我有以下查询:
SELECT DISTINCT ON (ps.p)
m.groundtruth, ps.p, ARRAY_AGG(m.anchor_id), ARRAY_AGG(m.id)
FROM
measurement m
JOIN
(SELECT unnest(point_array) AS p) AS ps
ON ST_DWithin(ps.p, m.groundtruth, distance)
GROUP BY ps.p, m.groundtruth
ORDER BY ps.p, RANDOM()
输出如下所示:
groundtruth | p | anchor_array | id_array
------------------------------------------------------
G1 | P1 | {1,3,3,3,4} | {1,2,3,4,5}
G2 | P1 | {1,5,7} | {6,7,8}
G1 | P2 | {1,3,3,3,4} | {1,2,3,4,5}
替代查询:
SELECT DISTINCT ON (ps.p)
m.groundtruth, ps.p, ARRAY_AGG(row(m.anchor_id, m.id))
...
输出:
groundtruth | p | combined_array
-----------------------------------------------------------
G1 | P1 | {(1,1),(3,2),(3,3),(3,4),(4,5)}
G2 | P1 | {(1,6),(5,7),(7,8)}
G1 | P2 | {(1,1),(3,2),(3,3),(3,4),(4,5)}
我想要达到的目标:
- 摆脱anchor_array中的重复条目
- 并且对于每个已删除的项目:从 id_array 中删除具有相同索引的项目
或者对于替代查询和输出:
- 使每个元组与元组的第一个条目相关
结果应该是什么样子:
groundtruth | p | anchor_array | id_array
------------------------------------------------------
G1 | P1 | {1,3,4} | {1,2,5}
G2 | P1 | {1,5,7} | {6,7,8}
G1 | P2 | {1,3,4} | {1,2,5}
或者对于替代查询和输出:
groundtruth | p | combined_array
-----------------------------------------------------------
G1 | P1 | {(1,1),(3,2),(4,5)}
G2 | P1 | {(1,6),(5,7),(7,8)}
G1 | P2 | {(1,1),(3,2),(4,5)}
PS 我忽略了示例输出中的随机化部分以获得更好的概述。
真实结果集:
p ; groundtruth ; ids
---------------------------------------------------------------------------------------------
"0101000000EE7C3F355EF24F4019390B7BDA011940";"010100000094F6065F98E44F40A930B610E4A01B40";"{"(29,250)","(30,251)","(31,241)","(32,263)","(33,243)","(34,264)","(35,277)"}"
"0101000000EE7C3F355EF24F40809F71E140681940";"010100000094F6065F98E44F40A930B610E4A01B40";"{"(29,250)","(30,251)","(31,257)","(32,276)","(33,272)","(34,264)","(35,249)"}"
"0101000000EE7C3F355EF24F40E605D847A7CE1940";"0101000000EE7C3F355EF24F4019390B7BDA011940";"{"(30,194)","(31,181)","(32,168)","(33,124)","(34,141)","(35,4)"}"
"0101000000EE7C3F355EF24F404C6C3EAE0D351A40";"010100000014D044D8F0DC4F4073BA2C2636DF1C40";"{"(30,281)","(31,278)","(32,297)","(33,284)","(34,294)","(35,303)"}"
"0101000000EE7C3F355EF24F40B3D2A414749B1A40";"0101000000DE9387855AEB4F4062670A9DD7581A40";"{"(30,235)","(31,214)","(32,220)","(33,221)","(34,217)","(35,232)"}"
"0101000000EE7C3F355EF24F4019390B7BDA011B40";"0101000000AF94658863D54F40A7E8482EFF211E40";"{"(27,316)","(31,329)","(32,334)","(33,340)","(34,327)","(35,324)"}"
"0101000000EE7C3F355EF24F40809F71E140681B40";"0101000000DE9387855AEB4F4062670A9DD7581A40";"{"(30,224)","(31,210)","(32,220)","(33,230)","(34,226)","(35,213)"}"
"0101000000EE7C3F355EF24F40E605D847A7CE1B40";"010100000014D044D8F0DC4F4073BA2C2636DF1C40";"{"(30,281)","(31,304)","(32,288)","(33,293)","(34,306)","(35,295)"}"
"0101000000EE7C3F355EF24F404C6C3EAE0D351C40";"010100000094F6065F98E44F40A930B610E4A01B40";"{"(29,250)","(30,256)","(31,257)","(32,271)","(33,254)","(34,260)","(35,277)"}"
"0101000000EE7C3F355EF24F4019390B7BDA011D40";"010100000007F0164850C44F405F46B1DCD24A2040";"{"(31,383)","(32,409)","(33,390)","(34,411)","(35,407)"}"