- name
- caching
- model
- standard
- description
- Caching strategies, invalidation, eviction policies, HTTP caching, distributed caching, and anti-patterns. Use when designing cache layers, choosing eviction policies, debugging stale data, or optimizing read-heavy workloads.
Caching Patterns
A well-placed cache is the cheapest way to buy speed. A misplaced cache is the most expensive way to buy bugs.
Cache Strategies
| Strategy | How It Works | When to Use |
|---|
| Cache-Aside (Lazy) | App checks cache → miss → reads DB → writes to cache | Default choice — general purpose |
| Read-Through | Cache fetches from DB on miss automatically | ORM-integrated caching, CDN origin fetch |
| Write-Through | Writes go to cache AND DB synchronously | Read-heavy with strong consistency |
| Write-Behind | Writes go to cache, async flush to DB | High write throughput, eventual consistency OK |
| Refresh-Ahead | Cache proactively refreshes before expiry | Predictable access patterns, low-latency critical |
Cache-Aside Flow:
App ──► Cache ──► HIT? ──► Return data
│
▼ MISS
Read DB ──► Store in Cache ──► Return data
Cache Invalidation
| Method | Consistency | When to Use |
|---|
| TTL-based | Eventual (up to TTL) | Simple data, acceptable staleness |
| Event-based | Strong (near real-time) | Inventory, profile updates |
| Version-based | Strong | Static assets, API responses, config |
| Tag-based | Strong | CMS content, category-based purging |
TTL Guidelines
| Data Type | TTL | Rationale |
|---|
| Static assets (CSS/JS/images) | 1 year + cache-busting hash | Immutable by filename |
| API config / feature flags | 30–60 seconds | Fast propagation needed |
| User profile data | 5–15 minutes | Tolerable staleness |
| Product catalog | 1–5 minutes | Balance freshness vs load |
| Session data | Match session timeout | Security requirement |
HTTP Caching
Cache-Control Directives
| Directive | Meaning |
|---|
max-age=N | Cache for N seconds |
s-maxage=N | CDN/shared cache max age (overrides max-age) |
no-cache | Must revalidate before using cached copy |
no-store | Never cache anywhere |
must-revalidate | Once stale, must revalidate |
private | Only browser can cache, not CDN |
public | Any cache can store |
immutable | Content will never change (within max-age) |
stale-while-revalidate=N | Serve stale for N seconds while fetching fresh |
Common Recipes
# Immutable static assets (hashed filenames)
Cache-Control: public, max-age=31536000, immutable
# API response, CDN-cached, background refresh
Cache-Control: public, s-maxage=60, stale-while-revalidate=300
# Personalized data, browser-only
Cache-Control: private, max-age=0, must-revalidate
ETag: "abc123"
# Never cache (auth tokens, sensitive data)
Cache-Control: no-store
Conditional Requests
| Mechanism | Request Header | Response Header | How It Works |
|---|
| ETag | If-None-Match: "abc" | ETag: "abc" | Hash-based — 304 if match |
| Last-Modified | If-Modified-Since: <date> | Last-Modified: <date> | Date-based — 304 if unchanged |
Prefer ETag over Last-Modified — ETags detect content changes regardless of timestamp granularity.
Application Caching
| Solution | Speed | Shared Across Processes | When to Use |
|---|
| In-memory LRU | Fastest | No | Single-process, bounded memory, hot data |
| Redis | Sub-ms (network) | Yes | Production default — TTL, pub/sub, persistence |
| Memcached | Sub-ms (network) | Yes | Simple key-value at extreme scale |
| SQLite | Fast (disk) | No | Embedded apps, edge caching |
Redis vs Memcached
| Feature | Redis | Memcached |
|---|
| Data structures | Strings, hashes, lists, sets, sorted sets | Strings only |
| Persistence | AOF, RDB snapshots | None |
| Pub/Sub | Yes | No |
| Max value size | 512 MB | 1 MB |
| Verdict | Default choice | Pure cache at extreme scale |
Distributed Caching
| Concern | Solution |
|---|
| Partitioning | Consistent hashing — minimal reshuffling on node changes |
| Replication | Primary-replica — writes to primary, reads from replicas |
| Failover | Redis Sentinel or Cluster auto-failover |
Rule of thumb: 3 primaries + 3 replicas minimum for production Redis Cluster.
Cache Eviction Policies
| Policy | How It Works | When to Use |
|---|
| LRU | Evicts least recently accessed | Default — general purpose |
| LFU | Evicts least frequently accessed | Skewed popularity distributions |
| FIFO | Evicts oldest entry | Simple, time-ordered data |
| TTL | Evicts after fixed duration | Data with known freshness window |
Redis default is noeviction. Set maxmemory-policy to allkeys-lru or volatile-lru for production.
Caching Layers
Browser Cache → CDN → Load Balancer → App Cache → DB Cache → Database
| Layer | What to Cache | Invalidation |
|---|
| Browser | Static assets, API responses | Versioned URLs, Cache-Control |
| CDN | Static files, public API responses | Purge API, surrogate keys |
| Application | Computed results, DB queries, external API | Event-driven, TTL |
| Database | Query plans, buffer pool, materialized views | ANALYZE, manual refresh |
Cache Stampede Prevention
When a hot key expires, hundreds of requests simultaneously hit the database.
| Technique | How It Works |
|---|
| Mutex / Lock | First request locks, fetches, populates; others wait |
| Probabilistic early expiration | Random chance of refreshing before TTL |
| Request coalescing | Deduplicate in-flight requests for same key |
| Stale-while-revalidate | Serve stale, refresh asynchronously |
Cache Warming
| Strategy | When to Use |
|---|
| On-deploy warm-up | Predictable key set, latency-sensitive |
| Background job | Reports, dashboards, catalog data |
| Shadow traffic | Cache migration, new infrastructure |
| Priority-based | Limited warm-up time budget |
Cold start impact: A full cache flush can increase DB load 10–100x. Always warm gradually or use stale-while-revalidate.
Monitoring
| Metric | Healthy Range | Action if Unhealthy |
|---|
| Hit rate | > 90% | Low → cache too small, wrong TTL, bad key design |
| Eviction rate | Near 0 steady state | High → increase memory or tune policy |
| Latency (p99) | < 1ms (Redis) | High → network issue, large values, hot key |
| Memory usage | < 80% of max | Approaching max → scale up or tune eviction |
NEVER Do
- NEVER cache without a TTL or invalidation plan — data rots; every entry needs an expiry path
- NEVER treat cache as durable storage — caches evict, crash, and restart; always fall back to source of truth
- NEVER cache sensitive data (tokens, PII) without encryption — cache breaches expose everything in plaintext
- NEVER ignore cache stampede on hot keys — one expired popular key can take down your database
- NEVER use unbounded in-memory caches in production — memory grows until OOM-killed
- NEVER cache mutable data with
immutable Cache-Control — browsers will never re-fetch - NEVER skip monitoring hit/miss rates — you won't know if your cache is helping or hurting
