Considering I have a simple aggregation with a window defined without any watermark say.
df
.groupBy(window(col("time"), "30 minutes","10 minutes").as("time"))
.aggr ....
Here as our window is 30 minutes
, and a sliding interval of 10 minutes
Yes it will slide / compute every 10 minutes (sliding interval) providing an overlapping window of 30 mins. You do not define if using event or ingest time. If one uses event time, then the late handling, out of order is handled to update include such data in updated windows as time goes by.
Following on from previous question, this is not the same as watermarking. Watermarking means that after a period, late-arriving data is dropped and thus the effect described above has a time consideration to contend with. That is to say, some older windows will not be updated.