MongoDB 基礎命令行

本文專門介紹MongoDB的命令行操作。其實，這些操作在MongoDB官網提供的Quick Reference上都有，但是英文的，為了方便，這里將其稍微整理下，方便查閱。

登錄和退出

mongo命令直接加MongoDB服務器的IP地址（比如：mongo 10.77.20.xx），就可以利用Mongo的默認端口號(27017)登陸Mongo，然后便能夠進行簡單的命令行操作。

至於退出，直接exit，然后回車就好了。

 

[plain] 

$ mongo 10.77.20.xx  

MongoDB shell version: 2.0.4  

connecting to: 10.77.20.xx/test  

> show collections  

> exit  

bye  

從以上可以看出，登錄后mongo會自動連上一個名為test的數據庫。如果這個數據庫不存在，那么mongo會自動建立一個名為test的數 據庫。上面的例子，由於Mongo服務器上沒有名為test的db，因此，mongo新建了一個空的名為test的db。其中，沒有任何 collection。

 

database級操作

2.1 查看服務器上的數據庫  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

  

2.2 切換數據庫  

切換到blogtest數據庫(從默認的test數據庫)  

> use blogtest  

switched to db blogtest  

mongo中，db代表當前使用的數據庫。這樣，db就從原來的test，變為現在的blogtest數據庫。  

  

2.3 查看當前數據庫中的所有集合  

> show collections  

book  

system.indexes  

user  

  

2.4 創建數據庫  

mongo中創建數據庫采用的也是use命令，如果use后面跟的數據庫名不存在，那么mongo將會新建該數據庫。不過，實際上只執行use命令后，mongo是不會新建該數據庫的，直到你像該數據庫中插入了數據。  

> use test2  

switched to db test2  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

到這里並沒有看到剛才新建的test2數據庫。  

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

該操作會在test2數據庫中新建一個hello集合，並在其中插入一條記錄。  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

test2   0.203125GB  

> show collections  

hello  

system.indexes  

這樣，便可以看到mongo的確創建了test2數據庫，其中有一個hello集合。  

  

2.5 刪除數據庫  

> db.dropDatabase()  

{ "dropped" : "test2", "ok" : 1 }  

> show dbs  

admin   (empty)  

back_up (empty)  

blogtest    0.203125GB  

local   44.056640625GB  

test    (empty)  

  

2.6 查看當前數據庫  

> db  

test2  

可以看出刪除test2數據庫之后，當前的db還是指向它，只有當切換數據庫之后，test2才會徹底消失。

 

collection級操作

3.1 新建collection  

> db.createCollection("Hello")  

{ "ok" : 1 }  

> show collections  

Hello  

system.indexes  

從上面2.4也可以看出，直接向一個不存在的collection中插入數據也能創建一個collection。  

> db.hello2.insert({"name":"lfqy"})  

> show collections  

Hello  

hello2  

system.indexes  

  

3.2 刪除collection  

> db.Hello.drop()  

true  

返回true說明刪除成功，false說明沒有刪除成功。  

> db.hello.drop()  

false  

不存在名為hello的collection，因此，刪除失敗。  

  

3.3 重命名collection  

將hello2集合重命名為HELLO  

> show collections  

hello2  

system.indexes  

> db.hello2.renameCollection("HELLO")  

{ "ok" : 1 }  

> show collections  

HELLO  

system.indexes  

  

3.4 查看當前數據庫中的所有collection  

>show collections  

  

3.5 建立索引在HELLO集合上，建立對ID字段的索引，1代表升序。  

>db.HELLO.ensureIndex({ID:1})  

Record級的操作

這一小節從這里開始，我們用事先存在的blogtest數據庫做測試，其中有兩個Collection，一個是book，另一個是user。

4.1 插入操作

 

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4.1.1 向user集合中插入兩條記錄  

> 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 同樣也可以用save完成類似的插入操作  

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

 

4.2 查找操作

 

4.2.1 查找集合中的所有記錄

[plain] 

