Scaling by Caching

Save the result of a request with a given set of parameters.
In a future request with the same parameter (maybe) return the same result
System level caching. Storage:
- In ‘local’ memory
- In ‘remote’ memory
- In database
- In Cloud
Awareness of order of magnitude speed of operations:
- Access registers inside CPU
- Access CPU caches
- Access standard RAM
- Access local disk
  - Access files
  - Access local database
- Access over network
  - To a nearby server
  - To a nearby database server
- Access over the internet
  - To a remote server
  - To a remote database server
  - To a remote Web Service
Memoization:
- caching applied to an individual method
- A basic programming technique
- Simple
Name-value databases
- Very fast searches and lookups
- Distributed searches and distributed databases
- Robust across system and application failures
Database Caching
- To a certaine extent, it’s what databases do
- Caching both at the server (postgres itself)
- And at the client (the postgres and activerecord subsystems)
- Yet a lot more can be done
Server page caching
- Done at the server
- Don’t regenerate the page if it’s requested again
- As long as you know it hasn’t changed
- Page fragment caching, including “russian doll caching”
- A key feature of good frameworks