MongoDB学习笔记

从接触计算机学习开始，我所使用的数据库就是mysql，Oracle这样的关系型数据库。早就听说了NoSQL的概念，也对其有代表性的非关系型数据库mongoDB有所耳闻，一直想学习学习这项从未使用过的技术，可是由于种种原因，又没有时间来学习学习这项新的概念。也就是这么巧，目前的工作中，使用到的就是mongoDB，那么借此机会，正好好好学习学习这个新的数据库。

安装及部署

学习一个新的数据库，当然首先第一步就是要来安装数据库以及部署数据库服务。

安装

在mongoDB官网下载相关的安装包，就像安装qq一样的安装mongodb。我选择在我电脑上以F:\software\mongodb\server此路径来作为mongodb的安装路径。然后在server同级目录下创建data目录，在其下创建两个目录db和log，分别用于存放数据库数据和mongo的日志。如图所示：

vcno1sPOqsrWtq/G9LavoaPSsr7NysfLtaOs1NrO0tDo0qrKudPDbW9uZ29EQrXEyrG68qOsztK/ydLUzai5/cP8we7Q0MP8we5uZXQgc3RhcnQgbXlNb25nb8C0xvS2r21vbmdvtcS3/s7xoaPV4rj2w/zB7tTa1rTQ0M3qsr3W6DK1xMqxuvLSsrvh1NrI1da+zsS8/tbQ09DL+czhyr6hozwvcD4NCjxwPtbBtMujrG1vbmdvREK1xLCy17C8sLK/yvC+zc3qs8nBy6GjPC9wPg0KPHA+sNez1c7KzOKjujwvcD4NCtTauNW/qsq8sLLXsNTL0NC1xMqxuvKjrL/JxNy74bP2z9bI58/CzbzL+cq+tcTH6b/2o7o8YnIgLz4NCjxpbWcgYWx0PQ=="这里写图片描述" src="/uploadfile/Collfiles/20160905/20160905095654118.jpg" title="\" />
这是由于关闭了mongo的服务端，又去创建新的连接所导致的。
所以要么一直开着mongod，要么将其注册为windows的服务。只要服务一直在，mongoDB就在。
MongoDB的相关概念

下面的表格对关系型数据库mysql和非关系型数据库MongoDB做了相关概念的对比。

mysql
MongoDB
解释
database
database
数据库
table
collection
mysql称之为表，mongoDB称之为集合
row
document
mysql称之为数据行，mongoDB称之为数据文档
colunm
field
mysql称之为数据列，mongoDB称之为字段
primary key
primary key
mysql需要设置主键，mongoDB自动维护主键_id

下面是mongoDB常用的命令。

命令
解释
show dbs
显示所有的数据库名称
show collections
显示当前数据库中的所有集合
show users
显示当前数据库中的所有用户
show logs
显示可以访问的所有日志的名称
show log [name]
输出指定的日志，默认name为global
use [db_name]
使用给定的数据库，如果没有，则为创建
MongoDB的使用

数据库的新增与删除

新增：使用下面的命令来新增数据库。当执行了use命令后，调用show dbs并不能看到刚刚新增的数据库，那是因为数据库中没有数据，所以咱们通过命令db.集合名.insert向新增的数据库中新建一个集合并为其插入一条数据，之后再使用show dbs命令时就可以看到刚刚新建的数据库了。

> use earltest
switched to db earltest
> db
earltest
> show dbs
local 0.000GB
> db.mongotest.insert({"name":"练习"})
WriteResult({ "nInserted" : 1 })
> show dbs
earltest 0.000GB
local 0.000GB

删除：使用db.集合名.drop()来删除当前数据库下的指定的集合。使用db.dropDatabase()来删除当前使用的数据库。删除之后，调用show dbs就显示数据库已经被删除了。

> use local
switched to db local
> db.test.drop()
true
> db.dropDatabase()
{ "dropped" : "local", "ok" : 1 }
> show dbs
>

