将多个分组查询组合成单个查询
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【中文标题】将多个分组查询组合成单个查询【英文标题】：combine multiple group by queries into single query 【发布时间】：2019-11-05 03:29:52 【问题描述】：我有一个结构类似于下面的表，我有四个分组变量（区域、站点、方法、客户端）和三个指标，每个指标都有一个随附的“桶”列，该列给出了指标的五分位数。我想通过分组变量和五分位桶执行一些聚合。现在我必须运行三个单独的查询并尝试加入导致重复的结果。有没有办法将下面的三个查询合并为一个？
Select Region,Site,Method,Client,A_bucket,
count(ID) as nA,
avg(Metric_A) as Avg.A,
min(Metric_A) as Min.A
From S_overflow
Group By Region,Site,Method,Client,A_bucket
和
Select Region,Site,Method,Client,B_bucket,
count(ID) as nB,
avg(Metric_B) Avg.B,
min(Metric_B) Min.B
From S_overflow
Group By Region,Site,Method,Client,B_bucket
和
Select Region,Site,Method,Client,C_bucket,
count(ID) as nC,
avg(Metric_C) as avg.C,
min(Metric_C) as Min.C
From S_overflow
Group By Region,Site,Method,Client,C_bucket
这是我能够通过按区域、站点、方法、客户端、存储桶和绑定列对每个结果进行排序而在 R 中获得的所需结果，但是我需要一个 HQL (SQL) 解决方案，并且希望将三个查询减为一个。
期望的结果
+--------+---------------+--------+--------+-----------+----+-------------+-------+-----------+----+-------------+-------+-----------+----+-------------+-------+
| Region | Site | Method | Client | A_buckets | nA | Avg.A | min.A | B_buckets | nB | Avg.B | min.B | C_buckets | nC | avg.C | min.C |
+--------+---------------+--------+--------+-----------+----+-------------+-------+-----------+----+-------------+-------+-----------+----+-------------+-------+
| A | Local | 1 | mno | 1 | 2 | 73.5 | 22 | 1 | 2 | 153 | 49 | 1 | 2 | 80.5 | 49 |
| A | Local | 1 | mno | 2 | 2 | 343.5 | 302 | 2 | 2 | 420 | 363 | 2 | 2 | 395 | 355 |
| A | Local | 1 | mno | 3 | 2 | 495.5 | 415 | 3 | 2 | 682.5 | 631 | 3 | 2 | 564.5 | 485 |
| A | Local | 1 | mno | 4 | 2 | 585.5 | 585 | 4 | 2 | 795 | 762 | 4 | 2 | 696.5 | 677 |
| A | Local | 1 | mno | 5 | 2 | 751 | 600 | 5 | 2 | 946.5 | 908 | 5 | 2 | 848 | 717 |
| A | Local | 1 | rst | 1 | 2 | 24.5 | 21 | 1 | 2 | 241.5 | 203 | 1 | 2 | 156.5 | 97 |
| A | Local | 1 | rst | 2 | 2 | 232.5 | 193 | 2 | 2 | 360.5 | 316 | 2 | 2 | 280 | 234 |
| A | Local | 1 | rst | 3 | 2 | 421.5 | 377 | 3 | 2 | 465.5 | 438 | 3 | 2 | 374 | 331 |
| A | Local | 1 | rst | 4 | 2 | 647.5 | 602 | 4 | 2 | 690.5 | 687 | 4 | 2 | 542 | 466 |
| A | Local | 1 | rst | 5 | 2 | 937.5 | 937 | 5 | 2 | 956.5 | 924 | 5 | 2 | 797 | 691 |
| A | Local | 1 | xyz | 1 | 2 | 209 | 70 | 1 | 2 | 224 | 80 | 1 | 2 | 159.5 | 148 |
| A | Local | 1 | xyz | 2 | 2 | 477 | 382 | 2 | 2 | 425 | 410 | 2 | 2 | 232.5 | 205 |
| A | Local | 1 | xyz | 3 | 2 | 611.5 | 596 | 3 | 2 | 521 | 444 | 3 | 2 | 315.5 | 287 |
| A | Local | 1 | xyz | 4 | 2 | 677 | 634 | 4 | 2 | 726.5 | 628 | 4 | 2 | 375 | 367 |
| A | Local | 1 | xyz | 5 | 2 | 829.5 | 807 | 5 | 2 | 843 | 838 | 5 | 2 | 671.5 | 570 |
