CouchDB: The Definitive Guide

Chapter 15. Scaling Basics

Scaling is an overloaded term. Finding a discrete definition is tricky. Everyone and her grandmother have their own idea of what scaling means. Most definitions are valid, but they can be contradicting. To make things even worse, there are a lot of misconceptions about scaling. To really define it, one needs a scalpel to find out the important bits.

First, scaling doesn’t refer to a specific technique or technology; scaling, or scalability, is an attribute of a specific architecture. What is being scaled varies for nearly every project.

Scaling is specialization.

—Joe Stump, Lead Architect of Digg.com and SimpleGeo.com

Joe’s quote is the one that we find to be the most accurate description of scaling. It is also wishy-washy, but that is the nature of scaling. An example: a website like Facebook.com— with a whole lot of users and data associated with those users and with more and more users coming in every day—might want to scale over user data that typically lives in a database. In contrast, Flickr.com at its core is like Facebook with users and data for users, but in Flickr’s case, the data that grows fastest is images uploaded by users. These images do not necessarily live in a database, so scaling image storage is Flickr’s path to growth.

Note

It is common to think of scaling as scaling out. This is shortsighted. Scaling can also mean scaling in—that is, being able to use fewer computers when demand declines. More on that later.

These are just two services. ...