5
create table store (id integer primary key, name text);

create table opening (store integer references store(id),
  wday text, start integer, end integer);

insert into store (name) values ('foo'), ('bar');

insert into opening (store, wday, start, end)
  values (1, 'mon', 0, 60),
         (1, 'mon', 60, 120),
         (1, 'tue', 180, 240),
         (1, 'tue', 300, 360),
         (2, 'wed', 0, 60),
         (2, 'wed', 60, 120),
         (2, 'thu', 180, 240);

我试图在一个查询中以 JSON 格式在工作日之前查询所有商店及其各自的营业时间。

{
  "1": {
    "name": "foo",
    "openings": {
      "mon": [ [ 0, 60 ], [ 60, 120 ] ],
      "tue": [ [180, 240 ], [ 300, 360 ] ]
    }
  },
  "2": {
    "name": "bar",
    "openings": { 
      "wed": [ [0,60], [60,120] ],
      "thu": [ [180,240] ]
    }
  }
}

这是我尝试过的演变。我想我想念一种做多层次的方法json_group_object

select * from opening;
store       wday        start       end
----------  ----------  ----------  ----------
1           mon         0           60
1           mon         60          120
1           tue         180         240
1           tue         300         360
2           wed         0           60
2           wed         60          120
2           thu         180         240
select * from opening group by store;
store       wday        start       end
----------  ----------  ----------  ----------
1           mon         0           60
2           wed         0           60
select json_group_object(store, wday) from opening group by store;
json_group_object(store, wday)
-----------------------------------------
{"1":"mon","1":"mon","1":"tue","1":"tue"}
{"2":"wed","2":"wed","2":"thu"}
select store, wday, json_group_array(json_array(start, end))
  from opening group by store, wday;
store       wday        json_group_array(json_array(start, end))
----------  ----------  ----------------------------------------
1           mon         [[0,60],[60,120]]
1           tue         [[180,240],[300,360]]
2           thu         [[180,240]]
2           wed         [[0,60],[60,120]]
select json_object('id', store,
  'openings', json_group_object(wday, json_group_array(json_array(start, end)))
) from opening group by store, wday;
Error: near line 17: misuse of aggregate function json_group_array()
select json_object('id', store,
  'openings', json_object(wday, json_group_array(json_array(start, end)))
) from opening group by store, wday;
{"id":1,"openings":{"mon":[[0,60],[60,120]]}}
{"id":1,"openings":{"tue":[[180,240],[300,360]]}}
{"id":2,"openings":{"thu":[[180,240]]}}
{"id":2,"openings":{"wed":[[0,60],[60,120]]}}

我怎样才能在这里分组相同的ID?

将为与 a 对应的每个唯一值返回一行group by。因此,最外面的select必须有一个group by store.

select json_group_object(store, x)
from (
  select
    store,
    json_object(
      'id', store,
      'openings', json_object(wday, json_group_array(json_array(start, end)))
    ) x
  from opening group by store, wday
) group by store;

然而,这个内部查询返回文字 JSON。将内部 JSON 解码然后在最外面的查询中对其进行编码似乎很愚蠢。

{"1":"{\"id\":1,\"openings\":{\"mon\":[[0,60],[60,120]]}}","1":"{\"id\":1,\"openings\":{\"tue\":[[180,240],[300,360]]}}"}

{"2":"{\"id\":2,\"openings\":{\"thu\":[[180,240]]}}","2":"{\"id\":2,\"openings\":{\"wed\":[[0,60],[60,120]]}}"}

Postgres 中的 IIRC 这个返回 JSON 的内部查询不会返回文字 JSON,但无论哪种方式,我都对如何继续感到困惑。

谢谢你的帮助。

4

1 回答 1

5

添加示例以供一般参考。Shawn关于json(x)在外部选择中使用的观点是关键。这是一个包含多层嵌套数组的示例

样本数据:select * from tblSmall

region|subregion    |postalcode|locality                       |lat    |lng    |
------|-------------|----------|-------------------------------|-------|-------|

