MongoDB 聚合操作：实战演示 $graphLookup 查询方法

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"
   ]
}