为什么mongoDB计数不正确？

Question

我的数据如下所示：

 {
       "_id":ObjectId("516fbf68067323ce2ea5b4b8"),
       "title":"GVPKFlFIXdLUaLM",
       "release_year":1913,
       "country_of_origin":"sWdXLXUfun",
       "length_in_minutes":147,
       "plot_summary":"bmwYkyyiSymHJYoXEPauPNjdKoFANDgcDImVelDGPuPJmLhyWOuNXjurNyGp",
       "director":"rNDFhhxGIo",
       "language":"oYeWskT",
       "popularity":5.2,
       "genre":"jDwdaMhuT",
       "actors":[
          {
             "id":2740,
             "name":"actor2740",
             "dob":1989,
             "alt_name":"PBpXPqJwmftpfcR",
             "pob":"DFoxETDuhAdDGNE"
          },
          {
             "id":3143,
             "name":"actor3143",
             "dob":1953,
             "alt_name":"AHnVvTviSKuvNZO",
             "pob":"KBUdvbnvNkXmddk"
          }
       ]
    }

一开始我以为Mongo里有个bug。我尝试使用聚合函数来解决一个假设的业务问题。(编辑:我并不是说我解决了一个mongo问题，也不是说我希望人们帮助我创建一个算法，只是为了确认MongoDB的一个潜在bug )

db.movies.aggregate([{$match:{popularity:{$gte:7.3}}},
     {$project:{actors:1,popularity:1}},
     {$unwind:"$actors"},
     {$group:{_id:"$actors.id",avgPop:{$avg:"$popularity"},
              docsByTag : { $sum : 1 }, popSum:{$sum:"$popularity"}}},
    {$match:{avgPop:{$gte:7.5}}}]);

我关注的结果(编辑$sum: 1而不是0)

{
            "_id" : 1383,
            "avgPop" : 8.772857142857141,
            "docsByTag" : 28,
            "popSum" : 245.63999999999996
        },

但是当我用手验证结果时。

db.movies.find({"actors.name":"actor1383"},{title:1,popularity:1,_id: 0})

{ "title" : "kZFfBwtAfVNobEq", "popularity" : 8.54 }
{ "title" : "kyOeSorYUWyJmjK", "popularity" : 8.11 }
{ "title" : "rvSdJCgEkkpYgFB", "popularity" : 8.36 }
{ "title" : "SwcgHTgZqqcYJja", "popularity" : 8.68 }
{ "title" : "XmcidmdwtDlNoKw", "popularity" : 7.33 }
{ "title" : "gwThvrWifoKCvyG", "popularity" : 7.94 }
{ "title" : "RdUsAFIxTnntTZR", "popularity" : 6.91 }
{ "title" : "RwhJlORFdvtDtpO", "popularity" : 5.13 }
{ "title" : "TuDfcWhNkQFeycl", "popularity" : 9.93 }
{ "title" : "xTVkwnyvftKQraC", "popularity" : 7.27 }
{ "title" : "HYMjUFlSXgnWVTx", "popularity" : 6.94 }
{ "title" : "ZPPyAUdGMeVQhbK", "popularity" : 8.48 }
{ "title" : "kEITAiMMrWTECGM", "popularity" : 9.42 }
{ "title" : "asNsLYKjvHlihXZ", "popularity" : 9.86 }
{ "title" : "ctEmciXPhbMtspt", "popularity" : 8.85 }
{ "title" : "DHjFtctccwDHtlf", "popularity" : 5.5 }
{ "title" : "ElUqbLqkoKrJPVl", "popularity" : 8.26 }
{ "title" : "XdTCieKsWtTbfZa", "popularity" : 5.72 }
{ "title" : "EeNqOPSuKiHuWRs", "popularity" : 5.91 }
{ "title" : "YgysqxcesvPryMY", "popularity" : 6.05 }
{ "title" : "eARvpGydsWilquc", "popularity" : 7.34 }
{ "title" : "NDpdkhSUfePDYjH", "popularity" : 7.28 }
{ "title" : "wUGKLBwijftQKgU", "popularity" : 8.97 }
{ "title" : "UHVGUmAcjBgAPBp", "popularity" : 7.44 }
{ "title" : "NKTKEKfbxFrudVi", "popularity" : 9.4 }
{ "title" : "AeByTKwsEQuQBYG", "popularity" : 8.97 }
{ "title" : "nZskARfGbhYRxdY", "popularity" : 9.16 }
{ "title" : "nBenZrikXFFrrnq", "popularity" : 7.58 }
{ "title" : "GdEFwoKgqjhHvjM", "popularity" : 6.3 }
{ "title" : "grpKTHgnYcDNyXH", "popularity" : 7.16 }
{ "title" : "hXhOqknvjIYJIaT", "popularity" : 5.24 }
{ "title" : "rggTJENnVeuqQVI", "popularity" : 9.95 }
{ "title" : "ABvGVFHkgOumMPO", "popularity" : 9.56 }
{ "title" : "baVkepHniIURUFH", "popularity" : 9.28 }
{ "title" : "PUYXlhPwbanMDmT", "popularity" : 9.6 }
{ "title" : "IJbqonvsVeorDMv", "popularity" : 7.82 }
{ "title" : "iAhyATKYpCVjtMw", "popularity" : 5.88 }
{ "title" : "uDECLFQGTOVnyvC", "popularity" : 6.25 }
{ "title" : "rTwfCYLfLwgPcbH", "popularity" : 8.38 }
{ "title" : "GRyKjecBHQhvYJk", "popularity" : 9.11 }
{ "title" : "GyEaSHoprUvGmZM", "popularity" : 9.92 } 

