SparkSQL---实战应用

数据集 ：http://grouplens.org/datasets/movielens/ MovieLens 1M Datase

相关数据文件 ：

users.dat ---UserID::Gender::Age::Occupation::Zip-code

movies.dat --- MovieID::Title::Genres

ratings.dat ---UserID::MovieID::Rating::Timestamp

SogouQ.mini

完成以下业务需求：

1. 年龄段在“18-24”的男性年轻人，最喜欢看哪10部

2.得分最高的10部电影；看过电影最多的前10个人；女性看多最多的10部电影；男性看过最多 的10部电影

3.利用数据集SogouQ2012.mini.tar.gz 将数据按照访问次数进行排序，求访问量前10的网站

代码如下：

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.Dataset


object hw_SparkSql {
    case class User(uid: String, xb: String,age:Int,V4:String,V5:String)
    case class Movie(mid:String,name:String,t:String)
    case class Rating(uid: String, mid: String,V3:Double,V4:String)
    case class Brower(V1: String, V2: String,V3:String,V4:String,V5:String,V6:String)

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

    val conf = new SparkConf().setAppName("ReadJSON").setMaster("local").set("spark.executor.memory","50g").set("spark.driver.maxResultSize","50g")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    
　　//隐式转换
    import sqlContext.implicits._ 
    val UserInfo = sc.textFile("C:\\Users\\BIGDATA\\Desktop\\文件\\BigData\\Spark\\3.SparkCore_2\\data\\data\\users.dat").map(_.split("::")).map(p => User(p(0), p(1),p(2).trim().toInt,p(3),p(4))).toDF()
    UserInfo.registerTempTable("User")
    val MovieInfo = sc.textFile("C:\\Users\\BIGDATA\\Desktop\\文件\\BigData\\Spark\\3.SparkCore_2\\data\\data\\movies.dat").map(_.split("::")).map(p => Movie(p(0),p(1),p(2))).toDF()
    MovieInfo.registerTempTable("Movie")
    val RatingsInfo = sc.textFile("C:\\Users\\BIGDATA\\Desktop\\文件\\BigData\\Spark\\3.SparkCore_2\\data\\data\\ratings.dat").map(_.split("::")).map(p => Rating(p(0), p(1),p(2).toDouble,p(3))).toDF()
    RatingsInfo.registerTempTable("Rating")
     val BrowerInfo = sc.textFile("C:\\Users\\BIGDATA\\Desktop\\文件\\BigData\\Spark\\3.SparkCore_2\\SogouQ2012.mini\\SogouQ.mini").map(_.split("\t")).map(p =>Brower(p(0), p(1),p(2),p(3),p(4),p(5))).toDF()
    BrowerInfo.registerTempTable("Brower")
    
    //年龄段在“18-24”的男性年轻人，最喜欢看哪10部
    val top10_M_18_24 = sqlContext.sql("select x.n as name,count(*) as count from ( select distinct Rating.mid as m, Rating.uid as u, Movie.name as n  FROM Rating,User,Movie WHERE User.age>=18 and User.age<=24 and User.xb=\"M\" and User.uid=Rating.uid and Movie.mid=Rating.mid)as x  group by x.n order by count desc  ")
    top10_M_18_24.show(10)
    
    
     //看过电影最多的前10个人  
     val  top10_pepole= sqlContext.sql("select uid,count(uid)as count from Rating group by uid order by count desc");  
     top10_pepole.show(10);  
     
      //得分最高的10部电影  
    val top10M_score=sqlContext.sql("select mid,(sum(V3)/count(V3)) as av  from Rating group by mid order by av desc")  
    top10M_score.show(10)
    
    //女性看的最多的10部电影
    val top10_Female = sqlContext.sql("select x.n,count(*) as c from ( select distinct Rating.mid as m, Rating.uid as u, Movie.name as n  FROM Rating,User,Movie WHERE User.xb=\"F\" and User.uid=Rating.uid and Movie.mid=Rating.mid)as x  group by x.n order by c desc  ")
    top10_Female.show(10)
    
    
    //男性看的最多的10部电影
    val top10_Male = sqlContext.sql("select x.n,count(*) as c from ( select distinct Rating.mid as m, Rating.uid as u, Movie.name as n  FROM Rating,User,Movie WHERE User.xb=\"M\" and User.uid=Rating.uid and Movie.mid=Rating.mid)as x  group by x.n order by c desc  ")
    top10_Male.show(10)
    
    //访问量前10的网站
    val Top10_brower = sqlContext.sql("select V6 as name,count(*) as count from Brower group by V6 order by count desc  ")
    Top10_brower.show(10)
    }

}

SparkSQL---实战应用

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