On 2014-01-12 1:17 PM, Larry Martell wrote:
> I've been asked to do something that I do not think is possible in SQL.
> I have a query that has this basic form:
> SELECT a, b, c, d, AVG(e), STD(e), CONCAT(x, ',', y) as f
> FROM t
> GROUP BY a, b, c, d, f
> x and y are numbers (378.18, 2213.797 or 378.218, 2213.949 or
> 10053.490, 2542.094).
> The business issue is that if either x or y in 2 rows that are in the
> same a, b, c, d group are within 1 of each other then they should be
> grouped together. And to make it more complicated, the tolerance is
> applied as a rolling continuum. For example, if the x and y in a set
> of grouped rows are:
> row 1: 1.5, 9.5
> row 2: 2.4, 20.8
> row 3: 3.3, 40.6
> row 4: 4.2, 2.5
> row 5: 5.1, 10.1
> row 6: 6.0, 7.9
> row 7: 8.0, 21.0
> row 8: 100, 200
> 1 through 6 get combined because all their X values are within the
> tolerance of some other X in the set that's been combined. 7's Y value
> is within the tolerance of 2's Y, so that should be combined as well.
> 8 is not combined because neither the X or Y value is within the
> tolerance of any X or Y in the set that was combined.
> In python I can easily parse the data and identify the rows that need
> to be combined, but then I've lost the ability to calculate the
> average and std. The only way I can think of to do this is to remove
> the grouping from the SQL and do all the grouping and aggregating
> myself. But this query often returns 20k to 30k rows after grouping.
> It could easily be 80k to 100k rows that I have to process if I remove
> the grouping and I think that will be very slow.
> Anyone have any ideas?
Could you compute the row-to-row values & write them to a temp table,
then run the SQL that incorporates that result column?