6. Caching Strategies and Invalidation | Engineering Notes

🧠 Caching Strategy & Invalidation

I use caching to improve system performance by reducing repeated database work and latency, especially in read-heavy scenarios.

🎯 How I Think About Caching

Caching works by storing frequently accessed data closer to where it’s needed.

This helps to:

reduce response time

lower database load

improve overall throughput

I typically consider caching at multiple layers:

CDN or browser cache

API gateway / reverse proxy

application-level cache

distributed cache (e.g., Redis)

🔄 Cache Patterns I Use

Cache-Aside (Most Common)

check cache first

if miss → fetch from database → store in cache

👉 Simple and flexible, but requires careful invalidation

Read-Through

cache layer loads missing data automatically

👉 Cleaner abstraction, but depends on infrastructure

Write-Through

write to cache and database together

👉 Keeps data consistent, but increases write latency

Write-Behind

write to cache first, persist later

👉 Improves performance, but introduces consistency risk

🧩 Invalidation (Most Critical Part)

In my experience, caching is not the hard part — invalidation is.

Common strategies I use:

TTL (time-based expiration)

explicit invalidation on updates

versioned cache keys

event-driven invalidation

Each has tradeoffs:

TTL → simple but may serve stale data

explicit invalidation → accurate but requires discipline

⚖️ Design Thinking

Before adding caching, I evaluate:

what the real bottleneck is (latency, throughput, DB load)

whether stale data is acceptable

what happens on cache miss or failure

how cache will be warmed

If data must be strictly consistent:

👉 I avoid caching or limit it to non-critical data

📌 Practical Approach

I don’t add caching by default.

I first identify:

hot paths

repeated queries

acceptable staleness window

ownership of invalidation

💬 Summary

My caching approach focuses on:

improving performance without breaking consistency

treating invalidation as a first-class concern

applying caching only where it provides real value

This ensures systems remain both fast and reliable.