Your server/network goes down, but you still want to maintain availability and let your users view and manipulate their data. So now users make edits while offline, and when they come back online you discover they made edits to the same rows in the DB. Now what do you do?
The problem really is about concurrency control - a DB creates a single source of truth so it can be either on or off. But with GoatDB we have multiple sources of truth which are equally valid, and a way to merge their states after the fact.
Think about what Git does for code - if GitHub somehow lost all their data, every dev in the world still has a valid copy and can safely restore GitHub's state. GoatDB does the same but for your app's data rather than source code
So now users make edits while offline, and when they come back online you discover they made edits to the same rows in the DB. Now what do you do?
Store changes in a queue as commands and apply them in between reads if that's what you want. This is really simple stuff. A few hundred thousand items and a few users is not a large scale or a concurrency problem.
Yup. Go ahead and try it, then you'll discover that:
- The queue introduces delays so this doesn't play nice with modern collaborative editing experience (think google docs, slack, etc)
- Let's say change A set a field to 1, and change B set the same field to 2. GoatDB allows you to easily get either 1, 2 or 3 (sum) or apply a custom resolution rule
Your only choices before goat to solve this were: Operational Transformation, raw CRDTs or differential synchronization. GoatDB combines CRDTs with commit graphs so it can do stuff other approaches don't at an unmatched speed
People were working at this scale 30 years ago on 486 web servers.