spark练习——影评案例

package movie_rating

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

object Utils {
    //初始化SparkConf对象
    private[movie_rating] val conf = new SparkConf().setAppName("FileReview").setMaster("local")
    //初始化sc对象
    private[movie_rating]  val sc = new SparkContext(conf)
    sc.setLogLevel("ERROR")

    //读取hdfs上的数据
    private[movie_rating] val movie = sc.textFile("hdfs://myha01/mydata/film_review/movies.dat")
    private[movie_rating] val ratings = sc.textFile("hdfs://myha01/mydata/film_review/ratings.dat")
    private[movie_rating] val users = sc.textFile("hdfs://myha01/mydata/film_review/users.dat")

    //将原始数据转为RDD格式
    private[movie_rating] val movieRdd: RDD[(String, String, String)] = movie.map(_.split("::")).map(m => (m(0), m(1), m(2)))
    private[movie_rating] val ratingsRdd: RDD[(String, String, String, String)] = ratings.map(_.split("::")).map(r => (r(0), r(1), r(2), r(3)))
    private[movie_rating] val usersRdd: RDD[(String, String, String, String, String)] = users.map(_.split("::")).map(u => (u(0), u(1), u(2), u(3), u(4)))

}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand01 {
    /**
      * 1、求被评分次数最多的 10 部电影，并给出评分次数（电影名，评分次数）
      */
    def main(args: Array[String]): Unit = {

        //获取电影id与对应的评分次数
        val movieID_rating: RDD[(String, Int)] = Utils.ratingsRdd.map(x => (x._2, 1))
        val movieID_times: RDD[(String, Int)] = movieID_rating.reduceByKey(_ + _).sortBy(_._2, false)
        //获得电影id和电影名
        val movieID_name: RDD[(String, String)] = Utils.movieRdd.map(x => (x._1, x._2))
        //关联movieID_times和movieID_name，获得电影id，电影名，评分次数
        val result: RDD[(String, Int)] = movieID_times.join(movieID_name).sortBy(_._2._1, false).map(x => (x._2._2, x._2._1))
        //输出结果
        result.take(10).foreach(println(_))
    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand02 {
    /**
      * 2、分别求男性，女性当中评分最高的 10 部电影（性别，电影名，影评分）
      */
    def main(args: Array[String]): Unit = {
        //(userID, sex)
        val userID_sex: RDD[(String, String)] = Utils.usersRdd.map(x => (x._1, x._2))
        //(userID, (movieID, rating))
        val userID_movieID_rating: RDD[(String, (String, String))] = Utils.ratingsRdd.map(x => (x._1, (x._2, x._3)))

        //(userID, (sex, (movieID, rating)))  ---> (sex, movieID, rating)
        val movieID_rating: RDD[(String, String, String)] = userID_sex.join(userID_movieID_rating).map(x => (x._2._1, x._2._2._1, x._2._2._2))

        //((sex, movieID), Iterable[(sex, movieID, rating)])  ---> (movieID, (sex, avg))
        val movieID_sex_avg: RDD[(String, (String, Double))] = movieID_rating.groupBy(x => (x._1, x._2)).map(x => {
            var sum, avg = 0d
            val list: List[(String, String, String)] = x._2.toList

            if (list.size > 50) {
                list.map(x => ( sum += x._3.toInt ))
                avg = sum * 1.0 / list.size
            }
            (x._1._2, (x._1._1, avg))
        })
        //(movieID, movieName)
        val movieID_movieName: RDD[(String, String)] = Utils.movieRdd.map(x => (x._1, x._2))
        //sex_movieID_avg与movie进行关联 (movieID, ((sex, avg), movieName)) ---> (sex, movieName, avg)
        val sex_movieName_avg: RDD[(String, String, Double)] = movieID_sex_avg.join(movieID_movieName)
            .map(x => (x._2._1._1, x._2._2, x._2._1._2)).sortBy(x => (x._1, x._3), false)

        sex_movieName_avg.take(10).foreach(println(_))
        sex_movieName_avg.filter(_._1 == "F").take(10).foreach(println(_))

    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand03 {
    /**
      * 3、分别求男性，女性看过最多的 10 部电影（性别，电影名）
      */
    def main(args: Array[String]): Unit = {
        //(userID, sex)
        val userID_sex: RDD[(String, String)] = Utils.usersRdd.map(x => (x._1, x._2))
        //(userID, movieID)
        val userID_movieID: RDD[(String, String)] = Utils.ratingsRdd.map(x => (x._1, x._2))
        //(movieID, name)
        val movieID_name: RDD[(String, String)] = Utils.movieRdd.map(x => (x._1, x._2))

