flink的自定义window聚合统计（增量，全量）_flink sql 全量统计

window聚合统计（增量，全量）

timewindow，countwindow就不说了，不知道的百度

自定义widow：

window的数值聚合统计
对于某一个window内的数值统计，我们可以增量的聚合统计或者全量的聚合统计
增量聚合统计
窗口当中每加入一条数据，就进行一次统计
reduce(reduceFunction)
aggregate(aggregateFunction)
sum(),min(),max()
 

import org.apache.flink.api.common.functions.ReduceFunction
import org.apache.flink.streaming.api.datastream.DataStreamSink
import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment}
import org.apache.flink.streaming.api.windowing.time.Time
 
object FlinkTimeCount {
  def main(args: Array[String]): Unit = {
    val environment: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
    import org.apache.flink.api.scala._
 
    val socketStream: DataStream[String] = environment.socketTextStream("node01",8000)
    val print: DataStreamSink[(Int, Int)] = socketStream
      .map(x => (1, x.toInt))
      .keyBy(0)
      .timeWindow(Time.seconds(5))
      .reduce(new ReduceFunction[(Int, Int)] {
        override def reduce(t: (Int, Int), t1: (Int, Int)): (Int, Int) = {
          (t._1, t._2 + t1._2)
        }
      }).print()
 
    environment.execute("startRunning")
  }
}

全量聚合统计
等到窗口截止，或者窗口内的数据全部到齐，然后再进行统计，可以用于求窗口内的数据的最大值，或者最小值，平均值等
apply(windowFunction)
process(processWindowFunction)
processWindowFunction比windowFunction提供了更多的上下文信息
通过全量聚合统计，求取每3条数据的平均值
 

import org.apache.flink.api.java.tuple.Tuple
import org.apache.flink.streaming.api.datastream.DataStreamSink
import org.apache.flink.streaming.api.scala.function.ProcessWindowFunction
import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment, WindowedStream}
import org.apache.flink.streaming.api.windowing.windows.{GlobalWindow, TimeWindow}
import org.apache.flink.util.Collector
 
 
object FlinkCountWindowAvg {
 
  def main(args: Array[String]): Unit = {
    val environment: StreamExecutionEnvironment = StreamExecutionEnvironment.getExecutionEnvironment
 
    import org.apache.flink.api.scala._
    val socketStream: DataStream[String] = environment.socketTextStream("node01",9000)
    //统计一个窗口内的数据的平均值
    val socketDatas: DataStreamSink[Double] = socketStream.map(x => (1, x.toInt))
      .keyBy(0)
      //.timeWindow(Time.seconds(10))
      .countWindow(3)
      //通过process方法来统计窗口的平均值
      .process(new MyProcessWindowFunctionclass).print()
    //必须调用execute方法，否则程序不会执行
    environment.execute("count avg")
  }
}
 
/**ProcessWindowFunction 需要跟四个参数
  * 输入参数类型，输出参数类型，聚合的key的类型，window的下界
  *
  */
class MyProcessWindowFunctionclass extends ProcessWindowFunction[(Int , Int) , Double , Tuple , GlobalWindow]{
  override def process(key: Tuple, context: Context, elements: Iterable[(Int, Int)], out: Collector[Double]): Unit = {
    var totalNum = 0;
    var countNum = 0;
    for(data <-  elements){
      totalNum +=1
      countNum += data._2
    }
    out.collect(countNum/totalNum)
  }
}