Here’s a real-world scenario. You set up a Redis database, and it’s doing wonders at speeding up your application. But as the data flows in and the volume increases, you notice a potential issue: the cache is filling up. What will happen when it’s full? You may have heard about cache eviction but, perhaps, you’re fuzzy on the details.
You’re not alone. Whether you’re a developer in a budding company or a system administrator in a large corporation, it’s important to understand cache eviction and know when and how to implement it. In this guide, we explore why.
Cache eviction policies are a critical aspect of cache management when you use Redis (or any system that relies on caching… but we speak here from our own expertise). It addresses the challenges of cache size and memory usage. As the cache reaches its limits, it must make a crucial decision: should new data be rejected, or should space be created by discarding old data?
This is where cache eviction comes into play. However you resolve it, cache eviction involves determining which cache entries to retain and which to discard when a cache fills up. It’s necessary to achieve or maintain optimal application performance and consistency.
Eviction, in the context of caching, does not involve any landlords or overdue rents. Rather, it refers to the process of removing specific data from a cache. Because when a cache reaches its maximum storage capacity, some data must be removed to make space for new data–just like a bookshelf where you cannot force another book into the space available.
Cache eviction strategies are protocols that dictate how a system responds when the cache is full. The decision about which data to evict (that is, remove) is made programmatically based on one of several strategies. Common strategies include:
The effectiveness of these strategies depends on the specific use case.
While Redis does have a default eviction policy (volatile-LRU), relying solely on it without understanding its implications can be risky. Applications serve diverse user needs, which means that data patterns and eviction requirements can vary significantly. Setting the right eviction policy can prevent potential headaches.
Before even thinking of eviction, it’s essential to know when to act. This is where monitoring tools come into play.
Redis provides tools like the INFO command for monitoring cache performance, while third-party monitoring tools like New Relic and Datadog offer more detailed analysis.
Tuning cache performance involves adjusting cache settings and eviction policies based on the information you discover from monitoring performance. In distributed caching scenarios, monitoring and tuning become even more critical to ensure consistent and efficient cache management across multiple nodes. (We have additional advice for what to look for in a third-party monitoring tool.)
In Redis, the cache entry data structure is managed by the maxmemory configuration directive, which sets the memory limit. The maxmemory-policy configuration directive guides Redis in making its eviction decisions based on the chosen cache eviction policy. Both the maxmemory-policy and the eviction policy are among the configuration settings stored in the redis.conf configuration file.
There are quite a few Redis eviction policies, but you probably care most about these.
Removes the least recently used cache entries, whether or not they have an expiration time set.
Removes the least recently used cache entries with an expiration time set. This is suitable for scenarios where data needs to be refreshed periodically.
When Redis needs to make room for new data, this policy removes the least frequently used keys.
Similar to allkeys-lfu, this policy applies only to keys with an expiration time set.
This policy removes keys with the shortest TTL first.
Instead of evicting any keys, this policy returns an error when the memory limit is reached and a write command is received. (Don’t throw out anything in the closet. Send an alarm!)
Each policy has its strengths and weaknesses. The best one for you depends on your specific needs.
It’s important to have a well-structured cache, combined with the right cache eviction policy in order to achieve performance goals when you have vast amounts of data. Redis, with its versatile capabilities, serves as an excellent caching solution and a powerful asset for applications handling large datasets. Effective cache management not only expedites data retrieval through cache hits but also mitigates the impact of cache misses, making Redis a reliable and efficient caching solution for diverse use cases.
Discover the intricacies of scaling cache with our comprehensive guide: The Definitive Guide to Caching at Scale with Redis. Learn the basics of caching to advanced enterprise application techniques in this one-stop resource.