Mongodb graphLookup

Question

由于MongoDB最近引入了graphLookup，我一直在尝试寻找它是否可以容纳一个简单的社会关系图。我目前使用neo4j就是为了这个目的。

我将graphLookup理解为一种递归搜索，它只是通过每个文档的“connectFromField”更深入地搜索。

虽然我能够做基本的事情，但我想为每个关系提供更多的属性。例如，这里的第一个示例：(Employees and reporting hierarchy)

https://docs.mongodb.com/manual/reference/operator/aggregation/graphLookup/

{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" }

如果我需要向'reportsTo‘值添加一个开始日期，如下所示：

{ "_id" : 2, "name" : "Eliot", "reportsTo" : {"name": "Dev", "from": "date"  } }

恐怕这是不受支持的。

我想知道是否有人这样使用过MongoDB。

Answer 1

假设我们插入了以下文档：

> db.employees.insertMany([
... { "_id" : 1, "name" : "Dev" },
... { "_id" : 2, "name" : "Eliot", "reportsTo" : { name: "Dev", "from": ISODate("2016-01-01T00:00:00.000Z") } },
... { "_id" : 3, "name" : "Ron", "reportsTo" : { name: "Eliot", "from": ISODate("2016-01-01T00:00:00.000Z") } },
... { "_id" : 4, "name" : "Andrew", "reportsTo" : { name: "Eliot", "from": ISODate("2016-01-01T00:00:00.000Z") } },
... { "_id" : 5, "name" : "Asya", "reportsTo" : { name: "Ron", "from": ISODate("2016-01-01T00:00:00.000Z") } },
... { "_id" : 6, "name" : "Dan", "reportsTo" : { name: "Andrew", "from": ISODate("2016-01-01T00:00:00.000Z") } },
... ]);
{ "acknowledged" : true, "insertedIds" : [ 1, 2, 3, 4, 5, 6 ] }

然后，我们可以只使用.通过以下聚合查询从嵌入的文档中获取字段：

db.employees.aggregate([
{
   $graphLookup: {
      from: "employees",
      startWith: "Eliot",
      connectFromField: "reportsTo.name",
      connectToField: "name",
      as: "reportingHierarchy"
   }
}
])

然后，它将返回以下结果：

{
        "_id" : 1,
        "name" : "Dev",
        "reportingHierarchy" : [
                {
                        "_id" : 1,
                        "name" : "Dev"
                },
                {
                        "_id" : 2,
                        "name" : "Eliot",
                        "reportsTo" : {
                                "name" : "Dev",
                                "from" : ISODate("2016-01-01T00:00:00Z")
                        }
                }
        ]
}
{
        "_id" : 2,
        "name" : "Eliot",
        "reportsTo" : {
                "name" : "Dev",
                "from" : ISODate("2016-01-01T00:00:00Z")
        },
        "reportingHierarchy" : [
                {
                        "_id" : 1,
                        "name" : "Dev"
                },
                {
                        "_id" : 2,
                        "name" : "Eliot",
                        "reportsTo" : {
                                "name" : "Dev",
                                "from" : ISODate("2016-01-01T00:00:00Z")
                        }
                }
        ]
}
{
        "_id" : 3,
        "name" : "Ron",
        "reportsTo" : {
                "name" : "Eliot",
                "from" : ISODate("2016-01-01T00:00:00Z")
        },
        "reportingHierarchy" : [
                {
                        "_id" : 1,
                        "name" : "Dev"
                },
                {
                        "_id" : 2,
                        "name" : "Eliot",
                        "reportsTo" : {
                                "name" : "Dev",
                                "from" : ISODate("2016-01-01T00:00:00Z")
                        }
                }
        ]
}
{
        "_id" : 4,
        "name" : "Andrew",
        "reportsTo" : {
                "name" : "Eliot",
                "from" : ISODate("2016-01-01T00:00:00Z")
        },
        "reportingHierarchy" : [
                {
                        "_id" : 1,
                        "name" : "Dev"
                },
                {
                        "_id" : 2,
                        "name" : "Eliot",
                        "reportsTo" : {
                                "name" : "Dev",
                                "from" : ISODate("2016-01-01T00:00:00Z")
                        }
                }
        ]
}
{
        "_id" : 5,
        "name" : "Asya",
        "reportsTo" : {
                "name" : "Ron",
                "from" : ISODate("2016-01-01T00:00:00Z")
        },
        "reportingHierarchy" : [
                {
                        "_id" : 1,
                        "name" : "Dev"
                },
                {
                        "_id" : 2,
                        "name" : "Eliot",
                        "reportsTo" : {
                                "name" : "Dev",
                                "from" : ISODate("2016-01-01T00:00:00Z")
                        }
                }
        ]
}
{
        "_id" : 6,
        "name" : "Dan",
        "reportsTo" : {
                "name" : "Andrew",
                "from" : ISODate("2016-01-01T00:00:00Z")
        },
        "reportingHierarchy" : [
                {
                        "_id" : 1,
                        "name" : "Dev"
                },
                {
                        "_id" : 2,
                        "name" : "Eliot",
                        "reportsTo" : {
                                "name" : "Dev",
                                "from" : ISODate("2016-01-01T00:00:00Z")
                        }
                }
        ]
}

然后，我们还可以使用聚合管道的其余部分来执行任何其他操作：

db.employees.aggregate([

   { $match: { "reportsTo.from": { $gt: ISODate("2016-01-01T00:00:00Z") } } },
   { $graphLookup: { ... } },
   { $project: { ... }
]);

有关管道阶段的信息，请参见https://docs.mongodb.com/v3.2/reference/operator/aggregation-pipeline/。