# .bash_profile
# Get the aliases and functions
if [ -f ~/.bashrc ]; then
        . ~/.bashrc
fi
# User specific environment and startup programs
PATH=$PATH:$HOME/bin
export PATH

export JAVA_HOME=/home/ebupt/eb/jdk1.7.0_60
export PATH=$JAVA_HOME/bin:$PATH
export CLASSPATH=.:$CLASSPATH:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar

export HADOOP_HOME=$HOME/hadoop
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin

export ZOOKEEPER_HOME=$HOME/zookeeper
export PATH=$ZOOKEEPER_HOME/bin:$PATH

export HIVE_HOME=$HOME/hive
export PATH=$HIVE_HOME/bin:$PATH

export HBASE_HOME=$HOME/hbase
export PATH=$PATH:$HBASE_HOME/bin

export MAVEN_HOME=$HOME/eb/apache-maven-3.0.5
export PATH=$PATH:$MAVEN_HOME/bin

export STORM_HOME=$HOME/storm
export PATH=$PATH:$STORM_HOME/storm-yarn-master/bin:$STORM_HOME/storm-0.9.0-wip21/bin

export SCALA_HOME=$HOME/scala
export PATH=$PATH:$SCALA_HOME/bin

export SPARK_HOME=$HOME/spark
export PATH=$PATH:$SPARK_HOME/bin

export SHARK_HOME=$HOME/shark
export PATH=$PATH:$SHARK_HOME/bin

7.2 同步脚本：syncInstall.sh

scp -r /home/ebupt/eb/scala-2.11.2 ebupt@eb175:/home/ebupt/eb/
scp -r /home/ebupt/eb/scala-2.11.2 ebupt@eb176:/home/ebupt/eb/
scp -r /home/ebupt/eb/spark-1.0.2-bin-hadoop2 ebupt@eb175:/home/ebupt/eb/
scp -r /home/ebupt/eb/spark-1.0.2-bin-hadoop2 ebupt@eb176:/home/ebupt/eb/
scp -r /home/ebupt/eb/spark-0.9.1-bin-hadoop2 ebupt@eb175:/home/ebupt/eb/
scp -r /home/ebupt/eb/spark-0.9.1-bin-hadoop2 ebupt@eb176:/home/ebupt/eb/
scp ~/.bash_profile ebupt@eb175:~/
scp ~/.bash_profile ebupt@eb176:~/

7.3 配置脚本：build.sh

#!/bin/bash
source ~/.bash_profile

ssh eb175 > /dev/null 2>&1 << eeooff
ln -s /home/ebupt/eb/scala-2.11.2/ /home/ebupt/scala
ln -s /home/ebupt/eb/spark-0.9.1-bin-hadoop2/ /home/ebupt/spark
ln -s /home/ebupt/eb/shark-0.9.1-bin-hadoop2/ /home/ebupt/shark
source ~/.bash_profile
exit
eeooff
echo eb175 done!

ssh eb176 > /dev/null 2>&1 << eeooffxx
ln -s /home/ebupt/eb/scala-2.11.2/ /home/ebupt/scala
ln -s /home/ebupt/eb/spark-0.9.1-bin-hadoop2/ /home/ebupt/spark
ln -s /home/ebupt/eb/shark-0.9.1-bin-hadoop2/ /home/ebupt/shark
source ~/.bash_profile
exit
eeooffxx
echo eb176 done!

