MongoDB在每日分组中聚合

Question

我在mongo中有一些看起来像这样的文档:

{
  _id : ObjectId("..."),
  "make" : "Nissan",
  ..
},
{
  _id : ObjectId("..."),
  "make" : "Nissan",
  "saleDate" :  ISODate("2013-04-10T12:39:50.676Z"),
  ..
}

理想情况下,我希望能够计算出每天销售的车辆数量.然后,我想要查看今天或者过去七天这样的窗口.

我能用一些丑陋的代码完成日常视图

db.inventory.aggregate(
  { $match : { "saleDate" : { $gte: ISODate("2013-04-10T00:00:00.000Z"), $lt: ISODate("2013-04-11T00:00:00.000Z")  } } } ,
  { $group : { _id : { make : "$make", saleDayOfMonth : { $dayOfMonth : "$saleDate" } }, cnt : { $sum : 1 } } }
)

然后产生结果

{
  "result" : [
    {
      "_id" : {
        "make" : "Nissan",
        "saleDayOfMonth" : 10
      },
      "cnt" : 2
    },
    {
      "_id" : {
        "make" : "Toyota",
        "saleDayOfMonth" : 10
      },
      "cnt" : 4
    },
  ],
  "ok" : 1
}

所以这没关系,但我更希望不必更改查询中的两个日期时间值.然后,正如我上面提到的,我希望能够运行此查询(再次,无需每次都修改它),并查看在过去一周内按天分类的相同结果.

哦,这是我一直用于查询的示例数据

db.inventory.save({"make" : "Nissan","saleDate" :  ISODate("2013-04-10T12:39:50.676Z")});
db.inventory.save({"make" : "Nissan"});
db.inventory.save({"make" : "Nissan","saleDate" :  ISODate("2013-04-10T11:39:50.676Z")});
db.inventory.save({"make" : "Toyota","saleDate" :  ISODate("2013-04-09T11:39:50.676Z")});
db.inventory.save({"make" : "Toyota","saleDate" :  ISODate("2013-04-10T11:38:50.676Z")});
db.inventory.save({"make" : "Toyota","saleDate" :  ISODate("2013-04-10T11:37:50.676Z")});
db.inventory.save({"make" : "Toyota","saleDate" :  ISODate("2013-04-10T11:36:50.676Z")});
db.inventory.save({"make" : "Toyota","saleDate" :  ISODate("2013-04-10T11:35:50.676Z")});

凯文,提前谢谢

Answer 1

在Mongo 2.8 RC2中有一个新的数据聚合运算符:$ dateToString,可用于按日分组,结果只有一个"YYYY-MM-DD":

文档中的示例:

db.sales.aggregate(
  [
     {
         $project: {
                yearMonthDay: { $dateToString: { format: "%Y-%m-%d", date: "$date" } },
                time: { $dateToString: { format: "%H:%M:%S:%L", date: "$date" } }
         }
     }
  ]
)

将导致:

{ "_id" : 1, "yearMonthDay" : "2014-01-01", "time" : "08:15:39:736" }

Answer 2

更新 更新后的答案基于3.6中的日期功能,并显示如何在没有销售的范围内包含日期(包括我在内的任何原始答案中未提及).

样本数据:

db.inventory.find()
{ "_id" : ObjectId("5aca30eefa1585de22d7095f"), "make" : "Nissan", "saleDate" : ISODate("2013-04-10T12:39:50.676Z") }
{ "_id" : ObjectId("5aca30eefa1585de22d70960"), "make" : "Nissan" }
{ "_id" : ObjectId("5aca30effa1585de22d70961"), "make" : "Nissan", "saleDate" : ISODate("2013-04-10T11:39:50.676Z") }
{ "_id" : ObjectId("5aca30effa1585de22d70962"), "make" : "Toyota", "saleDate" : ISODate("2013-04-09T11:39:50.676Z") }
{ "_id" : ObjectId("5aca30effa1585de22d70963"), "make" : "Toyota", "saleDate" : ISODate("2013-04-10T11:38:50.676Z") }
{ "_id" : ObjectId("5aca30effa1585de22d70964"), "make" : "Toyota", "saleDate" : ISODate("2013-04-10T11:37:50.676Z") }
{ "_id" : ObjectId("5aca30effa1585de22d70965"), "make" : "Toyota", "saleDate" : ISODate("2013-04-10T11:36:50.676Z") }
{ "_id" : ObjectId("5aca30effa1585de22d70966"), "make" : "Toyota", "saleDate" : ISODate("2013-04-10T11:35:50.676Z") }
{ "_id" : ObjectId("5aca30f9fa1585de22d70967"), "make" : "Toyota", "saleDate" : ISODate("2013-04-11T11:35:50.676Z") }
{ "_id" : ObjectId("5aca30fffa1585de22d70968"), "make" : "Toyota", "saleDate" : ISODate("2013-04-13T11:35:50.676Z") }
{ "_id" : ObjectId("5aca3921fa1585de22d70969"), "make" : "Honda", "saleDate" : ISODate("2013-04-13T00:00:00Z") }

