MongoDB底层数据结构常用命令

Posted 2022-11-23 _less is more

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1、数据结构

下图来自于StackOverflow

如上图，左边是B树（中间那是一杠，不是减号），右边是B+树

B树【MongoDB】

每个节点都有key和value，因此没有重复的key和value。但查找时会有更多【缓存未命中】的情况，因为当前遍历到的key即便不是我们要的，他对应的数据指针也会一起进入内存，导致内存中的key数量会比B+树少。

最快访问到一个想要节点的可以是O(1)，因此对于频繁访问的数据放到离根节点越近的地方，访问会越快，而B+树没有这个优势，因为B+中的内部节点只有key没有对应的data指针。

由于所有数据分布于整颗树上的每一个节点，因此如果要遍历一遍，需要遍历树中所有层，这样的遍历需要回溯，消耗更多栈空间。

B+树【mysql】

内部节点（非叶节点）只有key没有value，因此在内存中，一个内存页可以存储更多的key，因此在查找时比较key大小的时候，【缓存未命中】的情况会更少，因为cache中会有更多的key。

叶节点包含所有的完整的key和相对应的value，因此内部节点的key算是重复，但这样的重复有利于快速查找。

叶节点指针互相连接，因此遍历一遍只需要线性地依次查找所有的叶节点即可。对于MySQL需要访问一个区间数据的情况能提供更高效的查找。

本质区别

B树每个节点都有key和对应数据的指针；B+树只有叶节点才有key和对应数据的指针，内部节点只有用于查找的key
B树的叶节点之间没有指针；B+树叶节点之间有指针

2、BSON和JSON

JSON格式很常见


  "_id": 1,
  "name":  "first" : "John", "last" : "Backus" ,
  "contribs": [ "Fortran", "ALGOL", "Backus-Naur Form", "FP" ]

而BSON仅仅只是二进制版本的JSON，因此不具有可读性，像这样

\\x31\\x00\\x00\\x00
\\x04BSON\\x00
\\x26\\x00\\x00\\x00
\\x02\\x30\\x00\\x08\\x00\\x00\\x00awesome\\x00
\\x01\\x31\\x00\\x33\\x33\\x33\\x33\\x33\\x33\\x14\\x40
\\x10\\x32\\x00\\xc2\\x07\\x00\\x00
\\x00
\\x00

3、数据库操作
查看

show dbs

进入数据库，如果test数据库不存在，则自动创建

use test

删除数据库，在test数据库中，使用如下命令，则会删除当前正在使用的数据库

db.dropDatabase()

4、collection操作，相当于表
查看所有collections

show collections

创建collection

db.createCollection("mycollection")

删除collection

db.mycollection.drop()

向collection中插入，如果名为class的collection不存在则自动创建

db.class.insert('name':'ee547')

查看表中内容

> db.class.find()
 "_id" : ObjectId("632a16e412de6627982e581b"), "name" : "ee547"

5、实战1

创建一个collection

db.createCollection('my_data')

插入数据

db.my_data.insert([
 
 title: "MongoDB Overview",
 description: "MongoDB is no SQL database",
 by: "tutorials point",
 tags: ["mongodb", "database", "NoSQL"],
 likes: 100
 ,
 
 title: "NoSQL Database",
 description: "NoSQL database doesn't have tables",
 by: "tutorials point",
 tags: ["mongodb", "database", "NoSQL"],
 likes: 20
 
])

查看刚插入的数据

> db.my_data.find()
 "_id" : ObjectId("6333b19a3dc8391635c169b0"), "title" : "MongoDB Overview", "description" : "MongoDB is no SQL database", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 100 
 "_id" : ObjectId("6333b19a3dc8391635c169b1"), "title" : "NoSQL Database", "description" : "NoSQL database doesn't have tables", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 20

用更好观察类似JSON格式的方式输出

> db.my_data.find().pretty()

        "_id" : ObjectId("6333b19a3dc8391635c169b0"),
        "title" : "MongoDB Overview",
        "description" : "MongoDB is no SQL database",
        "by" : "tutorials point",
        "tags" : [
                "mongodb",
                "database",
                "NoSQL"
        ],
        "likes" : 100


