网站独立访客数UV的统计--Flink实现

问题描述：统计每一小时的网站独立访客数UV（Unique Visitor）

问题背景：这是尚硅谷大数据技术之电商用户行为数据分析的一道例题，属于海量数据去重问题。武晟然老师讲授的方法是，自定义布隆过滤器进行UV统计。受到老师的启发，并结合多篇网络文章，本文给出了 ①Flink自带的布隆过滤器 ②Redis整合布隆过滤器 两种方法，进行UV去重统计。

数据集：UserBehavior.csv

1. Flink自带布隆过滤器进行UV统计

package com.atguigu.networkflowanalysis

import java.lang
import java.text.SimpleDateFormat

import com.atguigu.networkflowanalysis.UserBehavior
import org.apache.flink.api.common.functions.AggregateFunction
import org.apache.flink.shaded.guava18.com.google.common.hash.{BloomFilter, Funnels}
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.function.ProcessWindowFunction
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.util.Collector
/**
 * @Author liu
 * @Date 2023-03-20
 */
object UvWithFlinkBloom {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
	env.setParallelism(1)
	
    val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
    val inputStream: DataStream[String] = env.readTextFile("D:\\ideaIU-2020.1.2\\IdeaProject\\UserBehaviorAnalysis\\NetworkFlowAnalysis\\src\\main\\resources\\UserBehavior.csv")

    val resultStream: DataStream[String] = inputStream
      .map(line => {
        val dataArr: Array[String] = line.split(",")
        UserBehavior(
          dataArr(0).toLong,
          dataArr(1).toLong,
          dataArr(2).toInt,
          dataArr(3),
          dataArr(4).toLong
        )
      })
      .filter(_.behavior == "pv")
      .assignAscendingTimestamps(_.timestamp * 1000L)
      .map(data=>("key",data.userId))
      .keyBy(_._1)
      .timeWindow(Time.hours(1))
      .aggregate(new AggregateFunction[(String, Long), (Long, BloomFilter[lang.Long]), Long] {
        
        override def createAccumulator() = (0, BloomFilter.create(Funnels.longFunnel(), 100000000, 0.01))

        override def add(value: (String, Long), accumulator: (Long, BloomFilter[lang.Long])) = {
          var uvCount: Long = accumulator._1
          val bloom: BloomFilter[lang.Long] = accumulator._2
          if (!bloom.mightContain(value._2)) {
            bloom.put(value._2)
            uvCount += 1
          }
          (uvCount, bloom)
        }

        override def getResult(accumulator: (Long, BloomFilter[lang.Long])) = accumulator._1

        override def merge(a: (Long, BloomFilter[lang.Long]), b: (Long, BloomFilter[lang.Long])) = ???
      }, new ProcessWindowFunction[Long, String, String, TimeWindow] {
        override def process(key: String, context: Context, elements: Iterable[Long], out: Collector[String]): Unit = {
          out.collect("窗口=" + sdf.format(context.window.getStart) + "~" + sdf.format(context.window.getEnd) + "\tUvCount=" + elements.iterator.next())
        }
      })
    resultStream.print()
    env.execute()
  }
}

2. Redis整合布隆过滤器进行UV统计

前置准备：Redis-5.0.8集群环境集成布隆过滤器

引入依赖：

<dependency>
    <groupId>redis.clientsgroupId>
    <artifactId>jedisartifactId>
    <version>3.0.0-m1version>
dependency>

<dependency>
    <groupId>com.redislabsgroupId>
    <artifactId>jrebloomartifactId>
    <version>2.2.2version>
dependency>     

启动Redis集群：

[xiaokang@hadoop01 ~]$ redis-cluster-start.sh

代码编写：

package com.atguigu.networkflowanalysis

import java.text.SimpleDateFormat
import java.util

import com.atguigu.networkflowanalysis.UserBehavior
import io.rebloom.client.ClusterClient
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.TimeCharacteristic
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.function.ProcessWindowFunction
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.util.Collector
import redis.clients.jedis.{HostAndPort, JedisPoolConfig}