        //(userID, (sex, movieID))  ---> (movieID, sex)
        val movieID_sex: RDD[(String, String)] = userID_sex.join(userID_movieID).map(x => (x._2._2, x._2._1))
        //关联movieID_sex和movieID_name    (movieID, (sex, name))  ---> (movieID, sex, name)
        val movieID_sex_name: RDD[(String, String, String)] = movieID_sex.join(movieID_name)
            .map(x => (x._1, x._2._1, x._2._2))

        //((sex, name), Iterable[(movieID, sex, name)])  ---> (sex, name, times)
        val sex_name_times: RDD[(String, String, Int)] = movieID_sex_name.groupBy(x => (x._2, x._3)).map(x => (x._1._1, x._1._2, x._2.toList.size)).sortBy(x => (x._1, x._3), false)
        //输出结果
        sex_name_times.take(10).foreach(println(_))
        sex_name_times.filter(_._1 == "F").take(10).foreach(println(_))
    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand04 {
    /**
      * 4、年龄段在“18-24”的男人，最喜欢看 10 部电影（输出电影id和电影名字）
      */
    def main(args: Array[String]): Unit = {
        // 年龄段在“18-24”的男人的userID     (userID, (sex, age))
        val userID_sex_age: RDD[(String, (String, Int))] = Utils.usersRdd.map(x => (x._1, (x._2, x._3.toInt))).filter(x =>{
            x._2._2 >= 18 && x._2._2 <= 24 && x._2._1 == "M"
        } )

        //(userID, (movieID, rating))
        val userID_movieID_rating: RDD[(String, (String, Int))] = Utils.ratingsRdd.map(x => (x._1, (x._2, x._3.toInt)))

        //关联userID与userID_movieID_rating    (userID, ((sex, age), (movieID, rating)))   ---> (movieID, rating)
        // --->(movieID, Iterable(movieID, rating))  ---> (movieID, avg)
        val movieID_avg : RDD[(String, Double)] = userID_sex_age.join(userID_movieID_rating).map(x => (x._2._2._1, x._2._2._2))
            .groupByKey().map(x => {
            var avg = 0d
            val len: Int = x._2.size
            if (len > 50){
                avg = 1.0 * x._2.sum / len
            }
            (x._1, avg)
        })

        //(movieID, name)
        val movieID_name: RDD[(String, String)] = Utils.movieRdd.map(x => (x._1, x._2))

        //关联movieID_avg与movieID_name    (movieID, (avg, name))
        val name_avg: RDD[(String, Double)] = movieID_avg.join(movieID_name).map(x => (x._2._2, x._2._1)).sortBy(_._2, false)

        //输出结果
        name_avg.take(10).foreach(println(_))
    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand05 {
    /**
      * 5、求 movieid = 2116 这部电影各年龄段（因为年龄就只有 7 个，就按这个 7 个分就好了）
      * 的平均影评（年龄段，影评分）
      */
    def main(args: Array[String]): Unit = {
        // 获得movieID = 2116  (userID, rating)
        val userID_rating: RDD[(String, Int)] = Utils.ratingsRdd.filter(_._2 == "2116").map(x => (x._1, x._3.toInt))
        //(userID, age)
        val userID_age: RDD[(String, String)] = Utils.usersRdd.map(x => (x._1, x._3))
        //关联userID_age和userID_rating   (userID, (age, rating)) --->(age, rating)  ---> (age, Iterable(rating))
        val age_avg: RDD[(String, Double)] = userID_age.join(userID_rating).map(x => (x._2._1, x._2._2)).groupByKey()
            .map(x => (x._1, x._2.sum * 1.0 / x._2.size))

        //输出结果
        age_avg.sortByKey().foreach(println(_))
    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand06 {
    /**
      * 6、求最喜欢看电影（影评次数最多）的那位女性评最高分的 10 部电影的平均影评分
      * （观影者userID，电影名，影评分)
      */
    def main(args: Array[String]): Unit = {
        //(userID, Iterable(userID, movieID, rating, time_stamp))  ---> (userID, times)
        val userID_times: RDD[(String, Int)] = Utils.ratingsRdd.groupBy(_._1).map(x => (x._1, x._2.size))

        //(userID, (sex, times))找到最喜欢看电影（影评次数最多）的那位女性的userID
        val userID: String = Utils.usersRdd.map(x => (x._1, x._2)).join(userID_times).filter(_._2._1 == "F")
            .sortBy(_._2._2, false).map(_._1).first()

        //获得userID用户评分最高的10部电影的movieID
        val movieID: Array[(String, Int)] = Utils.ratingsRdd.filter(_._1 == userID).map(x => (x._2, x._3.toInt))
            .sortBy(_._2, false).take(10)

