Explain the Role of Caching Layers in Scalable System Architecture

Concept

Caching is the process of storing frequently accessed data in a faster storage layer to reduce latency and backend load.
In large-scale systems, caching improves performance, scalability, and cost efficiency by avoiding redundant computation or data retrieval.

Caches are deployed at multiple layers — from browsers to CDNs to databases — forming a multi-tiered caching architecture.

1. Why Caching Matters

Without caching:

Every request hits the origin server or database.
Latency increases under load.
Systems require more compute to handle repetitive operations.

With caching:

Responses are served from memory or edge locations.
Database queries and computations are minimized.
End-user experience improves due to reduced round trips.

Example (safe for MDX):

Client → CDN Cache → App Cache → Database

2. Types of Caching in System Design

Layer	Example	Description
Client-Side Cache	Browser cache, Service Workers	Stores assets (HTML, JS, CSS) locally.
Edge Cache	CDN (Cloudflare, Akamai)	Delivers static assets from global edge nodes.
Application Cache	Redis, Memcached	Stores API responses or computed results.
Database Cache	Query-level cache (e.g., MySQL query cache)	Speeds up repeated queries.
OS / Hardware Cache	Disk or CPU cache	Manages frequently accessed data blocks.

Each layer reduces the workload of the layer below it, forming a cascading efficiency chain.

3. Common Caching Strategies

Strategy	Description	Example
Write-Through	Data written to cache and database simultaneously.	Ensures consistency but adds write latency.
Write-Back	Data written to cache first, then persisted later.	Improves speed but risks data loss on crash.
Write-Around	Writes go directly to DB; cache updated on read.	Prevents cache pollution on infrequent writes.
Read-Through	Cache fetches from DB automatically if miss occurs.	Simplifies cache logic for applications.

Example (safe for MDX):

if (cache.hit(key)) return cache.get(key)
else {
  value = db.query(key)
  cache.set(key, value)
  return value
}

4. Cache Invalidation Strategies

Keeping cache data consistent with source of truth is challenging.

Common Techniques:

Time-to-Live (TTL): Expire cache entries after defined intervals.
Write Invalidation: Remove cache entry when underlying data changes.
Versioning: Store data with version numbers or timestamps.
Event-Driven Updates: Publish/subscribe model to update cache across services.

Rule of thumb:

“There are only two hard things in Computer Science: cache invalidation and naming things.”

5. Cache Placement Patterns

Pattern	Description	Example
Global Distributed Cache	Shared across app instances	Redis cluster used by all web servers
Local Cache (In-Memory)	Specific to one app instance	LRU cache in memory per service
Edge Cache (CDN)	Closest to user	Cloudflare or Akamai delivering static assets
Hierarchical Cache	Multi-layer cascading	CDN → Redis → DB

Combining local and distributed caching balances latency vs consistency.

6. Cache Eviction Policies

When cache memory fills up, entries must be evicted.

Common Policies:

LRU (Least Recently Used): Remove least recently accessed items.
LFU (Least Frequently Used): Remove least frequently accessed items.
FIFO (First In, First Out): Evict oldest entries first.
Random Replacement: Useful when access patterns are unpredictable.

Example:

Cache = {A, B, C}
Access: B, C, D → Evict A (LRU)

7. Real-World Usage

Company	Example
Netflix	Uses distributed in-memory caching (EVCache, built on Memcached) to store video metadata and API responses.
Amazon	Employs multi-tier caching: CloudFront (CDN) → Elasticache (Redis) → Application servers.
YouTube	Uses regional edge caching to reduce latency for video delivery.

8. Metrics to Monitor

Cache Hit Ratio: % of requests served from cache (hits / total requests).
Eviction Rate: Frequency of cache removals.
Latency: Compare cache vs origin response times.
Memory Usage: Ensure cache doesn’t exhaust available resources.

Formula (safe for MDX):

Hit Ratio = Cache Hits / (Cache Hits + Cache Misses)

A hit ratio above 80% is typically considered effective in production systems.

9. Interview Tip

Explain layered caching (browser, CDN, app, DB).
Discuss trade-offs between freshness and performance.
Reference tools: Redis, Memcached, Cloudflare, CDN edge caches.
Mention challenges like cache invalidation and stale data.
Use real-world analogies: “Caching is like remembering answers to avoid recomputing them.”

Summary Insight

Caching is the cornerstone of scalability — it trades memory for speed. Well-designed caching layers reduce latency, offload databases, and allow systems to serve billions of requests efficiently.