文章目录
- MongoDB的聚合操作（Aggregate）
- MongoDB的管道（Pipline操作）
- MongoDB的聚合（Map Reduce）
- MongoDB的索引
更多相关内容可查看
MongoDB的聚合操作（Aggregate）
简单理解，其实本质跟sql一样，只不过写法不一样，仔细看以下示例
图例：
代码示例：
> db.orders.insertMany( [{ _id: 1, cust_id: "abc1", ord_date: ISODate("2012-11-02T17:04:11.102Z"), status: "A", amount: 50 },{ _id: 2, cust_id: "xyz1", ord_date: ISODate("2013-10-01T17:04:11.102Z"), status: "A", amount: 100 },{ _id: 3, cust_id: "xyz1", ord_date: ISODate("2013-10-12T17:04:11.102Z"), status: "D", amount: 25 },{ _id: 4, cust_id: "xyz1", ord_date: ISODate("2013-10-11T17:04:11.102Z"), status: "D", amount: 125 },{ _id: 5, cust_id: "abc1", ord_date: ISODate("2013-11-12T17:04:11.102Z"), status: "A", amount: 25 }] );
{ "acknowledged" : true, "insertedIds" : [ 1, 2, 3, 4, 5 ] }
> db.orders.find({})
{ "_id" : 1, "cust_id" : "abc1", "ord_date" : ISODate("2012-11-02T17:04:11.102Z"), "status" : "A", "amount" : 50 }
{ "_id" : 2, "cust_id" : "xyz1", "ord_date" : ISODate("2013-10-01T17:04:11.102Z"), "status" : "A", "amount" : 100 }
{ "_id" : 3, "cust_id" : "xyz1", "ord_date" : ISODate("2013-10-12T17:04:11.102Z"), "status" : "D", "amount" : 25 }
{ "_id" : 4, "cust_id" : "xyz1", "ord_date" : ISODate("2013-10-11T17:04:11.102Z"), "status" : "D", "amount" : 125 }
{ "_id" : 5, "cust_id" : "abc1", "ord_date" : ISODate("2013-11-12T17:04:11.102Z"), "status" : "A", "amount" : 25 }
>
> db.orders.aggregate([{ $match: { status: "A" } },{ $group: { _id: "$cust_id", total: { $sum: "$amount" } } },{ $sort: { total: -1 } }])
{ "_id" : "xyz1", "total" : 100 }
{ "_id" : "abc1", "total" : 75 }
根据上述不难看出具体是怎么操作的，对sql有一定基础的应该可以很容易看懂
MongoDB的管道（Pipline操作）
MongoDB的聚合管道（Pipline）将MongoDB文档在一个阶段（Stage）处理完毕后将结果传递给下一个阶段（Stage）处理。阶段（Stage）操作是可以重复的。
| 阶段 | 描述 | 类似于 SQL 中的 |
|---|---|---|
| $match | 用于过滤文档，只传递满足条件的文档到下一个阶段 | WHERE |
| $group | 用于将文档分组，并可用于计算聚合值（如总和、平均值、计数等） | GROUP BY |
| $project | 用于选择和重命名字段，或者创建计算字段 | SELECT |
| $sort | 用于对文档进行排序 | ORDER BY |
| $limit | 用于限制传递到下一个阶段的文档数量 | LIMIT |
| $skip | 用于跳过指定数量的文档 | OFFSET |
| $unwind | 用于将数组字段中的每个元素拆分为独立的文档 | N/A |
| $bucket | 根据指定的边界将文档分组到不同的桶中 | N/A |
| $facet | 允许在单个聚合管道中并行执行多个不同的子管道 | N/A |
代码示例：
$project
> db.orders.aggregate({ $project : {_id : 0 , // 默认不显示_idcust_id : 1 ,status : 1
... }});
{ "cust_id" : "abc1", "status" : "A" }
{ "cust_id" : "xyz1", "status" : "A" }
{ "cust_id" : "xyz1", "status" : "D" }
{ "cust_id" : "xyz1", "status" : "D" }
{ "cust_id" : "abc1", "status" : "A" }
>
$skip
> db.orders.aggregate({ $skip : 4 });
{ "_id" : 5, "cust_id" : "abc1", "ord_date" : ISODate("2013-11-12T17:04:11.102Z"), "status" : "A", "amount" : 25 }
>
$unwind
> db.inventory2.insertOne({ "_id" : 1, "item" : "ABC1", sizes: [ "S", "M", "L"] })
{ "acknowledged" : true, "insertedId" : 1 }
> db.inventory2.aggregate( [ { $unwind : "$sizes" } ] )
{ "_id" : 1, "item" : "ABC1", "sizes" : "S" }
{ "_id" : 1, "item" : "ABC1", "sizes" : "M" }
{ "_id" : 1, "item" : "ABC1", "sizes" : "L" }
$bucket