Delhi |Central Delhi|    110001|Connaught Place                |28.6431|77.2197|
Delhi |Central Delhi|    110001|Parliament House               |28.6407|77.2154|
Delhi |Central Delhi|    110003|Pandara Road                   |28.6431|77.2197|
Delhi |Central Delhi|    110004|Rashtrapati Bhawan             |28.6453|77.2128|
Delhi |Central Delhi|    110005|Karol Bagh                     |28.6514|77.1907|
Delhi |Central Delhi|    110005|Anand Parbat                   |28.6431|77.2197|
Delhi |North Delhi  |    110054|Civil Lines (North Delhi)      |28.6804|77.2263|
Delhi |North Delhi  |    110084|Burari                         |28.7557|77.1994|
Delhi |North Delhi  |    110084|Jagatpur                       |28.7414|77.2199|
Delhi |North Delhi  |    110086|Kirari Suleman Nagar           |28.7441|77.0732|

因为每个region都有多个subregion值,每个subregion都有多个postalcode值,每个postalcode都有多个locality值。

这是 sql :

select json_object('region', A2.region, 'subregions', json_group_array(json(A2.json_obj2))) from
  (select A1.region, json_object('subregion', 
                                 A1.subregion, 
                                 'postalCodes', 
                                 json_group_array(json(A1.json_obj1)) ) as json_obj2 from
    (select region, subregion, json_object('postalCode', 
                                           postalcode, 
                                           'localities', 
                                           json_group_array(json_object('locality', 
                                                                        locality, 'latitude', 
                                                                        lat, 'longitude', lng) ) ) as json_obj1
    from tblSmall where subregion in ('Central Delhi', 'North Delhi')
    group by region, subregion, postalcode) as A1
  group by A1.region, A1.subregion) as A2
group by A2.region

注意处理来自内部查询的 json 的解码/重新编码的json(A1.json_obj1)和位。json(A2.json_obj2)

这是结果(由于漂亮的打印而有点长) - 有一个subregions数组,其中包含一个postalcodes数组,其中包含一个localities数组:

{
  "region": "Delhi",
  "subregions": [
    {
      "subregion": "Central Delhi",
      "postalCodes": [
        {
          "postalCode": 110001,
          "localities": [
            {
              "locality": "Connaught Place",
              "latitude": 28.6431,
              "longitude": 77.2197
            },
            {
              "locality": "Parliament House",
              "latitude": 28.6407,
              "longitude": 77.2154
            }
          ]
        },
        {
          "postalCode": 110003,
          "localities": [
            {
              "locality": "Pandara Road",
              "latitude": 28.6431,
              "longitude": 77.2197
            }
          ]
        },
        {
          "postalCode": 110004,
          "localities": [
            {
              "locality": "Rashtrapati Bhawan",
              "latitude": 28.6453,
              "longitude": 77.2128
            }
          ]
        },
        {
          "postalCode": 110005,
          "localities": [
            {
              "locality": "Karol Bagh",
              "latitude": 28.6514,
              "longitude": 77.1907
            },
            {
              "locality": "Anand Parbat",
              "latitude": 28.6431,
              "longitude": 77.2197
            }
          ]
        },
        {
          "postalCode": 110060,
          "localities": [
            {
              "locality": "Rajender Nagar",
              "latitude": 28.5329,
              "longitude": 77.2004
            }
          ]
        },
        {
          "postalCode": 110069,
          "localities": [
            {
              "locality": "Union Public Service Commission",
              "latitude": 28.5329,
              "longitude": 77.2004
            }
          ]
        },
        {
          "postalCode": 110100,
          "localities": [
            {
              "locality": "Foreign Post Delhi IBC",
              "latitude": 28.6563,
              "longitude": 77.1366
            }
          ]
        }
      ]
    },
    {
      "subregion": "North Delhi",
      "postalCodes": [
        {
          "postalCode": 110054,
          "localities": [
            {
              "locality": "Timarpur",
              "latitude": 28.7038,
              "longitude": 77.2227
            },
            {
              "locality": "Civil Lines (North Delhi)",
              "latitude": 28.6804,
              "longitude": 77.2263
            }
          ]
        },
        {
          "postalCode": 110084,
          "localities": [
            {
              "locality": "Burari",
              "latitude": 28.7557,
              "longitude": 77.1994
            },
            {
              "locality": "Jagatpur",
              "latitude": 28.7414,
              "longitude": 77.2199
            }
          ]
        },
        {
          "postalCode": 110086,
          "localities": [
            {
              "locality": "Kirari Suleman Nagar",
              "latitude": 28.7441,
              "longitude": 77.0732
            }
          ]
        }
      ]
    }
  ]
}
于 2020-04-03T02:03:04.210 回答