Redis enhancements

LrU It is to eliminate the data that has not been used for the longest time . Five minutes ago and ten minutes ago Delete ten minutes

LfU Is eliminated for a period of time , Least used data . Five times, ten times Delete five times

Cache penetration :

Cache penetration refers to data that does not exist in cache or database , And users are constantly requesting , If initiated as id by “-1” Data or id For very large nonexistent data . The user at this time is likely to be the attacker , The attack will cause too much pressure on the database

Cache penetration Solution ：

1. Verification is added to the interface layer , Such as user authentication verification ,id Do basic verification ,id<=0 Direct interception of ;
2. Data not available from cache , In the database, there is no access to , At this time, you can also key-value Write as key-null,
 Cache effective time can be set as a short point , Such as 30 second （ Setting too long will cause normal conditions to be unavailable ）.
 This prevents the attacker from repeatedly using the same id Violent attack 
3. The bloon filter     

Cache breakdown :

Cache breakdown It refers to the data not in the cache but in the database （ Generally, the cache time expires ）, At this time, there are many concurrent users , At the same time, the read cache does not read the data , At the same time go to the database to get data , Causes the database pressure to increase instantaneously , Cause too much pressure

Breakdown is just the name of this phenomenon, which is not impermissible

Cache avalanche

Cache avalanche It refers to the time when the data in the cache reaches the expiration date in large quantities , And the amount of query data is huge , Causes database pressure to be too big even down machine .

A large number of breakdown occurs in a short time

Cache breakdown refers to concurrent query of the same data , The cache avalanche is that different data has expired , A lot of data can't be found to look up the database .

Cache avalanche Solution

1. The expiration time of cache data is set randomly , Prevent a large number of data expiration at the same time .

2. If the cache database is a distributed deployment , Distribute the hot data evenly in different cache databases .

3. Set hotspot data never to expire

Redis Persistence

Redis Store data in memory So many factors will lead to data loss

Once the data is lost, it needs to be cached again ( Get it again in the database ) if redis If there is new data in, it cannot be recovered

How to persist

1.RDB： Generate a snapshot of the current data and save it on the hard disk .

 advantage :
	 Because it's the whole Redis Binary format of data , Data recovery is the overall recovery 
 shortcoming :
	 Generated rdb File is a file on hard disk , Low efficiency of reading and writing 
     In case of sudden power failure , Only the last generated rdb Data in     Not every minute of every second  

 2.AOF： Record every data operation on the hard disk .

To reduce the size of logs ( redundancy )

Redis Support AOF rewrite The new command of deleting some data is also removed from the log , Achieve the purpose of reducing log capacity

Redis Storage principle

Redis colony