        //获得该10部电影的平均影评分
        val movieID_rating: RDD[(String, String)] = Utils.ratingsRdd.map(x => (x._2, x._3))

        //关联movieID和movieID_rating   (movieID, (rat1, rating))  ---> (movieID, Iterable(rating))  --> (movieID, avg)
        val movieID_avg = Utils.sc.makeRDD(movieID).join(movieID_rating).map(x => (x._1, x._2._2.toInt))
            .groupByKey().map(x => {
            var avg = 0d
            if (x._2.size >= 50) {
                avg = x._2.sum * 1.0 / x._2.size
            }
            (x._1, avg)
        })

        //(movieID, (name, avg))   ---> (UserID, name, avg)
        val userID_name_avg: RDD[(String, String, Double)] = Utils.movieRdd.map(x => (x._1, x._2))
            .join(movieID_avg).map(x => (userID, x._2._1, x._2._2)).sortBy(_._3, false)

        userID_name_avg.foreach(println(_))
    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand07 {
    /**
      * 7、求好片（评分>=4.0）最多的那个年份的最好看的 10 部电影（电影id， 电影名，平均评分）
      */
    def main(args: Array[String]): Unit = {
        //1、找到所有的好片的movieID
        //(movieID, rating) ---> (movieID, Iterable(rating))  ---> (movieID, avg)（avg >= 4.0）
        val movieID_avg :RDD[(String, Double)]= Utils.ratingsRdd.map(x => (x._2, x._3.toInt)).groupByKey().map(x =>{
            var avg = 0d
            if(x._2.size >= 50)
                avg = x._2.sum * 1.0 / x._2.size
            (x._1, avg)
        }).filter(_._2 >= 4.0)

        //(movieID, (name, year))
        val movieID_name_year: RDD[(String, (String, String))] = Utils.movieRdd.map(x => (x._1, (x._2, x._2.substring(x._2.length - 5, x._2.length - 1))))

        //2、找到好片最多的年代
        //关联movieID_avg与movieID_name_year，(movieID, (avg, (name, year)))   --> (year, Iterable(movieID))
        val year_count: (String, Int) = movieID_avg.join(movieID_name_year).map(x => (x._2._2._2, x._1))
            .groupByKey().map(x => (x._1, x._2.size)).sortBy(_._2, false).first()

        //3、找到该年最好看的10部电影
        //(movieID, name) ---> (movieID, (name, avg))  ---> (movieID, name, avg)
        val movieID_name_avg = movieID_name_year.filter(_._2._2 == year_count._1).map( x => (x._1, x._2._1))
            .join(movieID_avg).map(x => (x._1, x._2._1, x._2._2)).sortBy(_._3, false).take(10)

        //输出结果
        movieID_name_avg.foreach(println(_))

    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand08 {
    /**
      * 8、求 1997 年上映的电影中，评分最高的 10 部 Comedy 类电影（电影id，电影名字，类型，平均评分）
      */
    def main(args: Array[String]): Unit = {
        //(movieID, (name, year, type))
        val movieID_name_year_type: RDD[(String, (String, String, String))] = Utils.movieRdd
            .map(x => (x._1, (x._2, x._2.substring(x._2.length - 5, x._2.length - 1), x._3)))

        //找到所有1997年的comedy类型的电影 (movieID, (name, 1997, comedy))
        val movieID_name_1997_comedy: RDD[(String, (String, String, String))] = movieID_name_year_type.filter(x => {x._2._2 == "1997" && x._2._3.toLowerCase.contains("comedy")} )

        //(movieID, (rating, (name, 1997, comedy)))  ---> (movieID, (name, comedy, rating))
        val movieID_name_comedy_rating: RDD[(String, (String, String, String))] = Utils.ratingsRdd.map(x => (x._2, x._3))
            .join(movieID_name_1997_comedy).map(x => (x._1, (x._2._2._1, x._2._2._3, x._2._1)))


        //(movieID, Iterable(rating))  ---> (movieID, avg)
        val movieID_avg: Array[(String, Double)] = movieID_name_comedy_rating.map(x => (x._1, x._2._3.toInt))
            .groupByKey().map(x => {
            var avg = 0d
            if (x._2.size >= 50)
                avg = x._2.sum * 1.0 / x._2.size
            (x._1, avg)
        }).distinct().sortBy(_._2, false).take(10)

        //(movieID, (avg, (name, comedy, rating)))  ---> (movieID, name, comedy, avg)
        val movieID_name_comedy_avg: RDD[(String, String, String, Double)] = Utils.sc.makeRDD(movieID_avg)
            .join(movieID_name_comedy_rating).map(x => (x._1, x._2._2._1, x._2._2._2, x._2._1)).distinct().sortBy(_._4, false)

