Cache-Aside, Write-Through, Write-Behind: Six Caching Patterns, Two Decisions
Cross-posted from the Runsite blog . Cache-aside, read-through, write-through, write-behind, write-around, refresh-ahead. Put them in a list and they look like six competitors you're supposed to rank. They don't compete. Each one answers one of two independent questions: When a value is missing from the cache, who goes to the database to get it? When data changes, who writes it to the database,…
Cache-aside, read-through, write-through, write-behind, write-around and refresh-ahead are six distinct caching patterns. These patterns don't compete with each other but rather address two independent questions: what handles a cache miss and who writes the data to the database when it changes.
Most operations involve two axes - when to handle a cache miss and how to write data to the database. Cache-aside is the default pattern where an application handles both decisions. When a cache miss occurs, the application retrieves data from the database and stores it in the cache. For writes, the application updates the database and then removes the key from the cache. AWS describes the read side as "lazy loading" due to its common usage.
Cache-aside provides resilience; if the cache fails, normal read operations proceed since the database bears the load. However, it does have one downside - the first read of a key always results in a miss, causing every subsequent read to hit the database.
The write path often deletes the key after updating the database instead of updating the cache directly. This approach prevents race conditions where one reader sees outdated data after a write operation. Failure to implement this could result in serving incorrect data for the entire time-to-live period.
Write-through is another pattern, where the application writes to both the cache and the database before returning. This ensures consistency as all writes go through the cache. However, it comes at the cost of an additional database round trip on every write, potentially impacting performance for certain types of data like audit logs.
In conclusion, understanding these caching patterns and their implications is crucial for optimizing data retrieval and storage processes in software systems.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
