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Caching reduces latency and load by keeping frequently accessed data temporarily closer to consumers. It includes placement, consistency, invalidation, and capacity strategies as well as cache types (in-memory, CDN, HTTP, database). Useful for performance optimization but introduces trade-offs in consistency, complexity, and operational overhead.
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Caching temporarily stores frequently used data closer to the application so access is faster and the origin system is relieved.
Caching emerged from the problem that fast processors, applications, and networks often depend on much slower storage or remote services. In computer architecture it therefore keeps small copies close to where data is likely to be used again. The same idea was later formalized for the web in HTTP caching so responses can be reused, reducing latency and bandwidth use.
Think of a chain of intermediate stores. A request first checks the cache. If the entry is present and still valid, it is returned immediately. If not, the cache fetches data from the origin system, stores it, and then applies rules for freshness, revalidation, removal, and capacity to decide how long it stays.
A hit serves the answer from the cache; a miss forces access to the origin system.
An entry may be reused without checking again only within its freshness or validity rules.
The cache checks with the origin system whether a stored response can still be used.
Changed data is made invalid or removed on purpose so outdated responses are not served.
When space is limited, a strategy decides which entries must leave.
HTTP caching applies the general pattern to web responses and defines storage, age, and reuse rules.
Caching is useful when reads repeat, computations are expensive, many HTTP responses are similar, or backends are under load. It improves response time and scalability as long as freshness, invalidation, and memory limits are controlled. The trade-offs are added complexity, possible stale reads, and the choice between private, shared, or multi-layer caches.
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