Flink实时计算pv、uv的几种方法

本文首发于：Java大数据与数据仓库，Flink实时计算pv、uv的几种方法

实时统计pv、uv是再常见不过的大数据统计需求了，前面出过一篇SparkStreaming实时统计pv,uv的案例，这里用Flink实时计算pv,uv。

我们需要统计不同数据类型每天的pv，uv情况,并且有如下要求.

• 每秒钟要输出最新的统计结果；
• 程序永远跑着不会停,所以要定期清理内存里的过时数据；
• 收到的消息里的时间字段并不是按照顺序严格递增的,所以要有一定的容错机制；
• 访问uv并不一定每秒钟都会变化,重复输出对IO是巨大的浪费,所以要在uv变更时在一秒内输出结果,未变更时不输出；

Flink数据流上的类型和操作

DataStream是flink流处理最核心的数据结构，其它的各种流都可以直接或者间接通过DataStream来完成相互转换，一些常用的流直接的转换关系如图：

可以看出，DataStream可以与KeyedStream相互转换，KeyedStream可以转换为WindowedStream，DataStream不能直接转换为WindowedStream，WindowedStream可以直接转换为DataStream。各种流之间虽然不能相互直接转换，但是都可以通过先转换为DataStream，再转换为其它流的方法来实现。

在这个计算pv,uv的需求中就主要用到DataStream、KeyedStream以及WindowedStream这些数据结构。

这里需要用到window和watermark，使用窗口把数据按天分割，使用watermark可以通过“水位”来定期清理窗口外的迟到数据，起到清理内存的作用。

业务代码

我们的数据是json类型的，含有date,helperversion,guid这3个字段，在实时统计pv,uv这个功能中，其它字段可以直接丢掉，当然了在离线数据仓库中，所有有含义的业务字段都是要保留到hive当中的。

其它相关概念就不说了，会专门介绍，这里直接上代码吧。

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.ddxygq</groupId>
    <artifactId>bigdata</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <scala.version>2.11.8</scala.version>
        <flink.version>1.7.0</flink.version>
        <pkg.name>bigdata</pkg.name>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.11</artifactId>
            <version>{flink.version}</version>
  </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.11</artifactId>
            <version>flink.version</version>
  </dependency>

        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_2.11</artifactId>
            <version>{flink.version}</version>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.flink/flink-connector-kafka-0.8 -->
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-kafka-0.10_2.11</artifactId>
            <version>flink.version</version>
  </dependency>

    <build>
        <!--测试代码和文件-->
        <!--<testSourceDirectory>{basedir}/src/test</testSourceDirectory>-->
        <finalName>basedir/src/test</testSourceDirectory>−−><finalName>{pkg.name}</finalName>
        <sourceDirectory>src/main/java</sourceDirectory>
        <resources>
            <resource>
                <directory>src/main/resources</directory>
                <includes>
                    <include>*.properties</include>
                    <include>*.xml</include>
                </includes>
                <filtering>false</filtering>
            </resource>
        </resources>
        <plugins>
            <!-- 跳过测试插件-->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <configuration>
                    <skip>true</skip>
                </configuration>
            </plugin>
            <!--编译scala插件-->
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

主要代码，主要使用scala开发：

package com.ddxygq.bigdata.flink.streaming.pvuv

import java.util.Properties

import com.alibaba.fastjson.JSON
import org.apache.flink.runtime.state.filesystem.FsStateBackend
import org.apache.flink.streaming.api.CheckpointingMode
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor
import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment}
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.triggers.ContinuousProcessingTimeTrigger
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
import org.apache.flink.streaming.util.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala.extensions._
import org.apache.flink.api.scala._

/**
  * @ Author: keguang
  * @ Date: 2019/3/18 17:34
  * @ version: v1.0.0
  * @ description:
  */
object PvUvCount {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    // 容错
    env.enableCheckpointing(5000)
    env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
    env.setStateBackend(new FsStateBackend("file:///D:/space/IJ/bigdata/src/main/scala/com/ddxygq/bigdata/flink/checkpoint/flink/tagApp"))

    // kafka 配置
    val ZOOKEEPER_HOST = "hadoop01:2181,hadoop02:2181,hadoop03:2181"
    val KAFKA_BROKERS = "hadoop01:9092,hadoop02:9092,hadoop03:9092"
    val TRANSACTION_GROUP = "flink-count"
    val TOPIC_NAME = "flink"
    val kafkaProps = new Properties()
    kafkaProps.setProperty("zookeeper.connect", ZOOKEEPER_HOST)
    kafkaProps.setProperty("bootstrap.servers", KAFKA_BROKERS)
    kafkaProps.setProperty("group.id", TRANSACTION_GROUP)

