Load can be measured by a variety of metrics, including the number of requests per second received by a web server, the number of reads from vs writes to a cache, the frequency of a data back-up, the number of supported concurrent users, and more. When considering scalability, we generally question if the system can continue to perform well when one of the load parameters increases.

Examples

Let’s look at some well-known products and examples of their load parameters:

Microsoft Word: The number of geographically distributed users who can concurrently modify a document in collaboration mode without affecting usability for each user.

Facebook: The number of users able to view a live broadcast from, say, a celebrity. It becomes harder to broadcast a live feed to all users if there are many users viewing a particular live broadcast.

Google: Storage costs for indexing the web as the web grows.

Twitter: Adding a tweet by a popular user instantly to the timeline of all the followers. A tweet by an account with several million followers, if stored in a database, will be fetched millions of times when computing the timelines for each of the account followers and is a potential bottleneck.