Hadoop版本是2.2.0的稳定版本 下载地址
spark版本：spark-0.9.1-bin-hadoop2 下载地址
这里的spark有三个版本：
For Hadoop 1 (HDP1, CDH3): find an Apache mirror or direct file download
For CDH4: find an Apache mirror or direct file download
For Hadoop 2 (HDP2, CDH5): find an Apache mirror or direct file download
我的hadoop版本是hadoop2.2.0的，所以下载的是for hadoop2
关于spark的介绍可以参看
Apache Spark is a fast and general engine for large-scale data processing.
spark运行时需要scala环境，这里下载最新版本的scala
scala是一种可伸缩的语言是一种多范式的编程语言，一种类似java的编程，设计初衷是要集成面向对象编程和函数式编程的各种特性。Scala是在JVM上运行，Scala是一种纯粹的面向对象编程语言，而又无缝地结合了命令式和函数式的编程风格
ok 开始配置spark：
我是在hadoop的安装用户下面安装的，所以这里直接编辑/home/hadoop/.bashrc
[hadoop@localhost ~]$ cat .bashrc
# .bashrc
# Source global definitions
if [ -f /etc/bashrc ]; then
. /etc/bashrc
fi
# User specific aliases and functions
export HADOOP_HOME=/home/hadoop/hadoop
export HBASE_HOME=/home/hadoop/hbase
export HIVE_HOME=/home/hadoop/hive
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export YARN_HOME=/etc/home/hadoop
export YARN_CONF_DIR=$HADOOP_HOME/etc/hadoop
export SCALA_HOME=/home/hadoop/scala
export SPARK_HOME=/home/hadoop/spark
export PATH=${PATH}:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HBASE_HOME/bin:$HIVE_HOME/bin:$SCALA_HOME/bin:$SPARK_HOME/bin
export CLASSPATH=$CLASSPATH:$HADOOP/lib:$HBASE_HOME/lib
1.scala安装：
将scala解压到hadoop根目录下
ln -ls scala-2.11.0 scala#建立软链接
lrwxrwxrwx. 1 hadoop hadoop 12 May 21 09:15 scala -> scala-2.11.0
drwxrwxr-x. 6 hadoop hadoop 4096 Apr 17 16:10 scala-2.11.0
编辑.bashrc 加入 export SCALA_HOME=/home/hadoop/scala
export PATH=${PATH}:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HBASE_HOME/bin:$HIVE_HOME/bin:$SCALA_HOME/bin:$SPARK_HOME/bin
保存 并使环境变量生效 source .bashrc
验证安装：
[hadoop@localhost ~]$ scala -version
Scala code runner version 2.11.0 -- Copyright 2002-2013, LAMP/EPFL
能够正常显示版本说明安装成功
2：spark配置：
tar -xzvf spark-0.9.1-bin-hadoop2.tgz
ln -s spark-0.9.1-bin-hadoop2 spark
然后配置.bashrc
export SPARK_HOME=/home/hadoop/spark
export PATH=${PATH}:$HADOOP_HOME/bin:$HADOOP_HOME/sbin:$HBASE_HOME/bin:$HIVE_HOME/bin:$SCALA_HOME/bin:$SPARK_HOME/bin
编辑完成source .bashrc 使环境变量生效
spark-env.sh配置：
spark-env.sh是不存在的 需要从 cat spark-env.sh.template >> spark-env.sh 生成
然后编辑spark-env.sh
加入一下内容
export SCALA_HOME=/home/hadoop/scala
export JAVA_HOME=/usr/java/jdk
export SPARK_MASTER=localhost
export SPARK_LOCAL_IP=localhost
export HADOOP_HOME=/home/hadoop/hadoop
export SPARK_HOME=/home/hadoop/spark
export SPARK_LIBARY_PATH=.:$JAVA_HOME/lib:$JAVA_HOME/jre/lib:$HADOOP_HOME/lib/native
export YARN_CONF_DIR=$HADOOP_HOME/etc/hadoop
保存退出
3.启动spark
跟hadoop的目录结构相似 在spark下面的sbin里边放了启动和关闭的shell文件
-rwxrwxr-x. 1 hadoop hadoop 2504 Mar 27 13:44 slaves.sh
-rwxrwxr-x. 1 hadoop hadoop 1403 Mar 27 13:44 spark-config.sh
-rwxrwxr-x. 1 hadoop hadoop 4503 Mar 27 13:44 spark-daemon.sh
-rwxrwxr-x. 1 hadoop hadoop 1176 Mar 27 13:44 spark-daemons.sh
-rwxrwxr-x. 1 hadoop hadoop 965 Mar 27 13:44 spark-executor
-rwxrwxr-x. 1 hadoop hadoop 1263 Mar 27 13:44 start-all.sh
-rwxrwxr-x. 1 hadoop hadoop 2384 Mar 27 13:44 start-master.sh
-rwxrwxr-x. 1 hadoop hadoop 1520 Mar 27 13:44 start-slave.sh
-rwxrwxr-x. 1 hadoop hadoop 2258 Mar 27 13:44 start-slaves.sh
-rwxrwxr-x. 1 hadoop hadoop 1047 Mar 27 13:44 stop-all.sh
-rwxrwxr-x. 1 hadoop hadoop 1124 Mar 27 13:44 stop-master.sh
-rwxrwxr-x. 1 hadoop hadoop 1427 Mar 27 13:44 stop-slaves.sh
[hadoop@localhost sbin]$ pwd
/home/hadoop/spark/sbin
这里只需要运行start-all就可以了～～～
[hadoop@localhost sbin]$ ./start-all.sh
rsync from localhost
rsync: change_dir "/home/hadoop/spark-0.9.1-bin-hadoop2/sbin/localhost" failed: No such file or directory (2)
rsync error: some files/attrs were not transferred (see previous errors) (code 23) at main.c(1039) [sender=3.0.6]
starting org.apache.spark.deploy.master.Master, logging to /home/hadoop/spark/logs/spark-hadoop-org.apache.spark.deploy.master.Master-1-localhost.out
localhost: rsync from localhost
localhost: rsync: change_dir "/home/hadoop/spark-0.9.1-bin-hadoop2/localhost" failed: No such file or directory (2)
localhost: rsync error: some files/attrs were not transferred (see previous errors) (code 23) at main.c(1039) [sender=3.0.6]
localhost: starting org.apache.spark.deploy.worker.Worker, logging to /home/hadoop/spark/logs/spark-hadoop-org.apache.spark.deploy.worker.Worker-1-localhost.out