Spark 编程基础

基本框架

package week2

import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.SparkContext._

object WordCount1 {
  def main(args: Array[String]) {
    if (args.length == 0) {
      System.err.println("Usage: WordCount1 <file1>")
      System.exit(1)
    }

    val conf = new SparkConf().setAppName("WordCount1")
    val sc = new SparkContext(conf)


    sc.stop()
  }
}

RDD 方法

//parallelize演示
val num=sc.parallelize(1 to 10)
val doublenum = num.map(_*2)
val threenum = doublenum.filter(_ % 3 == 0)
threenum.collect
threenum.toDebugString

val num1=sc.parallelize(1 to 10,6)
val doublenum1 = num1.map(_*2)
val threenum1 = doublenum1.filter(_ % 3 == 0)
threenum1.collect
threenum1.toDebugString

threenum.cache()
val fournum = threenum.map(x=>x*x)
fournum.collect
fournum.toDebugString
threenum.unpersist()

num.reduce (_ + _)
num.take(5)
num.first
num.count
num.take(5).foreach(println)

//K-V演示
val kv1=sc.parallelize(List(("A",1),("B",2),("C",3),("A",4),("B",5)))
kv1.sortByKey().collect //注意sortByKey的小括号不能省
kv1.groupByKey().collect
kv1.reduceByKey(_+_).collect

val kv2=sc.parallelize(List(("A",4),("A",4),("C",3),("A",4),("B",5)))
kv2.distinct.collect
kv1.union(kv2).collect

val kv3=sc.parallelize(List(("A",10),("B",20),("D",30)))
kv1.join(kv3).collect
kv1.cogroup(kv3).collect

val kv4=sc.parallelize(List(List(1,2),List(3,4)))
kv4.flatMap(x=>x.map(_+1)).collect

//文件读取演示
val rdd1 = sc.textFile("hdfs://hadoop1:8000/dataguru/week2/directory/")
rdd1.toDebugString
val words=rdd1.flatMap(_.split(" "))
val wordscount=words.map(x=>(x,1)).reduceByKey(_+_)
wordscount.collect
wordscount.toDebugString

val rdd2 = sc.textFile("hdfs://hadoop1:8000/dataguru/week2/directory/*.txt")
rdd2.flatMap(_.split(" ")).map(x=>(x,1)).reduceByKey(_+_).collect

//gzip压缩的文件
val rdd3 = sc.textFile("hdfs://hadoop1:8000/dataguru/week2/test.txt.gz")   //MappedRDD[String]
//flatMap 简单说就是弄成一行
rdd3.flatMap(_.split(" ")).map(x=>(x,1)).reduceByKey(_+_).collect


//日志处理演示
//http://download.labs.sogou.com/dl/q.html 完整版(2GB)：gz格式
//访问时间\t用户ID\t[查询词]\t该URL在返回结果中的排名\t用户点击的顺序号\t用户点击的URL
//SogouQ1.txt、SogouQ2.txt、SogouQ3.txt分别是用head -n 或者tail -n 从SogouQ数据日志文件中截取
//结果对于各种partion -> bin/hdfs dfs -getmerge <partion file path> <outfile>
//搜索结果排名第1，但是点击次序排在第2的数据有多少?
val rdd1 = sc.textFile("hdfs://hadoop1:8000/dataguru/data/SogouQ1.txt")
val rdd2=rdd1.map(_.split("\t")).filter(_.length==6)
rdd2.count()
val rdd3=rdd2.filter(_(3).toInt==1).filter(_(4).toInt==2)
rdd3.count()
rdd3.toDebugString

//session查询次数排行榜
val rdd4=rdd2.map(x=>(x(1),1)).reduceByKey(_+_).map(x=>(x._2,x._1)).sortByKey(false).map(x=>(x._2,x._1))
rdd4.toDebugString
rdd4.saveAsTextFile("hdfs://hadoop1:8000/dataguru/week2/output1")


//cache()演示
//放入内存
//检查block命令：bin/hdfs fsck /dataguru/data/SogouQ3.txt -files -blocks -locations
val rdd5 = sc.textFile("hdfs://hadoop1:8000/dataguru/data/SogouQ3.txt")
rdd5.cache()
rdd5.count()    //会使其不会100%cache进来
rdd5.count()  //比较时间 因为数据没有全部cache 进内存


//join演示
val format = new java.text.SimpleDateFormat("yyyy-MM-dd")
case class Register (d: java.util.Date, uuid: String, cust_id: String, lat: Float,lng: Float)
case class Click (d: java.util.Date, uuid: String, landing_page: Int)
//映射成(k,v) map 此处 v 为一个类型 case class
val reg = sc.textFile("hdfs://hadoop1:8000/dataguru/week2/join/reg.tsv").map(_.split("\t")).map(r => (r(1), Register(format.parse(r(0)), r(1), r(2), r(3).toFloat, r(4).toFloat)))     
val clk = sc.textFile("hdfs://hadoop1:8000/dataguru/week2/join/clk.tsv").map(_.split("\t")).map(c => (c(1), Click(format.parse(c(0)), c(1), c(2).trim.toInt)))
reg.join(clk).take(2)
//Array[(String, (Register,Click))]