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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Caching is not one storage bin. It is a collection of copies held at different points—from a CPU chip to an operating system, application, browser, or CDN—so a system can avoid repeating expensive work. Each layer stores a different kind of data, serves a different reuse boundary, and follows its own rules for deciding whether a copy is still usable.
A map of the main caching layers
The layers below are a practical map, not a mandatory chain. A request or read may use only some of them; others may be bypassed, replicated, or controlled by different owners.
| Layer and location | What it stores | Typical reuse scope | What repeat work it can avoid |
|---|---|---|---|
| CPU caches, on the processor | Small blocks of memory called cache lines | Processor cores; sharing depends on the CPU design | Accessing main memory |
| Translation lookaside buffer (TLB), on the processor | Recent virtual-to-physical address translations | Processor execution | Repeating address-translation work |
| Operating-system page or file cache, in system memory | Filesystem data in memory pages | Processes on the same host, subject to OS behavior and access rules | Reading data again from physical storage |
| Application or database-facing cache, in a process, host, or network service | Computed results, records, query results, or service responses | Whatever clients the implementation permits | Recomputing results, repeating queries, or calling a service again |
| Browser Cache API, managed by web application code | Request-and-response pairs | The relevant browser context and application | Fetching a response again over the network |
| HTTP private cache, commonly in a browser | HTTP responses | One client | Re-downloading a response or contacting the origin |
| HTTP shared cache, such as a proxy or CDN | HTTP responses | Multiple users, when the response is safe and configured for sharing | Repeated origin requests and transfers |
| CDN or managed edge cache | Copies of content held at network locations near users | Users served by the configured provider and cache rules | Origin processing and long-distance delivery |
These entries describe common roles, not a standardized performance ranking. Capacity, eviction, sharing, and consistency vary by implementation, and there is no single benchmark that compares every layer on the same workload.
What happens close to the processor?
CPU caches hold memory data
CPU caches are small, fast stores that reduce how often a processor must wait for main memory. Many processors organize them as L1, L2, and sometimes L3 caches. Android Developers gives illustrative latencies of about 1 ns for L1, 3–5 ns for L2, and 10–20 ns for L3. These are representative mobile examples, not universal specifications: actual latency depends on processor architecture and changes over time.
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A TLB caches translations, not ordinary data
Programs use virtual addresses, which the system maps to physical memory. A translation lookaside buffer keeps recent mappings so the processor can avoid repeating some of that translation work. It is a cache in the broad sense, but it does not store the program’s ordinary data or instructions as a CPU data cache does.
How does the operating system cache filesystem data?
On Linux, the page cache is the normal path for filesystem reads and writes, and memory-mapped files use it too. A read may therefore be served from system memory instead of triggering a fresh physical-storage read. Direct I/O can bypass the page cache. Windows also documents read and write caching in system memory; modified, or “dirty,” data is flushed under operating-system control.
This layer operates below most applications, so software may benefit from it without managing its entries directly. Its exact behavior depends on the operating system and access method. Linux kernel documentation also discusses hardware-cache contention and diagnostic tools; a cache miss or slowdown can involve hardware behavior as well as filesystem I/O.
Where do application and database caches fit?
An application can keep a computed result or service response to avoid doing the same work again. A database-facing cache might retain records or query results. These caches may live inside an application process, on the same host, or in a separate network service; their scope depends on how the system is built.
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Many implementations use a time-to-live (TTL), after which an entry expires, or invalidate entries when underlying data changes. AWS’s Redis database-caching guidance describes adding jitter to expiration times: staggering expirations can reduce the pressure caused when many entries expire together. TTLs and invalidation do not guarantee that every result is current at every instant; the application must choose an acceptable freshness and consistency policy.
Which browser cache are you using?
The Cache API is application-managed
The browser Cache API lets scripts store and retrieve request-and-response pairs. It is distinct from relying only on the browser’s ordinary HTTP cache. The API does not automatically apply HTTP cache-control rules: application code decides how to match requests, update entries, and manage their lifetime. Storage lifetime is browser-dependent, so an application should not treat a stored entry as permanent.
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The ordinary HTTP cache follows HTTP rules
HTTP caches store responses for later reuse. A private cache is associated with an individual client; a shared cache, such as an intermediary proxy or CDN, can reuse a response for more than one user. That distinction matters when content is personalized: a response suitable for one user’s private cache may not be safe to share.
HTTP freshness directives and validators govern whether a stored response can be reused. In particular, no-cache means a cache must validate a response before reusing it; it does not mean “do not store.” no-store tells caches not to store the response, but it should not be described as a universal command that erases every existing browser entry. Browser back/forward behavior and already stored entries can make blanket claims about clearing misleading. A cookie alone also does not prove a response is personalized or define a complete shared-cache safety policy.
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What makes a CDN cache different?
A CDN holds copies at managed network edges so users can receive eligible content from a nearby location. The provider’s configuration and the origin’s response headers jointly affect caching; one provider’s defaults should not be assumed to apply to another.
Cloudflare documents distinct controls for CDN, browser, and other shared-cache TTLs, with precedence rules among them. Its documentation, updated April 16, 2026, covers CDN-Cache-Control and related controls. Cloudflare’s documentation updated September 14, 2026, says HTML and JSON are not cached by default and describes cache rules and response headers that can affect behavior. Those are Cloudflare-specific documented defaults, not general CDN behavior.
How to reason about freshness, sharing, and misses
When tracing a cached value, ask who owns the copy, what it represents, and what event makes it reusable or stale. A cache can reduce repeated work but also serve stale data if its freshness policy permits that. Invalidation may require expiry, explicit removal, validation against an origin, or recomputation; the right mechanism depends on the layer and the consequence of stale content.
- Locate the copy: distinguish chip-level data, host memory, an application or database service, a browser, and a shared network edge.
- Identify the unit: a CPU cache line, filesystem page, application object, query result, or HTTP response needs different invalidation logic.
- Check the reuse boundary: confirm whether the copy is local to a process or user, shared across a host, or reusable by multiple customers.
- Find the freshness rule: look for TTLs, HTTP directives and validators, application invalidation, or provider-specific cache settings.
- Trace a miss or stale result: determine which next layer is consulted and whether that step recomputes, rereads, revalidates, or fetches from an origin.
For a disk read, CPU caches, operating-system caching, application logic, a database buffer, a remote cache, and a client cache might all affect the path. But a particular operation need not pass through every layer. Systems references sometimes draw stacks extending through client, application, web server, caching server, database, operating system, filesystem, block device, controller, storage array, and on-disk caches; actual platforms differ, and that deeper list is illustrative rather than universal.
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