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  本片博文为大家带来的是计算各区域热门商品 Top3。

一. 需求

1.1 需求简介

这里的热门商品是从点击量的维度来看的.

计算各个区域前三大热门商品，并备注上每个商品在主要城市中的分布比例，超过两个城市用其他显示。

1.2 思路分析

使用 sql 来完成. 碰到复杂的需求, 可以使用 udf 或 udaf

1. 查询出来所有的点击记录, 并与 city_info 表连接, 得到每个城市所在的地区. 与 Product_info 表连接得到产品名称
2. 按照地区和商品 id 分组, 统计出每个商品在每个地区的总点击次数
3. 每个地区内按照点击次数降序排列
4. 只取前三名. 并把结果保存在数据库中
5. 城市备注需要自定义 UDAF 函数

二. 实际操作

1. 准备数据

  我们这次 Spark-sql 操作中所有的数据均来自 Hive.

  首先在 Hive 中创建表, 并导入数据.

  一共有 3 张表: 1 张用户行为表, 1 张城市表, 1 张产品表

• 1. 打开Hive

• 2. 创建三个表

CREATE TABLE `user_visit_action`(   `date` string,   `user_id` bigint,   `session_id` string,   `page_id` bigint,   `action_time` string,   `search_keyword` string,   `click_category_id` bigint,   `click_product_id` bigint,   `order_category_ids` string,   `order_product_ids` string,   `pay_category_ids` string,   `pay_product_ids` string,   `city_id` bigint) row format delimited fields terminated by 't';  CREATE TABLE `product_info`(   `product_id` bigint,   `product_name` string,   `extend_info` string) row format delimited fields terminated by 't';  CREATE TABLE `city_info`(   `city_id` bigint,   `city_name` string,   `area` string) row format delimited fields terminated by 't'; 

• 3. 上传数据

load data local inpath '/opt/module/datas/user_visit_action.txt' into table spark0806.user_visit_action; load data local inpath '/opt/module/datas/product_info.txt' into table spark0806.product_info; load data local inpath '/opt/module/datas/city_info.txt' into table spark0806.city_info; 

• 4. 测试是否上传成功

hive> select * from city_info; 

2. 显示各区域热门商品 Top3

• 1. 源码

 // user_visit_action  product_info  city_info 1. 先把需要的字段查出来   t1 select     ci.*,     pi.product_name,     click_product_id from user_visit_action uva join product_info pi on uva.click_product_id=pi.product_id join city_info ci on uva.city_id=ci.city_id  2. 按照地区和商品名称聚合 select     area,     product_name, count(*)  count from t1 group by area , product_name  3. 按照地区进行分组开窗 排序 开窗函数 t3 // (rank(1 2 2 4 5...) row_number(1 2 3 4...) dense_rank(1 2 2 3 4...)) select     area,     product_name,     count, rank() over(partition by area order by count desc) from  t2   4. 过滤出来名次小于等于3的 select     area,     product_name,     count from  t3 where rk <=3 

