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安装spark1.3.1单机环境

2016-07-11 14:50 330 查看
本文介绍安装spark单机环境的方法,可用于测试及开发。主要分成以下4部分:

(1)环境准备

(2)安装scala

(3)安装spark

(4)验证安装情况

1、环境准备

(1)配套软件版本要求:Spark runs on Java 6+ and Python 2.6+. For the Scala API, Spark 1.3.1 uses Scala 2.10. You will need to use a compatible Scala version (2.10.x).

(2)安装好linux、jdk、python, 一般linux均会自带安装好jdk与python,但注意jdk默认为openjdk,建议重新安装oracle jdk。

(3)IP:10.171.29.191  hostname:master

2、安装scala

(1)下载scala

wget http://downloads.typesafe.com/scala/2.10.5/scala-2.10.5.tgz
(2)解压文件

tar -zxvf scala-2.10.5.tgz

(3)配置环境变量

#vi/etc/profile

#SCALA VARIABLES START

export SCALA_HOME=/home/jediael/setupfile/scala-2.10.5

export PATH=$PATH:$SCALA_HOME/bin

#SCALA VARIABLES END

$ source /etc/profile

$ scala -version

Scala code runner version 2.10.5 -- Copyright 2002-2013, LAMP/EPFL

(4)验证scala

$ scala

Welcome to Scala version 2.10.5 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_51).

Type in expressions to have them evaluated.

Type :help for more information.

scala> 9*9

res0: Int = 81

3、安装spark

(1)下载spark

wget http://mirror.bit.edu.cn/apache/spark/spark-1.3.1/spark-1.3.1-bin-hadoop2.6.tgz
(2)解压spark

tar -zxvf http://mirror.bit.edu.cn/apache/spark/spark-1.3.1/spark-1.3.1-bin-hadoop2.6.tgz
(3)配置环境变量

#vi/etc/profile

#SPARK VARIABLES START

export SPARK_HOME=/mnt/jediael/spark-1.3.1-bin-hadoop2.6

export PATH=$PATH:$SPARK_HOME/bin

#SPARK VARIABLES END

$ source /etc/profile

(4)配置spark

 $ pwd

/mnt/jediael/spark-1.3.1-bin-hadoop2.6/conf

$ mv spark-env.sh.template spark-env.sh

$vi spark-env.sh

export SCALA_HOME=/home/jediael/setupfile/scala-2.10.5

export JAVA_HOME=/usr/java/jdk1.7.0_51

export SPARK_MASTER_IP=10.171.29.191

export SPARK_WORKER_MEMORY=512m

export master=spark://10.171.29.191:7070

$vi slaves

master

(5)启动spark

pwd

/mnt/jediael/spark-1.3.1-bin-hadoop2.6/sbin

$ ./start-all.sh

注意,hadoop也有start-all.sh脚本,因此必须进入具体目录执行脚本

$ jps

30302 Worker

30859 Jps

30172 Master

4、验证安装情况

(1)运行自带示例

$ bin/run-example  org.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/
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