shark0.8.1安裝部署

 

            作爲Hadoop生態系統一部分的Hive，使得用戶可以編寫類sql(HQL)語句後再由Hive進行轉化成不同的map-reduce任務交給hadoop來執行。Spark是一個分佈式的內存計算系統，主要充當分佈式計算的部分，比hadoop的map-reduce速度更快。shark是spark的一個組件，一個開源的、分佈式的、容錯的基於內存的分析系統，使用了現有的Hive客戶端和元數據存儲，兼容了Hive的功能，速度比hive快很多，是一個在spark之上調用shark來運行hive數據的系統，它把HQL轉化成多個小任務在spark上運行。  shark是大數據實時查詢分析的利器，支持HiveQL，Hive數據格式以及udf函數，shark SQL查詢比Hive快100倍，此外可以用來查詢HDFS，HBase數據，機器學習比hadoop快100倍。

•  Spark 0.8.1
•  Shark 0.8.1
•  Hive   0.9.0
•  Hadoop2.0.0-CDH4.4.0
•  Scala-0.9.3　　

以下是各種下載地址：直接wget即可。

名稱	下載地址
Spark 0.8.1	http://d3kbcqa49mib13.cloudfront.net/spark-0.8.1-incubating-bin-cdh4.tgz
Shark 0.8.1	https://github.com/amplab/shark/releases/download/v0.8.1/shark-0.8.1-bin-cdh4.tgz
Hive 0.9.0	https://github.com/amplab/shark/releases/download/v0.8.1/hive-0.9.0-bin.tgz
Hadoop2-CDH4.3	http://archive.cloudera.com/cdh4/cdh/4/hadoop-2.0.0-cdh4.3.0.tar.gz
Scala 0.9.3	http://www.scala-lang.org/files/archive/scala-2.9.3.tgz

 

 spark:        

$cd $YOUR_SPARK_HOME/
$vim conf/spark-env.sh

        修改spark-env.sh

SCALA_HOME=/usr/lib/spark-0.8.1-incubating-bin-cdh4/scala-2.9.3
JAVA_HOME=/usr/java/jdk1.7.0_25
export CLASSPATH=/usr/java/jdk1.7.0_25/lib
SPARK_MASTER_IP=192.168.10.220
SPARK_MASTER_PORT=8081
SPARK_MASTER_WEBUI_PORT=8090
SPARK_WORKER_CORES=2
SPARK_WORKER_MEMORY=4g
SPARK_WORKER_PORT=8091
SPARK_WORKER_WEBUI_PORT=8092
SPARK_WORKER_INSTANCES=1
export SPARK_JAVA_OPTS="-verbose:gc -XX:-PrintGCDetails -XX:+PrintGCTimeStamps"
export HADOOP_HOME=/usr/lib/hadoop

Hive:

         若使用mysql保存hive的元數據相關信息，需要拷貝mysql-connector-java-3.1.13-bin.jar 到$HIVE_HOME/lib 目錄下。

#cd $YOUR_HIVE_HOME

#cp conf/hive-env.sh.template conf/hive-env.sh

#cp conf/hive-default.xml.template conf/hive-site.xml

#vim conf/hive-env.sh

       修改hive-env.xml         

....
HADOOP_HOME=$YOUR_HADOOP2_HOME
....

       修改hive-site.xml

 

.....

<property>
<name>javax.jdo.option.ConnectionURL</name>
<value>jdbc:mysql://localhost:3306/SHARK_DATABASE?createDatabaseIfNotExist=true</value>
<description>JDBC connect string for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionDriverName</name>
<value>com.mysql.jdbc.Driver</value>
<description>Driver class name for a JDBC metastore</description>
</property>

<property>
<name>javax.jdo.option.ConnectionUserName</name>
<value>shark</value>
<description>username to use against metastore database</description>
</property>

<property>
<name>javax.jdo.option.ConnectionPassword</name>
<value>shark</value>
<description>password to use against metastore database</description>
</property>

......

 

  Shark:       

#cd $YOUR_SHARK_HOME
#cp conf/shark-env.sh.template conf/shark-env.sh
#vim conf/shark-env.sh

       修改shark-env.sh

......

export SCALA_HOME=/usr/lib/scala-2.9.3 export HADOOP_HOME=/usr/lib/hadoop export HIVE_HOME=/usr/lib/shark-0.8.1-bin-cdh4/hive-0.9.0-bin/ export MASTER=spark://192.168.10.220:8081 export SPARK_HOME=/usr/lib/spark-0.8.1-incubating-bin-cdh4 export HIVE_CONF_DIR=/usr/lib/shark-0.8.0-bin-cdh4/hive-0.9.0-bin/conf

# 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 " export SPARK_JAVA_OPTS

......  

啓動並測試     

#cd $YOUR_HADOOP_HOME
#bin/hadoop namenode -format
#sbin/start-all.sh

#cd $YOUR_SPARK_HOME
#bin/start-all.sh

#cd $YOUR_SHARK_HOME
#bin/shark
Starting the Shark Command Line Client
......
......
shark>show databases;
shark>create database SHARK_DB;
shark>use SHARK_DB;
shark>create table tbl_test(ID STRING);

 

可以看到shark作業按照隊列逐一運行，在spark集羣上運行狀態如下：

Spark Master at spark://192.168.10.220:8081

 

• URL: spark://192.168.10.220:8081
• Workers: 4
• Cores: 16 Total, 16 Used
• Memory: 57.6 GB Total, 4.0 GB Used
• Applications: 2 Running, 11 Completed

 

Workers

Id	Address	State	Cores	Memory
worker-20140518173608-CHBM220-34134	CHBM220:8081	ALIVE	4 (4 Used)	14.4 GB (1024.0 MB Used)
worker-20140518173608-CHBM221-41264	CHBM221:8081	ALIVE	4 (4 Used)	14.4 GB (1024.0 MB Used)
worker-20140518173610-CHBM223-36479	CHBM223:8081	ALIVE	4 (4 Used)	14.4 GB (1024.0 MB Used)
worker-20140518173611-CHBM224-41840	CHBM224:8081	ALIVE	4 (4 Used)	14.4 GB (1024.0 MB Used)

 

Running Applications

ID	Name	Cores	Memory per Node	Submitted Time	User	State	Duration
app-20140519085613-0012	Shark::CHBM220	0	1024.0 MB	2014/05/19 08:56:13	root	WAITING	9.4 min
app-20140519083651-0009	Shark::CHBM220	16	1024.0 MB	2014/05/19 08:36:51	root	RUNNING	29 min

 

Completed Applications

ID	Name	Cores	Memory per Node	Submitted Time	User	State	Duration
app-20140519083741-0011	Shark::CHBM220	0	1024.0 MB	2014/05/19 08:37:41	root	FINISHED	8.7 min
app-20140519083723-0010	Shark::CHBM220	0	1024.0 MB	2014/05/19 08:37:23	root	FINISHED	1 s