• 2. 运行结果

3. 定义udaf函数 得到需求结果

• 1. 源码

package com.buwenbuhuo.spark.sql.project  import java.text.DecimalFormat  import org.apache.spark.sql.Row import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction} import org.apache.spark.sql.types._   /**  **  *  * @author 不温卜火  *         *  * @create 2020-08-06 13:24  **  *         MyImapBox ：  https://buwenbuhuo.blog.csdn.net/  *  */ class CityRemarkUDAF extends UserDefinedAggregateFunction { // 输入数据的类型:  北京  String   override def inputSchema: StructType = { StructType(Array(StructField("city", StringType))) } // 缓存的数据的类型 每个地区的每个商品 缓冲所有城市的点击量 北京->1000, 天津->5000  Map,  总的点击量  1000/?   override def bufferSchema: StructType = { StructType(Array(StructField("map", MapType(StringType, LongType)), StructField("total", LongType))) } // 输出的数据类型  "北京21.2%，天津13.2%，其他65.6%"  String   override def dataType: DataType = StringType    // 相同的输入是否应用有相同的输出.   override def deterministic: Boolean = true // 给存储数据初始化   override def initialize(buffer: MutableAggregationBuffer): Unit = { //初始化map缓存 buffer(0) = Map[String, Long]() // 初始化总的点击量 buffer(1) = 0L   } // 分区内合并 Map[城市名, 点击量]   override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {     input match { case Row(cityName: String) => // 1. 总的点击量 + 1 buffer(1) = buffer.getLong(1) + 1L         // 2. 给这个城市的点击量 +1 =>   找到缓冲区的map，取出来这个城市原来的点击 + 1 ，再复制过去         val map: collection.Map[String, Long] = buffer.getMap[String, Long](0) buffer(0) = map + (cityName -> (map.getOrElse(cityName, 0L) + 1L)) case _ => } } // 分区间的合并   override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {     val map1 = buffer1.getAs[Map[String, Long]](0)     val map2 = buffer2.getAs[Map[String, Long]](0)      val total1: Long = buffer1.getLong(1)     val total2: Long = buffer2.getLong(1) // 1. 总数的聚合 buffer1(1) = total1 + total2     // 2. map的聚合 buffer1(0) = map1.foldLeft(map2) { case (map, (cityName, count)) =>         map + (cityName -> (map.getOrElse(cityName, 0L) + count)) } } // 最终的输出结果   override def evaluate(buffer: Row): Any = { // "北京21.2%，天津13.2%，其他65.6%"     val cityAndCount: collection.Map[String, Long] = buffer.getMap[String, Long](0)     val total: Long = buffer.getLong(1)      val cityCountTop2: List[(String, Long)] = cityAndCount.toList.sortBy(-_._2).take(2)     var cityRemarks: List[CityRemark] = cityCountTop2.map { case (cityName, count) => CityRemark(cityName, count.toDouble / total) } //    CityRemark("其他",1 - cityremarks.foldLeft(0D)(_+_.cityRatio))     cityRemarks :+= CityRemark("其他",cityRemarks.foldLeft(1D)(_ - _.cityRatio))     cityRemarks.mkString(",") } } case class CityRemark(cityName: String, cityRatio: Double) {   val formatter = new DecimalFormat("0.00%")    override def toString: String = s"$cityName:${formatter.format(cityRatio)}" } 

• 运行结果

4 .保存到Mysql

• 1. 源码

    val props: Properties = new Properties()     props.put("user","root")     props.put("password","199712")     spark.sql( """         |select         |    area, |    product_name, |    count, |    remark         |from t3         |where rk<=3 |""".stripMargin) .coalesce(1) .write       .mode("overwrite") .jdbc("jdbc:mysql://hadoop002:3306/rdd?useUnicode=true&characterEncoding=utf8", "spark0806", props) 

