CC00031.bdpositions——|Hadoop&实时数仓.V11|——|项目.v11|DWD层处理|数据仓库层数据处理.V1|

一、DW层处理

### --- DW层处理

~~~     DW(Data Warehouse 数据仓库层)。
~~~     包含DWD、DWS、DIM层，由ODS层数据加工而成。主要完成数据加工与整合，
~~~     建立一致性的维度，
~~~     构建可复用的面向分析和统计的明细事实表，
~~~     以及汇总公共粒度的指标。
~~~     DWD(Data Warehouse Detail 细节数据层)，是业务层与数据仓库的隔离层。
~~~     以业务过程作为建模驱动，基于每个具体的业务过程特点，构建细粒度的明细层事实表。
~~~     可以结合企业的数据使用特点，将明细事实表的某些重要维度属性字段做适当冗余，
~~~     也即宽表化处理；
~~~     DWS(Data Warehouse Service 服务数据层)，
~~~     基于DWD的基础数据，整合汇总成分析某一个主题域的服务数据。
~~~     以分析的主题为建模驱动，基于上层的应用和产品的指标需求，构建公共粒度的汇总指标事实表；
~~~     公共维度层(DIM)：基于维度建模理念思想，建立一致性维度；
~~~     TMP层 ：临时层，存放计算过程中临时产生的数据；

二、编程实现

### --- 编程实现DWD层数据处理：TimeJoin

package dw.dwd

import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.functions.co.ProcessJoinFunction
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.util.Collector
import org.apache.flink.api.scala._

object TimeJoin {

  case class UserClickLog(userId: String, eventTime: String, eventType: String, pageId: String)

  case class UserBrowseLog(userId: String,
                           eventTime: String,
                           productId: String,
                           productPrice: String)

  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
    env.setParallelism(1)

    val clickStream = env.fromElements(
      UserClickLog("user_1", "1500", "click", "page_1"),
      UserClickLog("user_1", "2000", "click", "page_1")
    )
      .assignAscendingTimestamps(_.eventTime.toLong*1000)
      .keyBy(_.userId)

    val browseStream = env.fromElements(
      UserBrowseLog("user_1", "1000", "product_1", "10"),
      UserBrowseLog("user_1", "1500", "product_1", "10"),
      UserBrowseLog("user_1", "1501", "product_1", "10"),
      UserBrowseLog("user_1", "1502", "product_1", "10")
    )
      .assignAscendingTimestamps(_.eventTime.toLong*1000)
      .keyBy(_.userId)

    clickStream.intervalJoin(browseStream)
      .between(Time.minutes(-10),Time.seconds(0))
      .process(new IntervalJoinFunc)
      .print()

    env.execute()

  }

  class IntervalJoinFunc extends ProcessJoinFunction[UserClickLog,UserBrowseLog,String] {
    override def processElement(left: UserClickLog, right: UserBrowseLog, ctx: ProcessJoinFunction[UserClickLog, UserBrowseLog, String]#Context, out: Collector[String]): Unit = {
      out.collect(left + "--->" + right)
    }
  }

}

### --- 编译打印

D:\JAVA\jdk1.8.0_231\bin\java.exe "-javaagent:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\lib\idea_rt.jar=62995:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\bin" -Dfile.encoding=UTF-8 -classpath 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link\flink-cep-scala_2.12\1.11.1\flink-cep-scala_2.12-1.11.1.jar;C:\Users\Administrator\.m2\repository\org\apache\flink\flink-cep_2.12\1.11.1\flink-cep_2.12-1.11.1.jar;C:\Users\Administrator\.m2\repository\redis\clients\jedis\3.3.0\jedis-3.3.0.jar;C:\Users\Administrator\.m2\repository\org\apache\commons\commons-pool2\2.6.2\commons-pool2-2.6.2.jar dw.dwd.TimeJoin
~~~输出参数：
UserClickLog(user_1,1500,click,page_1)--->UserBrowseLog(user_1,1000,product_1,10)
UserClickLog(user_1,1500,click,page_1)--->UserBrowseLog(user_1,1500,product_1,10)
UserClickLog(user_1,2000,click,page_1)--->UserBrowseLog(user_1,1500,product_1,10)
UserClickLog(user_1,2000,click,page_1)--->UserBrowseLog(user_1,1501,product_1,10)
UserClickLog(user_1,2000,click,page_1)--->UserBrowseLog(user_1,1502,product_1,10)

Process finished with exit code 0