索引
索引通常能够极大的提高查询的效率，如果没有索引，MongoDB在读取数据时必须扫描集合中的每个文件并选取那些符合查询条件的记录。
这种扫描全集合的查询效率是非常低的，特别在处理大量的数据时，查询可以要花费几十秒甚至几分钟，这对网站的性能是非常致命的。
索引是特殊的数据结构，索引存储在一个易于遍历读取的数据集合中，索引是对数据库表中一列或多列的值进行排序的一种结构。
默认是对_id进行索引的。
图例理解
以对users中score进行索引时查询的效果
索引的类型
对于索引，这里简单介绍下常用的类型，其它类型和例子可以参考官网文档
- 单一索引
- 复合索引
- 多键索引
ensureIndex() 方法
MongoDB使用 ensureIndex() 方法来创建索引。
语法
ensureIndex()方法基本语法格式如下所示：
db.COLLECTION_NAME.ensureIndex({KEY:1})
语法中 Key 值为你要创建的索引字段，1为指定按升序创建索引，如果你想按降序来创建索引指定为-1即可。
实例
db.col.ensureIndex({"title":1})
ensureIndex() 方法中你也可以设置使用多个字段创建索引（关系型数据库中称作复合索引）。
db.col.ensureIndex({"title":1,"description":-1})
ensureIndex() 接收可选参数，可选参数列表如下：
对索引的操作
- 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")
聚合 - Aggregation Pipline
类似于将SQL中的group by + order by + left join ... 等操作管道化。
常规使用
- 图例理解
- 准备数据
> 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 }
>
- 聚合操作
> db.orders.aggregate([
... { $match: { status: "A" } },
... { $group: { _id: "$cust_id", total: { $sum: "$amount" } } },
... { $sort: { total: -1 } }
... ])
{ "_id" : "xyz1", "total" : 100 }
{ "_id" : "abc1", "total" : 75 }
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。
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。
这里我们介绍一下聚合框架中常用的几个Stages：
$project：修改输入文档的结构。可以用来重命名、增加或删除域，也可以用于创建计算结果以及嵌套文档。$match：用于过滤数据，只输出符合条件的文档。$match使用MongoDB的标准查询操作。$limit：用来限制MongoDB聚合管道返回的文档数。$skip：在聚合管道中跳过指定数量的文档，并返回余下的文档。$unwind：将文档中的某一个数组类型字段拆分成多条，每条包含数组中的一个值。$group：将集合中的文档分组，可用于统计结果。$sort：将输入文档排序后输出。$geoNear：输出接近某一地理位置的有序文档。$bucket: 分组（分桶）计算。$facet: 多次分组计算。$out: 将结果集输出，必须是Pipline最后一个Stage。
举几个例子
- $project
> db.orders.aggregate(
... { $project : {
... _id : 0 , // 默认不显示_id
... cust_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" ] }
- facet
非常常用！
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"
}
]
}
]
}
聚合操作使用的比较频繁，在实际的工作中可以参考官方文档https://docs.mongodb.com/v3.6/reference/operator/aggregation-pipeline/。
更多关于 MongoDB 数据库的学习文章，请参阅：NoSQL 数据库之 MongoDB ，本系列持续更新中。
Aggregation Options参数
举一个explain参数为例，更多的相关Options可以参考官方文档 https://docs.mongodb.com/v3.6/reference/method/db.collection.aggregate/
- explain
> db.orders.aggregate(
... [
... { $match: { status: "A" } },
... { $group: { _id: "$cust_id", total: { $sum: "$amount" } } },
... { $sort: { total: -1 } }
... ],
... {
... explain: true
... }
... )
{
"serverInfo" : {
"host" : "pdai",
"port" : 27017,
"version" : "3.6.19",
"gitVersion" : "41b289ff734a926e784d6ab42c3129f59f40d5b4"
},
"stages" : [
{
"$cursor" : {
"query" : {
"status" : "A"
},
"fields" : {
"amount" : 1,
"cust_id" : 1,
"_id" : 0
},
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "testdb.orders",
"indexFilterSet" : false,
"parsedQuery" : {
"status" : {
"$eq" : "A"
}
},
"winningPlan" : {
"stage" : "COLLSCAN",
"filter" : {
"status" : {
"$eq" : "A"
}
},
"direction" : "forward"
},
"rejectedPlans" : [ ]
}
}
},
{
"$group" : {
"_id" : "$cust_id",
"total" : {
"$sum" : "$amount"
}
}
},
{
"$sort" : {
"sortKey" : {
"total" : -1
}
}
}
],
"ok" : 1
}
聚合 - Map Reduce
- 图例理解
官网给了个例子
- 准备数据
{
_id: ObjectId("50a8240b927d5d8b5891743c"),
cust_id: "abc123",
ord_date: new Date("Oct 04, 2012"),
status: 'A',
price: 25,
items: [ { sku: "mmm", qty: 5, price: 2.5 },
{ sku: "nnn", qty: 5, price: 2.5 } ]
}
- 计算每个顾客总花费：
map
var mapFunction1 = function() {
emit(this.cust_id, this.price);
};
reduce
var reduceFunction1 = function(keyCustId, valuesPrices) {
return Array.sum(valuesPrices);
};
out
db.orders.mapReduce(
mapFunction1,
reduceFunction1,
{ out: "map_reduce_example" }
)
- 计算每个订单中Items的均价
map
var mapFunction2 = function() {
for (var idx = 0; idx
