Based on Flink real-time project: user behavior analysis (III: Statistics of total website views (PV))

1. demand ：

1. Total site views （PV） The statistics of

2. about PV Make a brief introduction ：
One of the simplest indicators to measure website traffic , Is the page views of the website （Page View,PV）. Every time a user opens a page, he records 1 Time PV, If you open the same page several times, the total number of views will be . Generally speaking ,PV It's proportional to the number of visitors , however PV It doesn't directly determine the number of real visitors to the page , It's like a visitor constantly refreshing the page , You can also make very high PV.

3. Ideas ： Set the scrolling time window , Real time statistics of websites per hour

2. Code implementation

import org.apache.flink.api.scala._
import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
import org.apache.flink.streaming.api.windowing.time.Time

// Set input data type 
case class userBehavior(userId:Long,itemId:Long,categoryId:Int,behavior:String,timestamp:Long)

object PageView {
  def main(args: Array[String]): Unit = {

    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setParallelism(1)

    val data = env.readTextFile("E:\\WY\\programme\\MusicProject\\src\\main\\resources\\UserBehavior.csv")

    val dataStream = data.map(data => {
      val arr = data.split(",")
      userBehavior(arr(0).toLong, arr(1).toLong, arr(2).toInt, arr(3), arr(4).toLong)
    })

    val resultStream = dataStream
      .assignAscendingTimestamps(_.timestamp * 1000L)
      .filter(_.behavior == "pv")
      .map(x => ("pv", 1))
      .keyBy(_._1)
      .timeWindow(Time.seconds(60 * 60))
      .sum(1)

    resultStream.print()

    env.execute()

  }
}

3. Result display