    // watrmark 允许数据延迟时间
    val MaxOutOfOrderness = 86400 * 1000L

    // 消费kafka数据
    val streamData: DataStream[(String, String, String)] = env.addSource(
      new FlinkKafkaConsumer010[String](TOPIC_NAME, new SimpleStringSchema(), kafkaProps)
    ).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[String](Time.milliseconds(MaxOutOfOrderness)) {
      override def extractTimestamp(element: String): Long = {
        val t = JSON.parseObject(element)
        val time = JSON.parseObject(JSON.parseObject(t.getString("message")).getString("decrypted_data")).getString("time")
        time.toLong
      }
    }).map(x => {
      var date = "error"
      var guid = "error"
      var helperversion = "error"
      try {
        val messageJsonObject = JSON.parseObject(JSON.parseObject(x).getString("message"))
        val datetime = messageJsonObject.getString("time")
        date = datetime.split(" ")(0)
        // hour = datetime.split(" ")(1).substring(0, 2)
        val decrypted_data_string = messageJsonObject.getString("decrypted_data")
        if (!"".equals(decrypted_data_string)) {
          val decrypted_data = JSON.parseObject(decrypted_data_string)
          guid = decrypted_data.getString("guid").trim
          helperversion = decrypted_data.getString("helperversion")
        }
      } catch {
        case e: Exception => {
          println(e)
        }
      }
      (date, helperversion, guid)
    })
    // 这上面是设置watermark并解析json部分
    // 聚合窗口中的数据，可以研究下applyWith这个方法和OnWindowedStream这个类
    val resultStream = streamData.keyBy(x => {
      x._1 + x._2
    }).timeWindow(Time.days(1))
      .trigger(ContinuousProcessingTimeTrigger.of(Time.seconds(1)))
      .applyWith(("", List.empty[Int], Set.empty[Int], 0L, 0L))(
        foldFunction = {
          case ((_, list, set, _, 0), item) => {
            val date = item._1
            val helperversion = item._2
            val guid = item._3
            (date + "_" + helperversion, guid.hashCode +: list, set + guid.hashCode, 0L, 0L)
          }
        }
        , windowFunction = {
          case (key, window, result) => {
            result.map {
              case (leixing, list, set, _, _) => {
                (leixing, list.size, set.size, window.getStart, window.getEnd)
              }
            }
          }
        }
      ).keyBy(0)
      .flatMapWithState[(String, Int, Int, Long, Long),(Int, Int)]{
      case ((key, numpv, numuv, begin, end), curr) =>

        curr match {
          case Some(numCurr) if numCurr == (numuv, numpv) =>
            (Seq.empty, Some((numuv, numpv))) //如果之前已经有相同的数据,则返回空结果
          case _ =>
            (Seq((key, numpv, numuv, begin, end)), Some((numuv, numpv)))
        }
    }

    // 最终结果
    val resultedStream = resultStream.map(x => {
      val keys = x._1.split("_")
      val date = keys(0)
      val helperversion = keys(1)
      (date, helperversion, x._2, x._3)
    })

    resultedStream.print()
    env.execute("PvUvCount")

  }
}

使用List集合的size保存pv,使用Set集合的size保存uv，从而达到实时统计pv,uv的目的。

这里用了几个关键的函数：

applyWith：里面需要的参数，初始状态变量，和foldFunction ，windowFunction ；

存在的问题

显然，当数据量很大的时候，这个List集合和Set集合会很大，并且这里的pv是否可以不用List来存储，而是通过一个状态变量，不断做累加，对应操作就是更新状态来完成。

改进版

使用了一个计数器来存储pv的值。

packagecom.ddxygq.bigdata.flink.streaming.pvuv

import java.util.Properties

import com.alibaba.fastjson.JSON
import org.apache.flink.api.common.accumulators.IntCounter
import org.apache.flink.runtime.state.filesystem.FsStateBackend
import org.apache.flink.streaming.api.CheckpointingMode
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor
import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment}
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.triggers.ContinuousProcessingTimeTrigger
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
import org.apache.flink.streaming.util.serialization.SimpleStringSchema
import org.apache.flink.streaming.api.scala.extensions._
import org.apache.flink.api.scala._
import org.apache.flink.core.fs.FileSystem

object PvUv2 {
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment

    // 容错
    env.enableCheckpointing(5000)
    env.getCheckpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE)
    env.setStateBackend(new FsStateBackend("file:///D:/space/IJ/bigdata/src/main/scala/com/ddxygq/bigdata/flink/checkpoint/streaming/counter"))

