定义
$graphLookup在版本5.1中进行了更改。
对集合执行递归搜索，并提供按照递归深度和查询筛选器限制搜索的选项。
$graphLookup搜索过程总结如下：输入文档流入聚合操作的
$graphLookup阶段。$graphLookup将搜索定位到由from参数指定的集合（有关搜索参数的完整列表，请参见下文）。对于每份输入文档，搜索从
startWith指定的值开始。$graphLookup将startWith值与from集合中其他文档的connectToField指定字段进行匹配。对于每份匹配文档，
$graphLookup获取connectFromField的值，并检查from集合中的每份文档是否有匹配的connectToField值。对于每次匹配，$graphLookup将from集合中的匹配文档添加到由as参数命名的数组字段中。此步骤以递归方式继续，直到找不到更多匹配文档，或者直到操作达到
maxDepth参数指定的递归深度。然后，$graphLookup将数组字段追加到输入文档。$graphLookup在完成对所有输入文档的搜索后返回结果。
$graphLookup具有以下原型形式：{ $graphLookup: { from: <collection>, startWith: <expression>, connectFromField: <string>, connectToField: <string>, as: <string>, maxDepth: <number>, depthField: <string>, restrictSearchWithMatch: <document> }}$graphLookup接受包含以下字段的文档：字段说明from$graphLookup操作要搜索的目标集合，从而以递归方式将connectFromField与connectToField进行匹配。from集合必须与此操作中使用的所有其他集合位于同一数据库中。有关分片支持，请参阅 分片集合。
startWithconnectFromField字段名，其值
$graphLookup用于以递归方式匹配集合中其他文档的connectToField。如果值是数组，则每个元素都会单独完成遍历进程。connectToField其他文档中的字段名称，用于与
connectFromField参数指定的字段值相匹配。as添加到每个输出文档的数组字段的名称。包含在
$graphLookup阶段为访问文档而遍历的文档。不保证
as字段中返回的文档按任何顺序排列。maxDepth可选。指定最大递归深度的非负整数。
depthField可选。要添加到搜索路径中每个已遍历文档的字段的名称。该字段的值为文档的递归深度，并用
NumberLong表示。递归深度值从零开始，因此第一次查找对应于零深度。restrictSearchWithMatch
Considerations
分片集合
从 MongoDB 5.1 开始，可以在 $graphLookup 阶段的 from 参数中指定分片集合。
当以分片集合为目标时，您无法在事务中使用 $graphLookup 阶段。
最大深度
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 阶段必须保持在 100 兆字节的内存限制之内。如果为 aggregate() 操作指定了 allowDiskUse: true，则 $graphLookup 阶段将忽略该选项。如果 aggregate() 操作中还有其他阶段，则 allowDiskUse: true 选项对这些其他阶段有效。
有关更多信息，请参阅聚合管道限制。
未排序的结果
$graphLookup 阶段不返回排序结果。要对结果进行排序，请使用$sortArray 操作符。
视图和排序规则
如果执行的聚合涉及多个视图（如使用 $lookup 或 $graphLookup），则这些视图必须具有相同的排序规则。
示例
单个集合内
名为 employees 的集合包含以下文档：
db.employees.insertMany( [ { _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" }] )以下 $graphLookup 操作递归匹配 employees 集合中的 reportsTo 和 name 字段，返回每个人员的报告层次结构：
db.employees.aggregate( [ { $graphLookup: { from: "employees", startWith: "$reportsTo", connectFromField: "reportsTo", connectToField: "name", as: "reportingHierarchy" } }] )[ { _id: 1, name: 'Dev', reportingHierarchy: [] }, { _id: 2, name: 'Eliot', reportsTo: 'Dev', reportingHierarchy: [ { _id: 1, name: 'Dev' } ] }, { _id: 3, name: 'Ron', reportsTo: 'Eliot', reportingHierarchy: [ { _id: 2, name: 'Eliot', reportsTo: 'Dev' }, { _id: 1, name: 'Dev' } ] }, { _id: 4, name: 'Andrew', reportsTo: 'Eliot', reportingHierarchy: [ { _id: 2, name: 'Eliot', reportsTo: 'Dev' }, { _id: 1, name: 'Dev' } ] }, { _id: 5, name: 'Asya', reportsTo: 'Ron', reportingHierarchy: [ { _id: 2, name: 'Eliot', reportsTo: 'Dev' }, { _id: 3, name: 'Ron', reportsTo: 'Eliot' }, { _id: 1, name: 'Dev' } ] }, { _id: 6, name: 'Dan', reportsTo: 'Andrew', reportingHierarchy: [ { _id: 2, name: 'Eliot', reportsTo: 'Dev' }, { _id: 1, name: 'Dev' }, { _id: 4, name: 'Andrew', reportsTo: 'Eliot' } ] }]下表提供文档 { "_id" : 5, "name" : "Asya", "reportsTo" : "Ron" } 的遍历路径：
起始值 | 文档的 |
深度 0 | |
深度 1 | |
深度 2 | |
