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MongoDB基础教程
P粉469731340
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MongoDB 4.X教程

一、MongoDB介绍

MongoDB是一个基于分布式文件存储的数据库。由C++语言编写。旨在为WEB应用提供可扩展的高性能数据存储解决方案。

MongoDB是一个介于关系型数据库和非关系数据库之间的产品，是非关系数据库当中功能最丰富，最像关系数据库的。它支持的数据结构非常松散，是类似JSON的BJSON格式，因此可以存储比较复杂的数据类型。Mongo最大的特点是它支持的查询语言非常强大，其语法有点类似于面向对象的查询语言，几乎可以实现类似关系数据库单表查询的绝大部分功能，而且还支持对数据建立索引。

MongoDB数据操作基于json格式

  1. {
  2.     "userName":"admin",
  3.     "password":123456
  4. }

二、MongoDB安装

1. MongoDB下载

下载地址：https://www.mongodb.com/try/download/community

2.MongoDB安装

  • 下载的 .msi 文件，下载后双击该文件，按操作提示安装即可。

  • 安装过程中，你可以通过点击 “Custom(自定义)” 按钮来设置你的安装目录，建议不要安装在C盘。

  • img

  • 全程点击next安装，但是这一步需要注意，这里是安装可视化组件，默认是选择状态，这里需要取消选中，否则在安装的过程中要下载可视化组件，比较慢，甚至有时候会报错：

3.MongoDB环境变量配置

  • 在桌面右键 此电脑>>>属性>>>高级系统设置>>>高级>>>环境变量>>>找到path>>>选择编辑>>>新建

  • 在打开的环境变量中MongoDB安装的bin路径复制到新建目录中

  • 然后点击所有的确定即可完成环境变量配置

4.验证安装是否成功

  • 打开CMD命令窗口，输入mongo，出现以下提示信息，说明安装成功。
  1. C:\Users\***.DESKTOP-C1RC9P2>mongo
  2. MongoDB shell version v4.4.2-rc0
  3. connecting to: mongodb://127.0.0.1:27017/?compressors=disabled&gssapiServiceName=mongodb
  4. Implicit session: session { "id" : UUID("df31999e-cb62-4f71-8a18-7db8723c514f") }
  5. MongoDB server version: 4.4.2-rc0
  6. ---
  7. The server generated these startup warnings when booting:
  8.         2020-10-30T16:25:16.503+08:00: Access control is not enabled for the database. Read and write access to data and configuration is unrestricted
  9. ---
  10. ---
  11.         Enable MongoDB's free cloud-based monitoring service, which will then receive and display
  12.         metrics about your deployment (disk utilization, CPU, operation statistics, etc).
  14.         The monitoring data will be available on a MongoDB website with a unique URL accessible to you
  15.         and anyone you share the URL with. MongoDB may use this information to make product
  16.         improvements and to suggest MongoDB products and deployment options to you.
  18.         To enable free monitoring, run the following command: db.enableFreeMonitoring()
  19.         To permanently disable this reminder, run the following command: db.disableFreeMonitoring()

三、MongoDB使用基本介绍

  • MongoDB属于非关系型数据库，其数据库、表、字段等和关系型数据库（如：MySQL数据库）有一定的差别；

  • MongoDB中的集合就相当于关系型数据库中的表

  • MongoDB中的json字符串的键相当于关系型数据库中的列名；

  • 在操作MongoDB数据的时候全部使用json数据格式。

1.查看数据库名

  • 查看所有数据库名

    1. 命令：
    2.   show dbs
    1.   > show dbs
    2.   admin   0.000GB
    3.   config  0.000GB
    4.   local   0.000GB

  • 这三个数据库是默认系统数据库，不能删除。

2. 查看集合

  • 查看集合前提是要先指定使用哪一个数据库是用命令

    1. 命令：
    2.     ues 数据库名
    3.     show collections
    1. > use admin
    2. switched to db admin
    3. > show collections
    4. system.version
    5. >

