MongoDB 中，聚合（Aggregation）是一种用于处理和分析数据的强大工具。聚合框架提供了一组操作符，可以对集合中的文档进行分组、筛选、排序、转换等操作，以生成新的结果集。聚合操作可以用于复杂的数据处理和分析任务，例如统计、分组计算、数据透视等。
聚合框架的核心是管道（Pipeline），它由一系列操作符组成，每个操作符都会对输入文档进行处理，并将结果传递给下一个操作符。聚合操作符可以按照顺序组合，以实现复杂的数据处理逻辑。
以下是一些常用的聚合操作符：
$match：用于筛选文档，类似于查询中的 find() 方法。
$group：用于分组文档，并对每个组进行聚合计算，如求和、平均值等。
$project：用于投影文档，即选择需要的字段，并可以进行计算和重命名等操作。
$sort：用于对文档进行排序。
$limit：用于限制输出文档的数量。
$skip：用于跳过指定数量的文档。
$unwind：用于展开数组字段，将数组中的每个元素作为独立的文档。
以下是一个使用聚合框架的示例，假设有一个名为 orders 的集合，包含了订单信息：
db.orders.aggregate([
{ $match: { status: "completed" } }, // 筛选状态为 "completed" 的订单
{ $group: { _id: "$customer", total: { $sum: "$amount" } } }, // 按客户 customer 分组，并计算订单总金额
{ $sort: { total: -1 } }, // 按总金额降序排序
{ $limit: 10 } // 只返回前 10 个结果
]);上面的聚合操作首先筛选出状态为 “completed” 的订单，然后按客户分组，并计算每个客户的订单总金额，然后按总金额降序排序，并只返回前 10 个结果。
注意：聚合操作是在数据库服务器上执行的，可以利用数据库的计算能力和索引来提高性能。聚合操作还可以与其他查询操作（如查询条件、索引等）组合使用，以实现更复杂的数据处理需求。
MongoDB 中聚合的方法使用 aggregate()。aggregate() 方法的基本语法格式如下所示：
db.COLLECTION_NAME.aggregate(AGGREGATE_OPERATION)
其中，COLLECTION_NAME 为集合名称，AGGREGATE_OPERATION 为聚合操作对。
该示例将计算 col 集合中各种 sex 的 age 平均值。
（1）col 集合中的数据如下：
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }（2）计算各个 sex 下 age 的平均值：
> db.col.aggregate({ $group:{_id:"$sex", avg_age:{$avg:"$age"}} })
{ "_id" : "male", "avg_age" : 29 }
{ "_id" : "female", "avg_age" : 24.5 }以上示例类似的 sql 语句：
select sex, avg(age) from col group by sex;
在上面的例子中，我们通过字段 sex 字段对数据进行分组，并计算每个分组中 age 字段的平均值。
管道在 Unix 和 Linux 中一般用于将当前命令的输出结果作为下一个命令的参数。例如：
# 在文件中找出重复的行 $ sort testfile1 | uniq -d Hello 95 Linux 85 test 30
上面示例中，符号 | 就是管道符号。
在 MongoDB 中，MongoDB 的聚合管道将 MongoDB 文档在一个管道处理完毕后将结果传递给下一个管道处理，管道操作是可以重复的。
注意：聚合表达式处理输入文档并输出。表达式是无状态的，只能用于计算当前聚合管道的文档，不能处理其它的文档。
$project 操作符用于将聚合管道中的文档转换为指定的字段集合。它可以用于选择要返回的字段，计算新的字段值，重命名字段等操作。
$project 操作符接受一个包含字段映射的对象作为参数，其中键表示要包含或排除的字段，值表示是否包含或排除该字段。可以使用1表示包含该字段，0表示排除该字段。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 通过投影，仅输出 name 和 age 字段，默认情况下 _id 字段是被包含的
> db.col.aggregate({ $project:{ name:1, age:1 } })
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18 }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22 }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26 }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27 }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30 }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42 }
# 通过投影，禁止输出 _id 字段，允许输出 name 和 age 字段
> db.col.aggregate({ $project:{ _id:0, name:1, age:1 } })
{ "name" : "张三", "age" : 18 }
{ "name" : "李四", "age" : 22 }
{ "name" : "王五", "age" : 26 }
{ "name" : "赵六", "age" : 27 }
{ "name" : "顾七", "age" : 30 }
{ "name" : "何八", "age" : 42 }$project 操作符还支持一些表达式和操作符，可以用于计算新的字段值。常用的表达式和操作符包括算术操作符（如 $add、$subtract、$multiply、$divide）、逻辑操作符（如 $and、$or、$not）、条件操作符（如 $ifNull、$cond）、字符串操作符（如 $concat、$substr）、日期操作符（如 $year、$month、$dayOfMonth）等。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 27, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 27, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 22, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 $multiply 操作符对 age 字段的值乘以 10
> db.col.aggregate({ $project:{_id:0, name:1, age:{$multiply:["$age",10]}} })
{ "name" : "张三", "age" : 270 }
{ "name" : "李四", "age" : 220 }
{ "name" : "王五", "age" : 270 }
{ "name" : "赵六", "age" : 220 }
{ "name" : "顾七", "age" : 300 }
{ "name" : "何八", "age" : 420 }$match 操作符用于筛选文档，类似于查询中的find()方法。它可以根据指定的条件选择符合条件的文档，并将它们作为聚合管道的输入。
$match 操作符接受一个查询条件作为参数，可以使用各种查询运算符和表达式来构建条件。常用的查询运算符包括$eq（等于）、$ne（不等于）、$gt（大于）、$lt（小于）、$gte（大于等于）、$lte（小于等于）、$in（在给定数组中）、$nin（不在给定数组中）等。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 通过 $match 获取 age 大于 20 的文档，然后根据 sex 进行分组，计算每个分组 age 的平均值
> db.col.aggregate([ {$match:{age:{$gt:20}}}, {$group:{_id:"$sex", avg_age:{$avg:"$age"}}} ])
{ "_id" : "female", "avg_age" : 24.5 }
