I have recently started working with Kusto. I am stuck with a use case where i need to confirm the approach i am taking is right.
I have data in the following format
In the above example, if the status is 1 and if the time frame is equal to 15 seconds then i need to assume it as 1 occurrence.
So in this case 2 occurrence of status. My approach was
if the current and next rows status is equal to 1 then take the time difference and do row_cum_sum and break it if the next(STATUS)!=0.
Even though the approach is giving me correct output, I am assuming the performance can slow down once the size is increased.
I am looking for an alternative approach if any. Also adding the complete scenario to reproduce this with a sample data.
.create-or-alter function with (folder = "Tests", skipvalidation = "true") InsertFakeTrue() {
range LoopTime from ago(365d) to now() step 6s
| project TIME=LoopTime,STATUS=toint(1)
}
.create-or-alter function with (folder = "Tests", skipvalidation = "true") InsertFakeFalse() {
range LoopTime from ago(365d) to now() step 29s
| project TIME=LoopTime,STATUS=toint(0)
}
.set-or-append FAKEDATA <| InsertFakeTrue();
.set-or-append FAKEDATA <| InsertFakeFalse();
FAKEDATA
| order by TIME asc
| serialize
| extend cstatus=STATUS
| extend nstatus=next(STATUS)
| extend WindowRowSum=row_cumsum(iff(nstatus ==1 and cstatus ==1, datetime_diff('second',next(TIME),TIME),0),cstatus !=1)
| extend windowCount=iff(nstatus !=1 or isnull(next(TIME)), iff(WindowRowSum ==15, 1,iff(WindowRowSum >15,(WindowRowSum/15)+((WindowRowSum%15)/15),0)),0 )
| summarize IDLE_COUNT=sum(windowCount)