  • system.version就是admin这个数据库中的表

3.查询集合中的数据

  • 这里先简单介绍查询集合中的所有数据，方便后面学习。

  • 查询集合中的所有数据，这里查询的是系统数据库admin中的system.version集合

    1. 命令：
    2.     db.集合名.find()
    1. > db.system.version.find()
    2. { "_id" : "featureCompatibilityVersion", "version" : "4.4" }

  • 这里的_id是集合的键，每个集合里面默认存在，version是集合中的另一个键，相当于关系型数据库中的字段

四、创建数据库及添加数据

  • MongoDB不能够直接创建数据库，需要添加一条数据才能创建

1.创建数据库和插入数据

  • 先指定创建的数据

  • 然后执行添加数据命令

    1. > use company
    2. switched to db company
    3. > db.emp.insert({"empno":100,"ename":"admin","sex":"男","age":20,"salary":800.00,"deptno":10})
    4. WriteResult({ "nInserted" : 1 })

  • 这里插入了6列数据，分别是员工的编号，姓名，性别，年龄，薪资及所在的部门

  • WriteResult({ "nInserted" : 1 })表示一行数据插入成功,说明创建数据成功，同时添加了一套数据,我们可以继续添加

    1. >  db.emp.insert({"empno":101,"ename":"张三","sex":"女","age":30,"salary":2500.00,"deptno":20})})})})
    2. WriteResult({ "nInserted" : 1 })

  • 随着数据的插入，数据库也随着创建成功。

五、查询数据

查询所有数据，在查询数据前要先指定使用哪个数据库，再查询数据库中的所有集合，根据相应集合再查询数据。

1. 查询所有数据

  1. 命令：
  2.     > db.集合名.find()
  1. 指定对哪个数据库操作
  2.     > use company
  3.     switched to db company
  4. 查询指定数据库后里面的所有集合
  5.     > show collections
  6. emp
  7. 查询所有数据，一共12条数据：
  8.     > db.emp.find()
  10. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }
  11. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  12. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  13. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  14. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }
  15. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 34, "salary" : 12000, "deptno" : 10 }
  16. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 34, "salary" : 12000, "deptno" : 10 }
  17. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "empno" : 107, "ename" : "王三", "sex" : "女", "age" : 27, "salary" : 5000, "deptno" : 20 }
  18. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 33, "salary" : 10000, "deptno" : 30 }
  19. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }
  20. { "_id" : ObjectId("5f9c371b5df291fa8194b927"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }
  21. { "_id" : ObjectId("5f9c376a29491ade8d9d3e79"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 32, "salary" : 1600, "deptno" : 30 }

2.去掉集合中重复的数据

  1. 命令：
  2. > db.集合名.distinct("ename")
  1. > db.emp.distinct("ename")
  3. [
  4.         "admin",
  5.         "刘静",
  6.         "张三",
  7.         "张四",
  8.         "张良",
  9.         "李元芳",
  10.         "李元静",
  11.         "李四",
  12.         "李明",
  13.         "李菲菲",
  14.         "王三"
  15. ]

3. 查询年龄等于25的数据

  • 这里的“age”可以不加“ ”，直接写成{age:25}
  1. > db.emp.find({"age":25})
  3. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  4. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }

4. 查询ename=“李元芳“的数据

  1. > db.emp.find({"ename":"李元芳"})
  3. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }

5. 查询age>30岁的员工数据

  1. > db.emp.find({"age":{$gt:30}})
  3. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }
  4. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  5. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  6. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  7. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }

6. 查询age<25的员工数据

  1. > db.emp.find({"age":{$lt:25}})
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }

7.查询age>=30的员工数据

  1. > db.emp.find({"age":{$gte:30}})
  3. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  4. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  5. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }
  6. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  7. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  8. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  9. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }

8. 查询age<=30的员工数据

  1. > db.emp.find({"age":{$lte:30}})
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }
  4. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  5. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  6. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  7. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }
  8. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "empno" : 107, "ename" : "王三", "sex" : "女", "age" : 27, "salary" : 5000, "deptno" : 20 }
  9. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }

9. 查询age>=25并且age<=30的员工数据

  1. > db.emp.find({"age":{$gte:25,$lte:30}})
  3. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  4. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  5. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  6. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }
  7. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "empno" : 107, "ename" : "王三", "sex" : "女", "age" : 27, "salary" : 5000, "deptno" : 20 }
  8. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }

