$graphLookup (aggregation)

在本页面

在版本 3.4 中更改。

Definition

$graphLookup
- 对集合执行递归搜索，其中包含用于通过递归深度和查询过滤器限制搜索的选项。

$graphLookup搜索过程总结如下：

Importing 文档进入聚合操作的$graphLookup阶段。
$graphLookup将搜索定位到from参数指定的集合(有关搜索参数的完整列表，请参见下文)。
对于每个 Importing 文档，搜索均以startWith指定的值开始。
$graphLookup与from集合中其他文档中connectToField指定的字段匹配startWith值。
对于每个匹配的文档，$graphLookup取connectFromField的值，并在from集合中的每个文档中检查匹配的connectToField值。对于每个匹配项，$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 continue 进行，直到找不到更多匹配的文档，或者直到操作达到maxDepth参数指定的递归深度为止。然后$graphLookup将数组字段追加到 Importing 文档中。 $graphLookup完成对所有 Importing 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接收包含以下字段的文档：

Field	Description
`from`	$graphLookup操作要搜索的目标集合，将`connectFromField`与`connectToField`递归匹配。不能对`from`集合进行分片，并且必须与该操作中使用的任何其他集合位于同一数据库中。有关信息，请参见Sharded Collections。
`startWith`	Expression指定用于开始递归搜索的`connectFromField`的值。可选地，`startWith`可以是值的数组，每个值都分别经过遍历过程。
`connectFromField`	值$graphLookup用于与集合中其他文档的`connectToField`递归匹配的字段名称。如果值是数组，则每个元素都将分别经过遍历过程。
`connectToField`	其他文档中与`connectFromField`参数指定的字段值相匹配的字段名。
`as`	添加到每个输出文档中的数组字段的名称。包含在$graphLookup阶段遍历的文档。

Note

不能保证在as字段中返回的文档以任何 Sequences 排列。

|
| maxDepth | *可选.*非负整数，指定最大递归深度。
| depthField | *可选.*要添加到搜索路径中每个已遍历文档的字段名称。该字段的值是文档的递归深度，表示为NumberLong。递归深度值从零开始，因此第一次查找对应于零深度。
| restrictSearchWithMatch | *可选.*为递归搜索指定其他条件的文档。语法与query filter语法相同。

> [!NOTE|label:Note]

您不能在此过滤器中使用任何aggregation expression。例如，查询文档如

{ lastName: { $ne: "$lastName" } }

在这种情况下将无法找到lastName值与 Importing 文档的lastName值不同的文档，因为"$lastName"将充当字符串 Literals，而不是字段路径。

|

Considerations

Sharded Collections

from中指定的集合不能为sharded。但是，可以对运行aggregate()方法的集合进行分片。也就是说，在下面：

db.collection.aggregate([
   { $graphLookup: { from: "fromCollection", ... } }
])

collection可以被分片。
fromCollection无法分片。

要加入多个分片集合，请考虑：

修改 Client 端应用程序以执行手动查找，而不是使用$graphLookup聚合阶段。
如果可能，请使用嵌入式数据模型消除加入集合的需要。

Max Depth

将maxDepth字段设置为0等效于非递归$graphLookup搜索阶段。

Memory

$graphLookup阶段必须保持在 100 MiB 内存限制内。如果为aggregate()操作指定了allowDiskUse: true，则$graphLookup阶段将忽略该选项。如果在aggregate()操作中还有其他观察allowDiskUse: true的阶段，则allowDiskUse: true选项对这些其他阶段有效。

有关更多信息，请参见聚集管线限制。

视图和排序规则

如果执行涉及多个视图的聚合(例如$lookup或$graphLookup)，则这些视图必须具有相同的collation。

Examples

在一个集合内

名为employees的集合具有以下文档：

{ "_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$graphLookup操作在employees集合中的reportsTo和name字段上递归匹配，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"
      }
   }
] )

该操作返回以下内容：

{
   "_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" }
   ]
}

下表提供了文档{ "_id" : 5, "name" : "Asya", "reportsTo" : "Ron" }的遍历路径：

起始值	文档的`reportsTo`值： `{ ... "reportsTo" : "Ron" }`
Depth 0	`{ "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" }`
Depth 1	`{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }`
Depth 2	`{ "_id" : 1, "name" : "Dev" }`

输出生成层次结构Asya -> Ron -> Eliot -> Dev。

跨多个收藏集

像$lookup一样，$graphLookup可以访问同一数据库中的另一个集合。

在以下示例中，数据库包含两个集合：

具有以下文档的集合airports：

{ "_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" ] }

具有以下文档的集合travelers：

{ "_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，以下聚合操作将查找airports集合中的nearestAirport值，并将connects字段与airport字段递归匹配。该操作将最大递归深度指定为2。

db.travelers.aggregate( [
   {
      $graphLookup: {
         from: "airports",
         startWith: "$nearestAirport",
         connectFromField: "connects",
         connectToField: "airport",
         maxDepth: 2,
         depthField: "numConnections",
         as: "destinations"
      }
   }
] )

该操作返回以下结果：

{
   "_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) }
   ]
}

下表提供了递归搜索的遍历路径，深度为2，起始airport为JFK：

起始值	来自`travelers`集合的`nearestAirport`值： `{ ... "nearestAirport" : "JFK" }`
Depth 0	`{ "_id" : 0, "airport" : "JFK", "connects" : [ "BOS", "ORD" ] }`
Depth 1	{ "_id" : 1, "airport" : "BOS", "connects" : [ "JFK", "PWM"] } { "_id" : 2, "airport" : "ORD", "connects" : [ "JFK"] }
Depth 2	`{ "_id" : 3, "airport" : "PWM", "connects" : [ "BOS", "LHR" ] }`

使用查询过滤器

下面的示例使用一个集合，该集合包含一组文档，其中包含人的名字以及他们的朋友和爱好的数组。聚集操作找到一个特定的人，并遍历她的联系网络，以找到在他们的兴趣中列出golf的人。

名为people的集合包含以下文档：

{
  "_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" ]
 }

以下聚合操作分为三个阶段：

$match与包含字符串"Tanya Jordan"的name字段的文档匹配。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，以遍历Tanya Jordan's连接网络。此阶段使用restrictSearchWithMatch参数仅查找hobbies数组包含golf的文档。返回一个输出文档。
$project对输出文档进行整形。 connections who play golf中列出的名称取自 Importing 文档golfers数组中列出的文档的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"
    }
  }
] )

该操作返回以下文档：

{
   "_id" : 1,
   "name" : "Tanya Jordan",
   "friends" : [
      "Shirley Soto",
      "Terry Hawkins",
      "Carole Hale"
   ],
   "connections who play golf" : [
      "Joseph Dennis",
      "Tanya Jordan",
      "Angelo Ward",
      "Carole Hale"
   ]
}

Additional Resource

网络研讨会：在 MongoDB 中使用图数据

Docs

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