Redis cache problem

Preface ：

Every time you start a project, there will be too much data in the database, which will lead to too much data storage when you first enter , Multiple requestors of home page information can be used directly MySQL The database is slow , Affect the user experience by redis Solutions to cache problems , To improve energy .

Cache penetration

This means that the data requested by the client does not exist in the cache or in the database , Such a cache will never work , These requests will all go to the database

Problem solving ：

1. Caching empty objects

​ advantage ： Implement a simple , Convenient maintenance

​ shortcoming ： Extra memory consumption , May cause short-term inconsistencies

2. Bloon filtration

​ advantage ： Less memory usage , There is no excess Key

​ shortcoming ： The implementation is complex , There is a possibility of misjudgment

Cache breakdown

Cache breakdown means that the data queried by the user does not exist in the cache , But the back-end database exists , The reason for this phenomenon is that it is generally caused by key Due to expiration . For example, a hot data key, It receives a lot of concurrent access all the time , If at some point this key All of a sudden it failed , This causes a large number of concurrent requests to enter the back-end database , Cause its pressure to increase instantaneously . This phenomenon is called cache breakdown .

There are two solutions to cache breakdown ：

1) Change expiration time

Never expired data settings .

2) Distributed lock

Adopt the method of distributed lock , Redesign how cache is used , The process is as follows ：

• locked ： When we go through key When you go to query data , First query the cache , without , Lock through distributed lock , The first process to obtain the lock enters the back-end database to query , And slow the query results to Redis in .
• Unlock ： When other processes find that the lock is occupied by a process , It will enter the waiting state , Until unlocked , The other processes then access the cached key.

Cache avalanche

Cache avalanche refers to a large number of key At the same time , At this time, the amount of data access is very large , This leads to a sudden surge in the pressure on the back-end database , Even hang up , This phenomenon is called cache avalanche . It's different from cache breakdown , Cache breakdown occurs when the amount of concurrency is particularly large , A hot spot key Suddenly expired , The cache avalanche is a lot of key At the same time , So they are not an order of magnitude at all .

Solution

There are similarities between cache avalanche and cache breakdown , So you can also use the method that hot data never expires , To reduce large quantities of key At the same time . Another is to key Set random expiration time , avoid key Concentration expired .