    // kafka 配置
    val ZOOKEEPER_HOST = "hadoop01:2181,hadoop02:2181,hadoop03:2181"
    val KAFKA_BROKERS = "hadoop01:9092,hadoop02:9092,hadoop03:9092"
    val TRANSACTION_GROUP = "flink-count"
    val TOPIC_NAME = "flink"
    val kafkaProps = new Properties()
    kafkaProps.setProperty("zookeeper.connect", ZOOKEEPER_HOST)
    kafkaProps.setProperty("bootstrap.servers", KAFKA_BROKERS)
    kafkaProps.setProperty("group.id", TRANSACTION_GROUP)

    // watrmark 允许数据延迟时间
    val MaxOutOfOrderness = 86400 * 1000L

    val streamData: DataStream[(String, String, String)] = env.addSource(
      new FlinkKafkaConsumer010[String](TOPIC_NAME, new SimpleStringSchema(), kafkaProps)
    ).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[String](Time.milliseconds(MaxOutOfOrderness)) {
      override def extractTimestamp(element: String): Long = {
        val t = JSON.parseObject(element)
        val time = JSON.parseObject(JSON.parseObject(t.getString("message")).getString("decrypted_data")).getString("time")
        time.toLong
      }
    }).map(x => {
      var date = "error"
      var guid = "error"
      var helperversion = "error"
      try {
        val messageJsonObject = JSON.parseObject(JSON.parseObject(x).getString("message"))
        val datetime = messageJsonObject.getString("time")
        date = datetime.split(" ")(0)
        // hour = datetime.split(" ")(1).substring(0, 2)
        val decrypted_data_string = messageJsonObject.getString("decrypted_data")
        if (!"".equals(decrypted_data_string)) {
          val decrypted_data = JSON.parseObject(decrypted_data_string)
          guid = decrypted_data.getString("guid").trim
          helperversion = decrypted_data.getString("helperversion")
        }
      } catch {
        case e: Exception => {
          println(e)
        }
      }
      (date, helperversion, guid)
    })

    val resultStream = streamData.keyBy(x => {
      x._1 + x._2
    }).timeWindow(Time.days(1))
      .trigger(ContinuousProcessingTimeTrigger.of(Time.seconds(1)))
      .applyWith(("", new IntCounter(), Set.empty[Int], 0L, 0L))(
        foldFunction = {
          case ((_, cou, set, _, 0), item) => {
            val date = item._1
            val helperversion = item._2
            val guid = item._3
            cou.add(1)
            (date + "_" + helperversion, cou, set + guid.hashCode, 0L, 0L)
          }
        }
        , windowFunction = {
          case (key, window, result) => {
            result.map {
              case (leixing, cou, set, _, _) => {
                (leixing, cou.getLocalValue, set.size, window.getStart, window.getEnd)
              }
            }
          }
        }
      ).keyBy(0)
      .flatMapWithState[(String, Int, Int, Long, Long),(Int, Int)]{
      case ((key, numpv, numuv, begin, end), curr) =>

        curr match {
          case Some(numCurr) if numCurr == (numuv, numpv) =>
            (Seq.empty, Some((numuv, numpv))) //如果之前已经有相同的数据,则返回空结果
          case _ =>
            (Seq((key, numpv, numuv, begin, end)), Some((numuv, numpv)))
        }
    }

    // 最终结果
    val resultedStream = resultStream.map(x => {
      val keys = x._1.split("_")
      val date = keys(0)
      val helperversion = keys(1)
      (date, helperversion, x._2, x._3)
    })

    val resultPath = "D:\\space\\IJ\\bigdata\\src\\main\\scala\\com\\ddxygq\\bigdata\\flink\\streaming\\pvuv\\result"
    resultedStream.writeAsText(resultPath, FileSystem.WriteMode.OVERWRITE)
    env.execute("PvUvCount")

  }
}

参考资料

https://flink.sojb.cn/dev/event_time.html

http://wuchong.me/blog/2016/05/20/flink-internals-streams-and-operations-on-streams

https://segmentfault.com/a/1190000006235690