Redis summary

What is? Redis

Redis(Remote Dictionary Server) It's a use C language-written , Open source （BSD The license ） High performance non relational （NoSQL） Key value to database .

Redis You can store mappings between keys and five different types of values . The key type can only be string , Value supports five data types ： character string 、 list 、 aggregate 、 Hash table 、 Ordered set .

redis Can handle over per second 10 Ten thousand read and write operations , Is the fastest known Key-Value DB. in addition redis It is also commonly used as a distributed lock .

Redis Why fast ?

1. Completely based on memory , Most requests are purely memory operations , Very fast .
   At the same time, the data is stored in memory , Be similar to HashMap,HashMap The advantage is the time complexity of both the lookup and the operation O(1);
2. The data structure is simple , It's also easy to manipulate data ,Redis The data structure in is specially designed ;
3. Using single thread .
   Unnecessary context switches and race conditions are avoided , There is no switching consumption caused by multiple processes or multiple threads CPU, You don't have to worry about locks , There is no lock release operation , There is no performance penalty due to possible deadlocks ;
4. Using multiple channels I/O Reuse model , Non blocking IO;
5. Using the underlying model is different .
   The underlying implementation and application protocols for communicating with clients are different between them ,Redis Build it yourself VM Mechanism , Because the normal system calls system functions , It's a waste of time moving and requesting ;

Redis What are the common data types ?

Redis There are mainly 5 Type of data , Include String,List,Set,Zset,Hash
String （ character string 、 Integer or floating point ）： Do a simple key value pair cache
List list ： Store some list data structures
set unordered set ： intersection 、 Combine 、 Operation of difference set
hash Unordered hash table with key value pairs ： Structured data , Like an object
zset Ordered set ： De duplication but sorting

Redis RDB and AOF The difference between persistence , How to choose ?

What is? Redis Persistence ？

Persistence is to write data in memory to disk , Prevent memory data loss due to service outage .

Redis What is the persistence mechanism of ？

Redis There are two persistence mechanisms RDB（ Default ） and AOF Mechanism :

• RDB：Redis DataBase, Generate a snapshot of the current data and save it on the hard disk .
  RDB yes Redis Default persistence method . Save the memory data to the hard disk in the form of snapshot according to a certain time , The corresponding generated data file is dump.rdb. Through... In the configuration file save Parameter to define the period .

• AOF：Append Only File Persistence , Record every data operation on the hard disk .
  take Redis Each write command executed is recorded in a separate log file , When restarting Redis It will recover the data from the persistent log again .

RDB and AOF Advantages and disadvantages

• AOF File than RDB High update frequency , priority of use AOF Restore data .
• AOF Than RDB Safer and bigger
• RDB Performance ratio AOF good
• If both have priority loading AOF

How to choose

1、 Generally speaking , If you want to achieve enough PostgreSQL Data security of , Two persistence functions should be used at the same time . under these circumstances , When Redis It will be loaded prior to restart AOF File to restore the original data , Because in general AOF The data set saved by the file is better than RDB The data set of the file should be complete .

2、 If it can withstand data loss within a few minutes , Then you can only use RDB Persistence .
3、 Use only AOF Persistence , But it is not recommended . Because of timing generation RDB snapshot （snapshot） Very convenient for database backup , also RDB Data set recovery is also faster than AOF Fast recovery , besides , Use RDB You can also avoid AOF programmatic bug.

4、 If you only want the data to exist when the server is running , You can do it without any persistence .

How to solve cache breakdown 、 Cache penetration 、 Avalanche problem ?

Cache breakdown

Cache breakdown refers to 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 .
Unlike the cache avalanche , 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 .

Solution

1. Set hotspot data never to expire .
2. Add mutex lock

Cache penetration

Make a request for some data that must not exist , The request will penetrate the cache and reach the database .

Cache penetration refers to data that does not exist in cache or database , Causes all requests to fall to the database , Cause the database to bear a large number of requests in a short time and crash .

Solution

1. Cache an empty data for these nonexistent data ;
2. Filtering such requests .

Cache avalanche

Because the data is not loaded into the cache , Or cache data fails in a large area at the same time （ Be overdue ）, Or the cache server is down , This results in a large number of requests reaching the database .

In a system with a cache , The system relies heavily on caching , The cache shares a large portion of the data requests . When a cache avalanche occurs , The database can't handle such a large request , Cause database crash .

Solution ：

1. In order to prevent cache avalanche caused by cache expiration in a large area at the same time , By observing user behavior , Set the cache expiration time reasonably
   （ The expiration time of cache data is set randomly , Prevent a large number of data expiration at the same time .）
2. In general, concurrency is not particularly large , The most commonly used solution is lock queuing
3. Add the corresponding cache mark to each cache data , Whether the record cache is invalid , If the cache token fails , Then update the data cache .
4. In order to prevent cache avalanche caused by cache server downtime , You can use distributed caching , In the distributed cache, each node only caches part of the data , When a node goes down, it can ensure that the cache of other nodes is still available .
5. You can also warm up the cache , Avoid the cache avalanche caused by not caching a large amount of data soon after the system starts .