我有一張桌子。ShopUnit：名稱、價格、型別 (OFFER,CATEGORY)、parentID - (fk to shopUnit (self))。
我有身份證。我需要用這個 id 回傳行。和每個孩子。如果項或子項具有型別 == CATEGORY。我需要為一行的孩子設定價格 = AVG 值。
我想到了一個遞回
with recursive unit_tree as (
select s1.id,
s1.price,
s1.parent_id,
s1.type,
0 as level
from shop_unit s1
where s1.id = 'a'
union all
select s2.id,
s2.price,
s2.parent_id,
s2.type,
level 1
from shop_unit s2
join unit_tree ut on ut.id = s2.parent_id
)
select unit_tree.id,
unit_tree.parent_id,
unit_tree.type,
unit_tree.level,
unit_tree.price
from unit_tree;
但是我如何計算每個類別的平均值。
這是一個例子
{
"id": "3fa85f64-5717-4562-b3fc-2c963f66a111",
"name": "Категория",
"type": "CATEGORY",
"parentId": null,
"date": "2022-05-28T21:12:01.516Z",
"price": 6,
"children": [
{
"name": "Оффер 1",
"id": "3fa85f64-5717-4562-b3fc-2c963f66a222",
"price": 4,
"date": "2022-05-28T21:12:01.516Z",
"type": "OFFER",
"parentId": "3fa85f64-5717-4562-b3fc-2c963f66a111"
},
{
"name": "Подкатегория",
"type": "CATEGORY",
"id": "3fa85f64-5717-4562-b3fc-2c963f66a333",
"date": "2022-05-26T21:12:01.516Z",
"parentId": "3fa85f64-5717-4562-b3fc-2c963f66a111",
"price": 8,
"children": [
{
"name": "Оффер 2",
"id": "3fa85f64-5717-4562-b3fc-2c963f66a444",
"parentId": "3fa85f64-5717-4562-b3fc-2c963f66a333",
"date": "2022-05-26T21:12:01.516Z",
"price": 8,
"type": "OFFER"
}
]
}
]
}
uj5u.com熱心網友回復：
為了確定一個單元是否是另一個單元的子單元，您需要捕獲遞回 CTE 中每個元素的路徑。然后您可以LATERAL JOIN在 unit_tree 上使用 a 來查找并平均每個單元的孩子的價格。
WITH RECURSIVE shop_unit(id,name,price,parent_id,type) as (
(VALUES
('a','Propane',null,null,'CATEGORY'),
('b','Fuels',null,'a','CATEGORY'),
('c','HD5',5,'b','ITEM'),
('d','HD10',10,'b','ITEM'),
('e','Commercial',15,'b','ITEM'),
('f','Accessories',null,'a','CATEGORY'),
('g','Grill',100,'f','ITEM'),
('h','NFT',null,'f','CATEGORY'),
('i','bwaah.jpg',20000,'h','ITEM'),
('j','jaypeg.jpg',100000,'h','ITEM'),
('k','WD-40',2,null,'ITEM')
)
),
unit_tree as (
SELECT
s1.id,
s1.name,
s1.price,
s1.parent_id,
s1.type,
0 as level,
array[id] as path
FROM
shop_unit s1
WHERE
s1.id = 'a'
UNION ALL
SELECT
s2.id,
s2.name,
s2.price,
s2.parent_id,
s2.type,
level 1,
ut.path || s2.id as path --generate the path for every unit so that we can check if it is a child of another element
FROM
shop_unit s2
JOIN unit_tree ut ON ut.id = s2.parent_id
)
SELECT
ut.id,
ut.name,
ut.parent_id,
ut.type,
case when ut.type = 'CATEGORY' then ap.avg_price else ut.price end as price,
ut.level,
ut.path
FROM
unit_tree ut
-- The JOIN LATERAL subquery roughly means "for each row of ut run this query"
-- Must be a LEFT JOIN LATERAL in order to keep rows of ut that have no children.
LEFT JOIN LATERAL (
SELECT
avg(ut2.price) avg_price
FROM
unit_tree ut2
WHERE
ut.level < ut2.level --is deeper level
and ut.id = any(path) --is in the path
GROUP BY
ut.id
) ap ON TRUE
ORDER BY id
uj5u.com熱心網友回復：
您也可以使用遞回來構建所需的輸出 JSON：
with recursive top_down as (
select s.id, s.name, s.type, s."parentId", s.price, 1 as level,
array[s.id] as path, s.id as root
from shopunit s
where s."parentId" is null
union all
select c.id, c.name, c.type, c."parentId", c.price, p.level 1 as level,
p.path||c.id as path, p.root
from shopunit c
join top_down p on p.id = c."parentId"
), category_averages as (
select p."parentId", avg(c.price) as price, p.level, p.root
from top_down p
join top_down c
on p."parentId" = any(c.path)
group by p."parentId", p.level, p.root
), fill_missing as (
select s.id, s.name, s.type, s."parentId",
coalesce(a.price, s.price)::numeric(8,2) as price,
t.level, max(t.level) over (partition by t.root) as max_depth,
row_number() over (partition by s."parentId" order by s.id) as n,
count(1) over (partition by s."parentId") as max_n,
now() as date
from shopunit s
left join category_averages a on a."parentId" = s.id
join top_down t on t.id = s.id
), build_json as (
select id, "parentId", level, max_depth, n, max_n,
to_jsonb(fill_missing) - 'level' - 'max_depth' - 'n' - 'max_n' as j
from fill_missing
where level = max_depth
and n = max_n
union all
select next.id, next."parentId", next.level, next.max_depth, next.n, next.max_n,
case
when next.level = prev.level
then '[]'::jsonb||(to_jsonb(next) - 'level' - 'max_depth' - 'n' - 'max_n')||prev.j
else
jsonb_set(
to_jsonb(next) - 'level' - 'max_depth' - 'n' - 'max_n',
'{children}', '[]'::jsonb || prev.j
)
end as j
from fill_missing next
join build_json prev
on (prev.n = 1 and prev."parentId" = next.id and next.n = next.max_n)
or (prev.n > 1 and prev."parentId" = next."parentId" and next.n = prev.n - 1)
)
select id, jsonb_pretty(j) as j
from build_json
where "parentId" is null;
結果是：
{
"id": "3fa85f64-5717-4562-b3fc-2c963f66a111",
"date": "2022-06-11T16:14:44.11989 01:00",
"name": "Категория",
"type": "CATEGORY",
"price": 6.00,
"children": [
{
"id": "3fa85f64-5717-4562-b3fc-2c963f66a222",
"date": "2022-06-11T16:14:44.11989 01:00",
"name": "Оффер 1",
"type": "OFFER",
"price": 4.00,
"parentId": "3fa85f64-5717-4562-b3fc-2c963f66a111"
},
{
"id": "3fa85f64-5717-4562-b3fc-2c963f66a333",
"date": "2022-06-11T16:14:44.11989 01:00",
"name": "Подкатегория",
"type": "CATEGORY",
"price": 8.00,
"children": [
{
"id": "3fa85f64-5717-4562-b3fc-2c963f66a444",
"date": "2022-06-11T16:14:44.11989 01:00",
"name": "Оффер 2",
"type": "OFFER",
"price": 8.00,
"parentId": "3fa85f64-5717-4562-b3fc-2c963f66a333"
}
],
"parentId": "3fa85f64-5717-4562-b3fc-2c963f66a111"
}
],
"parentId": null
}
db<>在這里擺弄
（隱藏的查詢從您的示例 JSON 中填充表）
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標籤：sql PostgreSQL 递归