$denseRank
聚合运算符返回在$setWindowFields
阶段分区中文档的排名,排名的顺序由$setWindowFields
阶段sortBy
的字段值决定。
语法
{ $denseRank: { } }
$denseRank
不需要任何参数。
使用
$rank
和$denseRank
的不同点在于他们处理排序字段重复值的方式不同,如:排序字段的值有:7、9、9、10:
- $denseRank排名的值为1、2、2、3,重复值9的排名都是2,10的值则排名为3,所有的排名值都是连续的。
$rank
排名的值为1、2、2、4,重复值9的排名都为2,但是10的排名为4,中间有一个跳过的排名3。
对于排序字段为值为null
或字段值缺失的情况,排名基于BSON比较顺序。
举例
使用下面的脚本创建cakeSales
集合:
db.cakeSales.insertMany( [
{ _id: 0, type: "chocolate", orderDate: new Date("2020-05-18T14:10:30Z"),
state: "CA", price: 13, quantity: 120 },
{ _id: 1, type: "chocolate", orderDate: new Date("2021-03-20T11:30:05Z"),
state: "WA", price: 14, quantity: 140 },
{ _id: 2, type: "vanilla", orderDate: new Date("2021-01-11T06:31:15Z"),
state: "CA", price: 12, quantity: 145 },
{ _id: 3, type: "vanilla", orderDate: new Date("2020-02-08T13:13:23Z"),
state: "WA", price: 13, quantity: 104 },
{ _id: 4, type: "strawberry", orderDate: new Date("2019-05-18T16:09:01Z"),
state: "CA", price: 41, quantity: 162 },
{ _id: 5, type: "strawberry", orderDate: new Date("2019-01-08T06:12:03Z"),
state: "WA", price: 43, quantity: 134 }
] )
按整数字段进行密集排名分区
下面的例子在$setWindowFields
阶段使用$denseRank
依据quantity
得出各州蛋糕销售的密集等级
db.cakeSales.aggregate( [
{
$setWindowFields: {
partitionBy: "$state",
sortBy: { quantity: -1 },
output: {
denseRankQuantityForState: {
$denseRank: {}
}
}
}
}
] )
partitionBy: "state"
依据state
字段对文档进行分区,有CA
和WA
两个分区sortBy:{quantity:-1}
依据quantity
对分区内的文档按照从大到小进行排序,quantity
最大的排在最前面output
使用$densRank
将quantity
字段的密度排名赋予denseRankOrderDateForState
字段,结果如下:
{ "_id" : 4, "type" : "strawberry", "orderDate" : ISODate("2019-05-18T16:09:01Z"),
"state" : "CA", "price" : 41, "quantity" : 162, "denseRankQuantityForState" : 1 }
{ "_id" : 2, "type" : "vanilla", "orderDate" : ISODate("2021-01-11T06:31:15Z"),
"state" : "CA", "price" : 12, "quantity" : 145, "denseRankQuantityForState" : 2 }
{ "_id" : 0, "type" : "chocolate", "orderDate" : ISODate("2020-05-18T14:10:30Z"),
"state" : "CA", "price" : 13, "quantity" : 120, "denseRankQuantityForState" : 3 }
{ "_id" : 1, "type" : "chocolate", "orderDate" : ISODate("2021-03-20T11:30:05Z"),
"state" : "WA", "price" : 14, "quantity" : 140, "denseRankQuantityForState" : 1 }
{ "_id" : 5, "type" : "strawberry", "orderDate" : ISODate("2019-01-08T06:12:03Z"),
"state" : "WA", "price" : 43, "quantity" : 134, "denseRankQuantityForState" : 2 }
{ "_id" : 3, "type" : "vanilla", "orderDate" : ISODate("2020-02-08T13:13:23Z"),
"state" : "WA", "price" : 13, "quantity" : 104, "denseRankQuantityForState" : 3 }
按日期字段进行密集排名分区
下面的例子在$setWindowFields
阶段使用$denseRank
依据orderDate
得出各州蛋糕销售的密集等级
db.cakeSales.aggregate( [
{
$setWindowFields: {
partitionBy: "$state",
sortBy: { orderDate: 1 },
output: {
