更新更新后的答案基于 3.6 中的日期功能，并显示了如何在没有销售的范围内包含日期（包括我的任何原始答案中都没有提到）。
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") }
将startDate 和endDate 定义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"
]}
}
}}
}}
}}}
}}
])
在样本数据上给出结果：
{ "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" : { } }
这种聚合也可以通过两个$group 阶段和一个简单的$project 而不是$group 和一个复杂的$project 来完成。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"}}}
}}
])
同样的结果：
{ "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：
您可能想看看我的博客文章，了解如何在聚合框架here 中处理各种日期操作。
您可以做的是使用$project 阶段将您的日期截断为每日分辨率，然后对整个数据集（或只是其中的一部分）运行聚合，并按日期聚合。
使用您的示例数据，假设您想知道您在今年按品牌销售了多少辆汽车：
match={"$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" : {
"$sum" : 1
}
}
};
现在运行聚合会给你：
db.inventory.aggregate(match, proj1, proj2, group)
{
"result" : [
{
"_id" : {
"m" : "Toyota",
"d" : ISODate("2013-04-10T00:00:00Z")
},
"count" : 4
},
{
"_id" : {
"m" : "Toyota",
"d" : ISODate("2013-04-09T00:00:00Z")
},
"count" : 1
},
{
"_id" : {
"m" : "Nissan",
"d" : ISODate("2013-04-10T00:00:00Z")
},
"count" : 2
}
],
"ok" : 1
}
您可以添加另一个 {$project} 阶段来美化输出，您可以添加一个 {$sort} 步骤，但基本上对于每个日期，每个让您计算售出的数量。