> db.artwork.insertMany([{ "_id" : 1, "title" : "The Pillars of Society", "artist" : "Grosz", "year" : 1926,"price" : NumberDecimal("199.99") },{ "_id" : 2, "title" : "Melancholy III", "artist" : "Munch", "year" : 1902,"price" : NumberDecimal("280.00") },{ "_id" : 3, "title" : "Dancer", "artist" : "Miro", "year" : 1925,"price" : NumberDecimal("76.04") },{ "_id" : 4, "title" : "The Great Wave off Kanagawa", "artist" : "Hokusai","price" : NumberDecimal("167.30") },{ "_id" : 5, "title" : "The Persistence of Memory", "artist" : "Dali", "year" : 1931,"price" : NumberDecimal("483.00") },{ "_id" : 6, "title" : "Composition VII", "artist" : "Kandinsky", "year" : 1913,"price" : NumberDecimal("385.00") },{ "_id" : 7, "title" : "The Scream", "artist" : "Munch", "year" : 1893 },{ "_id" : 8, "title" : "Blue Flower", "artist" : "O'Keefe", "year" : 1918,"price" : NumberDecimal("118.42") }])
{"acknowledged" : true,"insertedIds" : [1,2,3,4,5,6,7,8]
}
> db.artwork.find({})
{ "_id" : 1, "title" : "The Pillars of Society", "artist" : "Grosz", "year" : 1926, "price" : NumberDecimal("199.99") }
{ "_id" : 2, "title" : "Melancholy III", "artist" : "Munch", "year" : 1902, "price" : NumberDecimal("280.00") }
{ "_id" : 3, "title" : "Dancer", "artist" : "Miro", "year" : 1925, "price" : NumberDecimal("76.04") }
{ "_id" : 4, "title" : "The Great Wave off Kanagawa", "artist" : "Hokusai", "price" : NumberDecimal("167.30") }
{ "_id" : 5, "title" : "The Persistence of Memory", "artist" : "Dali", "year" : 1931, "price" : NumberDecimal("483.00") }
{ "_id" : 6, "title" : "Composition VII", "artist" : "Kandinsky", "year" : 1913, "price" : NumberDecimal("385.00") }
{ "_id" : 7, "title" : "The Scream", "artist" : "Munch", "year" : 1893 } // 注意这里没有price，聚合结果中为Others
{ "_id" : 8, "title" : "Blue Flower", "artist" : "O'Keefe", "year" : 1918, "price" : NumberDecimal("118.42") }
> db.artwork.aggregate( [{$bucket: {groupBy: "$price",boundaries: [ 0, 200, 400 ],default: "Other",output: {"count": { $sum: 1 },"titles" : { $push: "$title" }}}}] )
{ "_id" : 0, "count" : 4, "titles" : [ "The Pillars of Society", "Dancer", "The Great Wave off Kanagawa", "Blue Flower" ] }
{ "_id" : 200, "count" : 2, "titles" : [ "Melancholy III", "Composition VII" ] }
{ "_id" : "Other", "count" : 2, "titles" : [ "The Persistence of Memory", "The Scream" ] }这里有很多朋友短时间内看不懂，其实bucket就是按照边界值进行分桶操作，以上案例就是价格字段在0-200放一个桶，200-400放一个桶，没有价格的数据放到other中
$bucket + $facet
db.artwork.aggregate( [{$facet: {"price": [{$bucket: {groupBy: "$price",boundaries: [ 0, 200, 400 ],default: "Other",output: {"count": { $sum: 1 },"artwork" : { $push: { "title": "$title", "price": "$price" } }}}}],"year": [{$bucket: {groupBy: "$year",boundaries: [ 1890, 1910, 1920, 1940 ],default: "Unknown",output: {"count": { $sum: 1 },"artwork": { $push: { "title": "$title", "year": "$year" } }}}}]}}
] )// 输出
{"year" : [{"_id" : 1890,"count" : 2,"artwork" : [{"title" : "Melancholy III","year" : 1902},{"title" : "The Scream","year" : 1893}]},{"_id" : 1910,"count" : 2,"artwork" : [{"title" : "Composition VII","year" : 1913},{"title" : "Blue Flower","year" : 1918}]},{"_id" : 1920,"count" : 3,"artwork" : [{"title" : "The Pillars of Society","year" : 1926},{"title" : "Dancer","year" : 1925},{"title" : "The Persistence of Memory","year" : 1931}]},{// Includes the document without a year, e.g., _id: 4"_id" : "Unknown","count" : 1,"artwork" : [{"title" : "The Great Wave off