10. 查询全部姓李的员工数据（模糊查询）

  • 模糊查询一般用于查询数据量比较小的集合数据
  1. > db.emp.find({"ename":/李/})
  3. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  4. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }
  5. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  6. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  7. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  8. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }

11. 查询姓名以“张”开头的员工数据

  1. > db.emp.find({"ename":/^张/})
  3. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  4. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  5. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }

12. 查询以姓名以“静”结尾的员工数据

  1. > db.emp.find({"ename":/静$/})
  3. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }
  4. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }

13. 查询指定列的员工数据

  • 查询所有员工年龄的数据
  1. > db.emp.find({},{"age":1})
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "age" : 20 }
  4. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "age" : 30 }
  5. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "age" : 25 }
  6. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "age" : 30 }
  7. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "age" : 28 }
  8. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "age" : 32 }
  9. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "age" : 35 }
  10. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "age" : 35 }
  11. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "age" : 27 }
  12. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "age" : 35 }
  13. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "age" : 35 }
  14. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "age" : 25 }
  • 查询所有员工的姓名和年龄的数据
  1. > db.emp.find({},{"ename":1,"age":1})
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "ename" : "admin", "age" : 20 }
  4. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "ename" : "张三", "age" : 30 }
  5. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "ename" : "张良", "age" : 25 }
  6. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "ename" : "李明", "age" : 30 }
  7. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "ename" : "李菲菲", "age" : 28 }
  8. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "ename" : "张四", "age" : 32 }
  9. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "ename" : "李四", "age" : 35 }
  10. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "ename" : "李四", "age" : 35 }
  11. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "ename" : "王三", "age" : 27 }
  12. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "ename" : "李元芳", "age" : 35 }
  13. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "ename" : "李元静", "age" : 35 }
  14. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "ename" : "刘静", "age" : 25 }
  • 查询age>=30的员工姓名、年龄和薪资
  1. > db.emp.find({"age":{$gte:30}},{"ename":1,"age":1,"salary":1})
  3. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "ename" : "张三", "age" : 30, "salary" : 2500 }
  4. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "ename" : "李明", "age" : 30, "salary" : 1800 }
  5. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "ename" : "张四", "age" : 32, "salary" : 8000 }
  6. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "ename" : "李四", "age" : 35, "salary" : 12000 }
  7. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "ename" : "李四", "age" : 35, "salary" : 12000 }
  8. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "ename" : "李元芳", "age" : 35, "salary" : 8000 }
  9. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "ename" : "李元静", "age" : 35, "salary" : 15000 }

14. 排序

  • 按照年龄升序排列
  1. > db.emp.find().sort({"age":1})
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }
  4. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  5. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }
  6. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "empno" : 107, "ename" : "王三", "sex" : "女", "age" : 27, "salary" : 5000, "deptno" : 20 }
  7. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }
  8. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  9. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  10. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }
  11. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  12. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  13. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  14. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }
  • 按照薪资降序排列
  1. > db.emp.find().sort({salary:-1})
  3. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }
  4. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  5. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  6. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }
  7. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  8. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "empno" : 107, "ename" : "王三", "sex" : "女", "age" : 27, "salary" : 5000, "deptno" : 20 }
  9. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }
  10. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }
  11. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  12. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  13. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  14. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }

15. 查询ename=admin且age=20的员工数据

  1. > db.emp.find({ename:"admin",age:20})
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }

16. 查询前5条数据

  1. > db.emp.find().limit(5)
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }
  4. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  5. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  6. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  7. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }

17. 查询10条以后的数据

  • 这里一共12条数据，查询10条以后的数据，结果是两条数据。
  1. > db.emp.find().skip(10)
  3. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }
  4. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }

18. 查询集合中的总数据

  • 一共12条数据
  1. > db.emp.find().count()
  3. 12
  • 查询salary>=5000的员工数量
  1. > db.emp.find({salary:{$gte:5000}}).count()
  3. 6
  • 验证（查询查询salary>=5000的员工数据，一共是6条。）
  1. > db.emp.find({salary:{$gte:5000}})
  3. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }
  4. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  5. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  6. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "empno" : 107, "ename" : "王三", "sex" : "女", "age" : 27, "salary" : 5000, "deptno" : 20 }
  7. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  8. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }

