我有下表：
CREATE TABLE PaL (
Event_Date DATE,
Country CHAR(2),
Category CHAR(255),
Revenue INTEGER(255),
Costs INTEGER(255)
);
INSERT INTO PaL
(Event_Date, Country, Category, Revenue, Costs)
VALUES
("2017-01-31", "DE", "Apparel", "692.09816652375", "-173.071989376023"),
("2017-02-28", "DE", "Apparel", "8419.9977988914", "-7622.61265984317"),
("2017-03-31", "DE", "Apparel", "2018.80471444031", "-1498.76213884283"),
("2017-04-30", "DE", "Apparel", "8863.15663035884", "-7965.69268589649"),
("2017-05-31", "DE", "Apparel", "6838.4514829573", "-1088.70351845663"),
("2017-06-30", "DE", "Apparel", "2025.73421386331", "-483.454199185678"),
("2017-07-31", "DE", "Apparel", "5389.0163788639", "-2643.93624645182"),
("2017-08-31", "DE", "Apparel", "6238.85870250446", "-1985.9879371866"),
("2017-09-30", "DE", "Apparel", "2294.62451106469", "-1864.98271539745"),
("2017-10-31", "DE", "Apparel", "4141.2074159951", "-197.773961036073"),
("2017-11-30", "DE", "Apparel", "1456.17584217397", "-1018.54129047119"),
("2017-12-31", "DE", "Apparel", "3623.54984724091", "-745.715567286581"),
("2017-01-31", "DE", "Shoes", "5955.20947079185", "-4745.39564508682"),
("2017-02-28", "DE", "Shoes", "9555.29563511224", "-5729.82601329738"),
("2017-03-31", "DE", "Shoes", "5490.36170257556", "-925.286457266534"),
("2017-04-30", "DE", "Shoes", "7652.35548686073", "-7335.32532050594"),
("2017-05-31", "DE", "Shoes", "9102.38987703511", "-5724.92574170071"),
("2017-06-30", "DE", "Shoes", "1703.95901703023", "-1678.19547060803"),
("2017-07-31", "DE", "Shoes", "3679.60045104324", "-2095.94207835501"),
("2017-08-31", "DE", "Shoes", "6672.43210841331", "-3475.55411014914"),
("2017-09-30", "DE", "Shoes", "7718.7744220635", "-1252.75877307055"),
("2017-10-31", "DE", "Shoes", "6976.01564153854", "-509.991595559256"),
("2017-11-30", "DE", "Shoes", "4725.46976319905", "-2835.09460170927"),
("2017-12-31", "DE", "Shoes", "8390.84483147949", "-7476.83516162742"),
("2017-01-31", "US", "Apparel", "939788.159047677", "-742666.846083707"),
("2017-02-28", "US", "Apparel", "826418.514009279", "-702997.151099908"),
("2017-03-31", "US", "Apparel", "775940.69563018", "-211238.971709086"),
("2017-04-30", "US", "Apparel", "516829.583069596", "-407521.856789393"),
("2017-05-31", "US", "Apparel", "635701.377748304", "-627829.016481388"),
("2017-06-30", "US", "Apparel", "757852.95599751", "-740948.867522139"),
("2017-07-31", "US", "Apparel", "154224.257732688", "-139805.456987081"),
("2017-08-31", "US", "Apparel", "102035.465731255", "-100103.875992667"),
("2017-09-30", "US", "Apparel", "880671.692714021", "-665324.083753931"),
("2017-10-31", "US", "Apparel", "187868.653562564", "-105676.793254039"),
("2017-11-30", "US", "Apparel", "994600.486892401", "-177382.899789215"),
("2017-12-31", "US", "Apparel", "813824.90461202", "-132527.311010471"),
("2017-01-31", "US", "Shoes", "661069.933966637", "-454778.427240679"),
("2017-02-28", "US", "Shoes", "675942.334464692", "-254489.623313569"),
("2017-03-31", "US", "Shoes", "473604.307973888", "-404226.047653847"),
("2017-04-30", "US", "Shoes", "872018.822577053", "-348781.396359871"),
("2017-05-31", "US", "Shoes", "718012.023481434", "-625306.312927362"),
("2017-06-30", "US", "Shoes", "688487.679029354", "-584512.575888519"),
("2017-07-31", "US", "Shoes", "690085.013711018", "-581753.771085971"),
("2017-08-31", "US", "Shoes", "204473.88894161", "-172301.871771595"),
("2017-09-30", "US", "Shoes", "516932.092423463", "-328002.737710081"),