• 2.运行结果

三. 完整代码

• 1. udaf

package com.buwenbuhuo.spark.sql.project  import java.text.DecimalFormat  import org.apache.spark.sql.Row import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction} import org.apache.spark.sql.types._   /**  **  *  * @author 不温卜火  *         *  * @create 2020-08-06 13:24  **  *         MyImapBox ：  https://buwenbuhuo.blog.csdn.net/  *  */ class CityRemarkUDAF extends UserDefinedAggregateFunction { // 输入数据的类型:  北京  String   override def inputSchema: StructType = { StructType(Array(StructField("city", StringType))) } // 缓存的数据的类型 每个地区的每个商品 缓冲所有城市的点击量 北京->1000, 天津->5000  Map,  总的点击量  1000/?   override def bufferSchema: StructType = { StructType(Array(StructField("map", MapType(StringType, LongType)), StructField("total", LongType))) } // 输出的数据类型  "北京21.2%，天津13.2%，其他65.6%"  String   override def dataType: DataType = StringType    // 相同的输入是否应用有相同的输出.   override def deterministic: Boolean = true // 给存储数据初始化   override def initialize(buffer: MutableAggregationBuffer): Unit = { //初始化map缓存 buffer(0) = Map[String, Long]() // 初始化总的点击量 buffer(1) = 0L   } // 分区内合并 Map[城市名, 点击量]   override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {     input match { case Row(cityName: String) => // 1. 总的点击量 + 1 buffer(1) = buffer.getLong(1) + 1L         // 2. 给这个城市的点击量 +1 =>   找到缓冲区的map，取出来这个城市原来的点击 + 1 ，再复制过去         val map: collection.Map[String, Long] = buffer.getMap[String, Long](0) buffer(0) = map + (cityName -> (map.getOrElse(cityName, 0L) + 1L)) case _ => } } // 分区间的合并   override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {     val map1 = buffer1.getAs[Map[String, Long]](0)     val map2 = buffer2.getAs[Map[String, Long]](0)      val total1: Long = buffer1.getLong(1)     val total2: Long = buffer2.getLong(1) // 1. 总数的聚合 buffer1(1) = total1 + total2     // 2. map的聚合 buffer1(0) = map1.foldLeft(map2) { case (map, (cityName, count)) =>         map + (cityName -> (map.getOrElse(cityName, 0L) + count)) } } // 最终的输出结果   override def evaluate(buffer: Row): Any = { // "北京21.2%，天津13.2%，其他65.6%"     val cityAndCount: collection.Map[String, Long] = buffer.getMap[String, Long](0)     val total: Long = buffer.getLong(1)      val cityCountTop2: List[(String, Long)] = cityAndCount.toList.sortBy(-_._2).take(2)     var cityRemarks: List[CityRemark] = cityCountTop2.map { case (cityName, count) => CityRemark(cityName, count.toDouble / total) } //    CityRemark("其他",1 - cityremarks.foldLeft(0D)(_+_.cityRatio))     cityRemarks :+= CityRemark("其他",cityRemarks.foldLeft(1D)(_ - _.cityRatio))     cityRemarks.mkString(",") } } case class CityRemark(cityName: String, cityRatio: Double) {   val formatter = new DecimalFormat("0.00%")    override def toString: String = s"$cityName:${formatter.format(cityRatio)}" } 

• 2. 主程序(具体实现)

package com.buwenbuhuo.spark.sql.project  import java.util.Properties  import org.apache.spark.sql.SparkSession  /**  **  *  * @author 不温卜火  *         *  * @create 2020-08-05 19:01  **  *         MyImapBox ：  https://buwenbuhuo.blog.csdn.net/  *  */ object SqlApp {   def main(args: Array[String]): Unit = {     val spark: SparkSession = SparkSession       .builder() .master("local[*]") .appName("SqlApp") .enableHiveSupport() .getOrCreate() import spark.implicits._     spark.udf.register("remark",new CityRemarkUDAF) // 去执行sql，从hive查询数据     spark.sql("use spark0806")     spark.sql( """         |select         |    ci.*, |    pi.product_name, |    uva.click_product_id         |from user_visit_action uva         |join product_info pi on uva.click_product_id=pi.product_id         |join city_info ci on uva.city_id=ci.city_id         | |""".stripMargin).createOrReplaceTempView("t1")      spark.sql( """         |select         |    area, |    product_name, | count(*) count, | remark(city_name) remark         |from t1         |group by area, product_name         |""".stripMargin).createOrReplaceTempView("t2")      spark.sql( """         |select         |    area, |    product_name, |    count, |    remark, | rank() over(partition by area order by count desc) rk         |from t2         |""".stripMargin).createOrReplaceTempView("t3")      val props: Properties = new Properties()     props.put("user","root")     props.put("password","199712")     spark.sql( """         |select         |    area, |    product_name, |    count, |    remark         |from t3         |where rk<=3 |""".stripMargin) .coalesce(1) .write       .mode("overwrite") .jdbc("jdbc:mysql://hadoop002:3306/rdd?useUnicode=true&characterEncoding=utf8", "spark0806", props) // 把结果写入到mysql中      spark.close() } } 

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