19. 分页查询

  • 按照每页显示5条数据查询，一共12条数据，就要查询3页
  1. 第一页：
  2. > db.emp.find().skip(0).limit(5)
  4. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }
  5. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  6. { "_id" : ObjectId("5f9c20945df291fa8194b91d"), "empno" : 102, "ename" : "张良", "sex" : "男", "age" : 25, "salary" : 3000, "deptno" : 20 }
  7. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  8. { "_id" : ObjectId("5f9c215f5df291fa8194b91f"), "empno" : 104, "ename" : "李菲菲", "sex" : "女", "age" : 28, "salary" : 4200, "deptno" : 20 }
  10. 第二页：
  11. > db.emp.find().skip(5).limit(5)
  13. { "_id" : ObjectId("5f9c21855df291fa8194b920"), "empno" : 105, "ename" : "张四", "sex" : "男", "age" : 32, "salary" : 8000, "deptno" : 30 }
  14. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  15. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  16. { "_id" : ObjectId("5f9c21d85df291fa8194b923"), "empno" : 107, "ename" : "王三", "sex" : "女", "age" : 27, "salary" : 5000, "deptno" : 20 }
  17. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  19. 第三页：
  20. > db.emp.find().skip(10).limit(5)
  22. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }
  23. { "_id" : ObjectId("5f9c227d5df291fa8194b926"), "empno" : 110, "ename" : "刘静", "sex" : "女", "age" : 25, "salary" : 3500, "deptno" : 10 }
  • skip的值=（页数-1）* 每页显示数量
  • skip（N）：表示要查询第N条数据后的数据

20.关键字or的查询方式

  • 查询年龄是30或者年龄是35的员工数据(注意写法)
  1. > db.emp.find({$or:[{age:30},{age:35}]})
  3. { "_id" : ObjectId("5f9c1dfc5df291fa8194b91c"), "empno" : 101, "ename" : "张三", "sex" : "女", "age" : 30, "salary" : 2500, "deptno" : 20 }
  4. { "_id" : ObjectId("5f9c21055df291fa8194b91e"), "empno" : 103, "ename" : "李明", "sex" : "女", "age" : 30, "salary" : 1800, "deptno" : 30 }
  5. { "_id" : ObjectId("5f9c219b5df291fa8194b921"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  6. { "_id" : ObjectId("5f9c21a75df291fa8194b922"), "empno" : 106, "ename" : "李四", "sex" : "男", "age" : 35, "salary" : 12000, "deptno" : 10 }
  7. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 35, "salary" : 8000, "deptno" : 30 }
  8. { "_id" : ObjectId("5f9c22445df291fa8194b925"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 35, "salary" : 15000, "deptno" : 36 }

21. 查询第一条数据

  1. > db.emp.findOne()
  2. {
  3.         "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"),
  4.         "empno" : 100,
  5.         "ename" : "admin",
  6.         "sex" : "男",
  7.         "age" : 20,
  8.         "salary" : 800,
  9.         "deptno" : 10
  10. }

或

  1. > db.emp.find().limit(1)
  3. { "_id" : ObjectId("5f9c1c5b5df291fa8194b91b"), "empno" : 100, "ename" : "admin", "sex" : "男", "age" : 20, "salary" : 800, "deptno" : 10 }

22. 查询当前表所在的数据库

  1. > db.emp.getDB()
  3. company

六、更新数据

  • 更新数据一定要有条件限制，并且需要加上$set否则会全部修改
  • 第一个参数是条件，后面的参数是要修改的数据或者是其他操作，比如批量操作

1. 更新员工李元静的年龄为32岁

  1. > db.emp.update({ename:"李元静"},{$set:{age:32}})
  3. WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })
  • 验证(查询姓名是李元静的员工数据)
  1. > db.emp.find({ename:"李元静"})
  3. { "_id" : ObjectId("5f9c376a29491ade8d9d3e79"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 32, "salary" : 15000, "deptno" : 36 }

