MongoDB命令行操作
2013-09-27 19:06
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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。
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。
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
3、collection级操作
4、针对集合中记录的操作
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
4.2 查找操作
4.2.1 查找集合中的所有记录
4.2.2 查找集合中的符合条件的记录
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。
4.2.4 查询记录的指定字段
4.2.5 查询指定字段的数据,并去重。
4.2.6 对查询结果集的操作
4.2.7 统计查询结果中记录的条数
4.3 删除操作
4.3.1 删除整个集合中的所有数据
4.3.2 删除集合中符合条件的所有记录
4.3.3 删除集合中符合条件的一条记录
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。
4.4.2 增值更新
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。
总之,希望对自己,对大家都有所帮助。
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。
$ mongo 10.77.20.xx MongoDB shell version: 2.0.4 connecting to: 10.77.20.xx/test PRIMARY> show collections PRIMARY> 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
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 PRIMARY> 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。 PRIMARY> 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。 PRIMARY> 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
3.1 新建collection > db.createCollection("Hello") { "ok" : 1 } PRIMARY> 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"}) PRIMARY> 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 PRIMARY> db.hello2.renameCollection("HELLO") { "ok" : 1 } PRIMARY> show collections HELLO system.indexes 3.4 查看当前数据库中的所有collection >show collections
4、针对集合中记录的操作
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
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
> 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
(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。
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
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。
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
(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
> 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 PRIMARY> 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
> db.user.remove({name:'lfqy'}) PRIMARY> 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 } PRIMARY> db.user.remove( {salary :{$lt:10}}) PRIMARY> db.user.find() { "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 } { "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 } { "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 } { "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 }
4.3.3 删除集合中符合条件的一条记录
> db.user.find() { "_id" : ObjectId("52453cfb25e437dfea8fd4f4"), "name" : "Gal Gadot", "gender" : "female", "age" : 28, "salary" : 11000 } { "_id" : ObjectId("52453d8525e437dfea8fd4f5"), "name" : "Mikie Hara", "gender" : "female", "age" : 26, "salary" : 7000 } { "_id" : ObjectId("52453e2125e437dfea8fd4f6"), "name" : "Wentworth Earl Miller", "gender" : "male", "age" : 41, "salary" : 33000 } { "_id" : ObjectId("52454155d8947fb70d000000"), "name" : "not known", "sex_orientation" : "male", "age" : 13, "salary" : 30000 } { "_id" : ObjectId("52455de325e437dfea8fd4fb"), "name" : "1", "gender" : "female", "age" : 28, "salary" : 1 } { "_id" : ObjectId("52455de925e437dfea8fd4fc"), "name" : "2", "gender" : "female", "age" : 28, "salary" : 2 } > db.user.remove({salary :{$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。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。
总之,希望对自己,对大家都有所帮助。
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