定义startDate和endDate作为变量并在聚合中使用它们:

startDate = ISODate("2013-04-08T00:00:00Z");
endDate = ISODate("2013-04-15T00:00:00Z");

db.inventory.aggregate([
  { $match : { "saleDate" : { $gte: startDate, $lt: endDate} } },
  {$addFields:{
     saleDate:{$dateFromParts:{
                  year:{$year:"$saleDate"},
                  month:{$month:"$saleDate"},
                  day:{$dayOfMonth:"$saleDate"}
     }},
     dateRange:{$map:{
        input:{$range:[0, {$subtract:[endDate,startDate]}, 1000*60*60*24]},
        in:{$add:[startDate, "$$this"]}
     }}
  }},
  {$unwind:"$dateRange"},
  {$group:{
     _id:"$dateRange", 
     sales:{$push:{$cond:[
                {$eq:["$dateRange","$saleDate"]},
                {make:"$make",count:1},
                {count:0}
     ]}}
  }},
  {$sort:{_id:1}},
  {$project:{
     _id:0,
     saleDate:"$_id",
     totalSold:{$sum:"$sales.count"},
     byBrand:{$arrayToObject:{$reduce:{
        input: {$filter:{input:"$sales",cond:"$$this.count"}},
        initialValue: {$map:{input:{$setUnion:["$sales.make"]}, in:{k:"$$this",v:0}}}, 
        in:{$let:{
           vars:{t:"$$this",v:"$$value"},
           in:{$map:{
              input:"$$v",
              in:{
                 k:"$$this.k",
                 v:{$cond:[
                     {$eq:["$$this.k","$$t.make"]},
                     {$add:["$$this.v","$$t.count"]},
                     "$$this.v"
                 ]}
              }
           }}
        }}
     }}}
  }}
])

在样本数据上,这给出了结果:

{ "saleDate" : ISODate("2013-04-08T00:00:00Z"), "totalSold" : 0, "byBrand" : {  } }
{ "saleDate" : ISODate("2013-04-09T00:00:00Z"), "totalSold" : 1, "byBrand" : { "Toyota" : 1 } }
{ "saleDate" : ISODate("2013-04-10T00:00:00Z"), "totalSold" : 6, "byBrand" : { "Nissan" : 2, "Toyota" : 4 } }
{ "saleDate" : ISODate("2013-04-11T00:00:00Z"), "totalSold" : 1, "byBrand" : { "Toyota" : 1 } }
{ "saleDate" : ISODate("2013-04-12T00:00:00Z"), "totalSold" : 0, "byBrand" : {  } }
{ "saleDate" : ISODate("2013-04-13T00:00:00Z"), "totalSold" : 2, "byBrand" : { "Honda" : 1, "Toyota" : 1 } }
{ "saleDate" : ISODate("2013-04-14T00:00:00Z"), "totalSold" : 0, "byBrand" : {  } }

这种聚合也可以用两个$group阶段完成,$project而不是简单而不是$group复杂$project.这里是:

db.inventory.aggregate([
   {$match : { "saleDate" : { $gte: startDate, $lt: endDate} } },
   {$addFields:{saleDate:{$dateFromParts:{year:{$year:"$saleDate"}, month:{$month:"$saleDate"}, day:{$dayOfMonth : "$saleDate" }}},dateRange:{$map:{input:{$range:[0, {$subtract:[endDate,startDate]}, 1000*60*60*24]},in:{$add:[startDate, "$$this"]}}}}},
   {$unwind:"$dateRange"},
   {$group:{
      _id:{date:"$dateRange",make:"$make"},
      count:{$sum:{$cond:[{$eq:["$dateRange","$saleDate"]},1,0]}}
   }},
   {$group:{
      _id:"$_id.date",
      total:{$sum:"$count"},
      byBrand:{$push:{k:"$_id.make",v:{$sum:"$count"}}}
   }},
   {$sort:{_id:1}},
   {$project:{
      _id:0,
      saleDate:"$_id",
      totalSold:"$total",
      byBrand:{$arrayToObject:{$filter:{input:"$byBrand",cond:"$$this.v"}}}
   }}
])