集合的新增与删除

新增
通过使用命令db.createCollect(集合名)来创建一个新的集合，例如：
> show dbs
earltest 0.000GB
> db.createCollection("student")
{ "ok" : 1 }
> show collections
student

以上语句相当于SQL的建表语句create table student(...)，只是没有在建表时定义表结构，这就是关系型数据库与非关系型数据库最主要的区别。没有表结构的约束，那么mongoDB使用起来就更加灵活。其实可以不用执行创建集合的命令，因为在插入文档时，如果数据库中没有相应的集合，那么mongo会自动创建这个集合，并完成插入操作。

删除
通过使用命令db.集合名.drop()来删除指定的集合，例如：
> show collections
student
> db.mongotest.drop()
true
> show collections
>

以上语句相当于SQL的删表语句drop table student。

文档的增删改查
mongoDB采用的数据结构是一种类似于JSON的BSON格式，即Binary JSON，二进制JSON格式。
常见的数据类型有以下这些：
数据类型
解释
String
字符串
Integer
整数类型，有32位和64位两种，分别记为Int32与Int64
Boolean
布尔类型，true还是false
Double
浮点类型
Arrays
数组，可以存放多个某一类型的数据
Object ID
用于存储文档的ID
新增
通过命令db.集合名.insert(document)来插入一个新的文档，例如：
> db.createCollection("student")
{ "ok" : 1 }
> show collections
student
>db.student.insert({"id":"1","name":"Adam","age":22,"sex":"male","major":"Psychology" })
WriteResult({ "nInserted" : 1 })
> db.student.find()
{ "_id" : ObjectId("57c83abfa33a42f78ac58e64"), "id" : "1", "name" : "Adam", "age" : 22, "sex" : "male", "major" : "Psychology" }
以上语句相当于SQL的向指定的表中插入一条数据，例如insert into student values("1","Adam","22","male","Psychology")
文章最后附有student集合的初始数据，可供需要练习的读者直接练习使用。
删除
通过命令db.集合名.remove(document)来删除相关文档，document为过滤条件，例如：
> db.student.find()
{ "_id" : ObjectId("57c83abfa33a42f78ac58e64"), "id" : "1", "name" : "Adam", "age" : 22, "sex" : "male", "major" : "Psychology" }
{ "_id" : ObjectId("57c83cc2a33a42f78ac58e65"), "id" : "2", "name" : "Alax", "age" : 21, "sex" : "male", "major" : "Biology" }
> db.student.remove({"id":"2"})
WriteResult({ "nRemoved" : 1 })
> db.student.find()
{ "_id" : ObjectId("57c83abfa33a42f78ac58e64"), "id" : "1", "name" : "Adam", "age" : 22, "sex" : "male", "major" : "Psychology" }

以上语句相当于SQL的从指定表中删除相关数据delete from student where id="2"

