What are the redis cache elimination strategies?

Redis provides a variety of cache elimination strategies, which can be selected according to business needs: Maximum memory limit: prevent insufficient memory crashes. Least used: Prioritize the elimination of rarely used data to improve the hit rate. Least recently accessed: Prioritize data that has been accessed less frequently. Elimination probability: Flexibly control the elimination probability to balance hit rate and memory usage. Elimination polling: Eliminate data evenly to prevent centralized elimination of large amounts of data.

Redis cache elimination strategy

Redis provides a variety of cache elimination strategies, which can be selected according to business needs, as follows It is a commonly used strategy:

1. Maximum memory limit (maxmemory)

• When the memory used by Redis exceeds the maximum memory limit, the elimination operation is triggered.
• Advantages: Simple and easy to use, preventing Redis from crashing due to insufficient memory.
• Disadvantages: It may cause commonly used data to be eliminated.

2. Least Used (LRU)

• Eliminate the least recently used key-value pair (LRU algorithm).
• Advantages: Prioritize the elimination of infrequently used data and improve the cache hit rate.
• Disadvantages: Unable to distinguish between frequently accessed data and temporarily unaccessed data.

3. Least Recently Visited (LFU)

• Eliminate the key-value pair with the least recent visits (LFU algorithm).
• Advantages: Considering the frequency of access, give priority to eliminating data with fewer access times.
• Disadvantages: Data with similar access times cannot be distinguished.

4. Elimination probability (volatile-ttl)

• Set a survival time (TTL) for each key-value pair. When the TTL reaches During the period, the key-value pair will be eliminated with a certain probability.
• Advantages: Flexible control of elimination probability, effective balance between hit rate and memory usage.
• Disadvantages: TTL needs to be set manually, and there may be inconsistencies.

5. Elimination polling (active decay)

• Divide the key-value pairs in the cache into multiple "buckets" and eliminate them regularly A key-value pair in each bucket.
• Advantages: Eliminate data evenly to prevent centralized elimination of large amounts of data.
• Disadvantages: Commonly used data may be eliminated, and the elimination frequency needs to be set appropriately.

Choose the appropriate strategy

Selecting the appropriate elimination strategy requires consideration of the following factors:

• Business needs: Prioritize the elimination of infrequently used data or prevent commonly used data from being eliminated.
• Access pattern: Data access frequency and time distribution.
• Performance requirements: The time-consuming and impact of the elimination operation on the system.
• Memory limit: The memory size available for Redis.

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