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 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 !