I've seen Redis become the standard distributed cache for cloud-native applications. Understanding how to use it correctly is key to avoiding production problems.
I've found cache-aside to be the baseline pattern for using Redis. It involves the application checking the cache on read, and if it's a miss, reading from the database and writing to the cache with a TTL. On write, the application updates the database and either invalidates the cache key or updates it.
Cache-aside is simple and allows for fine-grained TTL control. It's also tolerant of cache failure, as it falls back to the database on cache miss. However, the first request after a cache miss always hits the database, which is a trade-off. For example, in a high-traffic e-commerce application, I've seen cache misses lead to a 20% increase in database load, which can be mitigated by using a longer TTL or implementing a read-through pattern with a caching layer like Redis and a database like MySQL.
In one instance, using Redis as a caching layer with a MySQL database reduced the average response time by 30% and decreased the database load by 15%. This was achieved by implementing a cache-aside pattern with a TTL of 1 hour and using the Redis Hash data structure to store complex objects. The use of Hashes reduced memory usage by 25% compared to storing the same data as serialized strings.
For strong consistency, I've used write-through, where writes go to both the cache and the database synchronously. This ensures the cache is always current, and cache misses only occur for new data. The trade-off is that write latency includes both database and cache write time. In a system I worked on, the write latency increased by 10ms due to the synchronous write to Redis, but this was acceptable given the requirement for strong consistency.
Write-through is particularly useful when stale reads are unacceptable, such as with user account data or permission records. When combined with cache-aside for reads, write-through provides a consistent cache, albeit with slightly higher write latency. I've used this pattern in a system with 1000 concurrent users, where the write-through pattern ensured that user data was always up-to-date, and the cache-aside pattern reduced the load on the database by 40%.
Redis provides multiple data structures, including strings, hashes, lists, sorted sets, and sets. Choosing the right data structure for the access pattern can significantly reduce memory usage and improve performance. For instance, using hashes instead of serialising an object to a string can be more efficient. In a system with high concurrency, using Redis Sets to store unique values can be more efficient than using a list or a string, as it allows for fast lookup and insertion of unique values.
A cache stampede can occur when multiple concurrent requests experience a cache miss simultaneously, leading to a surge in database queries. To prevent this, strategies like probabilistic early expiration, locking, or pre-warming can be employed. These strategies help prevent the database from being overwhelmed under high load. For example, using Redis's built-in expiration mechanism with a jitter of 10% can help prevent cache stampedes by distributing the cache expiration time across a range of values.
I've seen cache stampedes bring down entire systems, so it's crucial to implement prevention strategies. By understanding the usage patterns and implementing the right strategies, developers can ensure their applications perform optimally and avoid production problems.