SparkR 读取数据& Spark运行的配置

1.本地LOCAL环境安装Spark并试运行配置(在Ubuntu系统下例子)

# 打开文件配置环境变量: JAVA,SCALA,SPARK,HADOOP,SBT
gedit /etc/profile
 
# 在文件中加入以下行
export JAVA_HOME=/usr/java/jdk1.8.0_51
export PATH=$JAVA_HOME/bin:$PATH
export CLASSPATH=$CLASSPATH:$JAVA_HOME/lib/dt.jar:$JAVA_HOME/lib/tools.jar
export SCALA_HOME=/usr/scala/scala-2.11.7
export PATH=$SCALA_HOME/bin:$PATH
export SPARK_HOME=/usr/spark/spark-1.4.1-bin-without-hadoop
export PATH=$SPARK_HOME/bin:$PATH
export SBT_HOME=/usr/scala/sbt
export PATH=$SBT_HOME/bin:$PATH
export HADOOP_HOME=/usr/hadoop/hadoop-2.7.0
export PATH=$HADOOP_HOME/bin:$PATH
export CLASSPATH=$CLASSPATH:$HADOOP_HOME/lib
 
# 更新系统文件
source /etc/profile

修改 Spark的配置文件 Spark-env.sh，将Spark-env.sh.template 文件修改名称并添加以下环境变量和类变量

export SCALA_HOME=/usr/scala/scala-2.11.7
export JAVA_HOME=/usr/java/jdk1.8.0_51
export HADOOP_CONF_DIR=/usr/hadoop/hadoop-2.7.0/etc/hadoop
export SPARK_LOCAL_IP=localhost
export SPARK_PUBLIC_DNS=localhost
 
export SPARK_CLASSPATH=${HADOOP_HOME}/share/hadoop/common/hadoop-common-2.7.0.jar:${HADOOP_HOME}/share/hadoop/common/hadoop-nfs-2.7.0.jar
export SPARK_CLASSPATH=${SPARK_CLASSPATH}:${HADOOP_HOME}/share/hadoop/common/lib/slf4j-log4j12-1.7.10.jar:${HADOOP_HOME}/share/hadoop/common/lib/slf4j-api-1.7.10.jar:${HADOOP_HOME}/share/hadoop/common/lib/log4j-1.2.17.jar:${HADOOP_HOME}/share/hadoop/common/lib/commons-configuration-1.6.jar:${HADOOP_HOME}/share/hadoop/common/lib/commons-collections-3.2.1.jar:${HADOOP_HOME}/share/hadoop/common/lib/guava-11.0.2.jar:${HADOOP_HOME}/share/hadoop/common/lib/commons-lang-2.6.jar:${HADOOP_HOME}/share/hadoop/common/lib/hadoop-auth-2.7.0.jar:${HADOOP_HOME}/share/hadoop/common/lib/jetty-6.1.26.jar
 
export SPARK_CLASSPATH=${SPARK_CLASSPATH}:${HADOOP_HOME}/share/hadoop/common/lib/jersey-server-1.9.jar:${HADOOP_HOME}/share/hadoop/common/lib/jersey-core-1.9.jar:${HADOOP_HOME}/share/hadoop/common/lib/jersey-json-1.9.jar:${HADOOP_HOME}/share/hadoop/common/lib/snappy-java-1.0.4.1.jar
 
export SPARK_CLASSPATH=${SPARK_CLASSPATH}:${HADOOP_HOME}/share/hadoop/mapreduce/hadoop-mapreduce-client-common-2.7.0.jar
 
export SPARK_CLASSPATH=${SPARK_CLASSPATH}:${SPARK_HOME}/lib/spark-assembly-1.4.1-hadoop2.2.0.jar:${SPARK_HOME}/lib/spark-1.4.1-yarn-shuffle.jar:${SPARK_HOME}/lib/spark-examples-1.4.1-hadoop2.2.0.jar

当执行./bin/spark-shell 命令行后，出现以下界面代表本地模式成功启动了Spark

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2.R执行Spark命令处理文件

library(SparkR)
# 新建一个SparkContent
sc <- sparkR.init(master="local")