准备工作
准备2个集合的数据，后面视图和索引都会用到
1个订单集合，一个收款信息集合
var orders = new Array();
var shipping = new Array();
var addresses = ["广西省玉林市", "湖南省岳阳市", "湖北省荆州市", "甘肃省兰州市", "吉林省松原市", "江西省景德镇", "辽宁省沈阳市", "福建省厦门市", "广东省广州市", "北京市朝阳区"];
for (var i = 10000; i < 20000; i++) {
var orderNo = i + Math.random().toString().substr(2, 5);
orders[i] = { orderNo: orderNo, userId: i, price: Math.round(Math.random() * 10000) / 100, qty: Math.floor(Math.random() * 10) + 1, orderTime: new Date(new Date().setSeconds(Math.floor(Math.random() * 10000))) };
var address = addresses[Math.floor(Math.random() * 10)];
shipping[i] = { orderNo: orderNo, address: address, recipienter: "Wilson", province: address.substr(0, 3), city: address.substr(3, 3) }
}
db.order.insert(orders);
db.shipping.insert(shipping);
视图
概述
A MongoDB view is a queryable object whose contents are defined by an aggregation pipeline on other collections or views. MongoDB does not persist the view contents to disk. A view’s content is computed on-demand when a client queries the view. MongoDB can require clients to have permission to query the view. MongoDB does not support write operations against views.
Mongodb的视图基本上和SQL的视图一样
- 数据源（集合或视图）
- 提供查询
- 不实际存储硬盘
- 客户端发起请求查询时计算而得
1. 创建视图
有两种方法创建视图
db.createCollection(
"",
{
"viewOn" : "",
"pipeline" : [],
"collation" : { }
}
) db.createView(
"",
"",
[],
{
"collation" : { }
}
) 一般使用db.createView
viewName : 必须，视图名称
source : 必须，数据源，集合/视图
[
1.1 单个集合创建视图
假设现在查看当天最高的10笔订单视图，例如后台某个地方需要实时显示金额最高的订单
db.createView(
"orderInfo", //视图名称
"order", //数据源
[
//筛选符合条件的订单，大于当天，这里要注意时区
{ $match: { "orderTime": { $gte: ISODate("2020-04-13T16:00:00.000Z") } } },
//按金额倒序
{ $sort: { "price": -1 } },
//限制10个文档
{ $limit: 10 },
//选择要显示的字段
//0: 排除字段，若字段上使用（_id除外），就不能有其他包含字段
//1: 包含字段
{ $project: { _id: 0, orderNo: 1, price: 1, orderTime: 1 } }
]
)然后就可以直接使用orderInfo这个视图查询数据
db.orderInfo.find({})返回结果
{ "orderNo" : "1755149436", "price" : 100, "orderTime" : ISODate("2020-04-14T13:49:42.220Z") }
{ "orderNo" : "1951423853", "price" : 99.99, "orderTime" : ISODate("2020-04-14T15:08:07.240Z") }
{ "orderNo" : "1196303215", "price" : 99.99, "orderTime" : ISODate("2020-04-14T15:15:41.158Z") }
{ "orderNo" : "1580069456", "price" : 99.98, "orderTime" : ISODate("2020-04-14T13:41:07.199Z") }
{ "orderNo" : "1114480559", "price" : 99.98, "orderTime" : ISODate("2020-04-14T13:31:58.150Z") }
{ "orderNo" : "1229542817", "price" : 99.98, "orderTime" : ISODate("2020-04-14T15:15:35.162Z") }
{ "orderNo" : "1208031402", "price" : 99.94, "orderTime" : ISODate("2020-04-14T14:13:02.160Z") }
{ "orderNo" : "1680622670", "price" : 99.93, "orderTime" : ISODate("2020-04-14T15:17:25.210Z") }
{ "orderNo" : "1549824953", "price" : 99.92, "orderTime" : ISODate("2020-04-14T13:09:41.196Z") }
{ "orderNo" : "1449930147", "price" : 99.92, "orderTime" : ISODate("2020-04-14T15:16:15.187Z") }1.2 多个集合创建视图
其实跟单个是集合是一样，只是多了$lookup连接操作符，视图根据管道最终结果显示，所以可以关联多个集合（若出现这种情况就要考虑集合设计是否合理，mongodb本来就是文档型数据库）
db.orderDetail.drop()
db.createView(
"orderDetail",
"order",
[
{ $lookup: { from: "shipping", localField: "orderNo", foreignField: "orderNo", as: "shipping" } },
{ $project: { "orderNo": 1, "price": 1, "shipping.address": 1 } }
]
)查询视图，得到如下结果
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6c3"), "orderNo" : "1000039782", "price" : 85.94, "shipping" : [ { "address" : "北京市朝阳区" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6c4"), "orderNo" : "1000102128", "price" : 29.04, "shipping" : [ { "address" : "吉林省松原市" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6c5"), "orderNo" : "1000214514", "price" : 90.69, "shipping" : [ { "address" : "湖南省岳阳市" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6c6"), "orderNo" : "1000337987", "price" : 75.05, "shipping" : [ { "address" : "辽宁省沈阳市" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6c7"), "orderNo" : "1000468969", "price" : 76.84, "shipping" : [ { "address" : "江西省景德镇" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6c8"), "orderNo" : "1000572219", "price" : 60.25, "shipping" : [ { "address" : "江西省景德镇" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6c9"), "orderNo" : "1000611743", "price" : 19.14, "shipping" : [ { "address" : "广东省广州市" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6ca"), "orderNo" : "1000773917", "price" : 31.5, "shipping" : [ { "address" : "北京市朝阳区" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6cb"), "orderNo" : "1000879146", "price" : 76.16, "shipping" : [ { "address" : "吉林省松原市" } ] }
{ "_id" : ObjectId("5e95af8c4ef6faf974b4a6cc"), "orderNo" : "1000945977", "price" : 93.98, "shipping" : [ { "address" : "辽宁省沈阳市" } ] }可以看到，mongodb不是像SQL那样把连接的表当成列列出，而是把连接结果放在数组里面，这很符合Mongodb文档型结构。
2. 修改视图
假设现在需要增加一个数量的字段
db.runCommand({
collMod: "orderInfo",
viewOn: "order",
pipeline: [
{ $match: { "orderTime": { $gte: ISODate("2020-04-13T16:00:00.000Z") } } },
{ $sort: { "price": -1 } },
{ $limit: 10 },
//增加qty
{ $project: { _id: 0, orderNo: 1, price: 1, qty: 1, orderTime: 1 } }
]
})当然，也可以删除视图，重新用db.createView()创建视图
3. 删除视图
db.orderInfo.drop();
索引
概述
Indexes support the efficient execution of queries in MongoDB. Without indexes, MongoDB must perform a collection scan, i.e. scan every document in a collection, to select those documents that match the query statement. If an appropriate index exists for a query, MongoDB can use the index to limit the number of documents it must inspect.
