Spark是一个基于内存计算的开源的集群计算系统，目的是让数据分析更加快速，Spark 是一种与 Hadoop 相似的开源集群计算环境，但是两者之间还存在一些不同之处，这些有用的不同之处使 Spark 在某些工作负载方面表现得更加优越，换句话说，Spark 启用了内存分布数据集，除了能够提供交互式查询外，它还可以优化迭代工作负载。尽管创建 Spark 是为了支持分布式数据集上的迭代作业，但是实际上它是对 Hadoop 的补充，可以在 Hadoop 文件系统中并行运行。

CDH5 Spark安装

1    Spark的相关软件包

spark-core: spark的核心软件包
spark-worker: 管理spark-worker的脚本
spark-master: 管理spark-master的脚本
spark-python: Spark的python客户端

2     Spark运行依赖的环境

CDH5
JDK

3     安装Spark

apt-get install spark-core spark-master spark-worker spark-python

        1     Configuring Spark(/etc/spark/conf/spark-env.sh)

SPARK_MASTER_IP, to bind the master to a different IP address or hostname
SPARK_MASTER_PORT / SPARK_MASTER_WEBUI_PORT, to use non-default ports
SPARK_WORKER_CORES, to set the number of cores to use on this machine
SPARK_WORKER_MEMORY, to set how much memory to use (for example 1000MB, 2GB)
SPARK_WORKER_PORT / SPARK_WORKER_WEBUI_PORT
SPARK_WORKER_INSTANCE, to set the number of worker processes per node
SPARK_WORKER_DIR, to set the working directory of worker processes

          2      Starting, Stopping, and Running Spark

service spark-master start
service spark-worker start

                    还有一个GUI界面在<master_host>:18080

5 Running Spark Applications

        1     Spark应用有三种运行模式：

                    Standalone mode：默认模式

                    YARN client mode：提交spark应用到YARN，spark驱动在spark客户端进程上。

          2     运行SparkPi在Standalone模式

source /etc/spark/conf/spark-env.sh
CLASSPATH=$CLASSPATH:/your/additional/classpath
$SPARK_HOME/bin/spark-class [<spark-config-options>]  \     
    org.apache.spark.examples.SparkPi  \  
    spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT 10

           3     运行SparkPi在YARN Client模式

source /etc/spark/conf/spark-env.sh
hdfs dfs -mkdir -p /user/spark/share/lib
hdfs dfs -put $SPARK_HOME/assembly/lib/spark-assembly_*.jar  /user/spark/share/lib/spark-assembly.jar
SPARK_JAR=hdfs://<nn>:<port>/user/spark/share/lib/spark-assembly.jar


source /etc/spark/conf/spark-env.sh
SPARK_CLASSPATH=/your/additional/classpath
SPARK_JAR=hdfs://<nn>:<port>/user/spark/share/lib/spark-assembly.jar
$SPARK_HOME/bin/spark-class [<spark-config-options>]  \    
    org.apache.spark.examples.SparkPi yarn-client 10

source /etc/spark/conf/spark-env.sh
SPARK_JAR=hdfs://<nn>:<port>/user/spark/share/lib/spark-assembly.jar
APP_JAR=$SPARK_HOME/examples/lib/spark-examples_<version>.jar
$SPARK_HOME/bin/spark-class org.apache.spark.deploy.yarn.Client \
      --jar $APP_JAR \
      --class org.apache.spark.examples.SparkPi \
      --args yarn-standalone \
      --args 10