{ "_id" : "male", "avg_age" : 32.666666666666664 }注意：$match 操作符可以利用集合上的索引来提高查询性能。因此，在使用 $match 操作符时，应该根据查询条件创建适当的索引，以获得更好的性能。
$skip 操作符用于在聚合管道中跳过指定数量的文档，并返回剩余的文档。它可以用于实现分页功能，或者在聚合操作中跳过不需要的文档。
$skip 操作符接受一个整数作为参数，表示要跳过的文档数量。该参数必须是一个非负整数。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 $skip 操作符跳过前 4 个文档
> db.col.aggregate({ $skip:4 })
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }注意：$skip 操作符应该尽量在聚合管道的早期使用，以避免在后续的操作中处理不必要的文档。如果在聚合管道的后期使用 $skip 操作符，将会对性能产生负面影响，因为在跳过文档之前，MongoDB 仍然需要执行前面的聚合操作。
另外，如果要实现分页功能，可以将 $skip 操作符与 $limit 操作符结合使用。$limit 操作符用于限制返回的文档数量，可以用于分页查询。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 27, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 27, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 22, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 跳过 col 集合前 3 个文档，然后仅仅获取 2 个文档
> db.col.aggregate([{ $skip:3 }, { $limit:2 }])
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 22, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }计算总和。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，计算每个分组 age 的总和
> db.col.aggregate({ $group:{_id:"$sex", age_sum:{$sum:"$age"}} })
{ "_id" : "male", "age_sum" : 116 }
{ "_id" : "female", "age_sum" : 49 }计算平均值。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，计算每个分组 age 的平均值
> db.col.aggregate({ $group:{_id:"$sex", age_avg:{$avg:"$age"}} })
{ "_id" : "female", "age_avg" : 24.5 }
{ "_id" : "male", "age_avg" : 29 }获取集合中所有文档对应值得最小值。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，获取每个分组最小的 age 值
> db.col.aggregate({ $group:{_id:"$sex", age_min:{$min:"$age"}} })
{ "_id" : "male", "age_min" : 18 }
{ "_id" : "female", "age_min" : 22 }获取集合中所有文档对应值得最大值。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，获取每个分组最大的 age 值
> db.col.aggregate({ $group:{_id:"$sex", age_max:{$max:"$age"}} })
{ "_id" : "female", "age_max" : 27 }
{ "_id" : "male", "age_max" : 42 }将值加入一个数组中，不会判断是否有重复的值。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，将每个分组的 age 组成一个数组
> db.col.aggregate({ $group:{_id:"$sex", ages:{$push:"$age"}} })
{ "_id" : "female", "ages" : [ 22, 22 ] }
{ "_id" : "male", "ages" : [ 27, 27, 30, 42 ] }将值加入一个数组中，会判断是否有重复的值，若相同的值在数组中已经存在了，则不加入。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 27, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 27, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 22, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，将每个分组的 age 组成一个数组
> db.col.aggregate({ $group:{_id:"$sex", ages:{$addToSet:"$age"}} })
{ "_id" : "male", "ages" : [ 27, 42, 30 ] }
{ "_id" : "female", "ages" : [ 22 ] }根据资源文档的排序获取第一个文档数据。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，获取每个分组文档中第一个文档的 age 值
> db.col.aggregate({ $group:{_id:"$sex", age:{$first:"$age"}} })
{ "_id" : "female", "age" : 22 }
{ "_id" : "male", "age" : 18 }根据资源文档的排序获取最后一个文档数据。例如：
# col 集合所有数据
> db.col.find()
{ "_id" : ObjectId("64e71af810366fa87109a12f"), "name" : "张三", "age" : 18, "email" : "zhangsan@outlook.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a130"), "name" : "李四", "age" : 22, "email" : "lisi@qq.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a131"), "name" : "王五", "age" : 26, "email" : "wangwu@sina.com.cn", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a132"), "name" : "赵六", "age" : 27, "email" : "zhaoliu@gmail.com", "sex" : "female" }
{ "_id" : ObjectId("64e71af810366fa87109a133"), "name" : "顾七", "age" : 30, "email" : "guqi@qq.com", "sex" : "male" }
{ "_id" : ObjectId("64e71af810366fa87109a134"), "name" : "何八", "age" : 42, "email" : "heba@outlook.com", "sex" : "male" }
# 使用 sex 进行分组，获取每个分组文档中最后一个文档的 age 值
> db.col.aggregate({ $group:{_id:"$sex", age:{$last:"$age"}} })
{ "_id" : "female", "age" : 27 }
{ "_id" : "male", "age" : 42 }
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