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A time-to-live (TTL) limits how long a cached value may remain fresh; it does not tell your application which data changes make that value obsolete. Reliable cache invalidation starts with the domain: identify what changed, which cached representations depend on it, and how that change reaches every cache that may hold them. TTL remains useful as a safety bound, but it is only one part of the policy.
What does a TTL solve—and what does it leave unresolved?
A cache stores a copy or derived representation of data so requests can avoid repeatedly doing the same work. A TTL gives that entry an age-based freshness window. When the window expires, the cache can fetch or revalidate the value rather than treating it as fresh.
This is useful when the data’s volatility and the acceptable period of staleness are understood. But a TTL does not know that a product’s price changed, which product lists include it, or whether an aggregate derived from those prices must be refreshed. Nor does expiration alone ensure that every cache has learned about a write. Microsoft’s caching guidance treats expiration as a policy choice, not a substitute for application-specific invalidation.
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So the useful question is not simply “How many seconds should this cache live?” It is also “What change makes this representation wrong, and what must happen when that change occurs?”
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Why can invalidation race with a write?
Dynamic caches change through both reads that fill entries and writes or invalidations that remove or update them. Those paths can overlap. For example, a read may start before a database write, pause, and then refill the cache with the old value after the write has already triggered an invalidation. A cache can therefore be invalidated correctly at one moment and still end up holding stale data if ordering and propagation are not handled.
Meta Engineering describes this coupled behavior in its account of TAO and Memcache: “For a dynamic cache, like TAO and Memcache, data gets mutated on both read (cache fill) and write (cache invalidation) paths.” Its 2022 article reports that TAO’s consistency improved from 99.9999 to 99.99999999 “by one measure”; that is a result for Meta’s system and measure, not a general benchmark for cache strategies. Meta Engineering’s explanation of cache consistency discusses the ordering problem in more detail.
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In a distributed system, the change may also need to reach more than one cache or region. A design must account for whether notifications are delivered, whether a stale fill can race with them, and what readers experience while a cache catches up.
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The domain boundary is where the application can name a real mutation and reason about what it affects. For each important change, map the source entity or event to the representations that depend on it. The specific answers depend on the application; these are design questions, not universal cache rules:
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- If a product changes, do cached product details, search results, category lists, or price aggregates include that value?
- If a user’s access level changes, which cached pages or API responses were assembled using the old permissions?
- Does a change affect one entity key, a collection, a computed result, or content selected by a URL, tag, or prefix?
- Can the application identify those affected keys precisely, or would it have to invalidate a broader group?
Once those dependencies are explicit, the system can select what to expire, purge, mark stale, or version. Without that map, “invalidate on update” is underspecified: the update may clear the entity entry while leaving a dependent list or aggregate unchanged.
Which invalidation policy fits the data?
These policies can be combined. Choose according to tolerated staleness, how reliably changes can be observed and propagated, how precisely affected entries can be targeted, refill load, failure behavior, and operational complexity.
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| Policy | What happens | Main trade-off |
|---|---|---|
| TTL or time-based expiration | An entry may be served during its freshness window; after it expires, the cache refills or revalidates it. | Simple and useful as a bound on age-based staleness, but a change may remain visible until expiration. Set the window according to volatility and tolerated staleness. |
| Explicit purge | Matching objects are removed so later requests must refill them. | Can remove affected content promptly when targeting is correct, but broad purges can send a burst of requests to the origin. Ensure the backend has the new content first, or the old response may be fetched and cached again. |
| Mark stale and revalidate | The cached object remains available but is marked stale; a subsequent request causes a conditional request to the origin. | Can avoid transferring an unchanged representation, but the reader’s wait and stale-serving behavior depend on configuration and origin response. |
| Event-driven invalidation | A change event, such as a publish/subscribe message, tells caches to invalidate affected entries. | Useful when changes are unpredictable, but correctness depends on identifying affected entries and on reliable message delivery and propagation; an event does not by itself guarantee immediate consistency. |
| Versioned keys | A newly published representation receives a new key or version, and readers request that version. | Avoids in-place mutation for that object class, but the application still needs to manage version selection and retention of old entries. |
For unpredictable data, the GOV.UK-hosted API design guidance recommends event-based invalidation as a design option alongside expiration. Neither strategy removes the need to define the affected data and the behavior when propagation or origin access fails.
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Provider terminology matters. In Cloudflare’s documentation, a purge removes matching content; invalidation instead keeps the content but marks it stale. “Invalidation” is therefore not a universal synonym for immediate deletion.
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Cloudflare says that “Invalidation does not fetch new content in advance.” When a stale object is requested, conditional revalidation can use an ETag or Last-Modified validator if available. The origin can respond with 304 Not Modified, allowing the cached response to be reused with a refreshed TTL, or return new cacheable content that replaces it. Depending on cache directives and settings, Cloudflare may serve stale content during background revalidation or if the origin fails. These are Cloudflare-specific behaviors, not guarantees for every cache. See Cloudflare’s invalidation documentation, last updated September 29, 2026.
Google Cloud CDN supports invalidation by URL or path patterns and by cache tags. Its documentation says a request takes effect in about 10 seconds, while noting that a small number of distributed caches may lag; it also permits up to 500 invalidation requests per minute. Those are published Cloud CDN service details, not general CDN guarantees. Google warns that invalidating too much can create a backend load spike, so target only what is needed when the design allows it. See the Google Cloud CDN invalidation overview.
What does HTTP invalidate automatically?
RFC 7234 says an HTTP cache must invalidate the effective request URI after a non-error response to a PUT, POST, or DELETE request. This protocol rule is narrower than an application’s dependency map: it does not automatically discover every other URL, cached query, list, or derived result that may depend on the changed resource. The application still needs a policy for those representations. RFC 7234 describes the HTTP caching behavior.
How to design an invalidation policy
- Name the mutation. Identify the domain event or write that changes the underlying data, rather than starting with a cache setting.
- Map its dependents. List the entity entries, collection views, aggregates, URLs, tags, or other representations that can become incorrect because of that mutation.
- Set the staleness budget. Decide how long old data may be served, including during revalidation or origin failure. Use a TTL where an age bound is valuable, but do not treat it as proof that a write propagated.
- Choose targeting and behavior. Decide whether to expire, purge, mark stale and revalidate, publish an invalidation event, or publish a new versioned key. Match the choice to the cache provider’s actual semantics.
- Account for ordering and delivery. Consider a read that began before the write, a refill after invalidation, delayed messages, and caches that have not yet received the change.
- Protect the origin. Estimate what happens if many entries are removed at once. Prefer precise invalidation where possible, and consider how refill traffic behaves after a broad purge.
- Define failure behavior. Make explicit whether requests wait for revalidation, may receive stale data while revalidation runs, or may receive stale data when the origin is unavailable.
The resulting policy is a contract between domain writes and cached reads: what changes, what becomes stale, how the cache learns about it, and what readers may see while the system converges.
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