修改
通过命令db.集合名.update(parameter)来更新相关文档数据。例如：
> db.student.find()
{ "_id" : ObjectId("57c977b3058cb6872afd9e9d"), "id" : "1", "name" : "Adam", "age" : 23, "sex" : "male", "major" : "Psychology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9e9e"), "id" : "2", "name" : "Alex", "age" : 21, "sex" : "male", "major" : "Biology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9e9f"), "id" : "3", "name" : "Andy", "age" : 19, "sex" : "male", "major" : "Chemistry" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea0"), "id" : "4", "name" : "Bill", "age" : 20, "sex" : "male", "major" : "Economics" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea1"), "id" : "5", "name" : "Daisy", "age" : 20, "sex" : "female", "major" : "Sociology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea2"), "id" : "6", "name" : "Elizabeth", "age" : 20, "sex" : "female", "major" : "Histroy" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea3"), "id" : "7", "name" : "Emily", "age" : 23, "sex" : "female", "major" : "Histroy" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea4"), "id" : "8", "name" : "Helena", "age" : 24, "sex" : "female", "major" : "Biology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea5"), "id" : "9", "name" : "Julia", "age" : 23, "sex" : "female", "major" : "Economics" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea6"), "id" : "10", "name" : "Simon", "age" : 21, "sex" : "male", "major" : "Chemistry" }
> db.student.update({"age":21},{$set:{"major":"Math"}})
WriteResult({ "nMatched" : 1, "nUpserted" : 0, "nModified" : 1 })
> db.student.find()
{ "_id" : ObjectId("57c977b3058cb6872afd9e9d"), "id" : "1", "name" : "Adam", "age" : 23, "sex" : "male", "major" : "Psychology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9e9e"), "id" : "2", "name" : "Alex", "age" : 21, "sex" : "male", "major" : "Math" }
{ "_id" : ObjectId("57c977b3058cb6872afd9e9f"), "id" : "3", "name" : "Andy", "age" : 19, "sex" : "male", "major" : "Chemistry" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea0"), "id" : "4", "name" : "Bill", "age" : 20, "sex" : "male", "major" : "Economics" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea1"), "id" : "5", "name" : "Daisy", "age" : 20, "sex" : "female", "major" : "Sociology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea2"), "id" : "6", "name" : "Elizabeth", "age" : 20, "sex" : "female", "major" : "Histroy" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea3"), "id" : "7", "name" : "Emily", "age" : 23, "sex" : "female", "major" : "Histroy" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea4"), "id" : "8", "name" : "Helena", "age" : 24, "sex" : "female", "major" : "Biology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea5"), "id" : "9", "name" : "Julia", "age" : 23, "sex" : "female", "major" : "Economics" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea6"), "id" : "10", "name" : "Simon", "age" : 21, "sex" : "male", "major" : "Chemistry" }

以上语句相当于SQL的更新语句update student set major='Math' where age='21',但是可以看到满足age=21的记录有两条，通过mongoDB的update方法更新后，只更新了一条数据。那是因为mongoDB默认更新只更新找到的第一个记录，所以如果需要满足我们的更新需求，那么需要为update方法再传递一个参数，multi，如下所示：

> db.student.update({"age":21},{$set:{"major":"Chinese"}},{multi:true})
WriteResult({ "nMatched" : 2, "nUpserted" : 0, "nModified" : 2 })
> db.student.find()
{ "_id" : ObjectId("57c977b3058cb6872afd9e9d"), "id" : "1", "name" : "Adam", "age" : 23, "sex" : "male", "major" : "Psychology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9e9e"), "id" : "2", "name" : "Alex", "age" : 21, "sex" : "male", "major" : "Chinese" }
{ "_id" : ObjectId("57c977b3058cb6872afd9e9f"), "id" : "3", "name" : "Andy", "age" : 19, "sex" : "male", "major" : "Chemistry" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea0"), "id" : "4", "name" : "Bill", "age" : 20, "sex" : "male", "major" : "Economics" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea1"), "id" : "5", "name" : "Daisy", "age" : 20, "sex" : "female", "major" : "Sociology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea2"), "id" : "6", "name" : "Elizabeth", "age" : 20, "sex" : "female", "major" : "Histroy" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea3"), "id" : "7", "name" : "Emily", "age" : 23, "sex" : "female", "major" : "Histroy" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea4"), "id" : "8", "name" : "Helena", "age" : 24, "sex" : "female", "major" : "Biology" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea5"), "id" : "9", "name" : "Julia", "age" : 23, "sex" : "female", "major" : "Economics" }
{ "_id" : ObjectId("57c977b3058cb6872afd9ea6"), "id" : "10", "name" : "Simon", "age" : 21, "sex" : "male", "major" : "Chinese" }

