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A cache keeps a temporary copy of selected data so repeat requests can avoid some work at the primary data store or origin. To use one safely in production, decide what is worth caching, how stale it may be, where it belongs, what happens when it fills or disappears, and how you will measure its effect.
How does a cache work?
A cache stores a subset of data temporarily. When an application needs a value, it may find that value in the cache and return it without repeating the original retrieval or computation. If the value is missing or expired, the application obtains it from the primary source and may store it for later requests.
Caching is most useful when requests reuse the same data and the system can tolerate the cache’s freshness behavior. It is not a durable source of truth: a cache can miss, evict data, or lose its contents. The application must still define what to do when the primary source has to be consulted.
What should I cache?
Start with a repeated, relatively expensive read or computation—not with a goal of caching everything. For each candidate, weigh how often it is reused against how quickly it changes, the impact of serving an old value, and the memory required to retain it.
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- Good candidates: values requested repeatedly whose source changes slowly enough, or whose consumers can accept a defined period of staleness.
- Weak candidates: values rarely requested, values that change so often that cached copies quickly become obsolete, or values for which even a brief stale response has unacceptable consequences.
- Capacity check: estimate the set of entries likely to be reused and compare it with available cache memory. A cache too small for the useful working set may continually displace entries that matter.
For example, an application might cache relatively stable reference data for longer than rapidly changing account data. That is a design choice, not a universal rule: specify what freshness each consumer requires before setting an expiry.
Which application caching pattern fits?
Cache-aside and write-through describe how an application populates or updates cache entries. They can be used together, but neither one alone defines the system’s consistency guarantees. Those depend on the application’s update ordering, concurrency behavior, and response to failures.
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| Pattern | How it works | Useful when | Trade-off |
|---|---|---|---|
| Cache-aside (lazy loading) | On a read, check the cache. On a miss, read from the primary store, populate the cache, and return the result. | You want the cache to fill with data that has actually been requested. | The first request after a miss does both the cache lookup and primary-store work, adding work and latency to that response. |
| Write-through | After a primary-store write, update the cache as part of the write flow. | You want written data to be more likely to be present on later reads. | It can use memory for data that is rarely read, and the application still needs a plan for repopulating entries after cache loss. |
| Combined approach | Update entries on writes and use cache-aside to populate on read misses. | Both write traffic and previously uncached reads should populate the cache. | The application must specify how the write path, miss path, and concurrent updates interact. |
How do I choose a TTL?
A time-to-live (TTL) sets how long a cache entry remains valid before it expires and the application must consult the origin again to refresh it. Choose it by considering how quickly the source value changes and the consequences of returning an outdated value. Slowly changing reference data may tolerate a longer TTL than frequently changing data, but the acceptable interval is specific to the data and its consumers.
- Write down the freshness requirement for each kind of value: how old may it be when served?
- Consider the source’s update rate and what a stale response would cause.
- Choose an expiry that fits that contract, then verify the result under real access patterns.
- Add jitter to expiry times when many entries would otherwise expire together. AWS’s Redis caching whitepaper recommends jitter to spread expirations and reduce the chance of a synchronized rush of requests reaching the backend.
There is no single TTL or hit-rate target that is right for every application. A TTL gives a time-based bound on how long an entry remains in the cache; it does not by itself promise immediate freshness after a source update.
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How do I invalidate a cache?
Expiration and active invalidation solve related but different problems. Expiration refreshes an entry after a chosen amount of time. Active invalidation removes or updates an entry when the application knows the underlying data has changed. A system may use either approach or combine them.
Define the contract for each update path: what happens to cached data when the source changes, and what may a reader see while that change is taking effect? For a cache-aside example, the application can remove the corresponding entry when it processes a source update; a later read then misses and fetches the current value from the primary store. That describes an update flow, not a guarantee that every concurrent reader immediately sees the new value. Specify the behavior your application can actually provide.
