I can't see how to solve this problem without double ranking:
You need to get the largest sums per customer & month.
You then need, for every month, to retrieve the top five of the found sums.
Here's how I would approach this:
;
WITH MaxSubtotals AS (
SELECT DISTINCT
CustomerID,
MonthDate = DATEADD(MONTH, DATEDIFF(MONTH, 0, DateOrdered), 0),
Subtotal = MAX(SUM(JobPrice)) OVER (
PARTITION BY Customer, DATEADD(MONTH, DATEDIFF(MONTH, 0, DateOrdered), 0)
ORDER BY SUM(JobPrice)
)
FROM Invoices
GROUP BY
CustomerID,
DateOrdered
),
TotalsRanked AS (
SELECT
CustomerID,
MonthDate,
Subtotal,
Ranking = ROW_NUMBER() OVER (PARTITION BY MonthDate ORDER BY Subtotal DESC)
FROM MaxDailyTotals
)
SELECT
Month = MONTH(i.MonthDate),
Year = YEAR(i.MonthDate),
c.CustomerCode,
i.Subtotal,
i.Ranking
FROM TotalsRanked i
INNER JOIN Customers ON i.CustomerID = c.ID
WHERE i.Ranking <= 5
;
The first CTE, MaxSubtotals, determines the maximum subtotals per customer & month. Involving DISTINCT and a window aggregating function, it is essentially a "shortcut" for the following two-step query:
SELECT
CustomerID,
MonthDate,
Subtotal = MAX(Subtotal)
FROM (
SELECT
CustomerID,
MonthDate = DATEADD(MONTH, DATEDIFF(MONTH, 0, DateOrdered), 0),
Subtotal = SUM(JobPrice)
FROM Invoices
GROUP BY
CustomerID,
DateOrdered
) s
GROUP BY
CustomerID,
MonthDate
The other CTE, TotalsRanked, simply adds ranking numbers for the found susbtotals, partitioning by customer and month. As a final step, you only need to limit the rows to those that have rankings not greater than 5 (or whatever you might choose another time).
Note that using ROW_NUMBER() to rank the rows in this case guarantees that you'll get no more than 5 rows with the Ranking <= 5 filter. If there were two or more rows with the same subtotal, the would get distinct rankings, and in the end you might end up with an output like this:
Month Year CustomerCode Subtotal Ranking
----- ---- ------------ -------- -------
1 2012 CCC 1500.00 1
1 2012 ELITE 1400.00 2
1 2012 NOC 900.00 3
1 2012 VBC 700.00 4
1 2012 HUCC 700.00 5
-- 1 2012 ABC 690.00 6 -- not returned
-- 1 2012 ... ... ...
Even though there might be other customers with Subtotals of 700.00 for the same month, they wouldn't be returned, because they would be assigned rankings after 5.
You could use RANK() instead of ROW_NUMBER() to account for that. But note that you might end up with more than 5 rows per month then, with an output like this:
Month Year CustomerCode Subtotal Ranking
----- ---- ------------ -------- -------
1 2012 CCC 1500.00 1
1 2012 ELITE 1400.00 2
1 2012 NOC 900.00 3
1 2012 VBC 700.00 4
1 2012 HUCC 700.00 4
1 2012 ABC 700.00 4
-- 1 2012 DEF 690.00 7 -- not returned
-- 1 2012 ... ... ...
Customers with subtotals less than 700.00 wouldn't make it to the output because they would have rankings starting with 7, which would correspond to the ranking of the first under-700.00 sum if ranked by ROW_NUMBER().
And there's another option, DENSE_RANK(). You might want to use it if you want up to 5 distinct sums per month in your output. With DENSE_RANK() your output might contain even more rows per month than it would have with RANK(), but the number of distinct subtotals would be exactly 5 (or fewer if the original dataset can't provide you with 5). That is, your output might then look like this:
Month Year CustomerCode Subtotal Ranking
----- ---- ------------ -------- -------
1 2012 CCC 1500.00 1
1 2012 ELITE 1400.00 2
1 2012 NOC 900.00 3
1 2012 VBC 700.00 4
1 2012 HUCC 700.00 4
1 2012 ABC 700.00 4
1 2012 DEF 650.00 5
1 2012 GHI 650.00 5
1 2012 JKL 650.00 5
-- 1 2012 MNO 600.00 5 -- not returned
-- 1 2012 ... ... ...
Like RANK(), the DENSE_RANK() function assigns same rankings to identical values, but, unlike RANK(), it doesn't produce gaps in the ranking sequence.
References: