Why is mongodb fast

• Write operations
  1) Write operations MongoDB The fundamental reason why it is faster than traditional databases is Mongo Memory mapping technology used , When writing data, it can be returned to the application as long as it is completed in memory , In this way, the concurrency is naturally high . The operation of saving to the hardware is completed asynchronously in the background . Be careful MongoDB stay 2.4 It is already written safely by default （ The specific implementation is in the driver ）.

• Read operations
  Read operations MongoDB The reason for the speed is ：
  1）MongoDB The design requires your usual data （working set) You can put it in memory . In this way, most operations only need to read memory , Nature soon .
  2） Document pattern design generally means that the data you need are relatively concentrated （ Memory or hard disk ）, As we all know, the most time-consuming hard disk reading and writing is the head positioning time generated by random reading and writing , Data aggregation reduces the need for relational databases to find data from all over the world （ then Join） Random reading time spent ; The other is Mongo It is a distributed cluster, so it can expand in parallel . At present, the general concurrency of millions of times is achieved simultaneously through clusters of dozens or hundreds of nodes . This point MySQL Basically can't do .NoSQL Not without sql, Just don't use relationships . It doesn't matter , It means that each data is like having a separate storage space , Then an aggregate index points to . Search performance will certainly improve .
  3) Language . Use javascript Syntax is more efficient 、 direct . These are MongoDB The reason for the high efficiency of relational databases . But you can't just focus on efficiency , There are also some disadvantages brought by the design of this kind of database . For example, the maintenance of data relations will bring a lot of redundant data 、 The client code needs a lot of work for the database IO operation 、 Data mining is difficult to realize, etc .