Full Stack Python

# Caching

Caching can reduce load on servers by storing the results of common operations and serving the precomputed answers to clients.

For example, instead of retrieving data from database tables that rarely change, you can store the values in-memory. Retrieving values from an in-memory location is far faster than retrieving them from a database (which stores them on a persistent disk like a hard drive). When the cached values change the system can invalidate the cache and re-retrieve the updated values for future requests.

A cache can be created for multiple layers of the stack.

## Caching Resources

memcached is a common in-memory caching system.

Redis is a key-value in-memory data store that can easily be configured for caching with libraries such as django-redis-cache.

Next read the web browsers section.