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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11A cache can return data faster because it serves a stored copy instead of asking the source of truth on every read. That copy creates a consistency problem: when the source changes, the cache—and any other cache holding the same data—does not automatically change with it. The right design depends on how stale a value may be, which reads must see a recent write, and what the system does when updates or invalidations fail.
Why a cache can return stale data after a database update
A cache is another place where data lives. In a cache-aside design, for example, an application reads a key from the cache first. On a miss, it reads the database and stores the result in the cache. A later request can then use that stored copy. This saves repeated source reads, but also means there are now at least two versions to coordinate.
After a database write, a cached value may remain unchanged until it expires or the application updates or deletes it. With several application instances using private local caches, each instance can hold a different version. A shared cache reduces that particular duplication, but it still needs a defined update policy.
Cache-aside is an application pattern, not automatic observation of every database change. An administrator, batch job, or separate service that updates the source without notifying the cache can leave the cached copy obsolete. Redis describes cache-aside and the limitations of invalidation in its cache-aside documentation and cache consistency guidance.
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How cache patterns differ
These strategies place the coordination work in different parts of the read and write path. None removes the need to define what freshness means for the application.
| Pattern | How it works | Freshness and failure trade-offs | Typical fit |
|---|---|---|---|
| Cache-aside (lazy loading) | The application checks the cache; on a miss, it reads the source and populates the cache. A write commonly updates the source and invalidates the key. | Demand-driven and flexible, but stale windows and races are possible. A writer that skips invalidation can leave stale data; cold reads still reach the source. | Repeated reads where some staleness is acceptable and the application can manage invalidation. |
| Write-through | The write path updates the source and cache synchronously. | Can make a successful write visible to subsequent cache reads if both updates succeed. A partial failure needs recovery handling; values that are not read may still occupy cache. | Read-after-write needs where synchronous coordination is acceptable. |
| Write-behind (write-back) | The cache accepts a write and persists it to the source asynchronously. | Moves work off the immediate write path, but acknowledged changes may be lost if the cache fails before persistence. | Write-heavy uses where delayed persistence and its risk are acceptable. |
| TTL (expiry) | Each entry expires after a configured duration. | Limits how long an entry can remain cached in the absence of refresh, but does not ensure immediate read-after-write consistency. Shorter TTLs can increase source reads and cache misses. | Data with a known stale-data tolerance and no stronger propagation requirement. |
| Invalidation or change propagation | A write or change event deletes or refreshes affected cache entries. | Can reduce stale windows, but delivery, ordering, retries, replay, and identifying dependent keys must be designed. All relevant writers must be observed. | Stronger freshness needs when changes can be propagated reliably. |
| Read from primary or bypass cache | Selected reads go directly to the authoritative store. | Avoids a cached copy on that path, while giving up some cache latency or load savings. | Decisions such as money, inventory, or permissions when stale data has a high cost. |
These are established implementation categories described in vendor documentation, not guarantees that a particular product will provide identical behavior in every configuration. AWS advises that “The patterns you choose to implement should be directly related to your caching and application objectives.” See its caching-pattern guidance.
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How invalidation races put old data back
Deleting a key after a write is useful, but by itself does not eliminate every race. Consider a cache miss that overlaps a database update:
- Reader A misses the cache and reads value A from the database.
- Writer B commits newer value B to the database and deletes the cache key.
- Reader A finishes its earlier read and writes old value A into the now-empty cache.
- Later readers receive A until another invalidation, refresh, or expiry corrects the entry.
The problem is ordering: the late cache fill can happen after the invalidation. An invalidation can also fail or be delayed, leaving an existing stale value in place. Redis documents these cache-fill interleavings in its cache consistency guidance. A design that needs a stronger guarantee must account for concurrent fills and failures, not just issue a delete after ordinary writes.
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TTL bounds an entry’s lifetime, not its staleness at every read
A time-to-live removes an entry after a configured period. If the source changes just after the cache is filled, readers may still get the old value during the remaining TTL. Expiry therefore gives a bound on how long an untouched entry can remain, assuming expiration works as configured; it does not make a write immediately visible through the cache.
Choosing a TTL means balancing the frequency and cost of source changes against cache misses, source load, and the harm caused by serving an old value. AWS recommends setting the expiry policy in light of data change rate and stale-data risk in its TTL guidance and data-access caching guidance. A short TTL is not a substitute for change propagation when a particular write must be reflected immediately.
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Multiple caches and read replicas add separate freshness risks
Consistency depends on the full read path, not just whether there is one cache product. Two application processes with private in-process caches can serve different versions. A remote shared cache addresses that duplication, but does not automatically coordinate cache writes with database writes.
A database or managed-cache read replica is a separate issue: replica reads can lag behind writes. Google Cloud warns that Memorystore for Redis read replicas may not provide read-your-writes consistency; see About read replicas. A primary read can avoid a replica-lag problem on that path, but it does not by itself repair a stale cache that the application still consults.
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Choose a freshness contract before choosing a pattern
Describe the requirement in terms a user or business decision can verify. Is it acceptable for another user to see an old profile briefly? Must someone see their own edit on the next read? Can a purchase decision use inventory that may be stale? Those answers determine whether a TTL is sufficient, whether writes must update or invalidate cache entries synchronously, or whether selected reads should bypass the cache.
- Freshness: define the maximum acceptable stale window and whether read-your-writes behavior is required.
- Writers: identify every service, job, administrator, or other process that can change the source and how its changes reach the cache.
- Failure recovery: specify what happens if the source write succeeds but the cache update fails, or if an invalidation is delayed, lost, or duplicated.
- Load and memory: account for cold misses, popular keys expiring together, source capacity during a miss surge, and cache space used by values that may not be read again.
- Operational complexity: weigh the ordering, retry, replay, and dependency-tracking work required for propagation against the cost of a stale read.
For a high-consequence decision, reading the authoritative source on that critical path may be safer and simpler than trying to make every cache copy appear immediately current. For less sensitive data, an explicitly accepted stale window can preserve the speed and load benefits of caching without pretending the copies are always synchronized.
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