> 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 查找集合中的符合條件的記錄

[plain] 

(1)單一條件  

a)Exact Equal:  

查詢age為了23的數據  

> db.user.find({"age":23})  

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

 

b)Great Than:  

查詢salary大於5000的數據  

> 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  

查詢name中包含'a'的數據  

> 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 }  

查詢name以G打頭的數據  

> db.user.find({name:/^G/})  

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

  

(2)多條件"與"  

查詢age小於30，salary大於6000的數據  

> 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)多條件"或"  

查詢age小於25，或者salary大於10000的記錄  

> 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 查詢第一條記錄

將上面的find替換為findOne()可以查找符合條件的第一條記錄。

[plain] 

將上面的find替換為findOne()可以查找符合條件的第一條記錄。  

> db.user.findOne({$or:[{salary:{$gt:10000}},{age:{$lt:25}}]})  
{  
    "_id" : ObjectId("52442736d8947fb501000001"),  
    "name" : "lfqy",  
    "gender" : "male",  
    "age" : 23,  
    "salary" : 15  
}  

4.2.4 查詢記錄的指定字段

[plain] 

查詢user集合中所有記錄的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：這里的1表示顯示此列的意思，也可以用true表示。  

 

4.2.5 查詢指定字段的數據，並去重。

[plain] 

查詢gender字段的數據，並去掉重復數據  

> db.user.distinct('gender')  

[ "male", "female" ]  

 

4.2.6 對查詢結果集的操作

(1)Pretty Print  

為了方便，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)指定結果集顯示的條目  

a)顯示結果集中的前3條記錄  

> 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)查詢第1條以后的所有數據  

> 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)對結果集排序  

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 統計查詢結果中記錄的條數

[plain] 

(1)統計集合中的所有記錄條數  

> db.user.find().count()  

5  

(2)查詢符合條件的記錄數  

查詢salary小於4000或大於10000的記錄數  

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

 

4.3 刪除操作

 

4.3.1 刪除整個集合中的所有數據

[plain] 

> db.test.insert({name:"asdf"})  

> show collections  

book  

system.indexes  

test  

user  

到這里新建了一個集合，名為test。  

刪除test中的所有記錄。  

> db.test.remove()  

PRIMARY> show collections  

book  

system.indexes  

test  

user  

> db.test.find()  

可見test中的記錄全部被刪除。  

注意db.collection.remove()和drop()的區別，remove()只是刪除了集合中所有的記錄，而集合中原有的索引等信息還在，而drop()則把集合相關信息整個刪除(包括索引)。

 

4.3.2 刪除集合中符合條件的所有記錄

[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  刪除集合中符合條件的一條記錄

> 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 }  

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

 

4.4 更新操作

4.4.1 賦值更新

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

criteria:update的查詢條件，類似sql update查詢內where后面的

objNew:update的對象和一些更新的操作符（如$,$inc...）等，也可以理解為sql update查詢內set后面的。

upsert : 如果不存在update的記錄，是否插入objNew,true為插入，默認是false，不插入。

multi : mongodb默認是false,只更新找到的第一條記錄，如果這個參數為true,就把按條件查出來多條記錄全部更新。

> 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

> 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 }  

關於更新操作（db.collection.update(criteria, objNew, upsert, multi )），要說明的是，如果upsert為true，那么在沒有找到符合更新條件的情況下，mongo會在集合中插入一條記錄其值滿足更新條件的記錄(其中的 字段只有更新條件中涉及的字段，字段的值滿足更新條件)，然后將其更新（注意，如果更新條件是$lt這種不等式條件，那么upsert插入的記錄只會包含 更新操作涉及的字段，而不會有更新條件中的字段。這也很好理解，因為沒法為這種字段定值，mongo索性就不取這些字段）。如果符合條件的記錄中沒有要更 新的字段，那么mongo會為其創建該字段，並更新。

上面大致介紹了MongoDB命令行中所涉及的操作，只是為了記錄和查閱。