2. 更新员工李元静的薪资为1600并且所在部门修改为30

  1. > db.emp.update({ename:"李元静"},{$set:{salary:1600,deptno:30}})
  3. WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })
  • 验证(查询姓名是李元静的员工数据)
  1. > db.emp.find({ename:"李元静"})
  3. { "_id" : ObjectId("5f9c376a29491ade8d9d3e79"), "empno" : 109, "ename" : "李元静", "sex" : "女", "age" : 32, "salary" : 1600, "deptno" : 30 }

3. 批量更新数据

  • 把年龄是35岁的更新为34岁
  • 设置第三个参数：{multi:true}
  1. > db.emp.update({age:35},{$set:{age:34}},{multi:true})
  3. WriteResult({ "nMatched" : 3, "nUpserted" : 0, "nModified" : 3 })
  • 通过db.emp.find()验证所有数据没有年龄为35的员工

4. $inc使用

  • $inc将一个字段的值增加或者减少
  • 把李元芳的年龄减少1岁，同时薪资加2000
  1. > db.emp.update({ename:"李元芳"},{$inc:{age:-1,salary:2000}})
  3. WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })
  • 验证
  1. > db.emp.find({ename:"李元芳"})
  3. { "_id" : ObjectId("5f9c22185df291fa8194b924"), "empno" : 108, "ename" : "李元芳", "sex" : "男", "age" : 33, "salary" : 10000, "deptno" : 30 }

七、删除操作

1. 删除指定条件的数据

  1. db.emp.remove({ename:"李元芳"})

2. 删除所有数据

  1. db.emp.remove({})

3. 删除集合

  1. db.emp.drop()

4.删除数据库

  1. db.dropDatabase()

5. 温馨提示

  • 对数据库数据执行删除操作时，记得加条件！

八、MongoDB数据库索引

  • MongoDB数据库索引是指对数据库集合中的一列或者多列进行排序的一种结构，可以大大缩减我们在使用数据库查询时候的时间，其用法和关系型数据库一样。

1. 模拟批量插入数据

  • 在使用数据库前，我们创建一个com数据库和users集合，模拟60万条数据
  • PS:插入60万条数据大概需要5分钟
  1. > use com
  2. switched to db com
  3. > for(var i=0;i<600000;i++){
  4. ... db.users.insert({userNo:i,userName:"张三"+i,age:28,phone:"13000"+i})
  5. ... }
  6. WriteResult({ "nInserted" : 1 })
  • 验证数据
  1. > db.users.find().count()
  2. 600000

2.无索引查询所耗费时间

  1. 命令： 
  2. db.users.find({userNo:599999}).explain("executionStats")
  1. > db.users.find({userNo:599999}).explain("executionStats")
  2. {
  3.         "queryPlanner" : {
  4.                 "plannerVersion" : 1,
  5.                 "namespace" : "com.users",
  6.                 "indexFilterSet" : false,
  7.                 "parsedQuery" : {
  8.                         "userNo" : {
  9.                                 "$eq" : 599999
  10.                         }
  11.                 },
  12.                 "winningPlan" : {
  13.                         "stage" : "COLLSCAN",
  14.                         "filter" : {
  15.                                 "userNo" : {
  16.                                         "$eq" : 599999
  17.                                 }
  18.                         },
  19.                         "direction" : "forward"
  20.                 },
  21.                 "rejectedPlans" : [ ]
  22.         },
  23.         "executionStats" : {
  24.                 "executionSuccess" : true,
  25.                 "nReturned" : 1,
  26.                 "executionTimeMillis" : 254,
  27.                 "totalKeysExamined" : 0,
  28.                 "totalDocsExamined" : 600000,
  29.                 "executionStages" : {
  30.                         "stage" : "COLLSCAN",
  31.                         "filter" : {
  32.                                 "userNo" : {
  33.                                         "$eq" : 599999
  34.                                 }
  35.                         },
  36.                         "nReturned" : 1,
  37.                         "executionTimeMillisEstimate" : 3,
  38.                         "works" : 600002,
  39.                         "advanced" : 1,
  40.                         "needTime" : 600000,
  41.                         "needYield" : 0,
  42.                         "saveState" : 600,
  43.                         "restoreState" : 600,
  44.                         "isEOF" : 1,
  45.                         "direction" : "forward",
  46.                         "docsExamined" : 600000
  47.                 }
  48.         },
  49.         "serverInfo" : {
  50.                 "host" : "thinkPadE580",
  51.                 "port" : 27017,
  52.                 "version" : "4.4.2-rc0",
  53.                 "gitVersion" : "b5fafa1f87dda6f8773c5a8a1a5e7776d4d94da7"
  54.         },
  55.         "ok" : 1
  56. }
  • 通过"executionTimeMillis" : 254可以知道查询所耗费时间为254毫秒，当然这与计算机配置性能有关。

