spark2 之 交互式用户行为分析

目录

• 1、离线式行为分析
• 2、交互式行为分析
• 3、DataSet 案例实战

1、离线式行为分析

每天凌晨对昨天的数据进行批量处理和分析，统计出各种指标和报表，放入MySQL等关系型数据库中。第二天就可以看到昨天以及昨天以前的数据分析结果。

2、交互式行为分析

用户在需要的时候，选择对应的查询和分析条件，然后由我们的系统立即运行一个大数据处理分析作业，在最短的时间内给用户提供他们想要的数据，这是所谓的交互式用户行为分析。

3、DataSe 案例实战

需求：

1. 统计指定时间内访问次数最多的十个用户
2. 统计指定时间内购买金额最多的十个用户
3. 统计最近一个周期比上一个周期访问次数最多的十个用户
4. 统计最近一个周期比上一个周期购买金额最多的十个用户
5. 统计指定时间范围内头七天访问次数最多的十个用户
6. 统计指定时间范围内头七天购买金额最多的十个用户

代码：

package com.study.spark2project

import org.apache.spark.sql.SparkSession

object UserActiveDegreeAnalyze {

  case class UserActionLog(logId:Long, userId:Long, actionTime:String, actionType:Long, purchaseMoney:Double)
  case class UserActionLogVO(logId:Long, userId:Long, actionValue:Long)
  case class UserActionLogWithPurchaseMoneyVO(logId:Long, userId:Long, purchaseMoney:Double)

  def  main(args:Array[String]): Unit ={
    val startDate = "2016-09-01";
    val endDate = "2016-11-01";

    val spark = SparkSession
      .builder()
      .appName("UserActiveDegreeAnalyze")
      .master("local")
      .config("spark.sql.warehouse.dir", "G:\\data\\spark-warehouse")
      .getOrCreate()

    import spark.implicits._
    import org.apache.spark.sql.functions._

    val userBaseInfo = spark.read.json("G:\\data\\user_base_info.json")
    val userActionLog = spark.read.json("G:\\data\\user_action_log.json")

      //统计指定时间内访问次数最多的十个用户
    userActionLog
      .filter("actionTime >= '"+ startDate +"' and actionTime <= '"+ endDate+"' and actionType = 0")
      .join(userBaseInfo, userActionLog("userId") === userBaseInfo("userId"))
      .groupBy(userBaseInfo("userId"), userBaseInfo("username"))
      .agg(count(userActionLog("logId")).alias("actionCount"))
      .sort($"actionCount".desc)
      .limit(10)
      .show()

     //统计指定时间内购买金额最多的十个用户
    userActionLog
      .filter("actionTime >= '"+ startDate +"' and actionTime <= '"+ endDate+"' and actionType = 1")
      .join(userBaseInfo, userActionLog("userId") === userBaseInfo("userId"))
      .groupBy(userBaseInfo("userId"), userBaseInfo("username"))
      .agg(round(sum(userActionLog("purchaseMoney")),2).alias("totalPurchaseMoney"))
      .sort($"totalPurchaseMoney".desc)
      .limit(10)
      .show()

    //统计最近一个周期比上一个周期访问次数最多的十个用户
    val userActionLogInFirstPeriod = userActionLog.as[UserActionLog]
      .filter("actionTime >= '2016-10-01' and actionTime <= '2016-10-31' and actionType = 0")
      .map{ userActionLogEntry => UserActionLogVO(userActionLogEntry.logId, userActionLogEntry.userId, 1)}
    val userActionLogInSecondPeriod = userActionLog.as[UserActionLog]
      .filter("actionTime >= '2016-01-01' and actionTime <= '2016-09-30' and actionType = 0")
      .map{ userActionLogEntry => UserActionLogVO(userActionLogEntry.logId, userActionLogEntry.userId, -1)}
    val userActionLogDS = userActionLogInFirstPeriod.union(userActionLogInSecondPeriod)
    userActionLogDS
      .join(userBaseInfo, userActionLogDS("userId") === userBaseInfo("userId"))
      .groupBy(userBaseInfo("userId"), userBaseInfo("username"))
      .agg(sum(userActionLogDS("actionValue")).alias("actionIncr"))
      .sort($"actionIncr".desc)
      .limit(10)
      .show()

    //统计最近一个周期比上一个周期购买金额最多的十个用户
    val userActionLogWithPurchaseMoneyInFirstPeriod = userActionLog.as[UserActionLog]
      .filter("actionTime >= '2016-10-01' and actionTime <= '2016-10-31' and actionType = 1")
      .map{userActionLogEntry => UserActionLogWithPurchaseMoneyVO(userActionLogEntry.logId,
        userActionLogEntry.userId, userActionLogEntry.purchaseMoney)}
    val userActionLogWithPurchaseMoneyInSecondPeriod = userActionLog.as[UserActionLog]
      .filter("actionTime >= '2016-09-01' and actionTime <= '2016-09-31' and actionType = 1")
      .map{userActionLogEntry => UserActionLogWithPurchaseMoneyVO(userActionLogEntry.logId,
        userActionLogEntry.userId, -userActionLogEntry.purchaseMoney)}
    val userActionLogWithPurchaseMoneyDS = userActionLogWithPurchaseMoneyInSecondPeriod
      .union(userActionLogWithPurchaseMoneyInFirstPeriod)

    userActionLogWithPurchaseMoneyDS
      .join(userBaseInfo, userBaseInfo("userId") === userActionLogWithPurchaseMoneyDS("userId"))
      .groupBy(userBaseInfo("userId"), userBaseInfo("username"))
      .agg(round(sum(userActionLogWithPurchaseMoneyDS("purchaseMoney")),2).alias("totalMoney"))
      .sort($"totalMoney".desc)
      .limit(10)
      .show()

     //统计指定时间范围内头七天访问次数最多的十个用户
    userActionLog
      .join(userBaseInfo, userBaseInfo("userId") === userActionLog("userId"))
      .filter(userBaseInfo("registTime") >= "2016-10-01"
           && userBaseInfo("registTime") <= "2016-10-31"
           && userActionLog("actionTime") >= userBaseInfo("registTime")
           && userActionLog("actionTime") <= date_add(userBaseInfo("registTime"), 7)
           && userActionLog("actionType") === 0)
      .groupBy(userBaseInfo("userId"), userBaseInfo("username"))
      .agg(count(userActionLog("logId")).alias("logCount"))
      .sort($"logCount".desc)
      .limit(10)
      .show()

    //统计指定时间范围内头七天购买金额最多的十个用户
    userActionLog
      .join(userBaseInfo, userBaseInfo("userId") === userActionLog("userId"))
      .filter(userBaseInfo("registTime") >= "2016-10-01"
           && userBaseInfo("registTime") <= "2016-10-31"
           && userActionLog("actionTime") >= userBaseInfo("registTime")
           && userActionLog("actionTime") <= date_add(userBaseInfo("registTime"), 7)
           && userActionLog("actionType") === 1)
      .groupBy(userBaseInfo("userId"), userBaseInfo("username"))
      .agg(round(sum(userActionLog("purchaseMoney")),2).alias("totalMoney"))
      .sort($"totalMoney".desc)
      .limit(10)
      .show()


  }

}

总结：
      

1. DataSet 的 api 实际上封装了 rdd 相关的算子，其更加丰富了rdd算子功能
2. DataFrame 的api 实际上封装了sql 相关的查询语法