apache-spark - 如果自上次事件以来传感器读数未发生变化，但未发送传感器读数，则如何计算窗口上的聚合？

Question

仅当自上次事件以来传感器值已发生更改时才发送新事件时，如何从传感器计算窗口上的聚合？传感器读数是在固定时间获取的，例如每5秒钟发送一次，但仅在自上次读取后读数发生变化时才转发。

因此，如果我想为每个设备创建signal_stength的平均值:

eventsDF = ... 
avgSignalDF = eventsDF.groupBy("deviceId").avg("signal_strength")

例如，设备在一分钟的时间范围内发送的事件:

event_time  device_id  signal_strength
12:00:00    1          5
12:00:05    1          4
12:00:30    1          5
12:00:45    1          6
12:00:55    1          5

带有未实际发送的事件的相同数据集已填写:

event_time  device_id  signal_strength
12:00:00    1          5
12:00:05    1          4
12:00:10    1          4
12:00:15    1          4
12:00:20    1          4
12:00:25    1          4
12:00:30    1          5
12:00:35    1          5
12:00:40    1          5
12:00:45    1          6
12:00:50    1          6
12:00:55    1          5

signal_strength sum是 57，而 avg是 57/12
如何通过 Spark 结构化流以及从推断值计算出的平均值来推断出这些丢失的数据？

注意:我已使用平均值作为聚合示例，但是该解决方案需要适用于任何聚合功能。

Answer 1

编辑:

我修改了逻辑以仅从过滤后的dataframe计算平均值，从而解决了差距。

//input structure
case class StreamInput(event_time: Long, device_id: Int, signal_strength: Int)
//columns for which we want to maintain state
case class StreamState(prevSum: Int, prevRowCount: Int, prevTime: Long, prevSignalStrength: Int, currentTime: Long, totalRow: Int, totalSum: Int, avg: Double)
//final result structure
case class StreamResult(event_time: Long, device_id: Int, signal_strength: Int, avg: Double)

val filteredDF = ???  //get input(filtered rows only)

val interval = 5  // event_time interval

// using .mapGroupsWithState to maintain state for runningSum & total row count till now

// you need to set the timeout threshold to indicate how long you wish to maintain the state
val avgDF = filteredDF.groupByKey(_.device_id)
  .mapGroupsWithState[StreamState, StreamResult](GroupStateTimeout.NoTimeout()) {

  case (id: Int, eventIter: Iterator[StreamInput], state: GroupState[StreamState]) => {
    val events = eventIter.toSeq

    val updatedSession = if (state.exists) {
      //if state exists update the state with the new values
      val existingState = state.get

      val prevTime = existingState.currentTime
      val currentTime = events.map(x => x.event_time).last
      val currentRowCount = (currentTime - prevTime)/interval
      val rowCount = existingState.rowCount + currentRowCount.toInt
      val currentSignalStength = events.map(x => x.signal_strength).last

      val total_signal_strength = currentSignalStength + 
        (existingState.prevSignalStrength * (currentRowCount -1)) + 
        existingState.total_signal_strength

      StreamState(
        existingState.total_signal_strength,
        existingState.rowCount,
        prevTime,
        currentSignalStength,
        currentTime,
        rowCount,
        total_signal_strength.toInt,
        total_signal_strength/rowCount.toDouble
      )

    } else {
      // if there are no earlier state
      val runningSum = events.map(x => x.signal_strength).sum
      val size = events.size.toDouble
      val currentTime = events.map(x => x.event_time).last
      StreamState(0, 1, 0, runningSum, currentTime, 1, runningSum, runningSum/size)
    }

    //save the updated state
    state.update(updatedSession)
    StreamResult(
      events.map(x => x.event_time).last,
      id,
      events.map(x => x.signal_strength).last,
      updatedSession.avg
    )
  }
}

val result = avgDF
  .writeStream
  .outputMode(OutputMode.Update())
  .format("console")
  .start

这个想法是要计算两个新的列:

totalRowCount:如果尚未过滤，则应该存在的行总数。

total_signal_strength:到现在为止signal_strength的运行总数。 (这也包括错过的行总数)。

其计算公式为:

total_signal_strength = 
  current row's signal_strength  +  
  (total_signal_strength of previous row * (rowCount -1)) + 
  //rowCount is the count of missed rows computed by comparing previous and current event_time.
  previous total_signal_strength

中间状态的格式:

+----------+---------+---------------+---------------------+--------+
|event_time|device_id|signal_strength|total_signal_strength|rowCount|
+----------+---------+---------------+---------------------+--------+
|         0|        1|              5|                    5|       1|
|         5|        1|              4|                    9|       2|
|        30|        1|              5|                   30|       7|
|        45|        1|              6|                   46|      10|
|        55|        1|              5|                   57|      12|
+----------+---------+---------------+---------------------+--------+

最终输出:

+----------+---------+---------------+-----------------+
|event_time|device_id|signal_strength|              avg|
+----------+---------+---------------+-----------------+
|         0|        1|              5|              5.0|
|         5|        1|              4|              4.5|
|        30|        1|              5|4.285714285714286|
|        45|        1|              6|              4.6|
|        55|        1|              5|             4.75|
+----------+---------+---------------+-----------------+