| B | International | 2 | mno | 1 | 3 | 175.6666667 | 15 | 1 | 3 | 114 | 58 | 1 | 3 | 202 | 159 |
| B | International | 2 | mno | 2 | 2 | 515.5 | 511 | 2 | 2 | 298 | 245 | 2 | 2 | 394 | 370 |
| B | International | 2 | mno | 3 | 3 | 614.3333333 | 573 | 3 | 3 | 569 | 508 | 3 | 3 | 493.3333333 | 430 |
| B | International | 2 | mno | 4 | 2 | 715.5 | 708 | 4 | 2 | 786.5 | 686 | 4 | 2 | 737 | 627 |
| B | International | 2 | mno | 5 | 2 | 814 | 760 | 5 | 2 | 941 | 901 | 5 | 2 | 861.5 | 847 |
| B | International | 2 | rst | 1 | 3 | 77 | 26 | 1 | 3 | 133.3333333 | 3 | 1 | 3 | 43 | 12 |
| B | International | 2 | rst | 2 | 2 | 329 | 225 | 2 | 2 | 262.5 | 243 | 2 | 2 | 259 | 192 |
| B | International | 2 | rst | 3 | 3 | 522.6666667 | 450 | 3 | 3 | 505 | 380 | 3 | 3 | 465.3333333 | 336 |
| B | International | 2 | rst | 4 | 2 | 701 | 578 | 4 | 2 | 713.5 | 673 | 4 | 2 | 790 | 755 |
| B | International | 2 | rst | 5 | 2 | 948 | 904 | 5 | 2 | 929.5 | 898 | 5 | 2 | 846.5 | 834 |
| B | International | 2 | xyz | 1 | 3 | 119.6666667 | 101 | 1 | 3 | 170.3333333 | 125 | 1 | 3 | 259.3333333 | 43 |
| B | International | 2 | xyz | 2 | 3 | 373 | 282 | 2 | 3 | 279.3333333 | 207 | 2 | 3 | 695.3333333 | 601 |
| B | International | 2 | xyz | 3 | 2 | 493 | 492 | 3 | 2 | 476 | 462 | 3 | 2 | 805 | 804 |
| B | International | 2 | xyz | 4 | 3 | 676.3333333 | 552 | 4 | 3 | 554.3333333 | 511 | 4 | 3 | 850 | 820 |
| B | International | 2 | xyz | 5 | 2 | 896 | 868 | 5 | 2 | 770.5 | 722 | 5 | 2 | 929.5 | 906 |
+--------+---------------+--------+--------+-----------+----+-------------+-------+-----------+----+-------------+-------+-----------+----+-------------+-------+
原始数据
+--------+---------------+--------+--------+----+----------+-----------+----------+-----------+----------+-----------+
| Region | Site | Method | Client | ID | Metric_A | A_buckets | Metric_B | B_buckets | Metric_C | C_buckets |
+--------+---------------+--------+--------+----+----------+-----------+----------+-----------+----------+-----------+
| A | Local | 1 | mno | 3 | 125 | 1 | 257 | 1 | 677 | 4 |
| A | Local | 1 | mno | 6 | 585 | 4 | 762 | 4 | 644 | 3 |
| A | Local | 1 | mno | 9 | 22 | 1 | 631 | 3 | 717 | 5 |
| A | Local | 1 | mno | 12 | 576 | 3 | 363 | 2 | 112 | 1 |
| A | Local | 1 | mno | 15 | 586 | 4 | 985 | 5 | 435 | 2 |
| A | Local | 1 | mno | 18 | 902 | 5 | 828 | 4 | 716 | 4 |
| A | Local | 1 | mno | 21 | 302 | 2 | 49 | 1 | 355 | 2 |
| A | Local | 1 | mno | 24 | 600 | 5 | 477 | 2 | 485 | 3 |
| A | Local | 1 | mno | 27 | 415 | 3 | 908 | 5 | 979 | 5 |
| A | Local | 1 | mno | 30 | 385 | 2 | 734 | 3 | 49 | 1 |
| A | Local | 1 | rst | 2 | 21 | 1 | 203 | 1 | 234 | 2 |
| A | Local | 1 | rst | 5 | 937 | 5 | 438 | 3 | 618 | 4 |
