Caching is an abstraction, not an optimization

Caching as Abstraction vs Optimization

• Many commenters argue caching is fundamentally an optimization: storing copies of data closer to where they’re used to reduce latency, always adding complexity on top of a correct system.
• Others say that, given multiple storage tiers already exist, hiding them behind a single “storage” interface is a useful abstraction; caching then becomes part of how that abstraction minimizes retrieval cost.
• Some see the disagreement as mostly semantic: caching-as-an-idea vs specific implementations vs the abstraction of a storage interface that may or may not cache.

Does Caching Simplify or Complicate Software?

• Strong view: adding a cache path alongside an uncached path necessarily increases complexity (keys, lifetimes, eviction, invalidation, failure modes).
• Counterpoint: compared to manually managing multiple storage tiers or custom data-movement logic, a well-designed caching layer can locally simplify code, at the cost of complexity moving elsewhere (infrastructure, runtime, DB).
• Several note “at what level?” matters: hardware designers, databases, and message queues absorb caching complexity so application code can be simpler.

Cache Invalidation, Consistency, and Distributed Systems

• Repeated emphasis that cache invalidation is hard once you have multiple writers/readers, nodes, or datacenters.
• Examples: build systems and make clean, SQL caches vs direct DB writes, CDC/replication, pub/sub invalidation, SNS/SQS setups, TTL-based caches (DNS).
• Discussion of eventual consistency, stale reads, thundering herds, and the need for push-based or batched-pull mechanisms; recognition that many real systems accept stale data to keep caching tractable.

Hardware, Databases, and Other Analogies

• CPU caches cited both as evidence that caching simplifies software (compared to explicit scratchpads) and that abstractions leak when performance matters.
• Database indexes and materialized views discussed as cache-like mechanisms that can also slow things down or complicate writes.
• Some note that most systems already rely on many hidden caches (CPU, OS, DB), so the real question is where you choose to expose or control them.

Confusion About the Article’s Framing

• Several readers found the article’s claim “caching is an abstraction, not an optimization” confusing or backwards: they’d prefer a baseline of “no cache” and then treating caching as an optional optimization behind a storage abstraction.
• Others reinterpret the piece as: “good caching = one consistent storage interface; bad caching = ad hoc tier juggling,” while stressing that caching overall remains an optimization strategy.