mongo 查询中的多个 $unwind 和 $group

Question

mongodb 查询有点挣扎。 我的 mongo 数据库具有以下结构：

name: String
test: String
competences: [{name: String, code: String, value: Float}]
subcompetences: [{name: String, code: String, value: Float}]

我的查询如下所示：

async function getAggregatedDataForCompetences(filter, category) {
    return await getCollection('competences').aggregate([
    {$match: { $or: filter }},
    {$unwind:  "$competences" },
    {$group: {
        _id: "$_id",
        code:  { $first: "$competences.code" },
        name:  { $first: "$competences.name"},
        avgValue: { $avg: "$competences.value" },
        subcompetences:  { $first: "$subcompetences"},
        }
    },
    {$unwind:  "$subcompetences" },
    {$group: {
        _id: "$subcompetences.code",
        code:  { $first: "$subcompetences.code" },
        name:  { $first: "$subcompetences.name"},
        avgValue: { $avg: "$subcompetences.value" },
    }}
    ]).toArray();
}

我想要做的是展开所有元素的第一个（能力）数组，将它们分组并计算每个项目的平均值。 对以下对象的子能力数组重复相同的过程。 结果，我只得到最后一个子能力数组的平均值。 你知道我怎样才能达到以下结果：

{
   competences: [{name: String, code: String, avgValue: Float}],
   subcompetences: [{name: String, code: String, avgValue: Float}]
}

Answer 1

$facet救援——“多组”运算符。 给定这样的输入：

var r =
[
{
    "_id" : 0,
    "name": "N1",
    "competences": [
{name: "AAA", code: "A", value: 1.1},
{name: "BBB", code: "B", value: 2.2},
{name: "CCC", code: "C", value: 3.3}
                    ],
    "subcompetences": [
{name: "DDD", code: "D", value: 4.4},
{name: "EEE", code: "E", value: 5.5},
{name: "FFF", code: "F", value: 6.6}
]
        }

,{
    "_id" : 1, "name": "N2",
    "competences": [
{name: "AAA", code: "A", value: 9.9},
{name: "BBB", code: "B", value: 8.8},
{name: "KKK", code: "K", value: 11.11}
                    ],
    "subcompetences": [
{name: "FFF", code: "F", value: 4.9},
{name: "GGG", code: "G", value: 6.7}
]
        }
 ];

然后$facet将允许您“并行”进行两组。 实际上，您可以同时执行两个或更多完整的管道（有一些限制）：

db.foo.aggregate([
{$facet: {
  "avg_competences": [
      {$unwind: "$competences"}
      ,{$group: {_id: "$competences.code",
           name: {$first: "$competences.name"},
           count: {$sum: 1},
           avgval: {$avg: "$competences.value"},
        }}
           ]

  ,"avg_subcompetences": [
      {$unwind: "$subcompetences"}
      ,{$group: {_id: "$subcompetences.code",
           name: {$first: "$subcompetences.name"},
           count: {$sum: 1},
           avgval: {$avg: "$subcompetences.value"},
        }}
           ]
    }
}

// The output of the stage above will be a *single* doc with two fields, 
// avg_competence and avg_subcompetences.  Let's add more fields to this doc!
,{$addFields: {N: {$reduce: {
                input: {$concatArrays: ["$avg_competences","$avg_subcompetences"]},
                initialValue: 0,
                in:{$sum: [ "$$value", "$$this.count"]}
            }}
    }}


   ]);

产生：

{
    "avg_competences" : [
        {
            "_id" : "K",
            "name" : "KKK",
            "count" : 1,
            "avgval" : 11.11
        },
        {
            "_id" : "C",
            "name" : "CCC",
            "count" : 1,
            "avgval" : 3.3
        },
        {
            "_id" : "B",
            "name" : "BBB",
            "count" : 2,
            "avgval" : 5.5
        },
        {
            "_id" : "A",
            "name" : "AAA",
            "count" : 2,
            "avgval" : 5.5
        }
    ],
    "avg_subcompetences" : [
        {
            "_id" : "G",
            "name" : "GGG",
            "count" : 1,
            "avgval" : 6.7
        },
        {
            "_id" : "F",
            "name" : "FFF",
            "count" : 2,
            "avgval" : 5.75
        },
        {
            "_id" : "E",
            "name" : "EEE",
            "count" : 1,
            "avgval" : 5.5
        },
        {
            "_id" : "D",
            "name" : "DDD",
            "count" : 1,
            "avgval" : 4.4
        }
    ],
    "N" : 11
}