| A | Local | 1 | rst | 8 | 28 | 1 | 924 | 5 | 97 | 1 |
| A | Local | 1 | rst | 11 | 377 | 3 | 989 | 5 | 326 | 2 |
| A | Local | 1 | rst | 14 | 602 | 4 | 687 | 4 | 216 | 1 |
| A | Local | 1 | rst | 17 | 693 | 4 | 405 | 2 | 903 | 5 |
| A | Local | 1 | rst | 20 | 938 | 5 | 316 | 2 | 466 | 4 |
| A | Local | 1 | rst | 23 | 193 | 2 | 280 | 1 | 331 | 3 |
| A | Local | 1 | rst | 26 | 272 | 2 | 694 | 4 | 691 | 5 |
| A | Local | 1 | rst | 29 | 466 | 3 | 493 | 3 | 417 | 3 |
| A | Local | 1 | xyz | 1 | 572 | 2 | 848 | 5 | 148 | 1 |
| A | Local | 1 | xyz | 4 | 627 | 3 | 628 | 4 | 287 | 3 |
| A | Local | 1 | xyz | 7 | 596 | 3 | 825 | 4 | 383 | 4 |
| A | Local | 1 | xyz | 10 | 807 | 5 | 410 | 2 | 367 | 4 |
| A | Local | 1 | xyz | 13 | 382 | 2 | 368 | 1 | 260 | 2 |
| A | Local | 1 | xyz | 16 | 852 | 5 | 598 | 3 | 570 | 5 |
| A | Local | 1 | xyz | 19 | 634 | 4 | 444 | 3 | 773 | 5 |
| A | Local | 1 | xyz | 22 | 720 | 4 | 80 | 1 | 344 | 3 |
| A | Local | 1 | xyz | 25 | 348 | 1 | 838 | 5 | 205 | 2 |
| A | Local | 1 | xyz | 28 | 70 | 1 | 440 | 2 | 171 | 1 |
| B | International | 2 | mno | 33 | 723 | 4 | 508 | 3 | 418 | 2 |
| B | International | 2 | mno | 36 | 868 | 5 | 686 | 4 | 159 | 1 |
| B | International | 2 | mno | 39 | 352 | 1 | 196 | 1 | 208 | 1 |
| B | International | 2 | mno | 42 | 520 | 2 | 887 | 4 | 847 | 4 |
| B | International | 2 | mno | 45 | 708 | 4 | 88 | 1 | 876 | 5 |
| B | International | 2 | mno | 48 | 511 | 2 | 351 | 2 | 370 | 2 |
| B | International | 2 | mno | 51 | 160 | 1 | 607 | 3 | 847 | 5 |
| B | International | 2 | mno | 54 | 577 | 3 | 245 | 2 | 239 | 1 |
| B | International | 2 | mno | 57 | 693 | 3 | 901 | 5 | 430 | 3 |
| B | International | 2 | mno | 60 | 760 | 5 | 592 | 3 | 627 | 4 |
| B | International | 2 | mno | 63 | 573 | 3 | 58 | 1 | 571 | 3 |
| B | International | 2 | mno | 66 | 15 | 1 | 981 | 5 | 479 | 3 |
| B | International | 2 | rst | 32 | 992 | 5 | 898 | 5 | 834 | 5 |
| B | International | 2 | rst | 35 | 578 | 4 | 754 | 4 | 336 | 3 |
| B | International | 2 | rst | 38 | 555 | 3 | 623 | 3 | 67 | 1 |
| B | International | 2 | rst | 41 | 824 | 4 | 3 | 1 | 755 | 4 |
| B | International | 2 | rst | 44 | 77 | 1 | 961 | 5 | 825 | 4 |
| B | International | 2 | rst | 47 | 26 | 1 | 380 | 3 | 412 | 3 |
| B | International | 2 | rst | 50 | 904 | 5 | 203 | 1 | 12 | 1 |
| B | International | 2 | rst | 53 | 433 | 2 | 282 | 2 | 50 | 1 |
| B | International | 2 | rst | 56 | 128 | 1 | 512 | 3 | 859 | 5 |
| B | International | 2 | rst | 59 | 563 | 3 | 194 | 1 | 326 | 2 |
| B | International | 2 | rst | 62 | 225 | 2 | 673 | 4 | 192 | 2 |
| B | International | 2 | rst | 65 | 450 | 3 | 243 | 2 | 648 | 3 |
| B | International | 2 | xyz | 31 | 101 | 1 | 185 | 1 | 804 | 3 |