它给出了大于或等于7.3的27个元素的子集

{ "title" : "kZFfBwtAfVNobEq", "popularity" : 8.54 }
{ "title" : "kyOeSorYUWyJmjK", "popularity" : 8.11 }
{ "title" : "rvSdJCgEkkpYgFB", "popularity" : 8.36 }
{ "title" : "SwcgHTgZqqcYJja", "popularity" : 8.68 }
{ "title" : "XmcidmdwtDlNoKw", "popularity" : 7.33 }
{ "title" : "gwThvrWifoKCvyG", "popularity" : 7.94 }
{ "title" : "TuDfcWhNkQFeycl", "popularity" : 9.93 }
{ "title" : "ZPPyAUdGMeVQhbK", "popularity" : 8.48 }
{ "title" : "kEITAiMMrWTECGM", "popularity" : 9.42 }
{ "title" : "asNsLYKjvHlihXZ", "popularity" : 9.86 }
{ "title" : "ctEmciXPhbMtspt", "popularity" : 8.85 }
{ "title" : "ElUqbLqkoKrJPVl", "popularity" : 8.26 }
{ "title" : "eARvpGydsWilquc", "popularity" : 7.34 }
{ "title" : "wUGKLBwijftQKgU", "popularity" : 8.97 }
{ "title" : "UHVGUmAcjBgAPBp", "popularity" : 7.44 }
{ "title" : "NKTKEKfbxFrudVi", "popularity" : 9.4 }
{ "title" : "AeByTKwsEQuQBYG", "popularity" : 8.97 }
{ "title" : "nZskARfGbhYRxdY", "popularity" : 9.16 }
{ "title" : "nBenZrikXFFrrnq", "popularity" : 7.58 }
{ "title" : "rggTJENnVeuqQVI", "popularity" : 9.95 }
{ "title" : "ABvGVFHkgOumMPO", "popularity" : 9.56 }
{ "title" : "baVkepHniIURUFH", "popularity" : 9.28 }
{ "title" : "PUYXlhPwbanMDmT", "popularity" : 9.6 }
{ "title" : "IJbqonvsVeorDMv", "popularity" : 7.82 }
{ "title" : "rTwfCYLfLwgPcbH", "popularity" : 8.38 }
{ "title" : "GRyKjecBHQhvYJk", "popularity" : 9.11 }
{ "title" : "GyEaSHoprUvGmZM", "popularity" : 9.92 }

