Spark SQL Window over interval of between two specified time boundaries - between 3 hours and 2 hours ago

Question

What is the proper way of specifying window interval in Spark SQL, using two predefined boundaries?

I am trying to sum up values from my table over a window of "between 3 hours ago and 2 hours ago".

When I run this query:

select *, sum(value) over (
partition by a, b
order by cast(time_value as timestamp)
range between interval 2 hours preceding and current row
) as sum_value
from my_temp_table;

That works. I get results that I expect, i.e. sums of values that fall into 2 hours rolling window.

Now, what I need is to have that rolling window not being bound to the current row but to take into account rows between 3 hours ago and 2 hours ago. I tried with:

select *, sum(value) over (
partition by a, b
order by cast(time_value as timestamp)
range between interval 3 hours preceding and 2 hours preceding
) as sum_value
from my_temp_table;

But I get extraneous input 'hours' expecting {'PRECEDING', 'FOLLOWING'} error.

I also tried with:

select *, sum(value) over (
partition by a, b
order by cast(time_value as timestamp)
range between interval 3 hours preceding and interval 2 hours preceding
) as sum_value
from my_temp_table;

but then I get different error scala.MatchError: CalendarIntervalType (of class org.apache.spark.sql.types.CalendarIntervalType$)

Third option I tried is:

select *, sum(value) over (
partition by a, b
order by cast(time_value as timestamp)
range between interval 3 hours preceding and 2 preceding
) as sum_value
from my_temp_table;

and it doesn't work as we would expect: cannot resolve 'RANGE BETWEEN interval 3 hours PRECEDING AND 2 PRECEDING' due to data type mismatch

I am having difficulties finding the docs for interval type as this link doesn't say enough and other information is kinda half baked. At least what I found.

Answer 1

Since range intervals didn't work their thing, I had to turn to an alternative approach. It goes something like this:

• prepare a list of intervals for which computation needs to be performed
• for each of the intervals, run the computation
  • each of those iterations produces a data frame
• after the iterations, we have a list of data frames
• union the data frames from the list into one bigger data frame
• write out the results

In my case, I had to run computations for each hour of the day and combine those "hourly" results, i.e. a list of 24 data frames, into one, "daily", data frame.

Code, from very high level perspective, looks like this:

val hourlyDFs = for ((hourStart, hourEnd) <- (hoursToStart, hoursToEnd).zipped) yield {
    val data = data.where($"hour" <= lit(hourEnd) && $"hour" >= lit(hourStart))
    // do stuff
    // return a data frame
}
hourlyDFs.toSeq().reduce(_.union(_))