I had this same issue in Redshift and ended up populating a table with values 1 to the maximum number of commas found (e.g. using max(regexp_count(...)) or something), then cross joining on the table with the csv column and calling split_part on the corresponding column and index (with the index coming from the numbers table). The cross join ensures that you index every value of the csv column.

What I think you'd do here (in modern implementations of SQL at least) is to split the values into an array using some convenient split function and self join on that array to pivot the arrays into rows.