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MongoDB 基础命令行

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互联网集市收集整理的这篇技术教程文章主要介绍了MongoDB 基础命令行，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含22509字，纯文字阅读大概需要33分钟。

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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。

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。   [plain]  $ 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。  

database级操作

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。

 

collection级操作

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})  

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   [plain]  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 [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 查找集合中的符合条件的记录 [plain]  (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:{$lt:25}}]})  
{ "_id" : ObjectId("52442736d8947fb501000001"), "name" : "lfqy", "gender" : "male", "age" : 23, "salary" : 15 }  
{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  
{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  
{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }
  4.2.3 查询第一条记录 将上面的find替换为findOne()可以查找符合条件的第一条记录。 [plain]  将上面的find替换为findOne()可以查找符合条件的第一条记录。   
> db.user.findOne({$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 [plain]  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。 [plain]  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 (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 [plain]  (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()     4.3 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 :{$lt:10}})  
> db.user.find()  
{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  
{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  
{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  
{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }
  4.3.3  删除集合中符合条件的一条记录 
> db.user.find()  
{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  
{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  
{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  
{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  
{ "_id" : ObjectId("52455de325e437dfea8fd4fb"), "name" : "1", "gender" : "female", "age" : 28, "salary" : 1 }  
{ "_id" : ObjectId("52455de925e437dfea8fd4fc"), "name" : "2", "gender" : "female", "age" : 28, "salary" : 2 }  
> db.user.remove({salary :{$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 :{$lt:10}},true)     4.4 更新操作 4.4.1 赋值更新 db.collection.update(criteria, objNew, upsert, multi ) criteria:update的查询条件，类似sql update查询内where后面的 objNew:update的对象和一些更新的操作符（如$,$inc...）等，也可以理解为sql update查询内set后面的。 upsert : 如果不存在update的记录，是否插入objNew,true为插入，默认是false，不插入。 multi : mongodb默认是false,只更新找到的第一条记录，如果这个参数为true,就把按条件查出来多条记录全部更新。 
> db.user.find()  
{ "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 }  
{ "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 }  
{ "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 }  
{ "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }  
{ "_id" : ObjectId("52455f8925e437dfea8fd4fd"), "name" : "lfqy", "gender" : "male", "age" : 28, "salary" : 1 }  
{ "_id" : ObjectId("5245607525e437dfea8fd4fe"), "name" : "lfqy", "gender" : "male", "age" : 28, "salary" : 2 }  
> db.user.update({name:‘lfqy‘},{$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 }

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。

MongoDB 基础命令行

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