8 遇到的问题及其解决办法

8.1 安装shark-0.9.1和spark-1.0.2时，运行shark shell，执行sql报错。

shark> select * from test;
17.096: [Full GC 71198K->24382K(506816K), 0.3150970 secs]
Exception in thread "main" java.lang.VerifyError: class org.apache.hadoop.hdfs.protocol.proto.ClientNamenodeProtocolProtos$SetOwnerRequestProto overrides final method getUnknownFields.()Lcom/google/protobuf/UnknownFieldSet;
 at java.lang.ClassLoader.defineClass1(Native Method)
 at java.lang.ClassLoader.defineClass(ClassLoader.java:800)
 at java.security.SecureClassLoader.defineClass(SecureClassLoader.java:142)
 at java.net.URLClassLoader.defineClass(URLClassLoader.java:449)
 at java.net.URLClassLoader.access$100(URLClassLoader.java:71)
 at java.net.URLClassLoader$1.run(URLClassLoader.java:361)
 at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
 at java.security.AccessController.doPrivileged(Native Method)
 at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
 at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
 at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)
 at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
 at java.lang.Class.getDeclaredMethods0(Native Method)
 at java.lang.Class.privateGetDeclaredMethods(Class.java:2531)
 at java.lang.Class.privateGetPublicMethods(Class.java:2651)
 at java.lang.Class.privateGetPublicMethods(Class.java:2661)
 at java.lang.Class.getMethods(Class.java:1467)
 at sun.misc.ProxyGenerator.generateClassFile(ProxyGenerator.java:426)
 at sun.misc.ProxyGenerator.generateProxyClass(ProxyGenerator.java:323)
 at java.lang.reflect.Proxy.getProxyClass0(Proxy.java:636)
 at java.lang.reflect.Proxy.newProxyInstance(Proxy.java:722)
 at org.apache.hadoop.ipc.ProtobufRpcEngine.getProxy(ProtobufRpcEngine.java:92)
 at org.apache.hadoop.ipc.RPC.getProtocolProxy(RPC.java:537)
 at org.apache.hadoop.hdfs.NameNodeProxies.createNNProxyWithClientProtocol(NameNodeProxies.java:334)
 at org.apache.hadoop.hdfs.NameNodeProxies.createNonHAProxy(NameNodeProxies.java:241)
 at org.apache.hadoop.hdfs.NameNodeProxies.createProxy(NameNodeProxies.java:141)
 at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:576)
 at org.apache.hadoop.hdfs.DFSClient.<init>(DFSClient.java:521)
 at org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFileSystem.java:146)
 at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2397)
 at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:89)
 at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2431)
 at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2413)
 at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:368)
 at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
 at org.apache.hadoop.hive.ql.Context.getScratchDir(Context.java:180)
 at org.apache.hadoop.hive.ql.Context.getMRScratchDir(Context.java:231)
 at org.apache.hadoop.hive.ql.Context.getMRTmpFileURI(Context.java:288)
 at org.apache.hadoop.hive.ql.parse.SemanticAnalyzer.getMetaData(SemanticAnalyzer.java:1274)
 at org.apache.hadoop.hive.ql.parse.SemanticAnalyzer.getMetaData(SemanticAnalyzer.java:1059)
 at shark.parse.SharkSemanticAnalyzer.analyzeInternal(SharkSemanticAnalyzer.scala:137)
 at org.apache.hadoop.hive.ql.parse.BaseSemanticAnalyzer.analyze(BaseSemanticAnalyzer.java:279)
 at shark.SharkDriver.compile(SharkDriver.scala:215)
 at org.apache.hadoop.hive.ql.Driver.compile(Driver.java:337)
 at org.apache.hadoop.hive.ql.Driver.run(Driver.java:909)
 at shark.SharkCliDriver.processCmd(SharkCliDriver.scala:338)
 at org.apache.hadoop.hive.cli.CliDriver.processLine(CliDriver.java:413)
 at shark.SharkCliDriver$.main(SharkCliDriver.scala:235)
 at shark.SharkCliDriver.main(SharkCliDriver.scala)

原因：不知道它在说什么，大概是说“protobuf”版本有问题.

解决：找到 jar 包 “hive-exec-0.11.0-shark-0.9.1.jar” 在$SHARK_HOME/lib_managed/jars/edu.berkeley.cs.shark/hive-exec, 删掉有关protobuf，重新打包，该报错不再有，脚本如下所示。

cd $SHARK_HOME/lib_managed/jars/edu.berkeley.cs.shark/hive-exec
unzip hive-exec-0.11.0-shark-0.9.1.jar
rm -f com/google/protobuf/*
rm  hive-exec-0.11.0-shark-0.9.1.jar
zip -r hive-exec-0.11.0-shark-0.9.1.jar *
rm -rf com hive-exec-log4j.properties javaewah/ javax/ javolution/ META-INF/ org/

8.2  安装shark-0.9.1和spark-1.0.2时，spark集群正常运行，跑一下简单的job也是可以的，但是shark的job始终出现Spark cluster looks dead, giving up. 在运行shark-shell（shark-withinfo ）时，都会看到连接不上spark的master。报错类似如下：

shark> select * from t1;
16.452: [GC 282770K->32068K(1005568K), 0.0388780 secs]
org.apache.spark.SparkException: Job aborted: Spark cluster looks down
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1028)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$abortStage$1.apply(DAGScheduler.scala:1026)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$abortStage(DAGScheduler.scala:1026)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$processEvent$10.apply(DAGScheduler.scala:619)
        at scala.Option.foreach(Option.scala:236)
        at org.apache.spark.scheduler.DAGScheduler.processEvent(DAGScheduler.scala:619)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$start$1$$anon$2$$anonfun$receive$1.applyOrElse(DAGScheduler.scala:207)
        at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498)
        at akka.actor.ActorCell.invoke(ActorCell.scala:456)
        at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237)
        at akka.dispatch.Mailbox.run(Mailbox.scala:219)
        at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386)
        at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
        at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
        at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
        at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
FAILED: Execution Error, return code -101 from shark.execution.SparkTask

原因：网上有很多人遇到同样的问题，spark集群是好的，但是shark就是不能够很好的运行。查看shark-0.9.1的release发现

Release date: April 10, 2014
Shark 0.9.1 is a maintenance release that stabilizes 0.9.0, which bumps up Scala compatibility to 2.10.3 and Hive compliance to 0.11. The core dependencies for this version are:
Scala 2.10.3
Spark 0.9.1
AMPLab’s Hive 0.9.0
(Optional) Tachyon 0.4.1

这是因为shark版本只兼容到spark-0.9.1，版本不兼容导致无法找到spark集群的master服务。

解决：回退spark版本到spark-0.9.1，scala版本不用回退。回退后运行正常。

9.集群成功运行

9.1启动spark集群standalone模式

[ebupt@eb174 ~]$ ./spark/sbin/start-all.sh 

9.2测试spark集群

[ebupt@eb174 ~]$ ./spark/bin/run-example org.apache.spark.examples.SparkPi 10 spark://eb174:7077

9.3 Spark Master UI：http://eb174:8080/

10 参考资料

1. Apache Spark
2. Apache Shark
3. Shark安装部署与应用
4. Spark github
5. Shark github
6. Spark 0.9.1和Shark 0.9.1分布式安装指南
7. google group-shark users
8. ERIC'S BLOG