索引能提供高效的查询，没有索引的查询，mongole执行集合扫描，相当于SQL SERVER的全表扫描，扫描每一个文档。
数据存在存储介质上，大多数情况是为了查询，查询的快慢直接影响用户体验，mongodb索引也是空间换时间，添加索引，CUD操作都会导致索引重新生成，影响速度。
1. 准备工作
1 准备200W条数据
var orderNo = 100 * 10000;
for (var i = 0; i < 100; i++) {
//分批次插入，每次20000条
var orders = new Array();
for (var j = 0; j < 20000; j++) {
var orderNo = orderNo++;
orders[j] = { orderNo: orderNo, userId: i + j, price: Math.round(Math.random() * 10000) / 100, qty: Math.floor(Math.random() * 10) + 1, orderTime: new Date(new Date().setSeconds(Math.floor(Math.random() * 10000))) };
}
//不需写入确认
db.order.insert(orders, { writeConcern: { w: 0 } });
}
2 mongodb的查询计划
db.collection.explain().
一般使用执行统计模式，例如
db.order.explain("executionStats").find({orderNo:1000000})返回的executionStats对象字段说明
部分字段说明
| 字段 | 说明 |
|---|---|
| executionSuccess | 是否执行成功 |
| nReturned | 返回匹配文档数量 |
| executionTimeMillis | 执行时间，单位：毫秒 |
| totalKeysExamined | 索引检索数目 |
| totalDocsExamined | 文档检索数目 |
查看未加索引前查询计划
db.order.explain("executionStats").find({orderNo:1000000})截取部分返回结果,可以看出
- executionTimeMillis : 用时1437毫秒
- totalDocsExamined : 扫描文档200W
- executionStages.stage : 集合扫描
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1,
"executionTimeMillis" : 1437,
"totalKeysExamined" : 0,
"totalDocsExamined" : 2000000,
"executionStages" : {
"stage" : "COLLSCAN",
3 查看当前集合统计信息
db.order.stats()截取部分信息，可以看出现在存储文件大小大概为72M
{
"ns" : "mongo.order",
"size" : 204000000,
"count" : 2000000,
"avgObjSize" : 102,
"storageSize" : 74473472,
2. 创建索引
db.order.createIndex({ orderNo: 1 }, { name: "ix_orderNo" })索引名称不是必须，若不指定，按 字段名称_排序类型组合自动生成，索引名称一旦创建不能修改，若要修改，只能删除索引重新生成索引，建议还是建索引的时候就把索引名称设置好。
1 执行查询计划
db.order.explain("executionStats").find({orderNo:1000000})截取部分结果，直观就可以感觉查询速度有了质的提升，再看查询计划更加惊讶
- nReturned : 匹配到1个文档
- executionTimeMillis : 0，呃。。
- totalKeysExamined : 总共检索了1个索引
- totalDocsExamined : 总共检索了1个文档
- executionStages.stage : FETCH，根据索引去检索指定文档，像SQL的Index Seek
"executionStats" : {
"executionSuccess" : true,
"nReturned" : 1,
"executionTimeMillis" : 0,
"totalKeysExamined" : 1,
"totalDocsExamined" : 1,
"executionStages" : {
"stage" : "FETCH"
这里只介绍最简单的单个字段索引，mongodb还有很多索引
- 复合索引（Compound Indexes）：对多个字段做索引
- 多键索引（Multikey Indexes）： 一个字段多个值做索引，通常是数组
- 全文索引（Text Indexes）： 对文本检索，可以对字段设置不同权重
- 通配符索引（Wildcard Indexes）：可以将对象的所有/指定的值做索引
- 更多
参考文章
Views — MongoDB Manual
Indexes — MongoDB Manual
转发请标明出处：https://www.cnblogs.com/WilsonPan/p/12704474.html
