Understanding Cache Breakdown and Effective Mitigation Strategies
The article explains the concept of cache breakdown—when an expiring cache key triggers a surge of database requests—and presents three practical mitigation approaches: using never‑expire for immutable data, applying distributed or local locks for infrequently updated data, and employing scheduled pre‑warming or expiration extension for frequently changing or slow‑refreshing caches.
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Please explain cache breakdown?
Concept of Cache Breakdown
A cache key with an expiration may, at the moment it expires, receive a massive number of requests that all hit the database, effectively punching a hole through the cache wall.
Solutions
Different scenarios can be addressed as follows:
If the cached data is essentially immutable, set the hot data to never expire.
If the data updates infrequently and the cache refresh process is quick, use a distributed mutex (e.g., Redis, Zookeeper) or a local lock so that only a few requests query the database while others wait for the new cache.
If the data updates frequently or cache refresh is time‑consuming, employ a scheduled thread to proactively rebuild the cache before expiration or extend the expiration time, ensuring continuous cache availability.
Reference: Advanced-Java
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Wukong Talks Architecture
Explaining distributed systems and architecture through stories. Author of the "JVM Performance Tuning in Practice" column, open-source author of "Spring Cloud in Practice PassJava", and independently developed a PMP practice quiz mini-program.
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