Mastering Cache Eviction Strategies in Redis: A Comprehensive Guide

Photo by Fredy Jacob on Unsplash

Mastering Cache Eviction Strategies in Redis: A Comprehensive Guide


3 min read

Play this article

Are you the proud architect of an application backed by a Redis database? Is the cache serving your data with the speed of a Cheetah on a hunt? If so, kudos! But have you thought about what happens when your cache eventually fills up? If you're fuzzy on the specifics of cache eviction, you’re certainly not alone. Whether you're a rookie developer or a seasoned system administrator, understanding cache eviction is essential. Let's explore why.

Understanding Cache Eviction

In the universe of Redis (or any caching system, but Redis is our star here), cache eviction policies are the underpinning of efficient cache management. The primary challenges these policies address are cache size and memory usage. When your cache reaches its saturation point, you're faced with a critical decision: should you turn away new data or discard old data to make room?

Enter cache eviction: the mechanism that decides which cache entries to keep and which to jettison. This process is crucial for maintaining optimal application performance.

Cache Eviction Strategies

When it comes to determining what data to remove, different strategies can be applied, such as:

  • Least Recently Used (LRU): Like deciding which old shirts to toss out of your wardrobe, LRU policy removes the least recently accessed cache entries.

  • Least Frequently Used (LFU): Think of a librarian removing the least borrowed books. LFU removes the cache entries accessed the least frequently.

  • Window TinyLFU (W-TinyLFU): This strategy keeps the most relevant entries by considering both recency and frequency, ideal for environments with varying access patterns.

  • Time To Live (TTL): Similar to food expiring in your fridge, TTL assigns each cache entry an "expiration date" and removes it once the time limit is reached.

Redis does have a default eviction policy called volatile-LRU. However, relying solely on default settings without understanding your specific needs can lead to suboptimal performance and even problems down the line.


Before you can effectively manage cache eviction, you need to know when to act. Redis has built-in tools like the INFO command for monitoring, and third-party tools like New Relic and Datadog can offer more granular insights.

How to Choose the Right Eviction Policy in Redis

Redis uses the maxmemory configuration directive to manage its cache entry data structure. You can set your desired eviction policy via the maxmemory-policy directive in the redis.conf configuration file. Here are some Redis eviction policies you might want to consider:

  • allkeys-lru: Affects all keys, irrespective of their expiration time.

  • volatile-lru: Affects only keys with an expiration time set.

  • allkeys-lfu: Targets least frequently used keys, regardless of expiration time.

  • volatile-lfu: Affects least frequently used keys that have an expiration time.

  • volatile-ttl: Targets keys with the shortest TTL first.

  • noeviction: Returns an error when the memory limit is reached during a write command.


A well-structured cache and the right cache eviction strategy can significantly boost your performance, especially when dealing with large datasets. Redis, with its array of capabilities, serves as a robust and efficient caching solution for a multitude of use-cases.