MongoDB聚合运算符：$denseRank

$denseRank聚合运算符返回在$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 }