Read from sql database in parallel using spark without knowing upper bound

Spark allows you to read in parallel from a sql db source, and one can partition based on a sliding window, for example (from the book, chapter 7)

 val colName = "count"
 val lowerBound = 0L
 val upperBound = 348113L // this is the max count in our table
 val numPartitions = 10

 spark.read.jdbc(url,
                tablename,
                colName,
                lowerBound,
                upperBound,
                numPartitions,
                props).count()

Here, the upper bound is known before hand.

Lets say, a table gets 'x' number of rows(which can be between 1-2 million) in a day and at the end of the day we submit a spark job, do some transformations and write to a Parquet/CSV/JSON. If we don't know before hand about how many rows will be written (as it varying from 1-2 million) to the SQL source database, in such a scenario what will be the best approach or practise to do a partition.

One way is to either have an estimation of your upper bound, but I am not sure this is a right approach.