("2017-10-31", "US", "Shoes", "609355.245817292", "-323624.391366703"),
("2017-11-30", "US", "Shoes", "313599.625504231", "-210253.020497022"),
("2017-12-31", "US", "Shoes", "723573.681040319", "-107333.764977439"),
("2017-01-31", "NZ", "Apparel", "81292.9610624533", "-27354.678748396"),
("2017-02-28", "NZ", "Apparel", "77473.6231986387", "-47920.2900396812"),
("2017-03-31", "NZ", "Apparel", "93819.4377266116", "-47582.1878235771"),
("2017-04-30", "NZ", "Apparel", "25580.8516093492", "-21277.2651303701"),
("2017-05-31", "NZ", "Apparel", "82842.9415935231", "-30714.5952859941"),
("2017-06-30", "NZ", "Apparel", "50878.6190715448", "-33047.3841488076"),
("2017-07-31", "NZ", "Apparel", "61423.3558286064", "-10811.2817583225"),
("2017-08-31", "NZ", "Apparel", "77517.2989019148", "-56818.7461336424"),
("2017-09-30", "NZ", "Apparel", "74046.1258000888", "-10108.0702908427"),
("2017-10-31", "NZ", "Apparel", "79490.650598675", "-68562.5753547413"),
("2017-11-30", "NZ", "Apparel", "65000.3971251097", "-25174.1329786955"),
("2017-12-31", "NZ", "Apparel", "99152.6457285608", "-42855.8431883814"),
("2017-01-31", "NZ", "Shoes", "20703.8970205884", "-11911.9616025915"),
("2017-02-28", "NZ", "Shoes", "72841.2537140946", "-14166.6747335237"),
("2017-03-31", "NZ", "Shoes", "45391.6550622383", "-40325.1638601903"),
("2017-04-30", "NZ", "Shoes", "58074.2843201579", "-54483.1122507654"),
("2017-05-31", "NZ", "Shoes", "52127.2701338519", "-28026.7984458694"),
("2017-06-30", "NZ", "Shoes", "32900.9222204099", "-22780.2637095601"),
("2017-07-31", "NZ", "Shoes", "18809.3868235169", "-11500.4020522949"),
("2017-08-31", "NZ", "Shoes", "67001.2729206886", "-53280.8129552599"),
("2017-09-30", "NZ", "Shoes", "26889.4058005421", "-24218.8734875798"),
("2017-10-31", "NZ", "Shoes", "56330.7544011198", "-51382.4201254223"),
("2017-11-30", "NZ", "Shoes", "60954.7129549264", "-19834.7256352672"),
("2017-12-31", "NZ", "Shoes", "97527.2014993995", "-83137.4844853141");
然后我使用以下查询从表中获取数据：
Select Country, Category, sum(Revenue) as Revenue, sum(Costs) as Costs
FROM Pal
WHERE Event_Date BETWEEN "2017-01-01" AND "2017-01-31"
GROUP BY Country, Category WITH ROLLUP
您还可以在sql fiddle here中找到包含数据的表
到目前为止，所有这些工作正常。
现在，我想知道如何避免WITH ROLLUP函数计算每个国家/地区下方的列的总数。相反，它应该只计算一次列总计，因此最终结果如下所示：
Country Category Revenue Costs
DE Apparel 692 -173
DE Shoes 5955 -4745
: : : :
: : : :
: : : :
US Shoes 661070 -454778
(null) (null) 1709502 -1241630
要实现此目的，我必须在SQL查询中进行哪些更改？
答案 0 :(得分：1)
MySQL不支持GROUPING SETS，这是您真正想要的。也许最简单的方法是使用UNION ALL：
SELECT Country, Category, SUM(Revenue) as Revenue, SUM(Costs) as Costs
FROM Pal
WHERE Event_Date BETWEEN '2017-01-01' AND '2017-01-31'
GROUP BY Country, Category
UNION ALL
SELECT NULL, NULL, SUM(Revenue) as Revenue, SUM(Costs) as Costs
FROM Pal
WHERE Event_Date BETWEEN '2017-01-01' AND '2017-01-31';
答案 1 :(得分：1)
您可以使用HAVING过滤出每个国家的小计：
Select Country, Category, sum(Revenue) as Revenue, sum(Costs) as Costs
FROM Pal
WHERE Event_Date BETWEEN "2017-01-01" AND "2017-01-31"
GROUP BY Country, Category WITH ROLLUP
HAVING (Country IS NULL AND Category IS NULL) OR (Country IS NOT NULL AND Category IS NOT NULL)
条件Country IS NULL AND Category IS NULL与末尾的总数匹配，条件Country IS NOT NULL AND Category IS NOT NULL与每个国家和类别的各个行匹配。
答案 2 :(得分：0)
通过汇总删除
Select Country, Category, sum(Revenue) as Revenue, sum(Costs) as Costs
FROM Pal
WHERE Event_Date BETWEEN "2017-01-01" AND "2017-01-31"
GROUP BY Country, Category
然后使用全部工会，就像先生@Gordon使用他的答案