MongoDB聚合：$graphLookup
$graphLookup聚合阶段在一个集合中执行递归搜索，可以使用选项来控制递归搜索的深度和条件。
$graphLookup搜索过程总结如下：
- 输入文档进入
$graphLookup聚合阶段。 $graphLookup的搜索目标是from参数指定的集合（搜索参数的完整列表见下文）。- 对于每个输入文档，搜索都从
startWith指定的值开始。 graphLookup使用startWith的值匹配由from指定的集合和connectToField指定的字段的值。- 对于每个匹配文档，
$graphLookup拿connectFromField的值来检查每个from参数指定的集合下的connectToField参数指定的字段的值，然后将匹配上的from集合的文档放到由as参数指定的数组中。
然后该步骤继续递归直到没有匹配的文档或操作达到由maxDepth参数指定的递归深度。然后$graphLookup把数组字段添加到输入文档。在完成所有的文档搜索后返回结果。
语法
{
$graphLookup: {
from: <collection>,
startWith: <expression>,
connectFromField: <string>,
connectToField: <string>,
as: <string>,
maxDepth: <number>,
depthField: <string>,
restrictSearchWithMatch: <document>
}
}
参数字段解释：
| 字段 | 描述 |
|---|---|
from |
$graphLookup操作搜索的目标集合，递归匹配connectFromField和connnectToField字段的值，from指定的集合必须与当前集合在同一个数据库，并且不可以是同一个集合 |
startWith |
可选，表达式，connectFromField字段进行递归搜索的起始值。startWith的值也可以是数组，其每个值都会被遍历处理 |
connectFromField |
指定一个字段名，其值用于递归搜索匹配。与集合中其他文档connectToField相对应，如果其值是数组，则会在遍历时单独处理每个元素 |
connectToField |
其他文档中的字段名称，用于匹配connectFromField参数指定的字段值 |
as |
添加到每个输出文档中的数组字段名称。包含在$graphLook阶段遍历的所有文档（注意，数组元素的顺序不保证） |
maxDepth |
可选，正整数，指定最大的递归深度 |
depthField |
可选，要添加到搜索路径中每个遍历文档的字段名称。该字段的值是文档的递归深度，长整数。递归深度值从零开始，因此第一次查找对应的深度为零 |
restrictSearchWithMatch |
可选，文档类型。为递归搜索指定额外的条件，其语法与查询过滤语法相同。可以在过滤条件中使用所有的聚合表达式，如：{ lastName: { $ne: "$lastName" } }，该表达式无法在该上下文中查找lastName值与输入文档的lastName值不同的文档，因为“$lastName”将充当字符串文本，而不是字段路径 |
使用
分片集合
从MongoDB 5.1开始，可以在from参数中指定分片集合，但不能在事务中使用分片集合。
最大递归深度
将maxDepth字段设置为0相当于一个非递归的$graphLookup搜索阶段
内存
$graphLookup阶段有100M内存的限制，如果想突破这个限制，可以为聚合指定allowDiskUse: true，该设置也会影响到$graphLookup中使用的其他聚合阶段。
视图和集合
如果执行涉及多个视图的聚合，如使用$lookup或$graphLookup，视图必须有相同的集合。
举例
单个集合
employees集合有下面的文档：
{ "_id" : 1, "name" : "Dev" }
{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }
{ "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" }
{ "_id" : 4, "name" : "Andrew", "reportsTo" : "Eliot" }
{ "_id" : 5, "name" : "Asya", "reportsTo" : "Ron" }
{ "_id" : 6, "name" : "Dan", "reportsTo" : "Andrew" }
下面的$graphLookup递归匹配employees集合中reportsTo和name字段，返回每个人的报告层次结构：
db.employees.aggregate( [
{
$graphLookup: {
from: "employees",
startWith: "$reportsTo",
connectFromField: "reportsTo",
connectToField: "name",
as: "reportingHierarchy"
}
}
] )
操作返回下面的结果：
{
"_id" : 1,
"name" : "Dev",
"reportingHierarchy" : [ ]
}
{
"_id" : 2,
"name" : "Eliot",
"reportsTo" : "Dev",
"reportingHierarchy" : [
{ "_id" : 1, "name" : "Dev" }
]
}
{
"_id" : 3,
"name" : "Ron",
"reportsTo" : "Eliot",
"reportingHierarchy" : [
{ "_id" : 1, "name" : "Dev" },
{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }
]
}
{
"_id" : 4,
"name" : "Andrew",
"reportsTo" : "Eliot",
"reportingHierarchy" : [
{ "_id" : 1, "name" : "Dev" },
{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }
