Introduction
Caching is often described as:
“Making things faster”
But in reality, caching is:
Trading complexity for performance
Done right:
- Your system feels instant
Done wrong:
- You serve stale data
- You create inconsistencies
- You introduce hard-to-debug issues
What is Caching?
Caching stores frequently accessed data in a faster layer.
Instead of:
- Querying database every time
You:
- Serve from cache
Types of Caching
1. In-Memory Caching
Stored in application memory.
Pros:
- Extremely fast
Cons:
- Not shared across servers
2. Distributed Cache (Redis)
Shared across services.
Pros:
- Scalable
- Fast
- Centralized
3. CDN Caching
Caches content near users.
Best for:
- Images
- Static assets
- APIs
What Should You Cache?
Good candidates:
- Frequently accessed data
- Rarely changing data
- Expensive queries
Examples:
- User profiles
- Product listings
- API responses
What NOT to Cache
Avoid caching:
- Highly dynamic data
- Sensitive data
- Real-time critical data
Cache Patterns
1. Cache-Aside (Lazy Loading)
Flow:
- Check cache
- If missing → fetch from DB
- Store in cache
Most common pattern.
2. Write-Through
- Write to cache and DB at the same time
Ensures consistency.
3. Write-Behind
- Write to cache first
- Update DB later
Improves performance but risky.
The Hard Problem: Cache Invalidation
This is where most systems fail.
When data changes:
- How do you update the cache?
Strategies:
- Time-based expiration (TTL)
- Event-based invalidation
- Manual clearing
Performance Impact
Without cache:
- DB gets overloaded
With cache:
- Faster responses
- Reduced load
- Better scalability
Common Mistakes
- Caching everything blindly
- Not invalidating cache
- Using wrong TTL
- Ignoring stale data
Executive_Summary
ID: 0x8
"Caching is powerful—but dangerous. It’s not just about speed. It’s about control over your data flow."