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AWS Well-Architected guidance, PERF03-BP05, recommends a cache invalidation strategy such as TTL that balances freshness with pressure on the backend datastore. The appropriate balance depends on the data and the consequences of staleness.
Where should a cache live?
Placement changes which requests can reuse an entry and what it costs to reach the cache. A system can also use more than one layer when each layer serves a distinct purpose.
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| Placement | What it changes | Trade-off to account for |
|---|---|---|
| Client-side | A client can reuse a local entry without a network lookup. | Different clients may hold duplicate entries, so local copies need an appropriate freshness policy. |
| Remote shared cache | Multiple clients can use centrally stored entries. | Each lookup adds a network hop; measure its cost as part of the full request path. |
| Multiple levels | Local and remote caches can serve different reuse patterns. | Each level has its own copy and freshness behavior, which the application must account for. |
| Edge cache for web delivery | A content-delivery network can serve cached objects from edge locations nearer viewers. AWS describes CloudFront as reducing origin requests and latency through edge caching. | Measure hit ratio for the deployment and define its denominator: AWS defines it as the proportion of requests served directly from cache. The documentation does not establish a general performance guarantee. |
How should memory limits and eviction work?
When a cache reaches its memory limit, its eviction policy determines whether entries are removed to make room or new writes are refused. Choose a policy based on how data is reused and the cost of losing particular entries.
- Least recently used (LRU): favors retaining entries accessed more recently.
- Least frequently used (LFU): favors retaining entries accessed more often.
- TTL-based or random eviction: selects entries according to expiry-related rules or at random, depending on the configured policy.
- No eviction: does not free memory by removing entries; AWS’s Redis whitepaper notes that writes are blocked when memory cannot be freed under this policy.
Evictions can be intentional, but if they are not part of the design, observed evictions may indicate that the deployment needs to scale up or out. Check whether the problem is memory capacity, key selection, or a mismatch between the policy and reuse pattern before increasing capacity.
How do I operate a cache safely in production?
Instrument the cache and the requests that depend on it. AWS Well-Architected’s 2024-06-27 guidance gives 80% or higher as a cache hit-rate goal and says lower values may indicate insufficient cache size or an access pattern that does not benefit from caching. Treat that figure as AWS guidance, not a universal benchmark or a promise of performance.
When reporting hit rate, state its scope and denominator—for example, which cache and which requests are counted. Pair it with measurements that help explain the result, such as evictions, cache lookup latency, errors, and load on the primary source. A low hit rate can reflect too little memory, unsuitable key selection, a workload with little reuse, or other design issues; investigate the cause rather than adding capacity automatically.
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- Manage connections: use connection pooling where supported to control how clients connect to the remote cache.
- Retry deliberately: where retries are supported, use exponential backoff rather than an uncontrolled burst of repeated requests.
- Plan for misses and loss: define how the application returns data when the cache is empty or unavailable, and account for the additional work sent to the origin.
- Define recovery behavior: decide how entries are repopulated after cache loss and whether warmup traffic could burden the primary source.
AWS identifies treating a cache as though it were durable and always available as an anti-pattern. Important data should remain recoverable from its authoritative source; the cache is an optimization around that source.
Quick Recap
A practical path from prototype to production
- Choose a candidate: identify a repeated read or computation and establish why avoiding repeated work matters for that request path.
- Define correctness: write down the acceptable staleness and the behavior expected when the source changes.
- Select a pattern and location: decide between cache-aside, write-through, or a combination, and between client-side, remote, or edge placement based on reuse and request flow.
- Set expiry and capacity behavior: choose TTLs from the freshness contract, add jitter where synchronized expiration is a concern, and select an eviction policy suited to the access pattern.
- Build in failure handling: specify timeout, retry, connection, miss, and cache-loss behavior so the cache does not become a hidden dependency with no recovery path.
- Measure before tuning: observe hit rate with a clear scope and denominator, along with evictions and the effect on the request path and primary source. Change one design factor at a time and verify that the change addresses the actual cause.
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