Difference between redis and mongodb (useful for interview)

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What is used in the project MongoDB, But why? In fact, there was not much consideration when selecting models , I just think the amount of data is relatively large , So using MongoDB.

Recently, I remembered why I used MongoDB, Just check , Summary summary summary ：

I used it before redis, It was used to store some thermal data , Not much , But the operation is very frequent . Now the project uses MongoDB, At present, it is a million level data , There will be tens of millions in the future 、 Billion level .

Just Redis and MongoDB Come on , It is generally called Redis cache 、MongoDB database . This is also reasonable and justified ,

Redis Mainly store data in memory , Its “ cache ” The nature of is far greater than its “ data storage “ The nature of , The addition, deletion, modification and query of data are just as simple as variable operation ;

MongoDB But one. “ Store the data ” The system of , Adding, deleting, modifying and querying can add many conditions , It's like SQL As flexible as a database , This is very useful in the interview .

Mongodb And Redis Application index comparison

MongoDB and Redis All are NoSQL, Using structured data storage . Both of them are used in the scene , There is a certain difference , This is mainly due to In the process of memory mapping , Persistence is handled differently .MongoDB Cluster deployment is recommended , Think more about the cluster solution ,Redis More emphasis on process sequential writing , Although it supports clustering , It is also limited to the main - Slave mode .

MongoDB Syntax and existing relational databases SQL Grammatical comparison

MongoDB grammar             MySql grammar 

db.test.find({'name':'foobar'})             <==>          select * from test where name='foobar'

db.test.find()                                      <==>          select *from test

db.test.find({'ID':10}).count()             <==>          select count(*) from test where ID=10

db.test.find().skip(10).limit(20)          <==>          select * from test limit 10,20

db.test.find({'ID':{$in:[25,35,45]}})     <==>          select * from test where ID in (25,35,45)

db.test.find().sort({'ID':-1})                 <==>          select * from test order by IDdesc

db.test.distinct('name',{'ID':{$lt:20}}) <==>          select distinct(name) from testwhere ID<20

db.test.group({key:{'name':true},cond:{'name':'foo'},reduce:function(obj,prev){prev.msum+=obj.marks;},initial:{msum:0}})     <==>     select name,sum(marks) from testgroup by name

db.test.find('this.ID<20',{name:1})    <==>           select name from test whereID<20

db.test.insert({'name':'foobar','age':25})    <==>       insertinto test ('name','age') values('foobar',25)

db.test.remove({})                                     <==>       delete * from test

db.test.remove({'age':20})                        <==>       delete test where age=20

db.test.remove({'age':{$lt:20}})                <==>        delete test where age<20

db.test.remove({'age':{$lte:20}})              <==>        delete test where age<=20

db.test.remove({'age':{$gt:20}})              <==>         delete test where age>20

db.test.remove({'age':{$gte:20}})            <==>         delete test where age>=20

db.test.remove({'age':{$ne:20}})             <==>         delete test where age!=20

db.test.update({'name':'foobar'},{$set:{'age':36}})<==> update test set age=36 where name='foobar'

db.test.update({'name':'foobar'},{$inc:{'age':3}})<==> update test set age=age+3 where name='foobar'

 Fuzzy query ：$regex

db.test.find({"name":{$regex:"aaa"}})

 Group number filtering 

db.getCollection('id_mapper').aggregate([{$group:{ _id :"$contract_id",count:{$sum:1}}},{$match:{count:{$gt:1}}}])

 Determine whether it is null 

db.getCollection('id_mapper').find({"sinocardid":{$in:[null]}})

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