Mongodb

MongoDB命令行操作

 

本文专门介绍MongoDB的命令行操作。其实，这些操作在MongoDB官网提供的Quick Reference上都有，但是英文的，为了方便，这里将其稍微整理下，方便查阅。

 

这里用来做测试的是远端(10.77.20.xx)的Mongo数据库。

 

1、登录和退出

 

mongo命令直接加MongoDB服务器的IP地址（比如：mongo 10.77.20.xx），就可以利用Mongo的默认端口号(27017)登陆Mongo，然后便能够进行简单的命令行操作。

至于退出，直接exit，然后回车就好了。

 

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$ mongo 10.77.20.xx  

MongoDB shell version: 2.0.4  

connecting to: 10.77.20.xx/test  

> show collections  

> exit  

bye  

从以上可以看出，登录后mongo会自动连上一个名为test的数据库。如果这个数据库不存在，那么mongo会自动建立一个名为test的数据库。上面的例子，由于Mongo服务器上没有名为test的db，因此，mongo新建了一个空的名为test的db。其中，没有任何collection。

2、database级操作

 

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2.1 查看服务器上的数据库  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

  

2.2 切换数据库  

切换到blogtest数据库(从默认的test数据库)  

> use blogtest  

switched to db blogtest  

mongo中，db代表当前使用的数据库。这样，db就从原来的test，变为现在的blogtest数据库。  

  

2.3 查看当前数据库中的所有集合  

> show collections  

book  

system.indexes  

user  

  

2.4 创建数据库  

mongo中创建数据库采用的也是use命令，如果use后面跟的数据库名不存在，那么mongo将会新建该数据库。不过，实际上只执行use命令后，mongo是不会新建该数据库的，直到你像该数据库中插入了数据。  

> use test2  

switched to db test2  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

到这里并没有看到刚才新建的test2数据库。  

> db.hello.insert({"name":"testdb"})  

该操作会在test2数据库中新建一个hello集合，并在其中插入一条记录。  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

test2   0.203125GB  

> show collections  

hello  

system.indexes  

这样，便可以看到mongo的确创建了test2数据库，其中有一个hello集合。  

  

2.5 删除数据库  

> db.dropDatabase()  

{ "dropped" : "test2", "ok" : 1 }  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

  

2.6 查看当前数据库  

> db  

test2  

可以看出删除test2数据库之后，当前的db还是指向它，只有当切换数据库之后，test2才会彻底消失。  

3、collection级操作

 

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3.1 新建collection  

> db.createCollection("Hello")  

{ "ok" : 1 }  

> show collections  

Hello  

system.indexes  

从上面2.4也可以看出，直接向一个不存在的collection中插入数据也能创建一个collection。  

> db.hello2.insert({"name":"lfqy"})  

> show collections  

Hello  

hello2  

system.indexes  

  

3.2 删除collection  

> db.Hello.drop()  

true  

返回true说明删除成功，false说明没有删除成功。  

> db.hello.drop()  

false  

不存在名为hello的collection，因此，删除失败。  

  

3.3 重命名collection  

将hello2集合重命名为HELLO  

> show collections  

hello2  

system.indexes  

> db.hello2.renameCollection("HELLO")  

{ "ok" : 1 }  

> show collections  

HELLO  

system.indexes  

  

3.4 查看当前数据库中的所有collection  

>show collections  

  

3.5 建立索引在HELLO集合上，建立对ID字段的索引，1代表升序。  

>db.HELLO.ensureIndex({ID:1})  

4、Record级的操作

这一小节从这里开始，我们用事先存在的blogtest数据库做测试，其中有两个Collection，一个是book，另一个是user。

4.1 插入操作

 

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4.1.1 向user集合中插入两条记录  

> db.user.insert({'name':'Gal Gadot','gender':'female','age':28,'salary':11000})  

> db.user.insert({'name':'Mikie Hara','gender':'female','age':26,'salary':7000})  

  

4.1.2 同样也可以用save完成类似的插入操作  

> db.user.save({'name':'Wentworth Earl Miller','gender':'male','age':41,'salary':33000})  

4.2 查找操作

 

4.2.1 查找集合中的所有记录

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> db.user.find()  

{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 15 }  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13 }  

4.2.2 查找集合中的符合条件的记录

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(1)单一条件  

a)Exact Equal:  

