Flink 窗口聚合函数之AggregatFunction实践

一、AggregatFunction概念

Flink 的AggregateFunction是一个基于中间计算结果状态进行增量计算的函数,AggregateFunction接口相对ReduceFunction更加灵活,实现复杂度也相对较高,输入数据类型和输出数据类型可以不一致,通常和WindowFunction一起结合使用。

二、案例实践:每隔3秒计算最近5秒内,每个基站的日志数量

1.创建日志数据对象

case class Log(sid:String,var callOut:String, var callIn:String, callType:String, callTime:Long, duration:Long)

2.业务实现

import org.apache.flink.api.common.functions.AggregateFunction
import org.apache.flink.streaming.api.scala._
import org.apache.flink.streaming.api.scala.function.WindowFunction
import org.apache.flink.streaming.api.windowing.assigners.SlidingProcessingTimeWindows
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.windows.TimeWindow
import org.apache.flink.util.Collector
 
/**
  * 增量聚合函数
  */
object TestAggregatFunctionByWindow {
 
  // 每隔3秒计算最近5秒内,每个基站的日志数量
  def main(args: Array[String]): Unit = {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    // source
    var stream = env.socketTextStream("flink101", 8888)
      .map(line => {
        var arr = line.split(",")
        Log(arr(0).trim,arr(1).trim, arr(2).trim, arr(3).trim, arr(4).trim.toLong, arr(5).trim.toLong)
      })
 
    stream.map(log=> (log.sid, 1))
      .keyBy(_._1)
        .window(SlidingProcessingTimeWindows.of(Time.seconds(5), Time.seconds(3)))
        .aggregate(new MyAggregateFunction, new MyWindowFunction)
 
    env.execute("TestAggregatFunctionByWindow")
  }
}
 
 
// 优点是简单实现聚合,缺点是不能输出key
// add方法,是来一条数据执行一次
// getResult 窗口结束的时候执行一次
class MyAggregateFunction extends AggregateFunction[(String, Int), Long, Long]{
  override def createAccumulator(): Long = 0  // 初始化累加器
 
  override def add(in: (String, Int), acc: Long): Long = acc + in._2  // 定义数据的添加逻辑
 
  override def getResult(acc: Long): Long = acc  // 定义计算结果的逻辑
 
  override def merge(acc: Long, acc1: Long): Long = acc + acc1  // 合并分区数据
}
 
// 为了输出key,输入数据来自于AggregateFunction,在窗口结束的时候先执行AggregateFunction对象的getResult方法,然后再执行apply方法
/** WindowFunction
  * * @param IN The type of the input value.
  * * @param OUT The type of the output value.
  * * @param KEY The type of the key.
  */
class MyWindowFunction extends WindowFunction[Long, (String, Long), String, TimeWindow] {
  override def apply(key: String, window: TimeWindow, input: Iterable[Long], out: Collector[(String, Long)]): Unit = {
    out.collect((key, input.iterator.next()))  // next得到第一个值,迭代器中只有一个值
  }
}

三、总结

AggregateFunction 接口中定义了三个 需要复写的方法,其中 add()定义数据的添加逻辑,getResult 定义了根据 accumulator 计 算结果的逻辑,merge 方法定义合并 accumulator 的逻辑。

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