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.
Hello, I'm Wufan.
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
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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