spark1.3.1使用基础教程 分类： B8_SPARK 2015-04-28 11:10 1651人阅读 评论(0) 收藏

 

spark能够经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，可做快速入门

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：

一、安装单机环境：http://blog.csdn.net/jediael_lu/article/details/45310321

二、快速入门，有简单的印象：本文http://blog.csdn.net/jediael_lu/article/details/45333195

三、学习scala

四、深刻一点：https://spark.apache.org/docs/latest/programming-guide.html

五、找其它专业资料或者在使用中学习

1、基础介绍
一、spark的全部操做均是基于RDD(Resilient Distributed Dataset)进行的，其中R（弹性）的意思为能够方便的在内存和存储间进行交换。
二、RDD的操做能够分为2类：transformation 和 action，其中前者从一个RDD生成另外一个RDD(如filter)，后者对RDD生成一个结果（如count)。

2、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，能够首先经过运行：

./bin/run-example SparkPi 10

检查是否正常，而后再使用下面介绍的shell来执行job。

 

一、快速入门
$ ./bin/spark-shell

（1）先将一个文件读入一个RDD中，而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能够组合使用
scala> textFile.filter(line => line.contains("Spark")).count()
res1: Long = 19

二、深刻一点
（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), (>...

三、缓存：将RDD写入缓存会大大提升处理效率
scala> linesWithSpark.cache()
res5: linesWithSpark.type = MapPartitionsRDD[2] at filter at <console>:23
scala> linesWithSpark.count()
res8: Long = 19

3、编码 scala代码，还不熟悉，之后再运行 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))   } }