| B | International | 2 | xyz | 34 | 127 | 1 | 125 | 1 | 701 | 2 |
| B | International | 2 | xyz | 37 | 492 | 3 | 462 | 3 | 601 | 2 |
| B | International | 2 | xyz | 40 | 924 | 5 | 612 | 4 | 906 | 5 |
| B | International | 2 | xyz | 43 | 786 | 4 | 722 | 5 | 784 | 2 |
| B | International | 2 | xyz | 46 | 442 | 2 | 511 | 4 | 43 | 1 |
| B | International | 2 | xyz | 49 | 552 | 4 | 207 | 2 | 820 | 4 |
| B | International | 2 | xyz | 52 | 868 | 5 | 819 | 5 | 953 | 5 |
| B | International | 2 | xyz | 55 | 494 | 3 | 540 | 4 | 855 | 4 |
| B | International | 2 | xyz | 58 | 131 | 1 | 267 | 2 | 875 | 4 |
| B | International | 2 | xyz | 61 | 282 | 2 | 201 | 1 | 806 | 3 |
| B | International | 2 | xyz | 64 | 395 | 2 | 364 | 2 | 156 | 1 |
| B | International | 2 | xyz | 67 | 691 | 4 | 490 | 3 | 579 | 1 |
+--------+---------------+--------+--------+----+----------+-----------+----------+-----------+----------+-----------+
【问题讨论】：
您的存储桶上似乎没有任何关系，请使用交叉连接 @ϻᴇᴛᴀʟ 我会调查的，但我认为这会给我带来比我想要的更多的行。我将编辑我的问题以包含所需的结果。 【参考方案1】：尝试使用交叉连接
select t1.Region, t1.Site, t1.Method, t1.Client, t1.A_bucket, count(t1.ID)
, t2.B_bucket, t2.Metric_B
, t3.C_bucket, t3.Metric_C
from
(select Region,Site,Method,Client,A_bucket, count(ID)
, avg(Metric_A), min(Metric_A) as Metric_A
from S_overflow
group By Region,Site,Method,Client,A_bucket) t1
cross join
(select Region,Site,Method,Client,B_bucket, min(Metric_B) as Metric_B
from S_overflow
group By Region,Site,Method,Client,B_bucket) t2
cross join
(select Region,Site,Method,Client,C_bucket, min(Metric_C) as Metric_C
from S_overflow
group By Region,Site,Method,Client,C_bucket) t3
【讨论】：
【参考方案2】：在看到@Metal 的建议并检查了我的代码后，我意识到解决方案让我眼前一亮，这段代码对我有用。
select t1.Region, t1.Site, t1.Method, t1.Client, t1.A_buckets, t1.A_cnt,t1.avg_A,t1.min_A,
t2.B_buckets, t2.B_cnt, t2.min_B,t2.avg_B,
t3.C_buckets, t3.C_cnt,t3.min_C, t3.avg_C
from
(select Region,Site,Method,Client,A_buckets, count(ID) as A_cnt,
avg(Metric_A) as avg_A, min(Metric_A) as min_A
from test3
group By Region,Site,Method,Client,A_buckets) t1
join
(select Region,Site,Method,Client,B_buckets, count(ID) as B_cnt, min(Metric_B) as min_B,avg(Metric_B)as avg_B
from test3
group By Region,Site,Method,Client,B_buckets) t2
on t1.Region=t2.Region
and t1.Site=t2.Site and t1.Method=t2.Method and t1.client=t2.Client and t1.A_buckets=t2.B_buckets
join
(select Region,Site,Method,Client,C_buckets, count(ID) as C_cnt, min(Metric_C) as min_C,avg(Metric_C)as avg_C
from test3
group By Region,Site,Method,Client,C_buckets) t3
on t2.Region=t3.Region
and t2.Site=t3.Site and t2.Method=t3.Method and t2.client=t3.Client and t2.B_buckets=t3.C_buckets
【讨论】：
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