输出生成层次结构Asya -> Ron -> Eliot -> Dev。
跨多个集合
与 $lookup 一样，$graphLookup 可以访问同一数据库中的另一个集合。
例如，创建一个包含两个集合的数据库：
包含以下文档的
airports集合：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" ] }] )包含以下文档的
travelers集合：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，以下聚合操作会在airports集合中查找nearestAirport值，并以递归方式将connects字段与airport字段进行匹配。该操作指定最大递归深度为2。
db.travelers.aggregate( [ { $graphLookup: { from: "airports", startWith: "$nearestAirport", connectFromField: "connects", connectToField: "airport", maxDepth: 2, depthField: "numConnections", as: "destinations" } }] )[ { _id: 1, name: 'Dev', nearestAirport: 'JFK', destinations: [ { _id: 0, airport: 'JFK', connects: [ 'BOS', 'ORD' ], numConnections: Long('0') }, { _id: 1, airport: 'BOS', connects: [ 'JFK', 'PWM' ], numConnections: Long('1') }, { _id: 3, airport: 'PWM', connects: [ 'BOS', 'LHR' ], numConnections: Long('2') }, { _id: 2, airport: 'ORD', connects: [ 'JFK' ], numConnections: Long('1') } ] }, { _id: 2, name: 'Eliot', nearestAirport: 'JFK', destinations: [ { _id: 0, airport: 'JFK', connects: [ 'BOS', 'ORD' ], numConnections: Long('0') }, { _id: 1, airport: 'BOS', connects: [ 'JFK', 'PWM' ], numConnections: Long('1') }, { _id: 3, airport: 'PWM', connects: [ 'BOS', 'LHR' ], numConnections: Long('2') }, { _id: 2, airport: 'ORD', connects: [ 'JFK' ], numConnections: Long('1') } ] }, { _id: 3, name: 'Jeff', nearestAirport: 'BOS', destinations: [ { _id: 0, airport: 'JFK', connects: [ 'BOS', 'ORD' ], numConnections: Long('1') }, { _id: 1, airport: 'BOS', connects: [ 'JFK', 'PWM' ], numConnections: Long('0') }, { _id: 4, airport: 'LHR', connects: [ 'PWM' ], numConnections: Long('2') }, { _id: 3, airport: 'PWM', connects: [ 'BOS', 'LHR' ], numConnections: Long('1') }, { _id: 2, airport: 'ORD', connects: [ 'JFK' ], numConnections: Long('2') } ] }]下表提供了递归搜索的遍历路径，深度为 2，其中起始 airport 为 JFK：
起始值 |
|
深度 0 | |
深度 1 | |
深度 2 | |
使用查询过滤器
以下示例使用一个包含一组文档的集合，其中包含人员姓名及其朋友和爱好的数组。聚合操作找到一个特定的人，并遍历她的人际网络，以找到在其爱好中列出golf的人。
一个名为 people 的集合包含以下文档：
db.people.insertMany( [ { _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" ] }] )以下聚合操作使用三个阶段：
$match匹配name字段包含 string"Tanya Jordan"的 document。返回一个输出 document。$graphLookup将输出文档的friends字段与集合中其他文档的name字段连接，以遍历Tanya Jordan's连接网络。该阶段使用restrictSearchWithMatch参数，只查找hobbies数组包含golf的文档。返回一个输出文档。$project会确定输出文档的形状。connections who play golf中列出的名称取自输入文档的golfers数组中所列文档的name字段。
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" } }] )[ { _id: 1, name: 'Tanya Jordan', friends: [ 'Shirley Soto', 'Terry Hawkins', 'Carole Hale' ], 'connections who play golf': [ 'Tanya Jordan', 'Joseph Dennis', 'Angelo Ward', 'Carole Hale' ] }]名为 employees 的集合包含以下文档：
{ _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" }以下 Employee 类对 employees集合中的文档进行建模：
[BsonIgnoreExtraElements]public class Employee{ [BsonId] public int Id { get; set; }
[BsonElement("name")] public string Name { get; set; } = null!;
[BsonElement("reportsTo")] public string? ReportsTo { get; set; }
[BsonElement("hobbies")] public List<string> Hobbies { get; set; } = new();
