考虑下表
Device
--------
id
name
type
--------
components
--------
id INT
type VARCHAR
--------
Manufacturers
-------------
id INT
name VARCHAR
country VARCHAR
-------------
Device_components
-----------------
deviceid REFERENCES Devices(id)
componentid REFERENCES Components(id)
-----------------
Component_Manufacturers
-----------------------
componentid REFERENCES Components(id)
manufacturerid REFERENCES Manufacturers(id)
-----------------------
我想查询数据库以返回如下内容：
{
"id": 1,
"name": "phone",
"components": [
{
"id": 1,
"type": "screen",
"manufacturers": [
{
"id": 1,
"name": "a",
"country": "Germany"
}
]
},
{
"id": 2,
"type": "keyboard",
"manufacturers": [
{
"id": 1,
"name": "a",
"country": "UK"
}
]
}
]
}
到目前为止，我分别从每个表中进行选择，然后在我的应用程序中组装 JSON 对象。
以下是一些示例数据：
CREATE TABLE IF NOT EXISTS Devices
(
ID SERIAL PRIMARY KEY NOT NULL,
NAME VARCHAR(30) UNIQUE NOT NULL
);
CREATE TABLE IF NOT EXISTS Components
(
ID SERIAL PRIMARY KEY NOT NULL,
NAME VARCHAR(30) UNIQUE NOT NULL
);
CREATE TABLE IF NOT EXISTS Manufacturers
(
ID SERIAL PRIMARY KEY NOT NULL,
NAME VARCHAR(30) UNIQUE NOT NULL,
COUNTRY VARCHAR(40)
);
CREATE TABLE IF NOT EXISTS Device_components
(
DeviceID INT REFERENCES Devices(ID),
ComponentID INT REFERENCES Components(ID)
);
CREATE TABLE IF NOT EXISTS Component_manufacturers
(
ComponentID INT REFERENCES Components(ID),
ManufacturerID INT REFERENCES Manufacturers(ID)
);
INSERT INTO Devices (NAME) VALUES ('phone');
INSERT INTO Devices (NAME) VALUES ('tablet');
INSERT INTO Devices (NAME) VALUES ('pc');
INSERT INTO Components (NAME) VALUES ('mouse');
INSERT INTO Components (NAME) VALUES ('camera');
INSERT INTO Components (NAME) VALUES ('screen');
INSERT INTO Manufacturers (NAME,Country) VALUES ('foo','france');
INSERT INTO Manufacturers (NAME,Country) VALUES ('bar','spain');
INSERT INTO Manufacturers (NAME,Country) VALUES ('baz','germany');
INSERT INTO Device_components VALUES (1,2);
INSERT INTO Device_components VALUES (1,3);
INSERT INTO Device_components VALUES (2,2);
INSERT INTO Device_components VALUES (2,3);
INSERT INTO Device_components VALUES (3,1);
INSERT INTO Device_components VALUES (3,2);
INSERT INTO Device_components VALUES (3,3);
INSERT INTO Component_manufacturers VALUES (1,1);
INSERT INTO Component_manufacturers VALUES (1,2);
INSERT INTO Component_manufacturers VALUES (1,3);
INSERT INTO Component_manufacturers VALUES (2,2);
INSERT INTO Component_manufacturers VALUES (3,3);
好的，所以您似乎想从连接树创建图形。这是很自然的事情想要做。
它不像在 Pg 中那样容易，主要是因为record伪类型中缺乏对列别名的支持。
这是我想出的：
SELECT row_to_json(r, true)
FROM (
SELECT
d.id,
d.name,
json_agg(c_row) AS components
FROM devices d
INNER JOIN device_components dc ON (dc.deviceid = d.id)
INNER JOIN (
SELECT
c.id,
c.name,
json_agg(m) AS manufacturers
FROM components c
INNER JOIN component_manufacturers cm ON (cm.componentid = c.id)
INNER JOIN manufacturers m ON (cm.manufacturerid = m.id)
GROUP BY c.id
) c_row ON (c_row.id = dc.componentid)
GROUP BY d.id
) r(id, name, component);
这里的一般思想是，在生成对象数组的对象嵌套的每个级别，json_agg在group_by. 该json_agg函数隐式调用row_to_json将行类型转换为 json。在SELECT子句中为合成列指定别名，以便在将行馈送到外部级别时 json 键名称是正确的。
由于未聚合外部级别，因此row_to_json在子查询上使用而不是使用json_agg. 如果您想要单个 json 结果而不是一组 json 行，您可以在外层更改row_to_json为json_agg。
我只在 9.3 上测试过，因为那是我安装的，而 sqlfiddle 似乎有一些问题。
更新：不幸的是json_agg9.2 上不存在，它是在 9.3 中添加的。但是，这是一个足够简单的案例，您可以array_agg改为使用，因此这应该适用于 9.2：
SELECT row_to_json(r, true)
FROM (
SELECT
d.id,
d.name,
array_agg(c_row) AS components
FROM devices d
INNER JOIN device_components dc ON (dc.deviceid = d.id)
INNER JOIN (
SELECT
c.id,
c.name,
array_agg(m) AS manufacturers
FROM components c
INNER JOIN component_manufacturers cm ON (cm.componentid = c.id)
INNER JOIN manufacturers m ON (cm.manufacturerid = m.id)
GROUP BY c.id
) c_row ON (c_row.id = dc.componentid)
GROUP BY d.id
) r(id, name, component);
理想情况下，PostgreSQL 会更自然地支持这种查询，没有子查询，但我认为我们需要一个新的连接类型，或者至少需要一些函数来与横向查询一起使用。无论如何，这一切都计划成一个文明的查询计划（尽管使用合适的索引会更好）：
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------
Subquery Scan on r (cost=1050.26..1282.94 rows=720 width=106)
-> GroupAggregate (cost=1050.26..1273.94 rows=720 width=188)
-> Merge Join (cost=1050.26..1226.42 rows=7704 width=188)
Merge Cond: (d.id = dc.deviceid)
-> Index Scan using devices_pkey on devices d (cost=0.15..58.95 rows=720 width=82)
-> Sort (cost=1050.11..1069.37 rows=7704 width=110)
Sort Key: dc.deviceid
-> Merge Join (cost=403.16..552.77 rows=7704 width=110)
Merge Cond: (c_row.id = dc.componentid)
-> Subquery Scan on c_row (cost=253.38..285.63 rows=720 width=110)
-> GroupAggregate (cost=253.38..278.43 rows=720 width=286)
-> Sort (cost=253.38..258.73 rows=2140 width=286)
Sort Key: c.id
-> Hash Join (cost=44.75..135.00 rows=2140 width=286)
Hash Cond: (cm.manufacturerid = m.id)
-> Hash Join (cost=26.20..87.03 rows=2140 width=86)
Hash Cond: (cm.componentid = c.id)
-> Seq Scan on component_manufacturers cm (cost=0.00..31.40 rows=2140 width=8)
-> Hash (cost=17.20..17.20 rows=720 width=82)
-> Seq Scan on components c (cost=0.00..17.20 rows=720 width=82)
-> Hash (cost=13.80..13.80 rows=380 width=208)
-> Seq Scan on manufacturers m (cost=0.00..13.80 rows=380 width=208)
-> Sort (cost=149.78..155.13 rows=2140 width=8)
Sort Key: dc.componentid
-> Seq Scan on device_components dc (cost=0.00..31.40 rows=2140 width=8)
(25 rows)