3. 创建索引

  • 为userNo创建索引
  1. 命令：
  2.     db.users.ensureIndex({userNo:1})
  1. > db.users.ensureIndex({userNo:1})
  2. {
  3.         "createdCollectionAutomatically" : false,
  4.         "numIndexesBefore" : 1,
  5.         "numIndexesAfter" : 2,
  6.         "ok" : 1
  7. }

4. 查询索引

  1. 命令：
  2.     db.users.getIndexes()
  1. > db.users.getIndexes()
  2. [
  3.         {
  4.                 "v" : 2,
  5.                 "key" : {
  6.                         "_id" : 1
  7.                 },
  8.                 "name" : "_id_"
  9.         },
  10.         {
  11.                 "v" : 2,
  12.                 "key" : {
  13.                         "userNo" : 1
  14.                 },
  15.                 "name" : "userNo_1"
  16.         }
  17. ]
  • _id为集合默认id索引,userNo是自定义索引

5. 使用索引查询所耗费时间

  1. > db.users.find({userNo:599999}).explain("executionStats")
  2. {
  3.         "queryPlanner" : {
  4.                 "plannerVersion" : 1,
  5.                 "namespace" : "com.users",
  6.                 "indexFilterSet" : false,
  7.                 "parsedQuery" : {
  8.                         "userNo" : {
  9.                                 "$eq" : 599999
  10.                         }
  11.                 },
  12.                 "winningPlan" : {
  13.                         "stage" : "FETCH",
  14.                         "inputStage" : {
  15.                                 "stage" : "IXSCAN",
  16.                                 "keyPattern" : {
  17.                                         "userNo" : 1
  18.                                 },
  19.                                 "indexName" : "userNo_1",
  20.                                 "isMultiKey" : false,
  21.                                 "multiKeyPaths" : {
  22.                                         "userNo" : [ ]
  23.                                 },
  24.                                 "isUnique" : false,
  25.                                 "isSparse" : false,
  26.                                 "isPartial" : false,
  27.                                 "indexVersion" : 2,
  28.                                 "direction" : "forward",
  29.                                 "indexBounds" : {
  30.                                         "userNo" : [
  31.                                                 "[599999.0, 599999.0]"
  32.                                         ]
  33.                                 }
  34.                         }
  35.                 },
  36.                 "rejectedPlans" : [ ]
  37.         },
  38.         "executionStats" : {
  39.                 "executionSuccess" : true,
  40.                 "nReturned" : 1,
  41.                 "executionTimeMillis" : 84,
  42.                 "totalKeysExamined" : 1,
  43.                 "totalDocsExamined" : 1,
  44.                 "executionStages" : {
  45.                         "stage" : "FETCH",
  46.                         "nReturned" : 1,
  47.                         "executionTimeMillisEstimate" : 0,
  48.                         "works" : 2,
  49.                         "advanced" : 1,
  50.                         "needTime" : 0,
  51.                         "needYield" : 0,
  52.                         "saveState" : 0,
  53.                         "restoreState" : 0,
  54.                         "isEOF" : 1,
  55.                         "docsExamined" : 1,
  56.                         "alreadyHasObj" : 0,
  57.                         "inputStage" : {
  58.                                 "stage" : "IXSCAN",
  59.                                 "nReturned" : 1,
  60.                                 "executionTimeMillisEstimate" : 0,
  61.                                 "works" : 2,
  62.                                 "advanced" : 1,
  63.                                 "needTime" : 0,
  64.                                 "needYield" : 0,
  65.                                 "saveState" : 0,
  66.                                 "restoreState" : 0,
  67.                                 "isEOF" : 1,
  68.                                 "keyPattern" : {
  69.                                         "userNo" : 1
  70.                                 },
  71.                                 "indexName" : "userNo_1",
  72.                                 "isMultiKey" : false,
  73.                                 "multiKeyPaths" : {
  74.                                         "userNo" : [ ]
  75.                                 },
  76.                                 "isUnique" : false,
  77.                                 "isSparse" : false,
  78.                                 "isPartial" : false,
  79.                                 "indexVersion" : 2,
  80.                                 "direction" : "forward",
  81.                                 "indexBounds" : {
  82.                                         "userNo" : [
  83.                                                 "[599999.0, 599999.0]"
  84.                                         ]
  85.                                 },
  86.                                 "keysExamined" : 1,
  87.                                 "seeks" : 1,
  88.                                 "dupsTested" : 0,
  89.                                 "dupsDropped" : 0
  90.                         }
  91.                 }
  92.         },
  93.         "serverInfo" : {
  94.                 "host" : "thinkPadE580",
  95.                 "port" : 27017,
  96.                 "version" : "4.4.2-rc0",
  97.                 "gitVersion" : "b5fafa1f87dda6f8773c5a8a1a5e7776d4d94da7"
  98.         },
  99.         "ok" : 1
  100. }
  • 用索引查询userNo的值，可以通过"executionTimeMillisEstimate" : 0知道，所耗费的时间为0毫秒，大大缩短了查询速度