[BsonElement("reportingHierarchy")] public List<Employee> ReportingHierarchy { get; set; } = new();}要使用MongoDB .NET/C# 驱动程序将 $graphLookup 阶段添加到聚合管道，请对 PipelineDefinition 对象调用 UnionWith() 方法。
以下示例创建了一个管道阶段，该阶段以递归方式匹配 employees集合中的 ReportsTo 和 Name 字段，从而返回每个人员的报告层次结构：
var pipeline = new EmptyPipelineDefinition<Employee>() .GraphLookup<Employee, Employee, Employee, string, string, string, List<Employee>, Employee>( from: _collection, connectFromField: e => e.ReportsTo!, connectToField: e => e.Name, startWith: e => e.ReportsTo!, : e => e.ReportingHierarchy);您可以使用 AggregateGraphLookupOptions 对象来指定要递归的深度以及深度字段的名称。以下代码示例执行与上一示例相同的 $graphLookup 操作，但指定最大递归深度为 1：
var pipeline = new EmptyPipelineDefinition<Employee>() .GraphLookup<Employee, Employee, Employee, string, string, string, List<Employee>, Employee>( from: _collection, connectFromField: e => e.ReportsTo!, connectToField: e => e.Name, startWith: e => e.ReportsTo!, : e => e.ReportingHierarchy, new AggregateGraphLookupOptions<Employee, Employee, Employee> { MaxDepth = 1 });您还可以使用 AggregateGraphLookupOptions对象指定文档必须与筛选器匹配的过滤， MongoDB才能将其包含在搜索中。以下代码示例执行与前面示例相同的 $graphLookup 操作，但仅包括 Hobbies字段包含 "golf" 的 Employee 文档：
var pipeline = new EmptyPipelineDefinition<Employee>() .GraphLookup<Employee, Employee, Employee, string, string, string, List<Employee>, Employee>( from: _collection, connectFromField: e => e.ReportsTo!, connectToField: e => e.Name, startWith: e => e.ReportsTo!, : e => e.ReportingHierarchy, new AggregateGraphLookupOptions<Employee, Employee, Employee> { MaxDepth = 1, RestrictSearchWithMatch = Builders<Employee>.Filter.AnyEq( e => e.Hobbies, "golf") });名为 employees 的集合包含以下文档：
db.employees.insertMany([ { _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" }]);要使用MongoDB Node.js驱动程序将 $graphLookup 阶段添加到聚合管道，请在管道对象中使用 $graphLookup操作符。
以下示例创建了一个管道阶段，将 reportsTo 字段递归匹配到 employees 集合中的 name 字段，从而在名为 reportingHierarchy 的新字段中返回每个人的报告层次结构。然后，示例运行聚合管道：
const pipeline = [ { $graphLookup: { from: "employees", connectFromField: "reportsTo", connectToField: "name", startWith: "$reportsTo", as: "reportingHierarchy" } }];
const cursor = collection.aggregate(pipeline);return cursor;要指定递归的深度，请使用 maxDepth 字段。以下代码示例执行与上一个示例相同的 $graphLookup 操作，但指定最大递归深度为 1：
const pipeline = [ { $graphLookup: { from: "employees", connectFromField: "reportsTo", connectToField: "name", startWith: "$reportsTo", as: "reportingHierarchy", maxDepth: 1 } }];
const cursor = collection.aggregate(pipeline);return cursor;要指定文档必须匹配的过滤器以便操作将其包含在搜索结果中，请使用 restrictSearchWithMatch 字段。以下代码示例执行与上一个示例相同的 $graphLookup 操作，但仅包括 employee 文档，其中 hobbies 字段包含 "golf"：
const pipeline = [ { $graphLookup: { from: "employees", connectFromField: "reportsTo", connectToField: "name", startWith: "$reportsTo", as: "reportingHierarchy", maxDepth: 1, restrictSearchWithMatch: { hobbies: "golf" } } }];
const cursor = collection.aggregate(pipeline);return cursor;了解详情
要学习；了解有关如何使用$graphLookup的更多信息，请参阅在MongoDB中使用图表数据。