MongoDB 查询:$near 与聚合

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问题描述

我有一个类似的收藏

{
    "_class" : "User",
    "_id" : "id1",
    "places" : [
            {
                    "_id" : "1",
                    "address" : "test1",
                    "location" : {
                            "latitude" : 1,
                            "longitude" : 1
                    }
            },
            {
                    "_id" : "2",
                    "address" : "test2",
                    "location" : {
                            "latitude" : 2,
                            "longitude" : 2
                    }
            },...
     ]
}

我正在尝试检索用户的每个位置(2 公里范围内).此查询不起作用:

I am trying to retrieve every place of a user (in a range of 2 km). This query doesn't work :

db.users.ensureIndex({"places.location":"2d"})

db.users.aggregate([
   {$match : { "_id" : "id1" } },
   {$unwind : "$places"},
   {$project:{_id:0, places:1},
   {$match :
      {"places.location" :
             { $near :
                 { $geometry :
                    { type : "2d" ,
                      coordinates : [ -1 , -2 ]
                    }
                 },
                 $maxDistance : 2000
              }
      }
    }])



Error: Printing Stack Trace
at printStackTrace (src/mongo/shell/utils.js:37:15)
at DBCollection.aggregate (src/mongo/shell/collection.js:897:9)
at (shell):1:10
JavaScript execution failed: aggregate failed: {
    "errmsg" : "exception: $near is not allowed inside of a $match aggregation expression",
    "code" : 16424,
    "ok" : 0

我不知道如何将 $near 与聚合一起使用.

I don't know how to use $near with aggregation.

当我尝试时

db.users.find(
                  {
                    "places.location" :
                        { $near:
                            { $geometry :
                                { type : "2d" ,
                                  coordinates : [ -1 , -2 ] }
                                },
                                $maxDistance : 2000
                        }
                    }

它返回给我 n 次给定用户(n = 用户的每个地方)，这就是为什么我决定使用聚合，但我被卡住了.

It returns me n times the given user (n = each place of the user), this is why i have decided to use aggregation, but i'm stuck.

提前谢谢

编辑

我试过 $geoNear :

I tried $geoNear :

db.users.aggregate(
{$geoNear : {
    near: [-1, 1],
    distanceField: "distance",
    query : {"_id" : "id1"},
    uniqueDocs: true,
    maxDistance : 2000  // In the specs, distance has to be in radians
}})

它会返回给我整个用户(包括所有地方，甚至是最远的地方).

and it returns me the whole user (with all the places, even the far ones).

所以我需要在搜索正确的位置之前修改文档.

So I need to rework the document before I search for the correct places.

检索正确的用户(匹配)
放松用户的每一个地方
然后搜索 2 公里范围内的每个地方(近/近)
重塑结果(项目)

我期待的结果是这样的:

The result I am expecting is something like :

"result" : [
        {
                "_id" : "1",
                "location" : {
                        "latitude" : 1,
                        "longitude" : 1
                }
        },
        {
                "_id" : "2",
                "location" : {
                        "latitude" : 2,
                        "longitude" : 2
                }
        }
]}

问题是我不能使用$geoNear之前的其他阶段，我的搜索参数必须在查询"字段中指定.我收到以下错误:

The problem is that I can't use other stages before $geoNear, my search parameters have to be specified in the "query" field.I get the following error :

$geoNear is only allowed as the first pipeline stage

编辑问题后

下面的查询将为您提供位置和距离，但不检索内部(数组元素)_id

Edit : after edit to question

The bellow query will give you location and distance, but doesn't retrieve the internal (array element) _id

db.users.aggregate(
  {$geoNear : {
    near: [-1, -2],
    distanceField: "distance",
    includeLocs: "location",
    query : {"_id" : "id1"},
    maxDistance : 2000
  }},
  {$project : {"location" : 1, "_id" : 0, "distance" : 1}}
)

注意添加includeLocs和删除uniqueDocs: true

要同时检索内部 _id，您必须(按照此示例)展开并有条件地进行项目左右，但我认为这不值得，除非您需要地址而不是 _id

To also retrieve the internal _id, you will have to (following this example) unwind and conditionally project or so, but I don't think it worth it, unless you need the address instead of the _id

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