I’m working on a SQL solution to squeeze the number of rows when there are matching “start” and “close” values across records. Here is an example input and the required output

Input:

+----+-----+-----+
|name|start|close|
+----+-----+-----+
|A   |120  |130  |
|A   |130  |140  |
|A   |140  |150  |
|A   |152  |160  |
|A   |160  |180  |
|B   |100  |130  |
|B   |130  |200  |
|B   |202  |250  |
|C   |300  |400  |
+----+-----+-----+

Required Output:

+----+-----+-----+
|name|start|close|
+----+-----+-----+
|A   |120  |150  |
|A   |152  |180  |
|B   |100  |200  |
|B   |202  |250  |
|C   |300  |400  |
+----+-----+-----+

I tried with lag() function, but not getting correct output.. the null boundaries are crossing.

with 
     t1 ( 
            select name, start, close, lag(close) over(partition by name order by start) pclose from meeting 
        ),
     t2 (
            select * from t1 where 1 = (case when pclose is null then 1
                                             when start = pclose then 0 else 1 end)
        )
   select * from t2 order by name, start

Any ANSI complaint solution is welcome as I can port it easily to spark.