/**
 * @Author liu
 * @Date 2023-03-20
 */
case class UserBehavior(
                         userId: Long,
                         itemId: Long,
                         categoryId: Int,
                         behavior: String,
                         timestamp: Long
                       )
object UvWithRedisBloomCluster {
  def main(args: Array[String]): Unit = {
    val env: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment

    val inputStream: DataStream[String] = env.readTextFile("D:\\ideaIU-2020.1.2\\IdeaProject\\UserBehaviorAnalysis\\NetworkFlowAnalysis\\src\\main\\resources\\UserBehavior.csv")
    val dataStream: DataStream[UserBehavior] = inputStream.map(line => {
      val dataArr: Array[String] = line.split(",")
      UserBehavior(
        dataArr(0).toLong,
        dataArr(1).toLong,
        dataArr(2).toInt,
        dataArr(3),
        dataArr(4).toLong
      )
    }).filter(_.behavior == "pv")
      .assignAscendingTimestamps(_.timestamp * 1000L)


    dataStream
      .map(data => ("key", data.userId))
      .keyBy(_._1) //全部分到同一个组
      .timeWindow(Time.hours(1))
      .trigger(new MyTrigger())
      .process(new ProcessWindowFunction[(String, Long), String, String, TimeWindow] {

        var hostAndPortSet: util.HashSet[HostAndPort] = _
        var config: JedisPoolConfig = _
        var clusterClientBf: ClusterClient = _
        var count: String = _
        var sdf: SimpleDateFormat=_

        override def open(parameters: Configuration) = {
          hostAndPortSet = new util.HashSet[HostAndPort]()
          hostAndPortSet.add(new HostAndPort("192.168.50.181", 7001))
          hostAndPortSet.add(new HostAndPort("192.168.50.181", 7002))
          hostAndPortSet.add(new HostAndPort("192.168.50.182", 7001))
          hostAndPortSet.add(new HostAndPort("192.168.50.182", 7002))
          hostAndPortSet.add(new HostAndPort("192.168.50.183", 7001))
          hostAndPortSet.add(new HostAndPort("192.168.50.183", 7002))

          config = new JedisPoolConfig()
          config.setMaxTotal(50)
          config.setMaxIdle(10)
          config.setMinIdle(5)
          config.setMaxWaitMillis(10000)

          clusterClientBf = new ClusterClient(hostAndPortSet, config)
          sdf= new SimpleDateFormat("yyyy/MM/dd-HH:mm:ss")
        }

        override def process(key: String, context: Context, elements: Iterable[(String, Long)], out: Collector[String]): Unit = {

          val windowStart: String = sdf.format(context.window.getStart)
          val windowEnd: String = sdf.format(context.window.getEnd)
          val windowRange=windowStart+"~~"+windowEnd

          if (!clusterClientBf.exists(windowRange)) {
            clusterClientBf.createFilter(windowRange, 100000000, 0.1)
          }


          val curValue: String = elements.iterator.next()._2.toString
          if (!clusterClientBf.exists(windowRange, curValue)) {
            clusterClientBf.add(windowRange, curValue)

            count = clusterClientBf.hget("uv", windowRange)
            if (count != null) {
              clusterClientBf.hset("uv", windowRange, (count.toLong+1).toString)
            }else{
              clusterClientBf.hset("uv", windowRange, 1L.toString)
            }
          }
        }
      })

    env.execute()

  }
}

class MyTrigger() extends Trigger[(String, Long), TimeWindow] {
  //每来一个数据的时候要做什么操作
  override def onElement(element: (String, Long), timestamp: Long, window: TimeWindow, ctx: Trigger.TriggerContext): TriggerResult = {
    TriggerResult.FIRE_AND_PURGE //计算和清空
    //FIRE //计算
    //PURGE //清空
    //CONTINUE //两者都不做
  }

  //系统时间有进展的时候要做什么操作
  override def onProcessingTime(time: Long, window: TimeWindow, ctx: Trigger.TriggerContext): TriggerResult = TriggerResult.CONTINUE

  //收到Watermark的时候，有Watermark改变的时候，要做什么操作
  override def onEventTime(time: Long, window: TimeWindow, ctx: Trigger.TriggerContext): TriggerResult = TriggerResult.CONTINUE

  override def clear(window: TimeWindow, ctx: Trigger.TriggerContext): Unit = {}
}

查看结果：

[xiaokang@hadoop01 ~]$ redis-cli -c -p 7001
127.0.0.1:7001> hgetall uv