I got this table right now:
Matt
--
W. Matthew Wilson
matt@xxxxxxxxxx
http://tplus1.com
select * from market_segment_dimension_values ;
+--------------------------+---------------+
| market_segment_dimension | value |
+--------------------------+---------------+
| geography | north |
| geography | south |
| industry type | retail |
| industry type | manufacturing |
| industry type | wholesale |
+--------------------------+---------------+
(5 rows)
The PK is (market_segment_dimension, value).
The dimension column refers to another table called market_segment_dimensions.
So, "north" and "south" are to values for the "geography" dimension.
In that data above, there are two dimensions. But sometimes there could be just one dimension, or maybe three, ... up to ten.
Now here's the part where I'm stumped.
I need to create a cartesian product of the dimensions.
I came up with this approach by hard-coding the different dimensions:
with geog as (
select value
from market_segment_dimension_values
where market_segment_dimension = 'geography'),
industry_type as (
select value
from market_segment_dimension_values
where market_segment_dimension = 'industry type')
select geog.value as g,
industry_type.value as ind_type
from geog
cross join industry_type
;
+-------+---------------+
| g | ind_type |
+-------+---------------+
| north | retail |
| north | manufacturing |
| north | wholesale |
| south | retail |
| south | manufacturing |
| south | wholesale |
+-------+---------------+
(6 rows)
But that won't work if I add a new dimension (unless I update the query). For example, maybe I need to add a new dimension called, say, customer size, which has values "big" and "small". A
I've got some nasty plan B solutions, but I want to know if there's some solution.
There's a really elegant solution in python using itertools.product, like this:
>>> list(itertools.product(*[['north', 'south'], ['retail', 'manufacturing', 'wholesale']]))
[('north', 'retail'),
('north', 'manufacturing'),
('north', 'wholesale'),
('south', 'retail'),
('south', 'manufacturing'),
('south', 'wholesale')]
All advice is welcome. Thanks in advance!
Matt
W. Matthew Wilson
matt@xxxxxxxxxx
http://tplus1.com