Redis (IV) - delete policy

Redis（ Four ）—— Deletion policy

1、 Data deletion strategy

• Delete regularly
• Lazy deletion
• Delete periodically

The storage structure of timeliness data

• Redis Data in , stay expire Is stored in it as a hash . Its value Is the address of the data in memory ,filed Is the corresponding life cycle

The goal of the data deletion strategy

In memory footprint and CPU Find a way between occupation Balance , Taking care of one thing and losing the other will result in the whole redis Performance degradation , Even cause server downtime or memory leakage

2、 Three deletion strategies

Delete regularly

• Create a timer , When key Set an expiration time , And when the expiration time arrives , By timer task Execute now Delete key
• advantage ： To save memory , Then delete , Free up unnecessary memory quickly
• shortcoming ：CPU Under great pressure , No matter what CPU How high is the load at this time , All occupied CPU, It will affect redis Server response time and instruction throughput
• summary ： Trade processor performance for storage （ Time for space ）

Lazy deletion

• Data arrival expiration time , Don't deal with it . The next time you access the data
  • If not expired , Return the data
  • Found expired , Delete , Return does not exist
• advantage ： save CPU performance , Delete when you find that you have to delete
• shortcoming ： There's a lot of memory pressure , Data that has occupied memory for a long time
• summary ： Swapping memory for processor performance （ Change space for time ）

Delete periodically

• Periodic polling redis Time sensitive data in the library , use The strategy of random extraction , Use the proportion of expired data to control the frequency of deletion
• characteristic 1：CPU Performance occupancy settings have peaks , The detection frequency can be customized
• characteristic 2： Memory pressure is not great , Cold data that has occupied memory for a long time will be continuously cleaned up
• summary ： Periodically spot check storage space （ Random sampling , Focus on spot check ）

3、 The eviction algorithm

When new data enters redis when , What if there's not enough memory ？

• Redis Using memory to store data , Before executing every command , Would call freeMemoryIfNeeded() Check if there is enough memory . If the memory does not meet the minimum storage requirements for newly added data ,redis To temporarily delete some data and clean up the storage space for the current instruction . The strategy for cleaning up data is called The eviction algorithm
• Be careful ： The process of evicting data is not 100% Can clean up enough available memory space , If not, repeat . After trying all the data , If the requirement of memory cleaning cannot be met , An error message will appear .

Related configurations that affect data eviction

• Maximum available memory

  maxmemory
  

  The proportion of physical memory occupied , The default value is 0, Means unrestricted . In the production environment, set according to the demand , Usually set at 50% above .

• Select the number of data to be deleted each time

  maxmemory-samples
  

  Data selection does not scan the entire database , Leading to serious performance consumption , Reduce read and write performance . Therefore, the method of randomly obtaining data is adopted as the data to be detected and deleted

• Deletion policy

  maxmemory-policy
  

  After reaching the maximum memory , The strategy of deleting the selected data

Related configurations that affect data eviction

LRU： The longest unused data

LFU： The least used data in a period of time

Data eviction policy configuration basis

• Use INFO command Output monitoring information , The query cache hit and miss The number of times , Tune according to business needs Redis To configure