Mongo按指定字段 分段分组 聚合统计

现在有一批数据如下（表名detectOriginalData）：

{
    "_id" : "760c29a2720ead1681184dfbef0aaae4",
    "imgSavePath" : "/opt/temp/face/publicceaf441cf933bba310e4.JPG",
    "faceDetail" : {
        "face_token" : "760c29a2720ead1681184dfbef0aaae4",
        "location" : {
            "left" : 110.04,
            "top" : 244.39,
            "width" : 311.0,
            "height" : 263.0,
            "rotation" : -2
        }
    },
    "cdt" : ISODate("2020-12-25T10:53:43.647+08:00")
}

现在，我们要统计faceDetail.location.width，找出width处于300-400之间，每隔10分一段（也就是300-310、310-320...390-400共10组），之间的faceToken和imgSavePath都有哪些

最后实现的一种为：

db.detectOriginalData.aggregate([
        {$match: {"faceDetail.location.width": {$lte: 400, $gte: 300}}},
        {$project: {val: "$faceDetail.location.width", ftk: "$faceDetail.face_token", imgPath: "$imgSavePath"}},
        {$group: {
            "_id": {
                $subtract: [
                {$subtract: ["$val", 0]},
                {$mod: [{$subtract: ["$val", 0]}, 10]}
                ]
            },
            ftkList: {$push: "$ftk"},
            imgList: {$push: "$imgPath"},
            ftkCount: {$sum: 1}
        }},
        {$sort: {_id: -1}}
])

下面为开始用的绕了弯路的一种实现方式，可以忽略。。。

db.detectOriginalData.aggregate([
        {$match: {"faceDetail.location.width": {$lte: 400, $gte: 300}}},
        {$project: {val: "$faceDetail.location.width", ftk: "$faceDetail.face_token"}},
        {$lookup:{
            from:"detectOriginalData",
            localField:"ftk",
            foreignField: "_id",
            as: "img"}
        },
        {$project: {val: 1, ftk: 1, imgPath: "$img.imgSavePath"}},
        {$unwind: "$imgPath"},
        {$group: {
            "_id": {
                $subtract: [
                {$subtract: ["$val", 0]},
                {$mod: [{$subtract: ["$val", 0]}, 10]}
                ]
            },
            ftkList: {$push: "$ftk"},
            imgList: {$push: "$imgPath"},
            ftkCount: {$sum: 1}
        }},
        {$sort: {_id: -1}}
])

最后的结果如下（_id=320，代表width处于320-330之间的数据）：

************2021-01-19 新增，测试小伙伴提了个统计需求。。。。。。

先看统计数据关联的另一张表（过滤详情表detectFilterDetail），大概数据结构如下（只截取部分字段）：

{
    "_id" : ObjectId("5feaa27fd873663e8085507d"),
    "faceToken" : "2268048d7df15fa15652cc745261404e",
    "paramRecordId" : "5feaa273d873663e80855047",
    "paramBoolean" : {
        "ageMax" : true,
        "ageMin" : true,
        "qualityBlur" : true,
        "qualityOcclusionMouth" : true,
        "locationWidthMin" : false,
        "locationHeightMin" : false
    },
    "filterCount" : 2,
    "filterKey" : [ 
        "locationWidthMin", 
        "locationHeightMin"
    ],
    "cdt" : ISODate("2020-12-29T11:29:03.651+08:00")
}

现在是想要统计，detectFilterDetail表的detectFilterDetail.paramBoolean.qualityOcclusionMouse为true的分布，也就是和上一个统计一样，统计每个分段里面，为true的数量有多少

琢磨了一会，大概实现sql如下：

db.detectFilterDetail.aggregate([
        {$match: {"paramRecordId": "5feaa273d873663e80855047", "paramBoolean.qualityOcclusionMouth": true}},
        {$project: {flag: "$paramBoolean.qualityOcclusionMouth", ftk: "$faceToken"}},
        {$lookup:{
            from:"detectOriginalData",
            localField:"ftk",
            foreignField: "_id",
            as: "f_ftk"}
        },
        {$project: {flag: 1, ftk: 1, val: "$f_ftk.faceDetail.quality.occlusion.mouth"}},
        {$unwind: "$val"},
        {$group: {
            "_id": {
                $subtract: [
                {$subtract: ["$val", 0]},
                {$mod: [{$subtract: ["$val", 0]}, 0.1]}
                ]
            },
            ftkList: {$push: "$ftk"},
            ftkCount: {$sum: 1}
        }},
        //{$group: {"_id": null, count: {$sum: 1}}}
        {$sort: {_id: -1}}
])

结果如下：

 PS：暂时做个记录，后续再稍微解释各个语句的大概作用