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How the four caching patterns differ
A cache stores copies of data so later requests may avoid retrieving or recalculating it from a slower or more expensive source. The patterns below describe how reads and writes interact with that copy; none is a universal ranking.
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| Pattern | Who loads a read miss? | When is the cache updated? | Potential fit | Main tradeoff |
|---|---|---|---|---|
| Cache-aside | Application | After a read miss; commonly invalidated after a write | Unpredictable demand when only requested data should be cached | Misses require an origin lookup; application code must handle invalidation and consistency |
| Read-through | Cache layer | The cache loads the value on a miss | Teams that want miss-loading behind a cache abstraction | Requires a cache or integration that supports the contract; freshness still needs a policy |
| Write-through | Application or cache write contract | Alongside or just after the primary-store update | Data likely to be read after a write, when fresh subsequent reads matter | More write work and cache space used for items that may never be read |
| Write-behind (write-back) | Not applicable to writes; reads depend on the read path | Immediately in the cache, later in the primary store | Write-intensive workloads that can tolerate deferred persistence | Delayed durability and consistency require reliable background processing |
What happens in each pattern?
Cache-aside: the application handles misses
The application checks the cache first. If the value is present, it returns it. If not, the application reads from the primary data store, places the result in the cache, and returns it. A common update flow writes to the primary store and invalidates the matching cache key; a later read repopulates the cache.
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Read-through: the cache handles misses
The application reads through the cache. When the requested value is absent, the cache layer fetches it from the backing store and populates itself before returning the result. AWS describes this as lazy loading on first access. The key distinction from cache-aside is ownership of the miss-loading logic: it belongs to the cache layer, not application code.
Read-through can centralize cache behavior, but it depends on a cache or integration that supports this contract. It does not eliminate the need to decide how long values remain valid or how updates elsewhere make cached copies fresh. The read-through and cache-aside flows are similar from the caller’s perspective, as AWS’s overview of caching patterns explains.
Write-through: update the cache with the database
With write-through, a change updates the primary database and the cache immediately afterward, or updates both through the cache’s write-through contract. Subsequent reads are more likely to find a recently updated value in the cache, which can reduce database reads.
Rank #2
The cost is extra work on writes, plus cache space and possible churn for items that are written but never read. Teams often pair write-through with lazy loading so misses and evictions can still be repopulated. AWS discusses the tradeoffs in its caching patterns guidance and 2025 Well-Architected Framework guidance on caching.
Write-behind: persist changes later
Write-behind, also called write-back, accepts a change in the cache first and persists it to the database later, often through an asynchronous queue. Because the caller need not wait for each database persistence operation, this pattern can suit write-intensive workloads.
The primary store temporarily lags behind the cache, changing the system’s durability and consistency behavior. The background path must reliably process queued writes and handle failures; otherwise, accepted changes may not reach the database as intended. Choose this pattern only when the application can tolerate that delay and the team can operate the persistence path. AWS describes this tradeoff in its caching patterns overview.
Rank #3
Which caching pattern should you use?
Start with the workload and the freshness requirement, not with a preferred pattern. Read-heavy workloads, especially those serving data that changes infrequently, are common candidates for caching. Caching may also help when the source is expensive to scale. Then decide who should own a cache miss and whether writes must update the cache immediately or may be deferred.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Choose cache-aside when demand is uncertain and you want application-controlled, on-demand population—provided you can manage invalidation and accept the miss path.
- Choose read-through when a supported cache integration can own miss loading and you want that behavior behind a cache abstraction.
- Choose write-through when data is likely to be read after updates and keeping the cache current is worth the additional write work.
- Consider write-behind when write throughput matters more than immediate visibility in the primary store, and deferred persistence is acceptable and operationally safe.
Cache-aside can be a poor fit for sensitive or security-related data that should always come from the primary source, a fully static dataset better primed at startup, or a workload where nearly every request misses. Those cases may call for a different serving strategy—or no cache at all.
How do you keep cached data fresh?
Expiration and invalidation are policy choices that trade freshness against reload work; there is no single time-to-live (TTL) that suits every kind of data. A TTL that is too short causes repeated origin retrieval. One that is too long can leave stale values in circulation. Cache-aside alone does not ensure that every writer invalidates or refreshes the corresponding entry.
Rank #4
Choose a policy based on how quickly a change must become visible and how costly it is to reload. For data that must always reflect the primary source, bypassing the cache may be safer. For data that can be briefly stale, expiration or deliberate invalidation may be appropriate. Microsoft’s cache-aside guidance and AWS’s 2025 caching recommendations describe these freshness and origin-load considerations.
Guard against popular-key expiration
If a heavily requested key expires, many simultaneous requests may miss and hit the primary store at once. This is known as a cache stampede. Redis documents mutex-lock and probabilistic early-refresh approaches to reduce this burst in its cache-aside guidance.
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Where should the cache live?
A local or client-side cache keeps data close to its caller and can reduce latency, but separate application instances may hold duplicate or inconsistent copies. A shared remote cache can offer shared capacity and improve consistency across clients, but each access adds a network hop. A multi-level design can combine local and shared caching when both properties matter, at the cost of more cache layers to keep coherent. AWS discusses these placement options in its 2025 Well-Architected Framework guidance.
How can you tell whether caching helps?
Measure the application rather than assuming a cache is beneficial. Track cache hit rate and request latency alongside primary-store load and cache memory use. AWS’s 2025 Well-Architected Framework recommends monitoring cache hit rate with a goal of 80% or higher; that is AWS operational guidance, not a universal benchmark or guarantee. AWS notes that a lower rate may signal insufficient cache capacity or an access pattern that does not benefit from caching.
Look at the whole path: if most requests miss, data changes so often that keeping it current is costly, or the remote-cache network hop saves less time than it adds, caching can make requests slower. The useful question is whether the chosen pattern reduces the total work for your actual access pattern without violating freshness or durability needs.
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