Spark SQL【电商购买数据分析】

Spark 数据分析 （Scala）

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.{SparkConf, SparkContext}import java.io.{File, PrintWriter}object Taobao {case class Info(userId: Long,itemId: Long,action: String,time: String)def main(args: Array[String]): Unit = {// 使用2个CPU核心val conf = new SparkConf().setMaster("local[2]").setAppName("tao bao product")val spark = SparkSession.builder().config(conf).getOrCreate()import spark.implicits._val sc = spark.sparkContext// 从本地文件系统加载文件生成RDD对象val rdd: RDD[Array[String]] = sc.textFile("data/practice2/Processed_UserBehavior.csv").map(_.split(","))// RDD 转为 DataFrame对象val df: DataFrame = rdd.map(attr => Info(attr(0).trim.toInt, attr(1).trim.toInt, attr(2), attr(3))).toDF()// Spark 数据分析//1.用户行为信息统计val behavior_count: DataFrame = df.groupBy("action").count()val result1 = behavior_count.toJSON.collectAsList().toString
//    val writer1 = new PrintWriter(new File("data/practice2/result1.json"))
//    writer1.write(result1)
//    writer1.close()//2.销量前十的商品信息统计val top_10_item:Array[(String,Int)] = df.filter(df("action") === "buy").select(df("itemId")).rdd.map(v => (v(0).toString,1)).reduceByKey(_+_).sortBy(_._2,false).take(10)val result2 = sc.parallelize(top_10_item).toDF().toJSON.collectAsList().toString
//    val writer2 = new PrintWriter(new File("data/practice2/result2.json"))
//    writer2.write(result2)
//    writer2.close()//3.购物数量前十的用户信息统计val top_10_user: Array[(String,Int)] = df.filter(df("action") === "buy").select(df("userId")).rdd.map(v => (v(0).toString, 1)).reduceByKey(_ + _).sortBy(_._2, false).take(10)val result3 = sc.parallelize(top_10_user).toDF().toJSON.collectAsList().toString
//    val writer3 = new PrintWriter(new File("data/practice2/result3.json"))
//    writer3.write(result3)
//    writer3.close()// 4.时间段内平台商品销量统计val buy_order_by_date: Array[(String,Int)] = df.filter(df("action") === "buy").select(df("time")).rdd.map(v => (v.toString().replace("[","").replace("]","").split(" ")(0),1)).reduceByKey(_+_).sortBy(_._1).collect()//转为dataframe
//    buy_order_by_date.foreach(println)/*(2017-11-25,21747)(2017-11-26,22265)(2017-11-27,24583)(2017-11-28,23153)(2017-11-29,24102)(2017-11-30,23994)(2017-12-01,23153)(2017-12-02,28512)*/val result4 = sc.parallelize(buy_order_by_date).toDF().toJSON.collectAsList().toStringval writer4 = new PrintWriter(new File("data/practice2/result4.json"))writer4.write(result4)writer4.close()sc.stop()spark.stop()}
}

数据可视化（pyecharts）