Spark window custom function - getting the total n

I have a timeseries dataset, which is partitioned by an id, and ordered by a timestamp. Sample:

  ID     Timestamp   Feature

 "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

I have some custom logic I need to compute, and it should be done per window, within each partition. I know Spark has a rich support for window functions, which I am trying to use for this purpose.

My logic requires the total number of elements in the current window/partition, as a scalar. I need that to do some specific computations(basically, a for loop up to that count).

I have tried to add a count column, by doing a

val window = Window.partitionBy("id").orderBy("timestamp") 
frame = frame.withColumn("my_cnt", count(column).over(window))

I need to do something like:

var i = 1
var y = col("Feature")
var result = y
while (i < /* total number of records within each partition goes here */) {
    result = result + lit(1) * lag(y, i).over(window) + /* complex computation */
    i = i + 1
}
dataFrame.withColumn("Computed_Value", result)

How can I get that total number of records within each partition, as a scalar value? I also have that count "my_cnt" value added, which adds the total values for the partition, but can't seem to be able to use it in my case.

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

So if you have the source

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))

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
    }
})

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")

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)))

This will result in something like:

+---+----------+-------+--------------------------+------------------+--------------------+
|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            |
+---+----------+-------+--------------------------+------------------+--------------------+