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 = 192、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)) } }