Flink自定义Assigning Timestamps和Watermarks 使用Scal语言

Flink自定义Assigning Timestamps和Watermarks 使用Scal语言

为了让event time工作，Flink需要知道事件的时间戳，这意味着流中的每个元素都需要分配其事件时间戳。这个通常是通过抽取或者访问事件中某些字段的时间戳来获取的。时间戳的分配伴随着水印的生成，告诉系统事件时间中的进度。下面介绍几种自定义事件时间戳方法
1.在数据流源中定义
可以看Flink静态Session Windows这边文章里面有
2.使用DataStream API中的assignAscendingTimestamps来指定时间戳。其中系统默认用此时间戳创建Watermark。注意:：数据源任务中的时间戳是递增的，这是很必要的。

  def main(args: Array[String]) {
    val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
    val input=env.fromCollection(List(("a",1L),("b",1L),("b",5L),("b",5L)))
    val timeWindow=input.assignAscendingTimestamps(t=>t._2)
    val result=timeWindow.keyBy(0).timeWindow(Time.milliseconds(4)).sum("_2")
    result.print()
    env.execute()
  }
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结果：

3.实现BoundedOutOfOrdernessTimestampExtractor类

val env = StreamExecutionEnvironment.getExecutionEnvironment
    env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
    env.getConfig.setAutoWatermarkInterval(90000)
    val input=env.fromCollection(List(("b",1L),("b",2L),("b",3L),("b",4L),("b",5L),("b",6L),("b",7L),("b",8L),("b",9L)))
    val timeWindow=input.assignTimestampsAndWatermarks(
      new BoundedOutOfOrdernessTimestampExtractor[(String, Long)](Time.milliseconds(1)) {
      override def extractTimestamp(element: (String, Long)): Long = element._2
    })
    val result=timeWindow.keyBy(0).timeWindow(Time.milliseconds(4)).sum("_2")
    result.print()
    env.execute()
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结果：

注意：此类有一个参数Time.milliseconds(1)，代表最长的时延1ms。可以查看源码
4实现AssignerWithPeriodicWatermarks接口

  def main(args: Array[String]) {
    val params = ParameterTool.fromArgs(args)
    val senv = StreamExecutionEnvironment.getExecutionEnvironment
      senv.getConfig.setAutoWatermarkInterval(900000)
    senv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
    val text = senv.socketTextStream("localhost", 9999)
      .assignTimestampsAndWatermarks(new TimestampExtractor)
    val counts = text.map {(m: String) => (m.split(",")(0), 1) }
      .keyBy(0)
      .timeWindow(Time.milliseconds(10))
      .sum(1)
    counts.print
    senv.execute("EventTime processing example")
  }
  class TimestampExtractor extends AssignerWithPeriodicWatermarks[String] with Serializable {
    private var currentMaxTimestamp = 0L
    private val maxOutOfOrderness = 3l
    override def extractTimestamp(e: String, prevElementTimestamp: Long) = {
      val timestamp=e.split(",")(1).toLong
     // println( e.split(",")(1).toLong)
      currentMaxTimestamp = Math.max(prevElementTimestamp,timestamp)
      e.split(",")(1).toLong
}
override def getCurrentWatermark(): Watermark = {
  println(currentMaxTimestamp-maxOutOfOrderness)
  new Watermark(currentMaxTimestamp-maxOutOfOrderness)
}

}
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输入：

结果：


注意：1.窗口触发需要要满足两个条件：1.watermark>=window_end_time,2,此窗口内有数据。
2.同时也说明watermark对window的分段之间没有关系，比如输入（a,13）,（a,12）,（a,16）都在10ms~20ms窗口内
5.实现AssignerWithPunctuatedWatermarks接口

def main(args: Array[String]) {
// Checking input parameters
val params = ParameterTool.fromArgs(args)

// set up the execution environment
val senv = StreamExecutionEnvironment.getExecutionEnvironment
  senv.getConfig.setAutoWatermarkInterval(900000)
senv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
val text = senv.socketTextStream("localhost", 9999)
  .assignTimestampsAndWatermarks(new TimestampExtractor)
val counts = text.map {(m: String) => (m.split(",")(0), 1) }
  .keyBy(0)
  .timeWindow(Time.milliseconds(10))
  .sum(1)
counts.print
senv.execute("EventTime processing example")

}

class TimestampExtractor extends AssignerWithPunctuatedWatermarks[String] with Serializable {
override def checkAndGetNextWatermark(lastElement: String, extractedTimestamp: Long): Watermark = {
  if(lastElement.split(",")(1).toLong%2==0)
    {
      println(extractedTimestamp)
      new Watermark(extractedTimestamp)
    }
  else null
}

override def extractTimestamp(element: String, previousElementTimestamp: Long): Long ={
  element.split(",")(1).toLong
}

}
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结果：
总结：其中2~4是固定时延间隔指定timestamps和watermark，5是根据事件的特殊条件。
从中可以看出watermark的含义是在固定时延间隔乱序，整体是有序的。

原文链接：https://blog.csdn.net/weixin_42412645/article/details/93378738