        "_id" : ObjectId("6333b19a3dc8391635c169b1"),
        "title" : "NoSQL Database",
        "description" : "NoSQL database doesn't have tables",
        "by" : "tutorials point",
        "tags" : [
                "mongodb",
                "database",
                "NoSQL"
        ],
        "likes" : 20

用条件过滤查找，这里是and，也可以是or，nor等等；这里表示查找title和by字段为特定字符串的document（即关系数据库中的row）

> db.my_data.find( $and: [ title: 'MongoDB Overview', by: 'tutorials point'])
 "_id" : ObjectId("6333b19a3dc8391635c169b0"), "title" : "MongoDB Overview", "description" : "MongoDB is no SQL database", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 100

更新title，可以看到将title为MongoDB Overview的document的title改为了New name

> db.my_data.find()
 "_id" : ObjectId("6333b19a3dc8391635c169b0"), "title" : "MongoDB Overview", "description" : "MongoDB is no SQL database", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 100 
 "_id" : ObjectId("6333b19a3dc8391635c169b1"), "title" : "NoSQL Database", "description" : "NoSQL database doesn't have tables", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 20 
> db.my_data.update(title:'MongoDB Overview',$set:title:'New name')
WriteResult( "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 )
> db.my_data.find()
 "_id" : ObjectId("6333b19a3dc8391635c169b0"), "title" : "New name", "description" : "MongoDB is no SQL database", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 100 
 "_id" : ObjectId("6333b19a3dc8391635c169b1"), "title" : "NoSQL Database", "description" : "NoSQL database doesn't have tables", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 20

同时更新多个document，找到by字段为tutorials point的documents，然后把他们的by字段改为tutorials point222，第三个参数中的multi：true即表示允许修改多个，不加这个则只会更新一个

> db.my_data.find()
 "_id" : ObjectId("6333b19a3dc8391635c169b0"), "title" : "New name", "description" : "MongoDB is no SQL database", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 100 
 "_id" : ObjectId("6333b19a3dc8391635c169b1"), "title" : "NoSQL Database", "description" : "NoSQL database doesn't have tables", "by" : "tutorials point", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 20 
> db.my_data.update(
...  by:'tutorials point',
...  $set:by:'tutorials point222',
...  multi:true
... )
WriteResult( "nMatched" : 2, "nUpserted" : 0, "nModified" : 2 )
> db.my_data.find()
 "_id" : ObjectId("6333b19a3dc8391635c169b0"), "title" : "New name", "description" : "MongoDB is no SQL database", "by" : "tutorials point222", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 100 
 "_id" : ObjectId("6333b19a3dc8391635c169b1"), "title" : "NoSQL Database", "description" : "NoSQL database doesn't have tables", "by" : "tutorials point222", "tags" : [ "mongodb", "database", "NoSQL" ], "likes" : 20

6、实战2

创建collection

db.createCollection("empDetails")

使用insertOne命令插入一个document并查看

> db.empDetails.insertOne(
...  First_Name: "Radhika",
...  Last_Name: "Sharma",
...  Date_Of_Birth:"1995-09-26",
...  e_mail: "radhika_sharma.123@gmail.com",
...  phone:"9848022338"
... )

        "acknowledged" : true,
        "insertedId" : ObjectId("6333b4013dc8391635c169b2")

> db.empDetails.find()
 "_id" : ObjectId("6333b4013dc8391635c169b2"), "First_Name" : "Radhika", "Last_Name" : "Sharma", "Date_Of_Birth" : "1995-09-26", "e_mail" : "radhika_sharma.123@gmail.com", "phone" : "9848022338"

使用insertMany同时插入多个，当然按照前面直接用insert命令插入多个也可以

> db.empDetails.insertMany([
...  
...  First_Name: "Doe",
...  Last_Name: "Joe",
...  Date_Of_Birth:"1995-10-24",
...  e_mail: "doe_joe.123@gmail.com",
...  phone:"1234567890"
...  ,
...  
...  First_Name: "Rachel",
...  Last_Name: "Christopher",
...  Date_Of_Birth:"1990-02-16",
...  e_mail: "Rachel_Christopher.123@gmail.com",
...  phone:"9000054321"
...  ,
...  
...  First_Name: "Fathima",
...  Last_Name: "Sheik",
...  Date_Of_Birth:"1990-02-16",
...  e_mail: "Fathima_Sheik.123@gmail.com",
...  phone:"9000054321"
...  
... ])

