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ArangoDB高级查询（二）
连接查询
在这之前我们插入如下的数据
{
"name": "Ned",
"surname": "Stark",
"alive": false,
"age": 41,
"traits": ["A","H","C","N","P"]
}
"traits": ["A","H","C","N","P"]是一个没有明显含义的字母表，它们是另一个集合的文档关键字，我们可以使用这些关键字来将这些字母解析为诸如“strong”之类的标签。为实际特征使用另一个集合的好处是，我们可以在以后轻松查询所有现有特征，并将标签以多种语言存储在中心位置。如果我们直接嵌入特征...
{
"name": "Ned",
"surname": "Stark",
"alive": false,
"age": 41,
"traits": [
{
"de": "stark",
"en": "strong"
},
{
"de": "einflussreich",
"en": "powerful"
},
{
"de": "loyal",
"en": "loyal"
},
{
"de": "rational",
"en": "rational"
},
{
"de": "mutig",
"en": "brave"
}
]
}
......保持特征变得非常困难。如果您要重命名或翻译其中的一个，则需要查找具有相同特征的所有其他字符文档，并在其中执行更改。如果我们只是在另一个集合中引用一个特征，它就像更新单个文档一样简单。
数据模型比较
导入特征
创建一个文档集合Traits
LET data =[
{ "_key": "A", "en": "strong", "de": "stark" },
{ "_key": "B", "en": "polite", "de": "freundlich" },
{ "_key": "C", "en": "loyal", "de": "loyal" },
{ "_key": "D", "en": "beautiful", "de": "schön" },
{ "_key": "E", "en": "sneaky", "de": "hinterlistig" },
{ "_key": "F", "en": "experienced", "de": "erfahren" },
{ "_key": "G", "en": "corrupt", "de": "korrupt" },
{ "_key": "H", "en": "powerful", "de": "einflussreich" },
{ "_key": "I", "en": "naive", "de": "naiv" },
{ "_key": "J", "en": "unmarried", "de": "unverheiratet" },
{ "_key": "K", "en": "skillful", "de": "geschickt" },
{ "_key": "L", "en": "young", "de": "jung" },
{ "_key": "M", "en": "smart", "de": "klug" },
{ "_key": "N", "en": "rational", "de": "rational" },
{ "_key": "O", "en": "ruthless", "de": "skrupellos" },
{ "_key": "P", "en": "brave", "de": "mutig" },
{ "_key": "Q", "en": "mighty", "de": "mächtig" },
{ "_key": "R", "en": "weak", "de": "schwach" }
]
只返回每个字符的traits属性：
FOR c IN Characters
RETURN c.traits
[
{ "traits": ["A","H","C","N","P"] },
{ "traits": ["D","H","C"] },
...
]
我们可以使用traits数组和DOCUMENT()函数一起使用元素作为文档键，并在Traits集合中查找它们：
FOR c IN Characters
RETURN DOCUMENT("Traits", c.traits)
[
[
{
"_key": "A",
"_id": "Traits/A",
"_rev": "_V5oRUS2---",
"en": "strong",
"de": "stark"
},
{
"_key": "H",
"_id": "Traits/H",
"_rev": "_V5oRUS6--E",
"en": "powerful",
"de": "einflussreich"
},
{
"_key": "C",
"_id": "Traits/C",
"_rev": "_V5oRUS6--_",
"en": "loyal",
"de": "loyal"
},
{
"_key": "N",
"_id": "Traits/N",
"_rev": "_V5oRUT---D",
"en": "rational",
"de": "rational"
},
{
"_key": "P",
"_id": "Traits/P",
"_rev": "_V5oRUTC---",
"en": "brave",
"de": "mutig"
}
],
[
{
"_key": "D",
"_id": "Traits/D",
"_rev": "_V5oRUS6--A",
"en": "beautiful",
"de": "schön"
},
{
"_key": "H",
"_id": "Traits/H",
"_rev": "_V5oRUS6--E",
"en": "powerful",
"de": "einflussreich"
},
{
"_key": "C",
"_id": "Traits/C",
"_rev": "_V5oRUS6--_",
"en": "loyal",
"de": "loyal"
}
],
...
]
上面返回的信息太冗余，我们只返回英文标签：
FOR c IN Characters
RETURN DOCUMENT("Traits", c.traits)[*].en
[
[
"strong",
"powerful",
"loyal",
"rational",
"brave"
],
[
"beautiful",
"powerful",
"loyal"
],
...
