TTL is useful for limiting how long cached data can remain fresh, but it cannot tell your application which changes make which cached views obsolete. Reliable invalidation starts with the domain: identify the changed data, the representations that depend on it, and how updates reach every cache that may hold them.
Why is cache invalidation a domain problem?
A cache stores a copy or derived representation of data. When that data changes, the application needs a rule connecting the mutation to the cached representations that depend on it. A TTL sets an age limit; it does not identify those dependencies or ensure an update reaches every relevant cache. Microsoft’s Azure caching guidance treats expiration as one part of caching design, while Meta Engineering’s account of TAO and Memcache describes the consistency challenges in dynamic caches.
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Start by asking what changed and what readers can see as a result. For example, if a product’s price changes, does the application cache only the product record, or also search results, category pages, and a price summary? Those are design questions, not universal dependencies: the application must determine which of its own keys and derived views depend on that data.
There is also an ordering problem. A request can begin before a write, then refill the cache with an old value after an invalidation has already happened. Distributed propagation and cache state that is not durable add more ways for a stale value to reappear or persist. Meta describes this as a challenge of dynamic caches, where both cache fills and writes affect state. Its 2022 article reports an improvement from 99.9999 to 99.99999999 consistency “by one measure” for TAO; that is a Meta-reported result for its system, not a general benchmark for invalidation strategies.
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What should a cache policy decide?
Choose a policy against the behavior the application needs, rather than treating “set a TTL” as a complete consistency plan. A single system can combine policies—for example, event-driven invalidation for a known update plus a TTL as a fallback bound.
| Policy | What happens | Best fit and trade-off |
|---|---|---|
| TTL (time-based expiration) | An entry may be served until its freshness window ends; it is then refilled or revalidated. | Simple when data volatility and acceptable staleness are understood. Old content can remain visible until expiry. Choose the window based on the tolerated age of stale data, not an unexplained round number. Microsoft’s caching guidance discusses expiration as a cache policy. |
| Explicit purge | Matching objects are removed, so later requests refill them. | Useful when matching content must be removed. The backend must already have the correct content, or the old response may be fetched and cached again. Purging too broadly can send a burst of requests to the origin. Google Cloud CDN’s invalidation guidance recommends limiting invalidation to what is needed. |
| Mark stale and revalidate | The entry remains but is marked stale; a later request triggers a conditional request to the origin. | Can avoid fetching new content in advance. With a validator such as an ETag or Last-Modified value, the origin may answer 304 Not Modified, allowing the cached response to be reused, or return new cacheable content. Depending on directives and settings, stale content may be served during background revalidation or if the origin fails. These are Cloudflare-specific behaviors. |
| Event-driven invalidation | A change event, such as a publish/subscribe message, tells caches to update or invalidate entries. | Can target changes whose timing is unpredictable, but adds a dependency on reliable event delivery and handling. Event notification is a design option, not a guarantee of immediate consistency. GOV.UK-hosted API design guidance recommends event-based invalidation for unpredictable data. |
| Versioned keys | New or immutable data is written under a new key or version, and readers request that version. | Avoids in-place mutation for the data class that uses it. The application still needs rules for assigning and publishing versions and retaining or removing old entries. The Software Engineering Guide discusses versioning among cache strategies. |
How do you choose among the policies?
Evaluate each candidate against the same operational questions. A policy that is easy to explain may still be wrong if it cannot target dependent data or its failure mode violates the application’s staleness requirements.
- Staleness tolerance: How long may a reader see old data? Can stale data be served during revalidation or when the origin is unavailable? The answer may differ by representation: an old profile image and an outdated account balance do not necessarily have the same acceptable freshness window.
- Change observability and propagation: Can the system identify the change and notify every cache that might hold a dependent representation? Account for races between reads, fills, writes, and invalidation, not just the happy-path event.
- Targeting: Can the change select the right URL, tag, prefix, host, entity key, or version without evicting unrelated content? The selectors and semantics available depend on the cache or provider.
- Refill load: What happens to origin traffic when entries expire or are removed? A broad invalidation can turn many cache hits into backend requests at once.
- Failure behavior: Does a request wait for revalidation, receive stale content while revalidation runs, or receive stale content after an origin error? Check the actual directives and service settings that govern the path.
- Operational complexity: TTL is comparatively simple but coarse. Event-based propagation needs dependable delivery and processing; versioned keys need lifecycle rules for versions and old entries.
What does HTTP invalidate automatically—and what does it not?
RFC 7234 specifies that an HTTP cache must invalidate the effective request URI after a non-error response to a PUT, POST, or DELETE request. This is protocol behavior for that URI. It does not discover every application-level dependency—for example, a cached query result, aggregate, or page that also reflects the changed resource. Those relationships still need an application design that connects changes to the affected representations.
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What do CDN purge and invalidation mean in practice?
Cloudflare: purge versus mark stale
Cloudflare distinguishes purging matching content from invalidating it: a purge removes the content, whereas invalidation leaves it in the cache but marks it stale for revalidation. Its documentation says, “Invalidation does not fetch new content in advance.” When a stale object is requested, conditional revalidation can use an ETag or Last-Modified validator. A 304 Not Modified response reuses the cached response and refreshes its TTL; a new cacheable response replaces it. Whether stale content is served during background revalidation or an origin failure depends on cache directives and settings. These details describe Cloudflare’s documented behavior, not a universal CDN rule. Read Cloudflare’s invalidation documentation.
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Google Cloud CDN: targeting, timing, and load
Google Cloud CDN documents invalidation by URL/path patterns and cache tags. Its documentation, accessed in 2026, says an invalidation 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 service-specific figures, not guarantees for other CDNs or general cache systems. Google warns that invalidating too much can create a backend load spike and advises invalidating only what is needed. See Google Cloud CDN’s invalidation overview.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you make the dependency rule explicit?
- Identify the mutation. Name the domain event or write that changes source data, such as a product price update or a profile edit. Be precise about what is committed and when readers should treat it as current.
- List dependent representations. Trace which cached objects, lists, search results, or aggregates include that data. Treat the list as application-specific; do not assume an entity key is the only affected cache entry.
- Define selectors and actions. Map each change to the appropriate keys, tags, paths, or versions, then decide whether to expire, purge, mark stale, refresh, or publish a new version.
- Specify ordering and delivery. Decide how cache fills that overlap a write are handled and what the system does if an event is delayed, duplicated, or not processed. In a distributed cache, document how each relevant location learns about the change.
- Set the fallback freshness bound. If event propagation or explicit invalidation fails, decide whether a TTL provides an acceptable upper bound on stale visibility. The TTL is a safeguard, not a substitute for the dependency mapping.
- Check the load and failure path. Consider simultaneous refill after a purge, origin failure during revalidation, and whether stale data is allowed in each case. Make the intended behavior observable so operators can distinguish a slow origin from missed invalidation.
The resulting rule should be understandable in domain terms: when this kind of data changes, these representations become stale or move to a new version, and this is what readers can see while the update propagates. That is the part a TTL alone cannot express.
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