update({query},{update},{multi})方法常用参数列表如下：

参数名
解释
query
相当于SQL的where
update
相当于SQL的set
multi
默认只更新找到的第一条记录，设置为true，则更新所有满足条件的记录
查询
接下来就是使用最多的查询操作了，对于查询的操作，我们与其来和SQL对比，这样更容易理解。
还是利用上面创建的学生表来进行查询。
查询所有的学生
mongoDB：db.student.find()
SQL：select * from student 查询所有男生
mongoDB：db.student.find({"sex":"male"})
SQL：select * from student where sex='male' 查询年龄小于23岁的所有男生的id，姓名及专业
mongoDB：db.student.find({"age":{$lt:23}},{id:"",name:"",major:""})
SQL：select id,name,major from student where age<23 按照年龄降序查询所有学生
mongoDB：db.student.find().sort({age:-1})
SQL：select * from student order by age desc 统计学生总数
mongoDB：db.student.count()
SQL：select count(*) from student 统计历史专业的学生人数
mongoDB：db.student.count({"major":"Histroy"})
SQL：select count(*) from student where major='Histroy' 统计各个专业的人数
mongoDB：db.student.aggregate([{$group:{_id:"$major","人数":{$sum:1}}}])
SQL：select count(*) from student group by major 统计每个专业的学生人数，并求他们的平均年龄
mongoDB：db.student.aggregate([{$group:{_id:"$major","学生人数":{$sum:1},"平均年龄":{$avg:"$age"}}}])
SQL：select major,count(major) as '学生人数',avg(age) as '平均年龄' from student group by major 查询第二条到第七条的记录
mongoDB：db.student.find().limit(6).skip(1)
SQL：select * from student limit 1,6; 查询中文专业的男生学生信息
mongoDB：db.student.find({$and:[{major:"Chinese"},{sex:"male"}]})
SQL：select * from student where major='Chinese' and sex='male'
查询中文专业或者男生的学生信息
mongoDB：db.student.find({$or:[{major:"Chinese"},{sex:"male"}]})
SQL：select * from student where major='Chinese' or sex='male'
查询男生与女生的平均年龄
mongoDB：db.student.aggregate([{$group:{_id:"$sex","平均年龄":{$avg:"$age"}}}])
SQL：select avg(age) as '平均年龄' from student group by age
查询学号是1，3，4，5，6的学生信息
mongoDB：db.student.find({"id":{$in:["1","3","4","5","6"]}})
SQL：select * from student where id in("1","3","4","5","6") 查询以A开头学生姓名的学生信息
mongoDB：db.student.find({"name":/^A/})
SQL：select * from student where name like 'A%'
总结

以上就是近期对于mongoDB数据库的一点学习小总结。当然这只是简单的查询，还没有涉及到复杂的查询，这主要是针对于刚刚接触NoSQL数据库，对一直以来使用关系型数据库的养成的查询习惯来说，一时间还不是很适应。随着大数据时代的到来，我们将会更多的使用到mongoDB这样的非关系型数据库，所以，在接下来的学习工作中，对于非关系型数据库还是要多多的去了解，学习，使用。

附以student集合初始数据：

db.student.insert(
[{"id":"1","name":"Adam","age":"22","sex":"male","major":"Psychology"},
{"id":"2","name":"Alex","age":"21","sex":"male","major":"Biology"},
{"id":"3","name":"Andy","age":"19","sex":"male","major":"Chemistry"},
{"id":"4","name":"Bill","age":"20","sex":"male","major":"Economics"},
{"id":"5","name":"Daisy","age":"20","sex":"female","major":"Sociology"},
{"id":"6","name":"Elizabeth","age":"20","sex":"female","major":"Histroy"},
{"id":"7","name":"Emily","age":"23","sex":"female","major":"Histroy"},
{"id":"8","name":"Helena","age":"24","sex":"female","major":"Biology"},
{"id":"9","name":"Julia","age":"23","sex":"female","major":"Economics"},
{"id":"10","name":"Simon","age":"21","sex":"male","major":"Chemistry"}
])