Cache Preloading and Warming
Cache preloading fills entries before traffic arrives, so users do not pay the latency of a cold cache during a deploy or after an eviction.
Definition
Cache preloading is the proactive population of a cache with expected keys, either at startup or in the background, so that the first real request finds a warm entry.
Context and scope
It is used after deployments, cache flushes, and known traffic spikes such as scheduled events.
Common misconceptions
- preloading guarantees every needed key will be present
- loading keys in an arbitrary order is as effective as by expected demand
Anti-patterns
- preloading an unbounded key space at startup
- warming with synthetic requests that never release their connections
Related concepts
Related examples
Authoritative source
Definition
Cache preloading is the proactive population of a cache with expected keys, either at startup or in the background, so that the first real request finds a warm entry.
It is used after deployments, cache flushes, and known traffic spikes such as scheduled events. Compared to simpler alternatives, this concept fits best when the trade-off it represents is the one the system actually cares about.
When to use it
A common use case is applying it at the boundary where the cost of getting it wrong is highest, and the scenario usually involves a concrete guarantee the system must uphold. Unlike an ad hoc approach, it makes that guarantee explicit, reviewable, and testable.
How it works
Loading the most probable keys first maximises the benefit, so warming should be driven by observed traffic rather than by iterating the entire data set.
Aggressive preloading can overload the origin, so it should be rate limited and staggered, especially when many instances warm simultaneously after a deploy.
Trade-offs and alternatives
The approach works best together with a sensible time to live, because a warm entry that expires before it is used delivers no benefit. The main drawback is the extra complexity it introduces, so the benefit should clearly outweigh that cost before a team adopts it.
Common misconceptions
A persistent misconception is that preloading guarantees every needed key will be present. Another is that loading keys in an arbitrary order is as effective as by expected demand. For example, an engineer who assumes otherwise may design around a guarantee that does not actually exist, which only becomes obvious under load or failure.
Anti-patterns to avoid
Two recurring anti-patterns are to preloading an unbounded key space at startup and to warming with synthetic requests that never release their connections. Both tend to surface as hard-to-debug production issues rather than obvious test failures. Related concepts include Distributed Caching, Write-Behind Caching, Cache Partitioning via Consistent Hashing.