Common Cache Issues and Their Solutions

1. Cache Penetration When a key is missing both in the cache and the database, often due to malicious requests or attacks, it leads to continuous database queries.

Solution: Intercept at the top level, reject unreasonable IDs or use a Bloom filter; if the DB returns null, store a null value in Redis with an appropriate expiration to avoid repeated DB hits.

2. Cache Avalanche A large number of different keys expire simultaneously, causing cache miss and overwhelming the database.

Solution: There is no perfect fix, but stagger key expiration times when setting them, and for hot data consider setting it to never expire.

3. Cache Breakdown A key expires while the database still has the data; if a massive concurrent request arrives before the cache is refreshed, the database gets overloaded.

Solution: Use locking—when the first request finds the cache empty, acquire a lock so subsequent requests wait, similar to a multithreaded singleton pattern.

4. Distributed Cache Efficiency Issues Distributed caches involve network transmission, which inevitably introduces performance overhead.

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