denseRankOrderDateForState: {
$denseRank: {}
}
}
}
}
] )
partitionBy: "state"
依据state
字段对文档进行分区,有CA
和WA
两个分区sortBy:{orderDate:-1}
依据orderDate
对分区内的文档按照从小到大进行排序,orderDate
最早的排在最前面output
使用$densRank
将orderDate
字段的密度排名赋予denseRankOrderDateForState
字段,结果如下:
{ "_id" : 4, "type" : "strawberry", "orderDate" : ISODate("2019-05-18T16:09:01Z"),
"state" : "CA", "price" : 41, "quantity" : 162, "denseRankOrderDateForState" : 1 }
{ "_id" : 0, "type" : "chocolate", "orderDate" : ISODate("2020-05-18T14:10:30Z"),
"state" : "CA", "price" : 13, "quantity" : 120, "denseRankOrderDateForState" : 2 }
{ "_id" : 2, "type" : "vanilla", "orderDate" : ISODate("2021-01-11T06:31:15Z"),
"state" : "CA", "price" : 12, "quantity" : 145, "denseRankOrderDateForState" : 3 }
{ "_id" : 5, "type" : "strawberry", "orderDate" : ISODate("2019-01-08T06:12:03Z"),
"state" : "WA", "price" : 43, "quantity" : 134, "denseRankOrderDateForState" : 1 }
{ "_id" : 3, "type" : "vanilla", "orderDate" : ISODate("2020-02-08T13:13:23Z"),
"state" : "WA", "price" : 13, "quantity" : 104, "denseRankOrderDateForState" : 2 }
{ "_id" : 1, "type" : "chocolate", "orderDate" : ISODate("2021-03-20T11:30:05Z"),
"state" : "WA", "price" : 14, "quantity" : 140, "denseRankOrderDateForState" : 3 }
重复值、null和字段缺失值的密度排名
创建一个cakeSalesWithDuplicates
集合:
db.cakeSalesWithDuplicates.insertMany( [
{ _id: 0, type: "chocolate", orderDate: new Date("2020-05-18T14:10:30Z"),
state: "CA", price: 13, quantity: 120 },
{ _id: 1, type: "chocolate", orderDate: new Date("2021-03-20T11:30:05Z"),
state: "WA", price: 14, quantity: 140 },
{ _id: 2, type: "vanilla", orderDate: new Date("2021-01-11T06:31:15Z"),
state: "CA", price: 12, quantity: 145 },
{ _id: 3, type: "vanilla", orderDate: new Date("2020-02-08T13:13:23Z"),
state: "WA", price: 13, quantity: 104 },
{ _id: 4, type: "strawberry", orderDate: new Date("2019-05-18T16:09:01Z"),
state: "CA", price: 41, quantity: 162 },
{ _id: 5, type: "strawberry", orderDate: new Date("2019-01-08T06:12:03Z"),
state: "WA", price: 43, quantity: 134 },
{ _id: 6, type: "strawberry", orderDate: new Date("2020-01-08T06:12:03Z"),
state: "WA", price: 41, quantity: 134 },
{ _id: 7, type: "strawberry", orderDate: new Date("2020-01-01T06:12:03Z"),
state: "WA", price: 34, quantity: 134 },
{ _id: 8, type: "strawberry", orderDate: new Date("2020-01-02T06:12:03Z"),
state: "WA", price: 40, quantity: 134 },
{ _id: 9, type: "strawberry", orderDate: new Date("2020-05-11T16:09:01Z"),
state: "CA", price: 39, quantity: 162 },
{ _id: 10, type: "strawberry", orderDate: new Date("2020-05-11T16:09:01Z"),
state: "CA", price: 39, quantity: null },
{ _id: 11, type: "strawberry", orderDate: new Date("2020-05-11T16:09:01Z"),
state: "CA", price: 39 }
