spark1.3.1使用基础教程

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



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))

  }

}