        "acknowledged" : true,
        "insertedIds" : [
                ObjectId("6333b44d3dc8391635c169b3"),
                ObjectId("6333b44d3dc8391635c169b4"),
                ObjectId("6333b44d3dc8391635c169b5")
        ]

> db.empDetails.find()
 "_id" : ObjectId("6333b4013dc8391635c169b2"), "First_Name" : "Radhika", "Last_Name" : "Sharma", "Date_Of_Birth" : "1995-09-26", "e_mail" : "radhika_sharma.123@gmail.com", "phone" : "9848022338" 
 "_id" : ObjectId("6333b44d3dc8391635c169b3"), "First_Name" : "Doe", "Last_Name" : "Joe", "Date_Of_Birth" : "1995-10-24", "e_mail" : "doe_joe.123@gmail.com", "phone" : "1234567890" 
 "_id" : ObjectId("6333b44d3dc8391635c169b4"), "First_Name" : "Rachel", "Last_Name" : "Christopher", "Date_Of_Birth" : "1990-02-16", "e_mail" : "Rachel_Christopher.123@gmail.com", "phone" : "9000054321" 
 "_id" : ObjectId("6333b44d3dc8391635c169b5"), "First_Name" : "Fathima", "Last_Name" : "Sheik", "Date_Of_Birth" : "1990-02-16", "e_mail" : "Fathima_Sheik.123@gmail.com", "phone" : "9000054321"

查看一个document，具体查看的是哪个则根据排序而定，通常是输出最开始插入的那一个，因为第一个插入的_id最小

> db.empDetails.findOne()

        "_id" : ObjectId("6333b4013dc8391635c169b2"),
        "First_Name" : "Radhika",
        "Last_Name" : "Sharma",
        "Date_Of_Birth" : "1995-09-26",
        "e_mail" : "radhika_sharma.123@gmail.com",
        "phone" : "9848022338"

赋值，并输出其_id

> employee = db.empDetails.findOne()

        "_id" : ObjectId("6333b4013dc8391635c169b2"),
        "First_Name" : "Radhika",
        "Last_Name" : "Sharma",
        "Date_Of_Birth" : "1995-09-26",
        "e_mail" : "radhika_sharma.123@gmail.com",
        "phone" : "9848022338"

> employee._id
ObjectId("6333b4013dc8391635c169b2")

7、_id

MongoDB中一个很重要的东西就是_id，由数据库管理系统自动生成，其类型是ObjectId，长度为12-byte，ObjectId("6333b401 3dc8391635 c169b2")中的一个数字是十六进制，用4个bits来表示，而一个byte是8个bits，因此12-byte则可以表示24个十六进制的数

前4个byte，代表这个ObjectId相对于Unix epoch的创建时间，用秒表示；Unix epoch 是UTC世界标准时间1970年一月一日0时0分0秒

中间5个byte，是随机生成的，这串数在此机器和进程上是唯一的

后3个byte，是递增的数，初始化为一个随机生成的数，而非从0开始计

8、实战：排序

直接插入多个document，collection被自动创建，根据borough字段按字母排序，1表示递增排序

> db.restaurants.insertMany( [
...   "_id" : 1, "name" : "Central Park Cafe", "borough" : "Manhattan",
...   "_id" : 2, "name" : "Rock A Feller Bar and Grill", "borough" : "Queens",
...   "_id" : 3, "name" : "Empire State Pub", "borough" : "Brooklyn",
...   "_id" : 4, "name" : "Stan's Pizzaria", "borough" : "Manhattan",
...   "_id" : 5, "name" : "Jane's Deli", "borough" : "Brooklyn",
... ] );
 "acknowledged" : true, "insertedIds" : [ 1, 2, 3, 4, 5 ] 
> db.restaurants.find().sort(  "borough": 1  )
 "_id" : 3, "name" : "Empire State Pub", "borough" : "Brooklyn" 
 "_id" : 5, "name" : "Jane's Deli", "borough" : "Brooklyn" 
 "_id" : 1, "name" : "Central Park Cafe", "borough" : "Manhattan" 
 "_id" : 4, "name" : "Stan's Pizzaria", "borough" : "Manhattan" 
 "_id" : 2, "name" : "Rock A Feller Bar and Grill", "borough" : "Queens"

按递减排序

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