查询age为了23的数据  

> db.user.find({"age":23})  

{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 15 }  

b)Great Than:  

查询salary大于5000的数据  

> db.user.find({salary:{$gt:5000}})  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

c)Fuzzy Match  

查询name中包含'a'的数据  

> db.user.find({name:/a/})  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

查询name以G打头的数据  

> db.user.find({name:/^G/})  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

  

(2)多条件"与"  

查询age小于30，salary大于6000的数据  

> db.user.find({age:{$lt:30},salary:{$gt:6000}})  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

  

(3)多条件"或"  

查询age小于25，或者salary大于10000的记录  

> db.user.find({$or:[{salary:{$gt:10000}},{age:{$lt:25}}]})  

{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 15 }  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

4.2.3 查询第一条记录

将上面的find替换为findOne()可以查找符合条件的第一条记录。

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将上面的find替换为findOne()可以查找符合条件的第一条记录。  

> db.user.findOne({$or:[{salary:{$gt:10000}},{age:{$lt:25}}]})  

{  

    "_id" : ObjectId("52442736d8947fb501000001"),  

    "name" : "lfqy",  

    "gender" : "male",  

    "age" : 23,  

    "salary" : 15  

}  

4.2.4 查询记录的指定字段

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查询user集合中所有记录的name,age,salary,sex_orientation字段  

> db.user.find({},{name:1,age:1,salary:1,sex_orientation:true})  

{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "age" : 23, "salary" : 15 }  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

注意：这里的1表示显示此列的意思，也可以用true表示。  

4.2.5 查询指定字段的数据，并去重。

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查询gender字段的数据，并去掉重复数据  

> db.user.distinct('gender')  

[ "male", "female" ]  

4.2.6 对查询结果集的操作

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(1)Pretty Print  

为了方便，mongo也提供了pretty print工具，db.collection.pretty()或者是db.collection.forEach(printjson)  

> db.user.find().pretty()  

{  

    "_id" : ObjectId("52442736d8947fb501000001"),  

    "name" : "lfqy",  

    "gender" : "male",  

    "age" : 23,  

    "salary" : 15  

}  

{  

    "_id" : ObjectId("52453cfb25e437dfea8fd4f4"),  

    "name" : "Gal Gadot",  

    "gender" : "female",  

    "age" : 28,  

    "salary" : 11000  

}  

{  

    "_id" : ObjectId("52453d8525e437dfea8fd4f5"),  

    "name" : "Mikie Hara",  

    "gender" : "female",  

    "age" : 26,  

    "salary" : 7000  

}  

{  

    "_id" : ObjectId("52453e2125e437dfea8fd4f6"),  

    "name" : "Wentworth Earl Miller",  

    "gender" : "male",  

    "age" : 41,  

    "salary" : 33000  

}  

{  

    "_id" : ObjectId("52454155d8947fb70d000000"),  

    "name" : "not known",  

    "sex_orientation" : "male",  

    "age" : 13  

}  

(2)指定结果集显示的条目  

a)显示结果集中的前3条记录  

> db.user.find().limit(3)  

{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 15 }  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

b)查询第1条以后的所有数据  

> db.user.find().skip(1)  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

c)对结果集排序  

升序  

> db.user.find().sort({salary:1})  

{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 15 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

降序  

> db.user.find().sort({salary:-1})  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 15 }  

4.2.7 统计查询结果中记录的条数

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(1)统计集合中的所有记录条数  

> db.user.find().count()  

5  

(2)查询符合条件的记录数  

查询salary小于4000或大于10000的记录数  

> db.user.find({$or: [{salary: {$lt:4000}}, {salary: {$gt:10000}}]}).count()  

4  

4.3 删除操作

 

4.3.1 删除整个集合中的所有数据

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> db.test.insert({name:"asdf"})  

> show collections  

book  

system.indexes  

test  

user  

到这里新建了一个集合，名为test。  

删除test中的所有记录。  

> db.test.remove()  

PRIMARY> show collections  

book  

system.indexes  

test  

user  

> db.test.find()  

可见test中的记录全部被删除。  

注意db.collection.remove()和drop()的区别，remove()只是删除了集合中所有的记录，而集合中原有的索引等信息还在，而drop()则把集合相关信息整个删除(包括索引)。  

