Spark 的 collect_list 函数允许您将窗口值聚合为列表.这个列表可以传递给一个udf来做一些复杂的计算

The collect_list function of Spark allows you to aggregate the windowed values as a list. This list can be passed to a udf to do some complex calculations

所以如果你有源

val data = List(
  ("XSC", "1986-05-21", 44.7530),
  ("XSC", "1986-05-22", 44.7530),
  ("XSC", "1986-05-23", 23.5678),
  ("TM", "1982-03-08", 22.2734),
  ("TM", "1982-03-09", 22.1941),
  ("TM", "1982-03-10", 22.0847),
  ("TM", "1982-03-11", 22.1741),
  ("TM", "1982-03-12", 22.1840),
  ("TM", "1982-03-15", 22.1344),
).toDF("id", "timestamp", "feature")
  .withColumn("timestamp", to_date('timestamp))

还有一些复杂的函数，包含在记录中的 UDF 中(例如表示为元组)

And some complex function, wrapped in a UDF on your record (represented as a Tuple for instance)

 val complexComputationUDF = udf((list: Seq[Row]) => {
  list
    .map(row => (row.getString(0), row.getDate(1).getTime, row.getDouble(2)))
    .sortBy(-_._2)
    .foldLeft(0.0) {
      case (acc, (id, timestamp, feature)) => acc + feature
    }
})

您可以定义一个窗口，将所有分区数据传递给每条记录，或者在有序窗口的情况下，将增量数据传递给每条记录

You can define either a window that passes all partitioned data to each record or, in case of an ordered window, an incremental data to each record

val windowAll = Window.partitionBy("id")
val windowRunning = Window.partitionBy("id").orderBy("timestamp")

把它们放在一个新的数据集中，比如:

And put it all together in a new dataset, like:

val newData = data
  // I assuming thatyou need id,timestamp & feature for the complex computattion. So I create a struct
  .withColumn("record", struct('id, 'timestamp, 'feature))
  // Collect all records in the partition as a list of tuples and pass them to the complexComupation
  .withColumn("computedValueAll",
     complexComupationUDF(collect_list('record).over(windowAll)))
  // Collect records in a time ordered windows in the partition as a list of tuples and pass them to the complexComupation
  .withColumn("computedValueRunning",
     complexComupationUDF(collect_list('record).over(windowRunning)))

这将导致类似:

+---+----------+-------+--------------------------+------------------+--------------------+
|id |timestamp |feature|record                    |computedValueAll  |computedValueRunning|
+---+----------+-------+--------------------------+------------------+--------------------+
|XSC|1986-05-21|44.753 |[XSC, 1986-05-21, 44.753] |113.07379999999999|44.753              |
|XSC|1986-05-22|44.753 |[XSC, 1986-05-22, 44.753] |113.07379999999999|89.506              |
|XSC|1986-05-23|23.5678|[XSC, 1986-05-23, 23.5678]|113.07379999999999|113.07379999999999  |
|TM |1982-03-08|22.2734|[TM, 1982-03-08, 22.2734] |133.0447          |22.2734             |
|TM |1982-03-09|22.1941|[TM, 1982-03-09, 22.1941] |133.0447          |44.4675             |
|TM |1982-03-10|22.0847|[TM, 1982-03-10, 22.0847] |133.0447          |66.5522             |
|TM |1982-03-11|22.1741|[TM, 1982-03-11, 22.1741] |133.0447          |88.7263             |
|TM |1982-03-12|22.184 |[TM, 1982-03-12, 22.184]  |133.0447          |110.91029999999999  |
|TM |1982-03-15|22.1344|[TM, 1982-03-15, 22.1344] |133.0447          |133.0447            |
+---+----------+-------+--------------------------+------------------+--------------------+