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A cache stores a temporary subset of data so repeated reads or computations can be served without repeating the full trip to a primary data source. Used where reuse is likely and the freshness trade-off is acceptable, caching can lower backend work and improve response time. In production, it also introduces decisions about stale values, invalidation, memory limits, network hops, and recovery when entries disappear.
How a cache works
A request first looks for a value in a cache. A hit returns the cached value; a miss requires another step, such as reading a database or computing the result, and may then populate the cache. The cache is not automatically the source of truth: it is a temporary copy whose contents can expire or be evicted.
Caching is most useful when the same data or result is requested repeatedly and the system can tolerate the cache’s freshness behavior. It is less useful when requests rarely reuse the same values, or when even a short-lived stale response would cause unacceptable consequences.
What should I cache?
Start with a candidate whose repeated retrieval or computation is costly enough to justify storing a copy. Then test it against both its access pattern and its correctness requirements.
#1 Best Overall
- Reuse: Do requests revisit the same keys often enough that entries are likely to be read again?
- Change rate: How often does the underlying value change, and how quickly must readers see an update?
- Staleness cost: What happens if a request receives an older value?
- Working set: Can the frequently reused keys fit in the available memory, or will entries be displaced before they are useful?
- Miss cost: Can the primary store handle a burst of misses, including after cache loss or a wave of expirations?
These questions often favor frequently reused reference or lookup data over values that are rarely requested or change constantly. They do not establish that every apparently reusable value should be cached: measure the actual workload and account for the consequences of stale data.
Choose a population pattern
The two common patterns differ in when they populate the cache. Neither one, by itself, guarantees strong consistency. The application still needs a defined contract for updates, concurrent requests, and failures.
| Pattern | What happens | Useful when | Cost to account for |
|---|---|---|---|
| Cache-aside (lazy loading) | On a read, check the cache. On a miss, fetch from the primary store, populate the cache, and return the result. | You want to cache data as it is requested and keep storage focused on keys with actual demand. | The first miss does both the cache lookup and primary-store work, adding work and latency to that request. |
| Write-through | After writing to the primary database, update the cache as part of the write flow. | You want written values to be more likely to be present for later reads and to reduce subsequent database reads. | Writes can fill memory with objects that are seldom read; a cache-loss plan must explain how entries return. |
Cache-aside in a read path
On a hit, return the cached value. On a miss, load from the primary store and populate the cache before returning. This is straightforward to introduce, but the miss path deserves attention: simultaneous misses for a popular key can all reach the origin unless the application controls that behavior. The cited guidance establishes the extra work on a miss; the right coordination mechanism depends on the application and is not prescribed here.
Write-through in an update path
Update the primary database, then update the corresponding cache entry in the write flow. Specify what the application does if one update succeeds and the other fails; otherwise the cache and primary store can disagree. Write-through can be combined with cache-aside so writes populate entries and later misses still load them lazily.
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Expiration and active invalidation solve related but different problems. Expiration is time-based: an entry ceases to be usable after its configured lifetime, so a later read must consult the origin. Active invalidation is event-driven: when the application knows source data changed, it removes or updates the corresponding cached value.
A TTL alone does not promise immediate freshness after a source update. If the application needs a stronger freshness contract, define how the update flow changes or removes the affected entry and what readers can observe while that work is in progress. AWS Well-Architected guidance, in PERF03-BP05 in its 2024-06-27 version, recommends an invalidation strategy such as a TTL that balances freshness with pressure on the backend datastore.
How do I choose a TTL?
Choose a TTL by weighing the source’s change rate against the harm of serving an old value. Slowly changing reference data may tolerate a longer validity period than frequently changing data. A shorter TTL forces more frequent origin reads; a longer one can leave old values available longer. There is no single TTL that is right for every key or application.
Where many entries are populated around the same time, give their expiration times some jitter. AWS’s Redis caching whitepaper recommends this to spread expirations rather than letting a large group of keys expire together and send a synchronized rush of requests to the backend.
Write down the intended freshness behavior for each important class of data: what staleness is acceptable, what triggers an early removal or update, and what the application does on a miss. That contract—not the TTL in isolation—sets what readers can expect.
Where should the cache live?
Cache placement changes both lookup cost and who can reuse an entry. A local cache can avoid a network lookup for requests handled by that client, but separate clients may hold duplicate entries. A remote cache centralizes entries for multiple clients, at the cost of an extra network hop. A multi-level arrangement can combine local and remote caches, but each level adds freshness and operational decisions.
| Placement | Potential benefit | Trade-off |
|---|---|---|
| Client-side or local | A local request can avoid a remote lookup. | Entries may be duplicated across clients. |
| Remote shared cache | Multiple clients can use centralized entries. | Requests incur a network hop to the cache. |
| Edge delivery cache | Can serve cached content from locations closer to viewers and reduce origin requests. | It is a delivery-layer choice; its behavior and hit ratio need to be evaluated for the deployed content and request scope. |
Amazon CloudFront’s developer documentation describes serving cached objects from edge locations closer to viewers to reduce origin requests and latency. This describes the intended benefit, not a guaranteed performance result for every deployment. CloudFront defines cache hit ratio as the proportion of requests served directly from cache. When reporting it, state which requests are in the denominator and what deployment or content scope the figure covers.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan memory and eviction
A cache has finite capacity, so its eviction policy determines which entries are removed when memory pressure requires space. Choose a policy that matches how reuse occurs and how costly it is to lose particular entries.
Best Value
- Least recently used (LRU): favors entries accessed more recently, which fits workloads where recent use predicts reuse.
- Least frequently used (LFU): favors entries used more often, which fits workloads where repeated frequency is a useful reuse signal.
- TTL-based, random, or other policies: AWS’s Redis caching whitepaper also enumerates policies based on TTL and random eviction. Their suitability depends on the workload.
- No eviction: under a
noevictionpolicy, writes are blocked when memory cannot be freed. The application must be prepared to handle those failed writes.
Evictions can be an intentional part of a bounded cache, but a sustained or unexpected rise can indicate a capacity mismatch. AWS notes that observed evictions may mean a deployment needs to scale up or out unless eviction is intentional. The whitepaper’s revision history lists April 1, 2022 as its latest revision.
Operate the cache as a dependency, not a durable store
Do not make a cache the only durable copy of important data or assume it will always be available. AWS Well-Architected identifies relying on a cache as though it were durable and always available as an anti-pattern. The primary store or another durable system must remain responsible for the data that cannot be lost.
Design and test the miss path for ordinary operation and for cache loss. A cold or emptied cache can send many requests to the origin; plan how the application will repopulate entries and how much additional backend load that creates. For remote caches, AWS also advises using client-side timeouts, connection pooling, retries, and exponential backoff where supported. Retries should be bounded and considered alongside origin capacity so a degraded dependency does not cause an uncontrolled wave of additional work.
Measure whether caching is helping
Track cache hit rate alongside evictions, cache errors, latency, and the load reaching the primary store. A hit rate alone cannot say whether the cached values are useful, fresh enough, or worth their memory and network costs.
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AWS Well-Architected gives 80% or higher as a cache hit-rate goal and says lower values may point to insufficient cache size or an access pattern that does not benefit from caching. Treat this as AWS’s operational guidance, not a universal benchmark or a target that every system must meet. A low rate can also reflect poor key selection or a workload with little reuse. Investigate which requests miss and why before simply adding capacity; report the metric with its request scope and denominator.
Use the measurements to decide whether the cache reduces origin work without violating the freshness contract. If entries churn before reuse, misses dominate, or cache access adds more cost than it saves, reconsider the keys, TTLs, placement, or whether that workload should be cached at all.
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