6. 删除索引

  1. 命令：
  2.      db.users.dropIndex({userNo:1})

验证

  1. > db.users.dropIndex({userNo:1})
  2. { "nIndexesWas" : 2, "ok" : 1 }
  • 可以看出，我们自定义的索引已经删除

7. 复合索引

  • 当要对多个字段进行经常性大量查询的时候，我们可以设置复合索引
  1. > db.users.ensureIndex({userNo:1,userName:1})
  2. {
  3.         "createdCollectionAutomatically" : false,
  4.         "numIndexesBefore" : 1,
  5.         "numIndexesAfter" : 2,
  6.         "ok" : 1
  7. }
  • 查看复合索引
  1. > db.users.getIndexes()
  2. [
  3.         {
  4.                 "v" : 2,
  5.                 "key" : {
  6.                         "_id" : 1
  7.                 },
  8.                 "name" : "_id_"
  9.         },
  10.         {
  11.                 "v" : 2,
  12.                 "key" : {
  13.                         "userNo" : 1,
  14.                         "userName" : 1
  15.                 },
  16.                 "name" : "userNo_1_userName_1"
  17.         }
  18. ]
  • userNo和userName是我们自定义的索引
  • 查询userName:”张三599999”所耗费时间
  1. > db.users.find({userNo:599999,userName:"张三599999"}).explain("executionStats")))
  2. {
  3.         "queryPlanner" : {
  4.                 "plannerVersion" : 1,
  5.                 "namespace" : "com.users",
  6.                 "indexFilterSet" : false,
  7.                 "parsedQuery" : {
  8.                         "$and" : [
  9.                                 {
  10.                                         "userName" : {
  11.                                                 "$eq" : "张三599999"
  12.                                         }
  13.                                 },
  14.                                 {
  15.                                         "userNo" : {
  16.                                                 "$eq" : 599999
  17.                                         }
  18.                                 }
  19.                         ]
  20.                 },
  21.                 "winningPlan" : {
  22.                         "stage" : "FETCH",
  23.                         "inputStage" : {
  24.                                 "stage" : "IXSCAN",
  25.                                 "keyPattern" : {
  26.                                         "userNo" : 1,
  27.                                         "userName" : 1
  28.                                 },
  29.                                 "indexName" : "userNo_1_userName_1",
  30.                                 "isMultiKey" : false,
  31.                                 "multiKeyPaths" : {
  32.                                         "userNo" : [ ],
  33.                                         "userName" : [ ]
  34.                                 },
  35.                                 "isUnique" : false,
  36.                                 "isSparse" : false,
  37.                                 "isPartial" : false,
  38.                                 "indexVersion" : 2,
  39.                                 "direction" : "forward",
  40.                                 "indexBounds" : {
  41.                                         "userNo" : [
  42.                                                 "[599999.0, 599999.0]"
  43.                                         ],
  44.                                         "userName" : [
  45.                                                 "[\"张三599999\", \"张三599999\"]"
  46.                                         ]
  47.                                 }
  48.                         }
  49.                 },
  50.                 "rejectedPlans" : [ ]
  51.         },
  52.         "executionStats" : {
  53.                 "executionSuccess" : true,
  54.                 "nReturned" : 1,
  55.                 "executionTimeMillis" : 2,
