correction:
2013/3/22 Misa Simic <misa.simic@xxxxxxxxx>
Hi,Not clear what is expected result - if you add new dimension...a) three columns? - well not possible to write SQL query which returns undefined number of columns... unfortunatelly - though I am not clear why :)b) But you can get the similar result as from python... my guess is you expect:('north', 'retail', small),('north', 'retail', big),('north', 'manufacturing', small),('north', 'manufacturing', big),('north', 'wholesale', small),('north', 'wholesale', big),('south', 'retail', small),('south', 'retail', big),('south', 'manufacturing', small),('south', 'manufacturing', big)('south', 'wholesale', small)('south', 'wholesale', big)In your dimensions table (called: market_dimensions) you would need one more column to define desired result orderi.e.market_segment_dimensionsmarket_segment_dimension , ordgeography, 1industry type, 2customer size, 3WITH RECURSIVE t (SELECT array_agg(value) AS values, ord + 1 AS next_dim_ord, ord AS agg_dimsFROM market_segment_dimension_valuesINNER JOIN market_segment_dimensions USING (market_segment_dimension)WHERE ord = 1UNION ALLSELECT array_agg(value) AS values, ord + 1 AS next_dim_ord, ord AS agg_dimsFROM tINNER JOIN market_segment_dimensions ON (ord = t.next_dim_ord)INNER JOIN market_segment_dimension_values USING (market_segment_dimension))SELECT values FROM t WHERE t.agg_dims = (SELECT MAX(ord) FROM market_segment_dimensions)2013/3/21 W. Matthew Wilson <matt@xxxxxxxxxx>
I got this table right now: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 valuefrom market_segment_dimension_valueswhere market_segment_dimension = 'geography'),industry_type as (select valuefrom market_segment_dimension_valueswhere market_segment_dimension = 'industry type')select geog.value as g,industry_type.value as ind_typefrom geogcross 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". AI'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