]
合并Characters和特征
我们traits解析为有意义的特征！我们需要合并字符文档和特质文档中的数据：
FOR c IN Characters
RETURN MERGE(c, { traits: DOCUMENT("Traits", c.traits)[*].en } )
[
{
"_id": "Characters/2861650",
"_key": "2861650",
"_rev": "_V1bzsXa---",
"age": 41,
"alive": false,
"name": "Ned",
"surname": "Stark",
"traits": [
"strong",
"powerful",
"loyal",
"rational",
"brave"
]
},
{
"_id": "Characters/2861653",
"_key": "2861653",
"_rev": "_V1bzsXa--B",
"age": 40,
"alive": false,
"name": "Catelyn",
"surname": "Stark",
"traits": [
"beautiful",
"powerful",
"loyal"
]
},
...
]
MERGE()函数将对象合并在一起。因为我们使用与原始字符属性{ traits: ... }具有相同属性名称特征的对象 ，所以后者被合并覆盖。
查询总数
for l in loginTb
filter l.cust_no=='123456'
COLLECT WITH COUNT INTO length
return length
#返回指定数据
FORu INusers
RETURN{name:u.name,friends:u.friends[*].name}
FORu INusers
LETfriends=(
FORf INfriends
FILTER u.id==f.userId
RETURNf
)
LETmemberships=(
FORm IN memberships
FILTER u.id==m.userId
RETURNm
)
RETURN{
"user" : u,
"friends" : friends,
"numFriends" : LENGTH(friends),
"memberShips" : memberships
}
地理空间查询
地点数据
让我们将一些拍摄位置插入到新集合Locations中，然后在AQL查询下运行：
创建地点集合
LET places = [
{ "name": "Dragonstone", "coordinate": [ 55.167801, -6.815096 ] },
{ "name": "King's Landing", "coordinate": [ 42.639752, 18.110189 ] },
{ "name": "The Red Keep", "coordinate": [ 35.896447, 14.446442 ] },
{ "name": "Yunkai", "coordinate": [ 31.046642, -7.129532 ] },
{ "name": "Astapor", "coordinate": [ 31.50974, -9.774249 ] },
{ "name": "Winterfell", "coordinate": [ 54.368321, -5.581312 ] },
{ "name": "Vaes Dothrak", "coordinate": [ 54.16776, -6.096125 ] },
{ "name": "Beyond the wall", "coordinate": [ 64.265473, -21.094093 ] }
]
FOR place IN places
INSERT place INTO Locations
查找附近的位置
查询距指定坐标位置最近的三个地址，near()函数默认查询附近100个位置。
FOR loc IN NEAR(Locations, 53.35, -6.26, 3)
RETURN {
name: loc.name,
latitude: loc.coordinate[0],
longitude: loc.coordinate[1]
}
[
{
"name": "Vaes Dothrak",
"latitude": 54.16776,
"longitude": -6.096125
},
{
"name": "Winterfell",
"latitude": 54.368321,
"longitude": -5.581312
},
{
"name": "Dragonstone",
"latitude": 55.167801,
"longitude": -6.815096
}
]
查找半径内的位置
WITHIN()，从参考点搜索给定半径内的位置。第四个参数，它指定半径而不是限制。半径的单位是米。该示例使用200,000米（200公里）的半径：
FOR loc IN WITHIN(Locations, 53.35, -6.26, 200 * 1000)
RETURN {
name: loc.name,
latitude: loc.coordinate[0],
longitude: loc.coordinate[1]
}
[
{
"name": "Vaes Dothrak",
"latitude": 54.16776,
"longitude": -6.096125
},
{
"name": "Winterfell",
"latitude": 54.368321,
"longitude": -5.581312
}
]
返回距离
NEAR()和WITHIN()可以通过添加一个可选的第五个参数返回到参考点的距离。它必须是一个字符串，它将用作距离为米的附加属性的属性名称：
FOR loc IN NEAR(Locations, 53.35, -6.26, 3, "distance")
RETURN {
name: loc.name,
latitude: loc.coordinate[0],
longitude: loc.coordinate[1],
distance: loc.distance / 1000
}
[
{
"name": "Vaes Dothrak",
"latitude": 54.16776,
"longitude": -6.096125,
"distance": 91.56658640314431
},
{
"name": "Winterfell",
"latitude": 54.368321,
"longitude": -5.581312,
"distance": 121.66399816395028
},
{
"name": "Dragonstone",
"latitude": 55.167801,
"longitude": -6.815096,
"distance": 205.31879386198324
}
]
示例查询中的值除以1000，将单位转换为公里。
本文标签： ArangoDB高级查询（二）
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