$ geoNear匹配最近的数组 [英] $geoNear matching nearest array

问题描述

我在Mongodb(3.4)的记录集合中有文档，如下所示:-

I have document in record collection Mongodb (3.4) like below :-

    { 
      "_id" : ObjectId("592d0c78555a7436b0883960"), 
      "userid" : 7, 
       "addresses" : [
        {
            "apporx" : 50.0, 
            "loc" : [
                -73.98137109999999, 
                40.7476039
            ]
        }, 
        {
            "apporx" : 15.0, 
            "loc" : [
                -73.982002, 
                40.74767
            ]          
        }, 
        {
            "apporx" :10.0, 
            "loc" : [
                -73.9819567, 
                40.7471609
            ]
        }
     ]
    }
`

I created index on this collection using below query :- 
`db.records.createIndex({'addresses.loc':1})`

when i execute my below query :-
`db.records.aggregate( 
      {$geoNear : {
        near : [ -73.9815103, 40.7475731 ],
        distanceField: "distance" 
    }});

这个结果给了我距离字段.现在您可以在我的文档中解释一下此多个元素中的哪个地址数组.我如何确定该结果对哪个元素为真?

this result gives me distance field.now can you explain me in my document which address array in this multiple elements exists. how can i determine for which element this result true?

另一个问题:-如果我在"addresses.apporx"上设置条件，例如大于或等于，那么有什么方法可以找到此条件的位置吗?

Another question :- if i make condition on "addresses.apporx" like greater then or equal then is there any way to find location of this condition?

推荐答案

首先，我强烈建议您如果要对真实世界坐标进行地理空间查询，请为您的集合创建一个"2dsphere"索引.

Firstly I strongly advise that you create a "2dsphere" index for your collection if you intend to to geospatial queries on real world coordinates.

请确保删除您可能一直在玩的其他索引:

Make sure to drop other indexes you may have been playing with:

db.records.dropIndexes();
db.records.createIndex({ "addresses.loc": "2dsphere" })

为了做您想做的事情，首先看一下稍作修改，其中还包括对

In order to do what you want, first take a look at the slight modification that also includes the includeLocs option to $geoNear

db.records.aggregate([
  { "$geoNear": {
     "near": [ -73.9815103, 40.7475731 ],
     "spherical": true,
     "distanceField": "distance",
     "includeLocs": "locs"
  }}
])

现在，您将看到如下所示的输出:

Now you will see output that looks like this:

{
        "_id" : ObjectId("592d0c78555a7436b0883960"),
        "userid" : 7,
        "addresses" : [
                {
                        "apporx" : 50,
                        "loc" : [
                                -73.98137109999999,
                                40.7476039
                        ]
                },
                {
                        "apporx" : 15,
                        "loc" : [
                                -73.982002,
                                40.74767
                        ]
                },
                {
                        "apporx" : 10,
                        "loc" : [
                                -73.9819567,
                                40.7471609
                        ]
                }
        ],
        "distance" : 0.0000019174641401278624,
        "locs" : [
                -73.98137109999999,
                40.7476039
        ]
}

所以返回的不仅是到最近点的距离，还包括所使用的匹配是哪个"位置.

So what that returned was not only the distance to the point that was nearest but "which" location was the match used.

因此，如果您想 $filter 数组以返回最接近的值，那么您可以:

So if you wanted to $filter the original array to return the nearest, then you can:

db.records.aggregate([
  { "$geoNear": {
     "near": [ -73.9815103, 40.7475731 ],
     "spherical": true,
     "distanceField": "distance",
     "includeLocs": "locs"
  }},
  { "$addFields": {
    "addresses": {
      "$filter": {
        "input": "$addresses",
        "as": "address",
        "cond": { "$eq": [ "$$address.loc", "$locs" ] }
      }
    }
  }}
])

然后返回仅具有该匹配项的数组:

And that returns the array with only that match:

{
        "_id" : ObjectId("592d0c78555a7436b0883960"),
        "userid" : 7,
        "addresses" : [
                {
                        "apporx" : 50,
                        "loc" : [
                                -73.98137109999999,
                                40.7476039
                        ]
                }
        ],
        "distance" : 0.0000019174641401278624,
        "locs" : [
                -73.98137109999999,
                40.7476039
        ]
}

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