PostgreSQL 如何在一组商品及其数量上找到完全相同的订单？

Question

我有一张订单表（每个订单（transaction_id）都有client_id和item_id） 我需要在订单中找到一组商品及其数量完全相同的订单

sql_1 = """
        CREATE TABLE IF NOT EXISTS orders (
            client_id varchar(10),
            item_id varchar(10),
            transaction_id varchar(10)
        )
    """
con.execute(sql_1)

sql_2 = """
        INSERT INTO orders values 
        ('CL1111','111','1001'),
        ('CL1111','222','1001'),
        ('CL1111','333','1001'),
        ('CL2222','111','1002'),
        ('CL2222','222','1002'),
        ('CL2222','333','1002'),
        ('CL3333','111','1003'),
        ('CL3333','222','1003'),
        ('CL3333','333','1003'),
        ('CL3333','444','1003'),
        ('CL4444','111','1004'),
        ('CL4444','222','1004'),
        ('CL4444','333','1004'),
        ('CL5555','111','1005'),
        ('CL5555','222','1005'),
        ('CL6666','111','1006'),
        ('CL6666','222','1006'),
        ('CL6666','333','1007')
    """
con.execute(sql_2)

我这样做了

sql = """
        with
        a as
            (
            select
                client_id, item_id, transaction_id,
                row_number() over (partition by transaction_id order by item_id) as rn
            from orders
            )
        select
            a.*
            , b.transaction_id as b_tr_id
        from
            a left join a as b
            on a.rn = b.rn
            and a.item_id = b.item_id
            and a.transaction_id != b.transaction_id
    """

df_SQL = pd.read_sql(sql, con)
df_SQL

但这并没有解决我的问题。 现在我不知道如何下订单，它在 item_id、数量和项目集上完全相同。

Answer 1

您的示例代码不包括数量！ 这是一个代码，它的结果与表中包含相同订单的行一样多：

sql = """
    with orders_mod as
    (
        select distinct
                "transaction_id",
                string_agg("item_id" || ' ' || "quantity"::text, ',') over (partition by "client_id") as "list_of_ordered_item_ids"
        from    orders
    )
    select distinct
            string_agg("transaction_id", ',') over (partition by "list_of_ordered_item_ids") as "list_of_the_same_orders",
            "list_of_ordered_item_ids"
    from    orders_mod
    ;
"""

df_SQL = pd.read_sql(sql, con)
df_SQL

Answer 2

请检查此查询，它在 dbfiddle

中返回正确的行

with agg as (
  SELECT client_id, 
         array_agg(array[item_id]) ai, array_agg(array[quantity]) aq
  from orders group by client_id 
  having count(distinct transaction_id) = 1 )
select client_id, ai items, aq quantities 
from (
  select client_id, ai , aq , count(1) over (partition by ai, aq) cnt
  from agg ) c
where cnt > 1

client_id	项目	数量
CL1111	{{111},{222},{333}}	{{1},{2},{1}}
CL4444	{{111},{222},{333}}	{{1},{2},{1}}

我不确定有多个交易的客户怎么样，但是在您的示例中，客户 CL5555 和 CL6666 不匹配，即使它们具有相同的交易 1005 和 1006。所以这里只比较具有一个不同交易的客户。

Answer 3

试试这个查询。聚合中的顺序子句对于可靠的结果是必要的。

with transactions as(
select client_id,transaction_id
   ,string_agg( (item_id ||'('||cast(quantity as varchar) || ')') ,',' order by item_id) itemlist
from orders
group by client_id,transaction_id
)
select * 
  ,row_number()over(partition by itemlist order by client_id,transaction_id) rn
from transactions
order by itemlist

使用测试数据，找到 transaction_id 1001-1004 和 1005-1006 的 2 个相同的组（不是你图片中的绿色）

结果是

client_id	交易编号	项目列表	rn
CL5555	1005	111(1),222(2)	1
CL6666	1006	111(1),222(2)	2
CL1111	1001	111(1),222(2),333(1)	1
CL4444	1004	111(1),222(2),333(1)	2
CL3333	1003	111(1),222(2),333(1),444(1)	1
CL2222	1002	111(2),222(1),333(1)	1
CL6666	1007	333(1)	1

测试数据

INSERT INTO orders (client_id,item_id,transaction_id,quantity)
values 
        ('CL1111','111','1001',1),
        ('CL1111','222','1001',2),
        ('CL1111','333','1001',1),
        ('CL2222','111','1002',2),
        ('CL2222','222','1002',1),
        ('CL2222','333','1002',1),
        ('CL3333','111','1003',1),
        ('CL3333','222','1003',2),
        ('CL3333','333','1003',1),
        ('CL3333','444','1003',1),
        ('CL4444','111','1004',1),
        ('CL4444','222','1004',2),
        ('CL4444','333','1004',1),
        ('CL5555','111','1005',1),
        ('CL5555','222','1005',2),
        ('CL6666','111','1006',1),
        ('CL6666','222','1006',2),
        ('CL6666','333','1007',1)
;