        //输出结果
        movieID_name_comedy_avg.foreach(println(_))
    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand09 {
    /**
      * 9、该影评库中各种类型电影中评价最高的 5 部电影（类型，电影名，平均影评分）
      */
    def main(args: Array[String]): Unit = {

        //获得所有电影的movieID，name，types      (movieID, (name, types))
        val movieID_name_types: RDD[(String, (String, String))] = Utils.movieRdd.map(x => (x._1, (x._2, x._3)))

        //获得所有的movieID，rating (movieID, rating)
        val movieID_rating: RDD[(String, String)] = Utils.ratingsRdd.map(x => (x._2, x._3))

        //关联movieID_name_types与movieID_rating   (movieID, ((name, types), rating))  ---> (types, name, rating)
        val types_name_rating: RDD[((String, String), Int)] = movieID_name_types.join(movieID_rating)
            .map(x => ((x._2._1._2, x._2._1._1), x._2._2.toInt))

        //((types, name), Iterable(rating))  ---> (types, name, avg)
        val types_name_avg: RDD[(String, String, Double)] = types_name_rating.groupByKey().map(x => {
            var avg = 0d
            if (x._2.size >= 50)
                avg = x._2.sum * 1.0 / x._2.size
            (x._1._1, x._1._2, avg)
        })

        //(types, name, avg)     划分types：将Action|Adventure|Comedy|Sci-Fi拆开
        var tempArray: Array[(String, String, Double)] = Array(("", "", 0d))

        types_name_avg.collect().foreach(x => {
            //Action|Adventure|Comedy|Sci-Fi   ---> Arrays(Action, Adventure, Comedy, Sci-Fi)
            val types: Array[String] = x._1.split("\\|")
            //将所有的types_name_avg中的元素拆分后存于tempArray数组中
            tempArray = types.map((_, x._2, x._3)).union(tempArray)
        })

        //(type, name, avg)  包含所有类型电影的排序
        val type_name_avg = Utils.sc.makeRDD(tempArray).filter(_._3 > 0).sortBy(x => (x._1, x._3), false)

        //(type, Iterable(type, name, avg))  打印前五
        type_name_avg.groupBy(_._1).sortByKey().foreach(x => {
            var count = 0
            val list: List[(String, String, Double)] = x._2.toList
            while(count < list.size  && count < 5){
                println(list(count))
                count += 1
            }
            println()
        })
    }
}

package movie_rating

import org.apache.spark.rdd.RDD

/**
  * Utils.usersRdd：对应字段中文解释：用户 id，性别，年龄，职业，邮政编码
  * Utils.movieRdd：对应字段中文解释：电影 ID，电影名字，电影类型
  * Utils.ratingsRdd：对应字段中文解释：用户 ID，电影 ID，评分，评分时间戳
  */
object Demand10 {
    /**
      * 10、各年评分最高的电影类型（年份，类型，影评分）
      */
    def main(args: Array[String]): Unit = {
        //(movieID, year)
        val movieID_year: RDD[(String, String)] = Utils.movieRdd.map(x => (x._1, (x._2.substring(x._2.length - 5, x._2.length - 1))))

        //(movieID, rating)  ---> (movieID, Iterable(rating)) ---> (movieID, avg)
        val moviID_avg: RDD[(String, Double)] = Utils.ratingsRdd.map(x => (x._2, x._3.toDouble)).groupByKey()
            .map(x => (x._1, x._2.sum / x._2.size))

        //关联movieID_year和moviID_avg   (movieID, (year, avg)) ---> (year, (movieID, avg))
        val year_mocvieID_avg: RDD[(String, (String, Double))] = movieID_year.join(moviID_avg)
            .distinct().map(x => (x._2._1, (x._1, x._2._2)))

        //(year, (movieID, avg))  ---> (year, Iterable((movieID, avg)))  ---> (movieID, (year, topavg))
        val year_movieID_topavg: RDD[(String, (String, Double))] = year_mocvieID_avg.groupByKey().map(x => {
            val list: List[(String, Double)] = x._2.toList.sortBy(-_._2)
            (list(0)._1, (x._1, list(0)._2))
        })

        //(movieID, (type, (year, topavg)) ---> (year, type, topavg)
        val year_type_topavg: RDD[(String, String, Double)] = Utils.movieRdd.map(x => (x._1, x._3))
            .join(year_movieID_topavg).map(x => (x._2._2._1, x._2._1, x._2._2._2)).sortBy(_._1, false)

        //输出结果
        year_type_topavg.foreach(println(_))
    }
}