] )
在集合中:
- 蛋糕销售的地点有加利福尼亚州(CA)和华盛顿州(WA)
- 文档6到8与文档5的
quantity
和state
相同 - 文档9与文档4的
quantity
和state
相同 - 文档10的
quantity
为null
- 文档11的
quantity
字段缺失
下面的例子在$setWindowFields
阶段使用$denseRank
依据quantity
对cakeSalesWithDuplicates
集合文档进行密度排名:
db.cakeSalesWithDuplicates.aggregate( [
{
$setWindowFields: {
partitionBy: "$state",
sortBy: { quantity: -1 },
output: {
denseRankQuantityForState: {
$denseRank: {}
}
}
}
}
] )
在本例中:
partitionBy: "state"
依据state
字段对文档进行分区,有CA
和WA
两个分区sortBy:{quantity:-1}
依据quantity
对分区内的文档按照从大到小进行排序,quantity
最大的排在最前面output
使用$densRank
将quantity
字段的密度排名赋予denseRankOrderDateForState
字段,结果如下:
{ "_id" : 4, "type" : "strawberry", "orderDate" : ISODate("2019-05-18T16:09:01Z"),
"state" : "CA", "price" : 41, "quantity" : 162, "denseRankQuantityForState" : 1 }
{ "_id" : 9, "type" : "strawberry", "orderDate" : ISODate("2020-05-11T16:09:01Z"),
"state" : "CA", "price" : 39, "quantity" : 162, "denseRankQuantityForState" : 1 }
{ "_id" : 2, "type" : "vanilla", "orderDate" : ISODate("2021-01-11T06:31:15Z"),
"state" : "CA", "price" : 12, "quantity" : 145, "denseRankQuantityForState" : 2 }
{ "_id" : 0, "type" : "chocolate", "orderDate" : ISODate("2020-05-18T14:10:30Z"),
"state" : "CA", "price" : 13, "quantity" : 120, "denseRankQuantityForState" : 3 }
{ "_id" : 10, "type" : "strawberry", "orderDate" : ISODate("2020-05-11T16:09:01Z"),
"state" : "CA", "price" : 39, "quantity" : null, "denseRankQuantityForState" : 4 }
{ "_id" : 11, "type" : "strawberry", "orderDate" : ISODate("2020-05-11T16:09:01Z"),
"state" : "CA", "price" : 39, "denseRankQuantityForState" : 5 }
{ "_id" : 1, "type" : "chocolate", "orderDate" : ISODate("2021-03-20T11:30:05Z"),
"state" : "WA", "price" : 14, "quantity" : 140, "denseRankQuantityForState" : 1 }
{ "_id" : 5, "type" : "strawberry", "orderDate" : ISODate("2019-01-08T06:12:03Z"),
"state" : "WA", "price" : 43, "quantity" : 134, "denseRankQuantityForState" : 2 }
{ "_id" : 6, "type" : "strawberry", "orderDate" : ISODate("2020-01-08T06:12:03Z"),
"state" : "WA", "price" : 41, "quantity" : 134, "denseRankQuantityForState" : 2 }
{ "_id" : 7, "type" : "strawberry", "orderDate" : ISODate("2020-01-01T06:12:03Z"),
"state" : "WA", "price" : 34, "quantity" : 134, "denseRankQuantityForState" : 2 }
{ "_id" : 8, "type" : "strawberry", "orderDate" : ISODate("2020-01-02T06:12:03Z"),
"state" : "WA", "price" : 40, "quantity" : 134, "denseRankQuantityForState" : 2 }
{ "_id" : 3, "type" : "vanilla", "orderDate" : ISODate("2020-02-08T13:13:23Z"),
"state" : "WA", "price" : 13, "quantity" : 104, "denseRankQuantityForState" : 3 }
从上面的结果可以看出:
- 数量和状态相同的文件具有相同的排名,排名是连续的
- 在 CA 分区的输出中,数量为空的文档和数量为缺失的文档排序最低。这种排序是 BSON 比较顺序的结果,在本例中,将空值和缺失值排序在数字值之后。