Cache consistency: strategies to keep data fresh

Cache consistency is crucial for maintaining the accuracy of data served by high-traffic systems, as caches that drift from their source databases can lead to issues like incorrect prices or outdated permissions. The discipline involves keeping the interval of disagreement between the cache and the source database as small as possible. Caches drift due to factors such as time-to-live (TTL) windows, write-ordering races, and multi-instance population races. Classic caching strategies like cache-aside and write-through offer different trade-offs between consistency and performance, but neither adequately addresses changes originating outside the application itself. Event-driven synchronization, through mechanisms like Redis keyspace notifications or change data capture (CDC) systems, provides a more dynamic solution by reacting to data changes in real time. Companies like Meta and Uber have invested heavily in refining their cache consistency strategies, often layering multiple mechanisms to achieve optimal freshness. Redis offers tools like Redis Data Integration (RDI) to automate parts of this process, ensuring that cache entries remain up-to-date with minimal custom coding, and it supports various data formats and database types.

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