spark1.3.1使用基礎教程

spark能夠經過交互式命令行及編程兩種方式來進行調用：
前者支持scala與python
後者支持scala、python與java

本文參考https://spark.apache.org/docs/latest/quick-start.html，可做快速入門

再詳細資料及用法請見https://spark.apache.org/docs/latest/programming-guide.html

建議學習路徑：

一、安裝單機環境：http://blog.csdn.net/jediael_lu/article/details/45310321

二、快速入門，有簡單的印象：本文http://blog.csdn.net/jediael_lu/article/details/45333195

三、學習scala

四、深刻一點：https://spark.apache.org/docs/latest/programming-guide.html

五、找其它專業資料或者在使用中學習

1、基礎介紹
一、spark的全部操做均是基於RDD(Resilient Distributed Dataset)進行的，其中R（彈性）的意思爲能夠方便的在內存和存儲間進行交換。
二、RDD的操做能夠分爲2類：transformation 和 action，其中前者從一個RDD生成另外一個RDD(如filter)，後者對RDD生成一個結果（如count)。

2、命令行方式

一、快速入門
$ ./bin/spark-shell

（1）先將一個文件讀入一個RDD中，而後統計這個文件的行數及顯示第一行。
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）統計包含spark的行數
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與count能夠組合使用
scala> textFile.filter(line => line.contains("Spark")).count()
res1: Long = 19

二、深刻一點
（1）使用map統計每一行的單詞數量，reduce找出最大的那一行所包括的單詞數量
scala> textFile.map(line => line.split(" ").size).reduce((a, b) => if (a > b) a else b)
res2: Int = 14

（2）在scala中直接調用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的實現
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), (>...

三、緩存：將RDD寫入緩存會大大提升處理效率
scala> linesWithSpark.cache()
res5: linesWithSpark.type = MapPartitionsRDD[2] at filter at <console>:23
scala> linesWithSpark.count()
res8: Long = 19

3、編碼

scala代碼，還不熟悉，之後再運行

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))
  }
}