You can use Spark sql window function , something like df.createOrReplaceTempView(“dfv”) Select count(eventid) over ( partition by start_time, end_time orderly start_time) from dfv
Sent from my iPhone > On Sep 26, 2018, at 11:32 AM, Debajyoti Roy <newroy...@gmail.com> wrote: > > The problem statement and an approach to solve it using windows is described > here: > > https://stackoverflow.com/questions/52509498/given-events-with-start-and-end-times-how-to-count-the-number-of-simultaneous-e > > Looking for more elegant/performant solutions, if they exist. TIA !
