Spark练习代码

1、scalaWordCount

package com._51doit.spark.day1

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object ScalaWorldCount {

def main(args: Array[String]): Unit = {

//第一步：创建SparkContext
val conf: SparkConf = new SparkConf().setAppName("ScalaWorldCount")
val sc = new SparkContext(conf)

//第二步，指定以后从hdfs中读取数据创建RDD（神奇的大集合）
//正确但是不建议sc.textFile(args(0)).flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).sortBy(_._2,false).saveAsTextFile(args(1))
val lines: RDD[String] = sc.textFile(args(0))

//切分压平
val words: RDD[String] = lines.flatMap(_.split(""))

//将单词和1组合在一起
val wordAndOne: RDD[(String, Int)] = words.map((_,1))

//聚合
val reduced: RDD[(String, Int)] = wordAndOne.reduceByKey(_+_,1)

//排序
val sorted: RDD[(String, Int)] = reduced.sortBy(_._2,false)

//将数据保存到HDFS中
sorted.saveAsTextFile(args(1))

//最后释放资源
sc.stop()

}
}

2、FavTeacherInSubject

package com._51doit.spark.day2

import java.net.URL

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object FavTeacherInSubject {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName(this.getClass.getSimpleName)

    val sc = new SparkContext(conf)

    val lines: RDD[String] = sc.textFile(args(0))

    //处理数据
    val subjectTeacherAndOne: RDD[((String, String), Int)] = lines.map(line => {
      val teacher = line.substring(line.lastIndexOf("/") + 1)
      val url = new URL(line)
      val host = url.getHost
      val subject = host.substring(0, host.indexOf("."))
      ((subject, teacher), 1)
    })

    //聚合
    val reduced: RDD[((String, String), Int)] = subjectTeacherAndOne.reduceByKey(_+_)

    //按照学科进行分组
    val grouped: RDD[(String, Iterable[((String, String), Int)])] = reduced.groupBy(_._1._1)

    //组内排序
    val sorted: RDD[(String, List[((String, String), Int)])] = grouped.mapValues(_.toList.sortBy(-_._2).take(2))

    //把数据保存到hdfs
    sorted.saveAsTextFile(args(1))

    sc.stop()

  }
}

3、(根据IP规则，计算用户的地区分布数量)

package com._51doit.spark.day3

import com._51doit.spark.utils.MyUtil
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

import scala.collection.mutable.ArrayBuffer

object IpLocation {

  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName(this.getClass.getSimpleName)

    val sc = new SparkContext(conf)

    //指定以后从哪里读取数据创建RDD
    val accessLog: RDD[String] = sc.textFile(args(0))

    //对数据进行处理
    val provinceAndOne: RDD[(String, Int)] = accessLog.map(line => {
      val fields = line.split("[|]")
      val ip = fields(1)
      val ipNum = MyUtil.ip2Long(ip)
      val province = IpRulesUtil.binarySearch(ipNum)
      (province, 1)
    })

    //聚合
    val reudced: RDD[(String, Int)] = provinceAndOne.reduceByKey(_+_)

    reudced.saveAsTextFile(args(1))

    sc.stop()
  }

}

package com._51doit.spark.day3

import java.io.{BufferedReader, InputStreamReader}
import java.net.URI

import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FSDataInputStream, FileSystem, Path}

import scala.collection.mutable.ArrayBuffer

object IpRulesUtil {

  //初始化一个集合
  val ipRules = new ArrayBuffer[(Long, Long, String)]()

  //hdfs的读取数据的过程
  val conf = new Configuration()
  val fs: FileSystem = FileSystem.get(URI.create("hdfs://node-1.51doit.com:9000"), conf)
  val in: FSDataInputStream = fs.open(new Path("/iprules/ip.txt"))
  val reader = new BufferedReader(new InputStreamReader(in))

  var line = reader.readLine()

  while (line != null) {
    val fields = line.split("[|]")
    val startNum = fields(2).toLong
    val endNum = fields(3).toLong
    val province = fields(6)
    ipRules.append((startNum, endNum, province))

    line = reader.readLine()
  }

  def binarySearch(ip: Long) : String = {
    var low = 0
    var high = ipRules.length - 1
    while (low <= high) {
      val middle = (low + high) / 2
      if ((ip >= ipRules(middle)._1) && (ip <= ipRules(middle)._2))
        return ipRules(middle)._3
      if (ip < ipRules(middle)._1)
        high = middle - 1
      else {
        low = middle + 1
      }
    }
    "未知"
  }
}