How would you handle invalidation, stampedes, and TTLs in a Spring cache?

Keeps cached reads acceptably fresh and prevents hot-key reloads from overwhelming the database across application instances.

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I start with the freshness requirement and update path. For cache-aside reads, a miss loads from the source and stores the result; successful writes evict or update affected keys. TTL is a safety net and a business staleness limit, not the only invalidation strategy. Keys include tenant and authorization scope so cached results cannot cross boundaries.

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Cache-aside, eviction timing, TTL, stale data, sync loading, distributed coordination, versioned keys, and metrics.

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A cache design is complete only when writes, expiry, concurrent misses, outages, and stale-data tolerance are defined.
I start with the freshness requirement and update path. For cache-aside reads, a miss loads from the source and stores the result; successful writes evict or update affected keys. TTL is a safety net and a business staleness limit, not the only invalidation strategy. Keys include tenant and authorization scope so cached results cannot cross boundaries.
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For a hot key, many simultaneous misses can reload the same data and overload the source. Spring's sync=true can coordinate supported same-key loads in one cache implementation, but multiple application nodes may still need provider-level locking, request coalescing, early refresh, or stale-while-revalidate behavior. I add TTL jitter so many keys do not expire together and use negative caching only with a short, deliberate policy.

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Using one TTL as the entire invalidation design.
Assuming sync=true coordinates every application node.
Caching authorization results without the full scope in the key.

How would you handle invalidation, stampedes, and TTLs in a Spring cache?

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