Caching Strategy

Caching Strategy

Purpose

Design a multi-layer caching architecture that minimizes latency and server load while maintaining data freshness. Produces a cache hierarchy diagram, key schema, TTL policy table, and invalidation flow for each cacheable resource.

Scope Constraints

• Reads: Application architecture diagrams, resource inventories, access pattern logs, existing cache configuration, Cache-Control headers, CDN settings.
• Cannot: Profile runtime performance bottlenecks or measure Core Web Vitals (use performance-audit). Cannot model traffic growth or define scaling triggers (use load-modeling). Cannot provision CDN or cache infrastructure.

Inputs

• Application architecture (server framework, database, CDN provider)
• Resource inventory (pages, API responses, static assets, database queries)
• Access patterns (read/write ratio, update frequency, personalization requirements)
• Freshness requirements (how stale can each resource be before it hurts the user?)
• Current caching setup (if any) and known pain points

Input Sanitization

No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets.

Procedure

Progress Checklist

• Step 1: Identify cacheable resources and access patterns
• Step 2: Design cache hierarchy
• Step 3: Define cache key schema
• Step 4: Specify TTL policies per resource type
• Step 5: Design invalidation strategy
• Step 6: Plan stale-while-revalidate patterns
• Step 7: Specify cache warming strategy for cold starts

Step 1: Identify Cacheable Resources and Access Patterns

Inventory all resources served by the application:

• Static assets (JS, CSS, images, fonts) — versioned or content-hashed?
• HTML pages — fully static, SSG, SSR, or dynamic?
• API responses — public or user-specific? How often do underlying data change?
• Database query results — read frequency vs write frequency per table/query
• Computed/aggregated data — dashboards, reports, search indexes

For each resource, document the read:write ratio and acceptable staleness window.

Step 2: Design Cache Hierarchy

Define what is cached at each layer, from closest to the user outward:

• Browser cache — immutable versioned assets, prefetched resources
• CDN / Edge cache — public HTML, public API responses, optimized images
• Application cache — session data, computed results, frequently-accessed records
• Database query cache — prepared statement results, materialized views

Document which layer is the primary cache for each resource type.

Step 3: Define Cache Key Schema

Design consistent, collision-free cache keys:

• Include resource type, identifier, and variant (e.g., page:blog:slug:v2:locale:en)
• Account for personalization dimensions (user role, locale, feature flags)
• Define key namespacing to support bulk invalidation (e.g., page:blog:*)
• Document key generation patterns for each resource type

Step 4: Specify TTL Policies per Resource Type

Assign time-to-live values based on staleness tolerance:

• Immutable assets — long TTL (1 year) with content-hash cache busting
• Semi-static content (blog posts, product pages) — medium TTL (minutes to hours)
• Dynamic/personalized content — short TTL (seconds) or no-cache with revalidation
• API responses — TTL matched to underlying data change frequency

Document TTL as both max-age and s-maxage where CDN behavior differs from browser.

Step 5: Design Invalidation Strategy

Define how stale cache entries are removed or refreshed:

• Event-driven invalidation — cache purge triggered by data mutations (webhook, pub/sub)
• Time-based expiry — TTL-only, no active invalidation
• Versioned keys — new key on data change, old key expires naturally
• Tag-based invalidation — associate cache entries with tags, purge by tag

Map each resource to its invalidation method. Document the mutation-to-purge flow.

Step 6: Plan Stale-While-Revalidate Patterns

Design graceful degradation for cache misses:

• Which resources support stale-while-revalidate? Define the stale window.
• Which resources require strict freshness (financial data, auth state)?
• Define fallback behavior when the origin is unavailable (serve stale? show error?)
• Document Cache-Control header construction for each resource class

Step 7: Specify Cache Warming Strategy for Cold Starts

Plan for empty caches after deployments, CDN purges, or scaling events:

• Which resources should be pre-warmed? (High-traffic pages, critical API responses)
• Warming method — build-time generation, deployment hook, background job, on-demand
• Warming priority order — warm the hottest paths first
• Cold start latency budget — acceptable response time before cache is warm

Compaction resilience: If context was lost during a long session, re-read the Inputs section to reconstruct what system is being analyzed, check the Progress Checklist for completed steps, then resume from the earliest incomplete step.

Handoff

• If profiling reveals bottlenecks beyond caching (render path, bundle size, database queries), hand off to tuner/performance-audit for full-stack performance analysis.
• If cache warming or invalidation volume suggests infrastructure scaling concerns, hand off to tuner/load-modeling for capacity planning and cost projections.

Output Format

# Caching Strategy: [Application Name]

## Cache Hierarchy

[User] → Browser Cache → CDN/Edge → App Cache → DB Query Cache → [Database]

| Layer | Resources Cached | TTL Range | Invalidation |
|-------|-----------------|-----------|-------------|
| Browser | ... | ... | ... |
| CDN/Edge | ... | ... | ... |
| Application | ... | ... | ... |
| DB Query | ... | ... | ... |

## Cache Key Reference

| Resource Type | Key Pattern | Variants | Example |
|--------------|-------------|----------|---------|
| ...          | ...         | ...      | ...     |

## TTL Policy Table

| Resource | Browser max-age | CDN s-maxage | stale-while-revalidate | Rationale |
|----------|----------------|-------------|----------------------|-----------|
| ...      | ...            | ...         | ...                  | ...       |

## Invalidation Flows

### [Resource Type]
1. Mutation occurs in [source]
2. [Event/webhook] triggers cache purge
3. [Layer] invalidates entries matching [pattern]
4. Next request triggers revalidation

## Cache Warming Plan

| Resource | Method | Trigger | Priority |
|----------|--------|---------|----------|
| ...      | ...    | ...     | ...      |

## Cache-Control Headers

| Route Pattern | Cache-Control Value |
|--------------|-------------------|
| ...          | ...               |

Quality Checks

• Every cacheable resource has an assigned cache layer and TTL
• Cache keys include all variant dimensions (locale, role, version)
• Invalidation strategy is defined for every resource that can be mutated
• Stale-while-revalidate windows are specified where appropriate
• Personalized content is excluded from shared caches (CDN/edge)
• Cache warming covers the highest-traffic paths
• Cache-Control headers are specified for all route patterns
• Cold start scenario is addressed with acceptable latency budgets

Evolution Notes