比聚合函数小1。

所以我认为aggregate是被破坏的，并将其重写为mapReduce

// make sure we're using the right db; this is the same as "use aggdb;" in shell
db = db.getSiblingDB("recommendations"); //Put your MongoLab database name here.



var mapFunc2 = function() {
                       for (var idx = 0; idx < this.actors.length; idx++) {
                           var key = this.actors[idx].id;
                           var value = {
                                         count: 1,
                                         pop: this.popularity
                                       };
                           emit(key, value);
                       }
                    };

var reduceFunc2 = function(keyActor, countObjVals) {


                     reducedVal = { actor: keyActor, count: 0, pop: 0, pop_list : [] };

                     for (var idx = 0; idx < countObjVals.length; idx++) {
                         reducedVal.count += countObjVals[idx].count;
                         reducedVal.pop += countObjVals[idx].pop;
                         reducedVal.pop_list = reducedVal.pop_list.concat(countObjVals[idx].pop);
                     }

                     return reducedVal;
                  };

var finalizeFunc2 = function (key, reducedVal) {

                       reducedVal.avg = reducedVal.pop/reducedVal.count;

                       return reducedVal;

                    };


result = db.movies.mapReduce( mapFunc2,
                     reduceFunc2,
                     {
                       out: { merge: "mre" },
                       query: { popularity:
                                  { $gte: 7.3 }
                              },
                       finalize: finalizeFunc2
                     }
                   )
cursor = db.map_reduce_example.find()                  

while(cursor.hasNext()){
    printjson(cursor.next());

}

结果又差了一分。

{
    "_id" : 1383,
    "value" : {
        "actor" : 1383,
        "count" : 28,
        "pop" : 245.63999999999996,
        "avg" : 8.772857142857141
    }
}

所以我开始调试，当涉及到保存数组中每个单独电影的受欢迎程度时，我看到了一些奇怪的事情。

{ "_id“：1，"value”：{ "actor“：1，"count”：13，"pop“：114.97，"pop_list”：7.47，8.52，9.95，17.4，7.4，19.43，8.46，17.21，9.24，9.89，"avg“：8.843846153846155 }}

在这里，奇怪的是计数是13，而元素的数量是10。

7.4 7.4
7.47    7.47
8.07    1
8.14    2
8.46    8.46
8.52    8.52
9.14    1
9.24    9.24
9.26    2
9.57    3
9.86    3
9.89    9.89
9.95    9.95

其中1,2,3对应于

1   17.21=9.14+8.07
2   17.4=8.14+9.26
3   19.43=9.57+9.86

{ "_id“：2，"value”：{ "actor“：2，"count”：14，"pop“：120.91999999999999，"pop_list”：35.239999999999995，7.58，35.56，9.35，25.839999999999996，7.35，"avg“：8.637142857142857 }}然而，上面的公式完全不清楚，因为我所有的平均值都只有2位小数精度。

在这一点上真的很困惑。我相信这篇文章会对其他被同样类型的计数问题困扰的人有所帮助。

Answer 1

聚合框架和mapreduce都犯了“错误”的可能性很小，所以我想请您验证一下，您是如何将它们的结果与您的期望进行比较的。

在聚合中，您可以对"actors.id"字段进行分组。但是您手动验证的查询是：

db.movies.find({"actors.name":"actor1383"},{title:1,popularity:1,_id: 0})

有证据表明你的"actors.name“和"actors.id”字段100%匹配吗？

当你做浮点运算时，高于2位的精度是正常的，不用担心。这与要求5和10的平均值并得到7.5没有什么不同，即使5和10在小数点后都没有数字。

还有另一个“差异”可能来自的地方。如果您有这样的文档：

{人气: 7.6，演员：[{ id: 1383，... }，{ id: 1383，... ... }

您现在只有一个顶层文档，但当您展开actors数组时，您现在有两个文档，这两个文档都具有actor.id 1383。您能验证每个参与者在每个顶级文档中只出现一次吗？如果不是，这将导致您看到的差异。