Optimizing cache elimination strategy. Cache eviction strategy is a crucial part of the cache system. When the cache space is insufficient, some data need to be eliminated according to certain strategies to make room for new data. Reasonable selection and optimization of elimination strategies can significantly improve the performance and hit rate of the cache system.
Common cache eviction strategies
- LRU (Least Recently Used) : Least recently used. Eliminate the least recently used data.
- LFU (Least Frequently Used) : Least frequently used. However, Eliminate the least frequently used data.
- FIFO (First In First Out) : First in, first out. The data that enters the cache first is eliminated first.
- Random elimination : randomly select a data to eliminate.
- TTL (Time To Live) : Set an expiration time for each data, and it will be automatically deleted when it expires.
Optimization of elimination strategy
- Mixed strategies :
- LRU+LFU : Combines the advantages of LRU and LFU, and comprehensively considers data access frequency and recent access time.
- LRU+TTL : For some data, set a fixed expiration Australia WhatsApp Number Data time and combine it with the LRU strategy.
- Tiered storage :
- The cache is divided into multiple levels, and different levels use different elimination strategies.
- Hot data is placed in the first-level cache, using the LRU strategy; unpopular data is placed in the second-level cache, using the TTL strategy.
- Data importance weighting :
- Set different importance weights for different data, and prioritize eliminating data with low importance.
- Dynamic Adjustment :
- Dynamically adjust the parameters of the elimination strategy based on system load and cache hit rate.
- Customized strategy :
- Design customized elimination strategies based on business characteristics.
Factors affecting elimination strategy
- Data access pattern : Different access patterns have different effects on the selection of elimination strategies.
- Cache capacity : The size of the cache capacity affects the frequency of the eviction strategy.
- Data life cycle : The length of the data life cycle will affect the choice of elimination strategy.
- System load : When the system load is too high, a more aggressive eviction strategy may be required.
Optimization example: Redis
However, Redis provides a variety of elimination strategies:
- volatile-lru : From the data set with set expiration time, prioritize the least recently used ones.
- allkeys-lru : From all data sets, prioritize the least recently used ones.
- volatile-lfu : From the data sets with set expiration times, prioritize the ones with the lowest frequency of use.
- allkeys-lfu : From all data sets, prioritize the ones with the lowest usage frequency.
- volatile-random : Randomly eliminate one from the data set with set expiration time.
- allkeys-random : Randomly eliminate one from all data sets.
- volatile-ttl : From the data sets with set expiration times, prioritize those that are about to expire.
- noeviction : Return an error when there is insufficient memory.
Summarize
However, Choosing a suitable cache elimination strategy is the key to optimizing cache system performance. It is necessary to comprehensively consider factors such as Optimizing cache elimination strategy business scenarios, data characteristics, and system load to select or customize a suitable elimination strategy.
Common optimization suggestions:
- Monitor cache hit rate : However, adjust the elimination strategy based on the hit rate.
- Regularly evaluate the elimination strategy : adjust the elimination strategy in a timely manner as the business changes.
- Consider data importance : Set higher priority for important data.
- Combined with business rules : Customize the elimination strategy according to business rules.