]
}
{
"_id" : 5,
"name" : "Asya",
"reportsTo" : "Ron",
"reportingHierarchy" : [
{ "_id" : 1, "name" : "Dev" },
{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" },
{ "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" }
]
}
{
"_id" : 6,
"name" : "Dan",
"reportsTo" : "Andrew",
"reportingHierarchy" : [
{ "_id" : 1, "name" : "Dev" },
{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" },
{ "_id" : 4, "name" : "Andrew", "reportsTo" : "Eliot" }
]
}
下表显示了文件的遍历路径：
{ "_id" : 5, "name" : "Asya", "reportsTo" : "Ron" }:
| 起始值 | 文档reportsTo的值 |
|---|---|
{ ... "reportsTo" : "Ron" } |
|
| 深度0 | { "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" } |
| 深度1 | { "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" } |
| 深度2 | { "_id" : 1, "name" : "Dev" } |
输出结果生成的层次结构Asya -> Ron -> Eliot -> Dev
多个集合
跟$lookup类似，$graphLookup可以跨同一数据库的集合
例如，在同一数据库中分别创建两个集合：
airports集合有下列文档：
db.airports.insertMany( [
{ "_id" : 0, "airport" : "JFK", "connects" : [ "BOS", "ORD" ] },
{ "_id" : 1, "airport" : "BOS", "connects" : [ "JFK", "PWM" ] },
{ "_id" : 2, "airport" : "ORD", "connects" : [ "JFK" ] },
{ "_id" : 3, "airport" : "PWM", "connects" : [ "BOS", "LHR" ] },
{ "_id" : 4, "airport" : "LHR", "connects" : [ "PWM" ] }
] )
travelers集合有以下文档：
db.travelers.insertMany( [
{ "_id" : 1, "name" : "Dev", "nearestAirport" : "JFK" },
{ "_id" : 2, "name" : "Eliot", "nearestAirport" : "JFK" },
{ "_id" : 3, "name" : "Jeff", "nearestAirport" : "BOS" }
] )
对于travelers集合中的每个文档，下面的聚合操作会查找airports集合中nearestAirport的值，并递归匹配connects字段和airport字段。该操作指定的最大递归深度为2。
db.travelers.aggregate( [
{
$graphLookup: {
from: "airports",
startWith: "$nearestAirport",
connectFromField: "connects",
connectToField: "airport",
maxDepth: 2,
depthField: "numConnections",
as: "destinations"
}
}
] )
操作返回下面的结果：
{
"_id" : 1,
"name" : "Dev",
"nearestAirport" : "JFK",
"destinations" : [
{ "_id" : 3,
"airport" : "PWM",
"connects" : [ "BOS", "LHR" ],
"numConnections" : NumberLong(2) },
{ "_id" : 2,
"airport" : "ORD",
"connects" : [ "JFK" ],
"numConnections" : NumberLong(1) },
{ "_id" : 1,
"airport" : "BOS",
"connects" : [ "JFK", "PWM" ],
"numConnections" : NumberLong(1) },
{ "_id" : 0,
"airport" : "JFK",
"connects" : [ "BOS", "ORD" ],
"numConnections" : NumberLong(0) }
]
}
{
"_id" : 2,
"name" : "Eliot",
"nearestAirport" : "JFK",
"destinations" : [
{ "_id" : 3,
"airport" : "PWM",
"connects" : [ "BOS", "LHR" ],
"numConnections" : NumberLong(2) },
{ "_id" : 2,
"airport" : "ORD",
"connects" : [ "JFK" ],
"numConnections" : NumberLong(1) },
{ "_id" : 1,
"airport" : "BOS",
"connects" : [ "JFK", "PWM" ],
"numConnections" : NumberLong(1) },
{ "_id" : 0,
"airport" : "JFK",
"connects" : [ "BOS", "ORD" ],
"numConnections" : NumberLong(0) } ]