4.3.2 删除集合中符合条件的所有记录

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> db.user.remove({name:'lfqy'})  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52455cc825e437dfea8fd4f8"), "name" : "2", "gender" : "female", "age" : 28, "salary" : 2 }  

{ "_id" : ObjectId("52455d8a25e437dfea8fd4fa"), "name" : "1", "gender" : "female", "age" : 28, "salary" : 1 }  

> db.user.remove( {salary :{$lt:10}})  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

4.3.3  删除集合中符合条件的一条记录

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> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52455de325e437dfea8fd4fb"), "name" : "1", "gender" : "female", "age" : 28, "salary" : 1 }  

{ "_id" : ObjectId("52455de925e437dfea8fd4fc"), "name" : "2", "gender" : "female", "age" : 28, "salary" : 2 }  

> db.user.remove({salary :{$lt:10}},1)  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52455de925e437dfea8fd4fc"), "name" : "2", "gender" : "female", "age" : 28, "salary" : 2 }  

当然，也可以是db.user.remove({salary :{$lt:10}},true)  

4.4 更新操作

4.4.1 赋值更新

db.collection.update(criteria, objNew, upsert, multi )

criteria:update的查询条件，类似sql update查询内where后面的

objNew:update的对象和一些更新的操作符（如$,$inc...）等，也可以理解为sql update查询内set后面的。

upsert : 如果不存在update的记录，是否插入objNew,true为插入，默认是false，不插入。

multi : mongodb默认是false,只更新找到的第一条记录，如果这个参数为true,就把按条件查出来多条记录全部更新。

 

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> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "name" : "lfqy", "gender" : "male", "age" : 28, "salary" : 1 }  

{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "name" : "lfqy", "gender" : "male", "age" : 28, "salary" : 2 }  

> db.user.update({name:'lfqy'},{$set:{age:23}},false,true)  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 1 }  

{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 2 }  

db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 1 }  

{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 2 }  

> db.user.update({name:'lfqy1'},{$set:{age:23}},true,true)  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 1 }  

{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 2 }  

{ "_id" : ObjectId("5245610881c83a5bf26fc285"), "age" : 23, "name" : "lfqy1" }  

> db.user.update({name:'lfqy'},{$set:{interest:"NBA"}},false,true)  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("5245610881c83a5bf26fc285"), "age" : 23, "name" : "lfqy1" }  

{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "age" : 23, "gender" : "male", "interest" : "NBA", "name" : "lfqy", "salary" : 1 }  

{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "age" : 23, "gender" : "male", "interest" : "NBA", "name" : "lfqy", "salary" : 2 }  

4.4.2 增值更新

[plain] 

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("5245610881c83a5bf26fc285"), "age" : 23, "name" : "lfqy1" }  

{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "age" : 23, "gender" : "male", "interest" : "NBA", "name" : "lfqy", "salary" : 1 }  

{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "age" : 23, "gender" : "male", "interest" : "NBA", "name" : "lfqy", "salary" : 2 }  

> db.user.update({gender:'female'},{$inc:{salary:50}},false,true)  

> db.user.find()  

{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11050 }  

{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7050 }  

{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  

{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  

{ "_id" : ObjectId("5245610881c83a5bf26fc285"), "age" : 23, "name" : "lfqy1" }  

{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "age" : 23, "gender" : "male", "interest" : "NBA", "name" : "lfqy", "salary" : 1 }  

{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "age" : 23, "gender" : "male", "interest" : "NBA", "name" : "lfqy", "salary" : 2 }  

关于更新操作（db.collection.update(criteria, objNew, upsert, multi )），要说明的是，如果upsert为true，那么在没有找到符合更新条件的情况下，mongo会在集合中插入一条记录其值满足更新条件的记录(其中的字段只有更新条件中涉及的字段，字段的值满足更新条件)，然后将其更新（注意，如果更新条件是$lt这种不等式条件，那么upsert插入的记录只会包含更新操作涉及的字段，而不会有更新条件中的字段。这也很好理解，因为没法为这种字段定值，mongo索性就不取这些字段）。如果符合条件的记录中没有要更新的字段，那么mongo会为其创建该字段，并更新。

上面大致介绍了MongoDB命令行中所涉及的操作，只是为了记录和查阅。细心的也许会发现，这篇文章，越往后我的耐心越少。期待有时间能分享一些not very navie的东西。