How to join table to itself N times?

On Wed, Mar 20, 2013 at 7:38 PM, W. Matthew Wilson <matt@tplus1.com> wrote:

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 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@tplus1.com
http://tplus1.com

You may try:

Select a.value, b.value
from market_segment_dimension_values as a,
from market_segment_dimension_values as b
where a.market_segment_dimension <> b.market_segment_dimension

-- AI

Matt Wilson wrote

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)

Most likely you can solve your problem by using the "hstore" extension. I
could be more certain of this if you actually state the
requirements/use-case/business-problem.

SQL requires that you know the column structure of the output so if hstore
does not suffice you will have to execute a dynamic query in your API after
querying the dimension map table to decide how many output columns you will
need. hstore avoids that by giving you a dynamic table-in-a-column.

David J.

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On Wed, Mar 20, 2013 at 5:38 PM, W. Matthew Wilson <matt@tplus1.com> wrote:

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.

If the number of dimensions is not fixed, then you'll probably have to
write a plpgsql function to first interrogate the data set for how
many dimensions there are and then to build an n-dimension query.
While joining a variable number of tables may be problematic as you
won't have a fixed number of columns, using a union might give you
what you want with a fixed number of columns.

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Wow, this is a fun puzzle. I'd love to be the first to solve it with
just SQL, but I don't have a solution yet. Here are some elements that
might be useful:

SELECT market_segment_dimension, array_agg(value)
FROM market_segment_dimension_values
GROUP BY market_segment_dimension;

the UNNEST function
the ROW function
window functions like row_number and nth_value
the crosstab function (requires installing an extension; this seems
like cheating if you ask me)

Good luck!
Paul

On Wed, Mar 20, 2013 at 7:14 PM, Scott Marlowe <scott.marlowe@gmail.com> wrote:

On Wed, Mar 20, 2013 at 5:38 PM, W. Matthew Wilson <matt@tplus1.com> wrote:

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.

If the number of dimensions is not fixed, then you'll probably have to
write a plpgsql function to first interrogate the data set for how
many dimensions there are and then to build an n-dimension query.
While joining a variable number of tables may be problematic as you
won't have a fixed number of columns, using a union might give you
what you want with a fixed number of columns.

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Okay, how about this (table names shortened):

create table m (d varchar(255) not null, v varchar(255) not null);
insert into m (d, v) values ('geography', 'north'), ('geography',
'south'), ('industry type', 'retail'), ('industry type',
'manufacturing'), ('industry type', 'wholesale');

WITH RECURSIVE t(combo, n) AS (
WITH dims AS (SELECT DISTINCT d, row_number() OVER () AS n FROM m GROUP BY d)
SELECT '{}'::text[], 1
UNION ALL
SELECT array_append(t2.combo::text[], m.v::text), t2.n+1
FROM t t2, dims
CROSS JOIN m
WHERE m.d = dims.d AND dims.n = t2.n
)
SELECT *
FROM t
WHERE n = (SELECT COUNT(DISTINCT d) + 1 FROM m);

Gives these results:

combo | n
-----------------------+---
{retail,north} | 3
{manufacturing,north} | 3
{wholesale,north} | 3
{retail,south} | 3
{manufacturing,south} | 3
{wholesale,south} | 3
(6 rows)

Paul

On Wed, Mar 20, 2013 at 8:40 PM, Paul Jungwirth
<pj@illuminatedcomputing.com> wrote:

Wow, this is a fun puzzle. I'd love to be the first to solve it with
just SQL, but I don't have a solution yet. Here are some elements that
might be useful:

SELECT market_segment_dimension, array_agg(value)
FROM market_segment_dimension_values
GROUP BY market_segment_dimension;

the UNNEST function
the ROW function
window functions like row_number and nth_value
the crosstab function (requires installing an extension; this seems
like cheating if you ask me)

Good luck!
Paul

On Wed, Mar 20, 2013 at 7:14 PM, Scott Marlowe <scott.marlowe@gmail.com> wrote:

On Wed, Mar 20, 2013 at 5:38 PM, W. Matthew Wilson <matt@tplus1.com> wrote:

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.

If the number of dimensions is not fixed, then you'll probably have to
write a plpgsql function to first interrogate the data set for how
many dimensions there are and then to build an n-dimension query.
While joining a variable number of tables may be problematic as you
won't have a fixed number of columns, using a union might give you
what you want with a fixed number of columns.

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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 order

i.e.

market_segment_dimensions
market_segment_dimension , ord
geography, 1
industry type, 2
customer size, 3

WITH RECURSIVE t (

SELECT array_agg(value) AS values, ord + 1 AS next_dim_ord, ord AS agg_dims
FROM market_segment_dimension_values
INNER JOIN market_segment_dimensions USING (market_segment_dimension)
WHERE ord = 1
UNION ALL
SELECT array_agg(value) AS values, ord + 1 AS next_dim_ord, ord AS agg_dims
FROM t
INNER 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@tplus1.com>

correction:

2013/3/22 Misa Simic <misa.simic@gmail.com>

correction:

WITH RECURSIVE t (

SELECT array_agg('{}'::text[], value) AS values, ord + 1 AS next_dim_ord,
ord AS agg_dims
FROM market_segment_dimension_values
INNER JOIN market_segment_dimensions USING (market_segment_dimension)
WHERE ord = 1
UNION ALL
SELECT array_agg(t.values, value) AS values, ord + 1 AS next_dim_ord, ord
AS agg_dims
FROM t
INNER 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)

2013/3/22 Misa Simic <misa.simic@gmail.com>