概述

GROUP BY子句将别名代表的数据行按指定的属性进行分组，分组后各组保留仅一行数据，其余行舍弃。GRIUP BY子句常与聚合、排序等操作配合使用。

语法

GROUP BY <expression> as <alias>, <expression> as <alias>, ...

• <expression>是分组依据；如果使用了多个别名，它们必须同源，此时按照从左到右的顺序逐级进行分组
• <alias>是为分组依据定义的别名，可省略

例如，对以下路径查询产生的数据流进行多重分组。先按照起点n1的形状分组，再按照终点n2的颜色分组，然后统计各组的路径数量:

n(as n1).re().n(as n2) as path
group by n1.shape, n2.color
return path, count(path)

示例

示例图集

在一个空图集中，依次运行以下各行语句创建示例图集：

create().node_schema("country").node_schema("movie").node_schema("director").edge_schema("filmedIn").edge_schema("direct")
create().node_property(@*, "name")
insert().into(@country).nodes([{_id:"C001", _uuid:1, name:"France"}, {_id:"C002", _uuid:2, name:"USA"}])
insert().into(@movie).nodes([{_id:"M001", _uuid:3, name:"Léon"}, {_id:"M002", _uuid:4, name:"The Terminator"}, {_id:"M003", _uuid:5, name:"Avatar"}])
insert().into(@director).nodes([{_id:"D001", _uuid:6, name:"Luc Besson"}, {_id:"D002", _uuid:7, name:"James Cameron"}])
insert().into(@filmedIn).edges([{_uuid:1, _from_uuid:3, _to_uuid:1}, {_uuid:2, _from_uuid:4, _to_uuid:1}, {_uuid:3, _from_uuid:3, _to_uuid:2}, {_uuid:4, _from_uuid:4, _to_uuid:2}, {_uuid:5, _from_uuid:5, _to_uuid:2}])
insert().into(@direct).edges([{_uuid:6, _from_uuid:6, _to_uuid:3}, {_uuid:7, _from_uuid:7, _to_uuid:4}, {_uuid:8, _from_uuid:7, _to_uuid:5}])

组内聚合

本例查找“国家-[]-电影-[]-导演”2步路径，按导演进行分组后，统计每组的路径条数：

n({@country}).e().n({@movie}).e().n({@director} as n)
group by n
return table(n.name, count(n))

|     n.name    | count(n) |
|---------------|----------|
| Luc Besson    | 2        |
| James Cameron | 3        |

如果要进行组内聚合运算，执行聚合运算的子句必须紧挨在GROUP BY子句后。

多重分组

本例查找“国家-[]-电影-[]-导演”2步路径，按国家和导演进行分组后，统计每组的路径条数：

n({@country} as a).e().n({@movie}).e().n({@director} as b)
group by a, b
return table(a.name, b.name, count(a))

| a.name |     b.name    | count(a) |
|--------|---------------|----------|
| France | Luc Besson    | 1        |
| France | James Cameron | 1        |
| USA    | Luc Besson    | 1        |
| USA    | James Cameron | 2        |