}
{
"_id" : 3,
"name" : "Jeff",
"nearestAirport" : "BOS",
"destinations" : [
{ "_id" : 2,
"airport" : "ORD",
"connects" : [ "JFK" ],
"numConnections" : NumberLong(2) },
{ "_id" : 3,
"airport" : "PWM",
"connects" : [ "BOS", "LHR" ],
"numConnections" : NumberLong(1) },
{ "_id" : 4,
"airport" : "LHR",
"connects" : [ "PWM" ],
"numConnections" : NumberLong(2) },
{ "_id" : 0,
"airport" : "JFK",
"connects" : [ "BOS", "ORD" ],
"numConnections" : NumberLong(1) },
{ "_id" : 1,
"airport" : "BOS",
"connects" : [ "JFK", "PWM" ],
"numConnections" : NumberLong(0) }
]
}
下表显示了递归搜索遍历的路径，最大深度为2，开始的airport为JFK：
| 开始值 | travelers集合中nearestAirport的值 |
|---|---|
{ ... "nearestAirport" : "JFK" } |
|
| 深度0 | { "_id" : 0, "airport" : "JFK", "connects" : [ "BOS", "ORD" ] } |
| 深度1 | { "_id" : 1, "airport" : "BOS", "connects" : [ "JFK", "PWM" ] }, { "_id" : 2, "airport" : "ORD", "connects" : [ "JFK" ] } |
| 深度2 | { "_id" : 3, "airport" : "PWM", "connects" : [ "BOS", "LHR" ] } |
查询条件
下面的示例使用了一个包含一组文档的集合，文档中包含人名及其朋友和爱好的数组。聚合操作会找到一个特定的人，并遍历她的社交网络，找到爱好为golf的人。
集合people包含了下列文档：
{
"_id" : 1,
"name" : "Tanya Jordan",
"friends" : [ "Shirley Soto", "Terry Hawkins", "Carole Hale" ],
"hobbies" : [ "tennis", "unicycling", "golf" ]
}
{
"_id" : 2,
"name" : "Carole Hale",
"friends" : [ "Joseph Dennis", "Tanya Jordan", "Terry Hawkins" ],
"hobbies" : [ "archery", "golf", "woodworking" ]
}
{
"_id" : 3,
"name" : "Terry Hawkins",
"friends" : [ "Tanya Jordan", "Carole Hale", "Angelo Ward" ],
"hobbies" : [ "knitting", "frisbee" ]
}
{
"_id" : 4,
"name" : "Joseph Dennis",
"friends" : [ "Angelo Ward", "Carole Hale" ],
"hobbies" : [ "tennis", "golf", "topiary" ]
}
{
"_id" : 5,
"name" : "Angelo Ward",
"friends" : [ "Terry Hawkins", "Shirley Soto", "Joseph Dennis" ],
"hobbies" : [ "travel", "ceramics", "golf" ]
}
{
"_id" : 6,
"name" : "Shirley Soto",
"friends" : [ "Angelo Ward", "Tanya Jordan", "Carole Hale" ],
"hobbies" : [ "frisbee", "set theory" ]
}
下面的聚合操作使用了3个阶段：
-
$match匹配name字段包含字符串"Tanya Jordan"的文档，返回一个输出文档。 -
$graphLookup将输出文档的friends字段与集合中其他文档的name字段连接起来，以遍历Tanya Jordan的社交网络。此阶段使用restrictSearchWithMatch参数只查找爱好数组中包含golf的文档。返回一个输出文档。 -
$project 塑造输出文档。列出的
connections who play golf的名字取自输入文档的golfers数组。
db.people.aggregate( [
{ $match: { "name": "Tanya Jordan" } },
{ $graphLookup: {
from: "people",
startWith: "$friends",
connectFromField: "friends",
connectToField: "name",
as: "golfers",
restrictSearchWithMatch: { "hobbies" : "golf" }
}
},
{ $project: {
"name": 1,
"friends": 1,
"connections who play golf": "$golfers.name"
}
}
] )
操作返回下面的文档：
{
"_id" : 1,
"name" : "Tanya Jordan",
"friends" : [
"Shirley Soto",
"Terry Hawkins",
"Carole Hale"
],
"connections who play golf" : [
"Joseph Dennis",
"Tanya Jordan",
"Angelo Ward",
"Carole Hale"
]
}