Kanagawa"}]}],"price" : [{"_id" : 0,"count" : 4,"artwork" : [{"title" : "The Pillars of Society","price" : NumberDecimal("199.99")},{"title" : "Dancer","price" : NumberDecimal("76.04")},{"title" : "The Great Wave off Kanagawa","price" : NumberDecimal("167.30")},{"title" : "Blue Flower","price" : NumberDecimal("118.42")}]},{"_id" : 200,"count" : 2,"artwork" : [{"title" : "Melancholy III","price" : NumberDecimal("280.00")},{"title" : "Composition VII","price" : NumberDecimal("385.00")}]},{// Includes the document without a price, e.g., _id: 7"_id" : "Other","count" : 2,"artwork" : [{"title" : "The Persistence of Memory","price" : NumberDecimal("483.00")},{"title" : "The Scream"}]}]
}这里代码太长，可能有朋友没有足够的耐心看完，$bucket + $facet是非常常用的场景，这里解释一下，就是将两组bucket跟组合到了一起进行返回，可以按我自己的理解一个bucket就是多个List数组，List<List>,而一个facet就是在这个bucket在套一层List
更多的聚合关键字可以查看官方文档：https://www.mongodb.com/zh-cn/docs/manual/reference/operator/aggregation-pipeline/
MongoDB的聚合（Map Reduce）
图例：
代码示例：
{ "_id": 1, "customerId": "A123", "amount": 100 }
{ "_id": 2, "customerId": "B456", "amount": 200 }
{ "_id": 3, "customerId": "A123", "amount": 150 }
{ "_id": 4, "customerId": "C789", "amount": 50 }
{ "_id": 5, "customerId": "B456", "amount": 300 }
使用 MapReduce 来计算每个 customerId 的总 amount。
// Map function
var mapFunction = function() {emit(this.customerId, this.amount);
};// Reduce function
var reduceFunction = function(customerId, amounts) {return Array.sum(amounts);
};// Execute MapReduce
db.orders.mapReduce(mapFunction,reduceFunction,{ out: "order_totals" }
);// 查看结果
db.order_totals.find().forEach(printjson);{ "_id": "A123", "value": 250 }
{ "_id": "B456", "value": 500 }
{ "_id": "C789", "value": 50 }
- Map Function: 对于每个文档，
emit函数将customerId作为键，amount作为值发射出去。 - Reduce Function: 对于每个唯一的
customerId，reduceFunction接收一个键和与该键相关联的所有值的数组，并返回这些值的总和。 - Output: 结果存储在
order_totals集合中，每个文档包含一个customerId和该客户的总订单金额。
MongoDB的索引
图例：
类型：
- 单一索引
{ "_id": 1, "username": "alice", "age": 30 }
{ "_id": 2, "username": "bob", "age": 25 }
db.users.createIndex({ username: 1 });
这里的 1 表示升序索引。对于降序索引，可以使用 -1
- 复合索引
db.users.createIndex({ username: 1, age: -1 });
- 多键索引
{ "_id": 1, "title": "MongoDB Basics", "tags": ["database", "NoSQL"] }
{ "_id": 2, "title": "Advanced MongoDB", "tags": ["database", "performance"] }
db.posts.createIndex({ tags: 1 });
- 文字索引
支持文本搜索。它们允许对字符串字段进行全文搜索。
{ "_id": 1, "content": "MongoDB is a NoSQL database" }
{ "_id": 2, "content": "Text search in MongoDB" }
我们可以在 content 字段上创建文字索引：
db.articles.createIndex({ content: "text" });
然后，我们可以执行全文搜索：
db.articles.find({ $text: { $search: "NoSQL" } });
- 地理空间索引
引用于加速地理位置查询。MongoDB 支持 2D 和 2DSphere 索引
{ "_id": 1, "name": "Central Park", "coordinates": [40.785091, -73.968285] }
{ "_id": 2, "name": "Golden Gate Bridge", "coordinates": [37.819929, -122.478255] }
我们可以在 coordinates 字段上创建 2DSphere 索引：
db.locations.createIndex({ coordinates: "2dsphere" });
- 哈希索引
用于均匀分布数据，适合需要高效等值查询的场景
{ "_id": 1, "sku": "A123" }
{ "_id": 2, "sku": "B456" }
我们可以在 sku 字段上创建哈希索引：
db.products.createIndex({ sku: "hashed" });
索引的操作：
查看集合索引
db.col.getIndexes()
查看集合索引大小
db.col.totalIndexSize()
删除集合所有索引
db.col.dropIndexes()
删除集合指定索引
db.col.dropIndex("索引名称")