相同的结果:

{ "saleDate" : ISODate("2013-04-08T00:00:00Z"), "totalSold" : 0, "byBrand" : { "Honda" : 0, "Toyota" : 0, "Nissan" : 0 } }
{ "saleDate" : ISODate("2013-04-09T00:00:00Z"), "totalSold" : 1, "byBrand" : { "Honda" : 0, "Nissan" : 0, "Toyota" : 1 } }
{ "saleDate" : ISODate("2013-04-10T00:00:00Z"), "totalSold" : 6, "byBrand" : { "Honda" : 0, "Toyota" : 4, "Nissan" : 2 } }
{ "saleDate" : ISODate("2013-04-11T00:00:00Z"), "totalSold" : 1, "byBrand" : { "Toyota" : 1, "Honda" : 0, "Nissan" : 0 } }
{ "saleDate" : ISODate("2013-04-12T00:00:00Z"), "totalSold" : 0, "byBrand" : { "Toyota" : 0, "Nissan" : 0, "Honda" : 0 } }
{ "saleDate" : ISODate("2013-04-13T00:00:00Z"), "totalSold" : 2, "byBrand" : { "Honda" : 1, "Toyota" : 1, "Nissan" : 0 } }
{ "saleDate" : ISODate("2013-04-14T00:00:00Z"), "totalSold" : 0, "byBrand" : { "Toyota" : 0, "Honda" : 0, "Nissan" : 0 } }

基于2.6的原始答案:

您可能需要查看我的博客文章,了解如何在此处处理聚合框架中的各种日期操作.

您可以做的是使用$project阶段将日期截断为每日分辨率,然后对整个数据集(或仅部分)进行聚合,并按日期和汇总进行聚合.

根据您的样本数据,假设您想知道今年按日期销售的汽车数量:

match={"$match" : {
               "saleDate" : { "$gt" : new Date(2013,0,1) }
      }
};

proj1={"$project" : {
        "_id" : 0,
        "saleDate" : 1,
        "make" : 1,
        "h" : {
            "$hour" : "$saleDate"
        },
        "m" : {
            "$minute" : "$saleDate"
        },
        "s" : {
            "$second" : "$saleDate"
        },
        "ml" : {
            "$millisecond" : "$saleDate"
        }
    }
};

proj2={"$project" : {
        "_id" : 0,
        "make" : 1,
        "saleDate" : {
            "$subtract" : [
                "$saleDate",
                {
                    "$add" : [
                        "$ml",
                        {
                            "$multiply" : [
                                "$s",
                                1000
                            ]
                        },
                        {
                            "$multiply" : [
                                "$m",
                                60,
                                1000
                            ]
                        },
                        {
                            "$multiply" : [
                                "$h",
                                60,
                                60,
                                1000
                            ]
                        }
                    ]
                }
            ]
        }
    }
};

group={"$group" : {
        "_id" : {
            "m" : "$make",
            "d" : "$saleDate"
        },
        "count" : {
            "$sum" : 1
        }
    }
};

现在运行聚合为您提供:

db.inventory.aggregate(match, proj1, proj2, group)
{
    "result" : [
        {
            "_id" : {
                "m" : "Toyota",
                "d" : ISODate("2013-04-10T00:00:00Z")
            },
            "count" : 4
        },
        {
            "_id" : {
                "m" : "Toyota",
                "d" : ISODate("2013-04-09T00:00:00Z")
            },
            "count" : 1
        },
        {
            "_id" : {
                "m" : "Nissan",
                "d" : ISODate("2013-04-10T00:00:00Z")
            },
            "count" : 2
        }
    ],
    "ok" : 1
}

您可以添加另一个{$ project}阶段以完成输出,并且您可以添加{$ sort}步骤,但基本上每个日期,对于每个日期,您可以计算出已售出的数量.