spark可以通过交互式命令行及编程两种方式来进行调用：
前者支持scala与python
后者支持scala、python与java
本文参考https://spark.apache.org/docs/latest/quick-start.html，可作快速入门
再详细资料及用法请见https://spark.apache.org/docs/latest/programming-guide.html
建议学习路径：
1、安装单机环境：http://blog.csdn.net/jediael_lu/article/details/45310321
2、快速入门，有简单的印象：本文http://blog.csdn.net/jediael_lu/article/details/45333195
3、学习scala
4、深入一点：https://spark.apache.org/docs/latest/programming-guide.html
5、找其它专业资料或者在使用中学习
一、基础介绍
1、spark的所有操作均是基于RDD(Resilient Distributed Dataset)进行的，其中R（弹性）的意思为可以方便的在内存和存储间进行交换。
2、RDD的操作可以分为2类：transformation 和 action，其中前者从一个RDD生成另一个RDD(如filter)，后者对RDD生成一个结果（如count)。
二、命令行方式
只要机器上有java与scala，spark无需任何安装、配置，就可直接运行，可以首先通过运行：
./bin/run-example SparkPi 10检查是否正常，然后再使用下面介绍的shell来执行job。
1、快速入门
$ ./bin/spark-shell
（1）先将一个文件读入一个RDD中，然后统计这个文件的行数及显示第一行。
scala> var textFile = sc.textFile("/mnt/jediael/spark-1.3.1-bin-hadoop2.6/README.md")
textFile: org.apache.spark.rdd.RDD[String] = /mnt/jediael/spark-1.3.1-bin-hadoop2.6/README.md MapPartitionsRDD[1] at textFile at <console>:21
scala> textFile.count()
res0: Long = 98
scala> textFile.first();
res1: String = # Apache Spark
（2）统计包含spark的行数
scala> val linesWithSpark = textFile.filter(line => line.contains("Spark"))
linesWithSpark: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[2] at filter at <console>:23
scala> linesWithSpark.count()
res0: Long = 19
（3）以上的filter与count可以组合使用
scala> textFile.filter(line => line.contains("Spark")).count()
res1: Long = 19
2、深入一点
（1）使用map统计每一行的单词数量，reduce找出最大的那一行所包括的单词数量
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
res2: Int = 14
（2）在scala中直接调用java包
scala> import java.lang.Math
import java.lang.Math
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => Math.max(a, b))
res2: Int = 14
（3）wordcount的实现
scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey((a, b) => a + b)
wordCounts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[8] at reduceByKey at <console>:24
scala> wordCounts.collect()
res4: Array[(String, Int)] = Array((package,1), (For,2), (processing.,1), (Programs,1), (Because,1), (The,1), (cluster.,1), (its,1), ([run,1), (APIs,1), (computation,1), (Try,1), (have,1), (through,1), (several,1), (This,2), ("yarn-cluster",1), (graph,1), (Hive,2), (storage,1), (["Specifying,1), (To,2), (page](http://spark.apache.org/documentation.html),1), (Once,1), (application,1), (prefer,1), (SparkPi,2), (engine,1), (version,1), (file,1), (documentation,,1), (processing,,2), (the,21), (are,1), (systems.,1), (params,1), (not,1), (different,1), (refer,2), (Interactive,2), (given.,1), (if,4), (build,3), (when,1), (be,2), (Tests,1), (Apache,1), (all,1), (./bin/run-example,2), (programs,,1), (including,3), (Spark.,1), (package.,1), (1000).count(),1), (HDFS,1), (Versions,1), (Data.,1), (>...
3、缓存：将RDD写入缓存会大大提高处理效率
scala> linesWithSpark.cache()
res5: linesWithSpark.type = MapPartitionsRDD[2] at filter at <console>:23
scala> linesWithSpark.count()
res8: Long = 19
三、编码
scala代码，还不熟悉，以后再运行
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
object SimpleApp {
def main(args: Array[String]) {
val logFile = "YOUR_SPARK_HOME/README.md" // Should be some file on your system
val conf = new SparkConf().setAppName("Simple Application")
val sc = new SparkContext(conf)
val logData = sc.textFile(logFile, 2).cache()
val numAs = logData.filter(line => line.contains("a")).count()
val numBs = logData.filter(line => line.contains("b")).count()
println("Lines with a: %s, Lines with b: %s".format(numAs, numBs))
}
}
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