Caching
Storing copies of data or computed results in faster storage to reduce latency and load on the original data source.
In-Depth Explanation
Caching stores frequently accessed data in fast storage (memory, CDN) to speed up retrieval and reduce load on primary data sources. Effective caching dramatically improves performance and reduces costs.
Types of caching:
- Application cache: In-memory cache within application
- Distributed cache: Shared cache across instances (Redis, Memcached)
- CDN cache: Edge caching for static content
- Database query cache: Cache query results
- API response cache: Cache API responses
Cache strategies:
- Cache-aside: Application checks cache first, loads from source if miss
- Read-through: Cache automatically loads from source on miss
- Write-through: Writes go to cache and source simultaneously
- Write-behind: Write to cache, async sync to source
Cache considerations:
- Expiration/TTL (time to live)
- Invalidation (when data changes)
- Cache size and eviction policies
- Consistency vs. performance trade-offs
Business Context
Caching can reduce latency by 10-100x for US businesses, dramatically cutting infrastructure costs on AWS, Azure, and GCP by offloading expensive AI operations.
How Clever Ops Uses This
We implement caching strategies for American business AI systems, using Redis and CDN caching to reduce costs and latency for US end users.
Example Use Case
"Caching embedding vectors so repeated queries for the same document don't require recomputation, reducing latency from 500ms to 5ms."