MongoDB（96）如何使用MongoDB的高级聚合功能？

MongoDB 的高级聚合框架（Aggregation Framework）提供了一种强大且灵活的方法来处理和分析数据。通过使用聚合管道（Aggregation Pipeline），你可以执行复杂的数据处理操作，如过滤、排序、分组和计算聚合结果等。

一、聚合管道简介

聚合管道由一系列阶段（stage）组成，每个阶段将输入文档转换为输出文档，然后将输出文档传递给下一个阶段。以下是一些常见的聚合阶段：

1. $match：过滤文档（相当于 SQL 中的 WHERE 子句）。
2. $group：分组文档并计算聚合结果（相当于 SQL 中的 GROUP BY 子句）。
3. $project：重新格式化文档，选择或计算新字段。
4. $sort：排序文档。
5. $limit：限制返回的文档数。
6. $skip：跳过指定数量的文档。
7. $lookup：联表操作（相当于 SQL 中的 JOIN）。
8. $unwind：展开数组字段。

二、示例数据集

假设我们有以下示例数据集：

[
  { "_id": 1, "name": "Alice", "age": 25, "city": "New York", "hobbies": ["reading", "hiking"], "score": 85 },
  { "_id": 2, "name": "Bob", "age": 30, "city": "San Francisco", "hobbies": ["cooking", "gaming"], "score": 90 },
  { "_id": 3, "name": "Charlie", "age": 35, "city": "New York", "hobbies": ["hiking", "traveling"], "score": 75 },
  { "_id": 4, "name": "David", "age": 40, "city": "San Francisco", "hobbies": ["reading", "cooking"], "score": 88 },
  { "_id": 5, "name": "Eve", "age": 45, "city": "New York", "hobbies": ["gaming", "traveling"], "score": 92 }
]

三、常见聚合操作示例

1. 使用 $match 过滤文档

过滤出住在 New York 的用户：

db.collection.aggregate([
  { $match: { city: "New York" } }
])

结果：

[
  { "_id": 1, "name": "Alice", "age": 25, "city": "New York", "hobbies": ["reading", "hiking"], "score": 85 },
  { "_id": 3, "name": "Charlie", "age": 35, "city": "New York", "hobbies": ["hiking", "traveling"], "score": 75 },
  { "_id": 5, "name": "Eve", "age": 45, "city": "New York", "hobbies": ["gaming", "traveling"], "score": 92 }
]

2. 使用 $group 进行分组和聚合

按城市分组，并计算每个城市的用户平均年龄和总分：

db.collection.aggregate([
  { $group: {
      _id: "$city",
      avgAge: { $avg: "$age" },
      totalScore: { $sum: "$score" }
  }}
])

结果：

[
  { "_id": "New York", "avgAge": 35, "totalScore": 252 },
  { "_id": "San Francisco", "avgAge": 35, "totalScore": 178 }
]

3. 使用 $project 选择和重命名字段

选择用户的姓名和年龄，并多添加一个新字段 isAdult 表明用户是否成年（假设成年年龄为18岁）：

db.collection.aggregate([
  { $project: {
      _id: 0,
      name: 1,
      age: 1,
      isAdult: { $gte: ["$age", 18] }
  }}
])

结果：

[
  { "name": "Alice", "age": 25, "isAdult": true },
  { "name": "Bob", "age": 30, "isAdult": true },
  { "name": "Charlie", "age": 35, "isAdult": true },
  { "name": "David", "age": 40, "isAdult": true },
  { "name": "Eve", "age": 45, "isAdult": true }
]

4. 使用 $sort 排序文档

按分数降序排序：

db.collection.aggregate([
  { $sort: { score: -1 } }
])

结果：

[
  { "_id": 5, "name": "Eve", "age": 45, "city": "New York", "hobbies": ["gaming", "traveling"], "score": 92 },
  { "_id": 2, "name": "Bob", "age": 30, "city": "San Francisco", "hobbies": ["cooking", "gaming"], "score": 90 },
  { "_id": 4, "name": "David", "age": 40, "city": "San Francisco", "hobbies": ["reading", "cooking"], "score": 88 },
  { "_id": 1, "name": "Alice", "age": 25, "city": "New York", "hobbies": ["reading", "hiking"], "score": 85 },
  { "_id": 3, "name": "Charlie", "age": 35, "city": "New York", "hobbies": ["hiking", "traveling"], "score": 75 }
]

5. 使用 $limit 和 $skip 实现分页

获取分数最高的前 3 名用户：

db.collection.aggregate([
  { $sort: { score: -1 } },
  { $limit: 3 }
])

结果：

[
  { "_id": 5, "name": "Eve", "age": 45, "city": "New York", "hobbies": ["gaming", "traveling"], "score": 92 },
  { "_id": 2, "name": "Bob", "age": 30, "city": "San Francisco", "hobbies": ["cooking", "gaming"], "score": 90 },
  { "_id": 4, "name": "David", "age": 40, "city": "San Francisco", "hobbies": ["reading", "cooking"], "score": 88 }
]

跳过前 2 名用户，获取接下来的 2 名用户：

db.collection.aggregate([
  { $sort: { score: -1 } },
  { $skip: 2 },
  { $limit: 2 }
])

结果：

[
  { "_id": 4, "name": "David", "age": 40, "city": "San Francisco", "hobbies": ["reading", "cooking"], "score": 88 },
  { "_id": 1, "name": "Alice", "age": 25, "city": "New York", "hobbies": ["reading", "hiking"], "score": 85 }
]

6. 使用 $lookup 进行联表操作

假设有另一个名为 orders 的集合，包含用户的订单信息：

[
  { "_id": 1, "userId": 1, "total": 100 },
  { "_id": 2, "userId": 2, "total": 200 },
  { "_id": 3, "userId": 1, "total": 150 },
  { "_id": 4, "userId": 3, "total": 250 }
]

使用 $lookup 将用户和订单信息联接起来：

db.collection.aggregate([
  { $lookup: {
      from: "orders",
      localField: "_id",
      foreignField: "userId",
      as: "orders"
  }}
])

结果：

[
  { "_id": 1, "name": "Alice", "age": 25, "city": "New York", "hobbies": ["reading", "hiking"], "score": 85, "orders": [{ "_id": 1, "userId": 1, "total": 100 }, { "_id": 3, "userId": 1, "total": 150 }] },
  { "_id": 2, "name": "Bob", "age": 30, "city": "San Francisco", "hobbies": ["cooking", "gaming"], "score": 90, "orders": [{ "_id": 2, "userId": 2, "total": 200 }] },
  { "_id": 3, "name": "Charlie", "age": 35, "city": "New York", "hobbies": ["hiking"]}