mongodb总结1

1：登陆进入bin目录下（win10 win+x 命令提示符管理员）
C:\Program Files\MongoDB\Server\3.2\bin
运行登陆命令
mongo 10.77.20.xx

2：退出登陆
exit 

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。

3：查看服务器上的数据库
show dbs;

4:查看当前数据库 
db ;
5:切换数据库
use test
6:创建数据库
use test2

7:删除数据库
> db.dropDatabase() 
{ "dropped" : "test2", "ok" : 1 } 
> show dbs 
 
-------------------------详细教程-----------------------
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
[plain]
> 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
[plain]
> 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 :{$lt:10}}) 
> db.user.find() 
{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 } 
{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 } 
{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 } 
{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 } 
4.3.3  删除集合中符合条件的一条记录
[plain]
> db.user.find() 
{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 } 
{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 } 
{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 } 
{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 } 
{ "_id" : ObjectId("52455de325e437dfea8fd4fb"), "name" : "1", "gender" : "female", "age" : 28, "salary" : 1 } 
{ "_id" : ObjectId("52455de925e437dfea8fd4fc"), "name" : "2", "gender" : "female", "age" : 28, "salary" : 2 } 
> db.user.remove({salary :{$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。

[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("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。