  56.                 "totalKeysExamined" : 1,
  57.                 "totalDocsExamined" : 1,
  58.                 "executionStages" : {
  59.                         "stage" : "FETCH",
  60.                         "nReturned" : 1,
  61.                         "executionTimeMillisEstimate" : 0,
  62.                         "works" : 2,
  63.                         "advanced" : 1,
  64.                         "needTime" : 0,
  65.                         "needYield" : 0,
  66.                         "saveState" : 0,
  67.                         "restoreState" : 0,
  68.                         "isEOF" : 1,
  69.                         "docsExamined" : 1,
  70.                         "alreadyHasObj" : 0,
  71.                         "inputStage" : {
  72.                                 "stage" : "IXSCAN",
  73.                                 "nReturned" : 1,
  74.                                 "executionTimeMillisEstimate" : 0,
  75.                                 "works" : 2,
  76.                                 "advanced" : 1,
  77.                                 "needTime" : 0,
  78.                                 "needYield" : 0,
  79.                                 "saveState" : 0,
  80.                                 "restoreState" : 0,
  81.                                 "isEOF" : 1,
  82.                                 "keyPattern" : {
  83.                                         "userNo" : 1,
  84.                                         "userName" : 1
  85.                                 },
  86.                                 "indexName" : "userNo_1_userName_1",
  87.                                 "isMultiKey" : false,
  88.                                 "multiKeyPaths" : {
  89.                                         "userNo" : [ ],
  90.                                         "userName" : [ ]
  91.                                 },
  92.                                 "isUnique" : false,
  93.                                 "isSparse" : false,
  94.                                 "isPartial" : false,
  95.                                 "indexVersion" : 2,
  96.                                 "direction" : "forward",
  97.                                 "indexBounds" : {
  98.                                         "userNo" : [
  99.                                                 "[599999.0, 599999.0]"
  100.                                         ],
  101.                                         "userName" : [
  102.                                                 "[\"张三599999\", \"张三599999\"]"
  103.                                         ]
  104.                                 },
  105.                                 "keysExamined" : 1,
  106.                                 "seeks" : 1,
  107.                                 "dupsTested" : 0,
  108.                                 "dupsDropped" : 0
  109.                         }
  110.                 }
  111.         },
  112.         "serverInfo" : {
  113.                 "host" : "thinkPadE580",
  114.                 "port" : 27017,
  115.                 "version" : "4.4.2-rc0",
  116.                 "gitVersion" : "b5fafa1f87dda6f8773c5a8a1a5e7776d4d94da7"
  117.         },
  118.         "ok" : 1
  119. }

  • 通过"executionTimeMillis" : 2,可以看出，查询userNo和userName只需要2毫秒

  • 注意：如果在一个集合中，对多个字段设置索引N（N!=1），在使用复合索引查询的时候，要连同第一个索引字段一起查询，如果只单单查询第N个,索引将没有效果。

8.唯一索引

  • 创建唯一索引的条件是，集合中字段的数据不能重复，但在缺省情况下创建是索引均不是唯一索引

  • 由于集合中的age都是一样的值，在给age创建唯一索引的时候会报错

  1. 命令：
  2.     > db.users.ensure({age:1},{unique:true})
  • 为age创建唯一索引，失败
  1. > db.users.ensure({age:1},{unique:true})
  2. TypeError: db.users.ensure is not a function :
  3. @(shell):1:1
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