Cloud-native computing offers the promise of infinite scalability -as your application makes greater demands on the infrastructure, the infrastructure magically grows to accommodate it. This works for every part of your application, except for the database.

Normally, you have to make a trade-off between scalability, functionality, and performance. On the one hand, you can deploy single-instances of commodity databases such as Postgres or Mysql, which don't scale well (if at all); or you can deploy highly-scalable engines such as Dynamo which scale infinitely, but cost a fortune and don't provide any complex queries at all (and are very tricky to set up even then). Occupying a middle-ground, there are products like Aurora, Hammer, and Citus, which between them explore a number of ways to achieve scalability in a relational model, but in each case you need to apply complex manual tuning, and even then the performance can collapse without warning.

Jules has been consulting on a project which leverages any commodity engine to deliver infinitely-scalable relational data delivering performance which is guaranteed to be at least as good as the original engine. Because it uses the underlying engine's wire protocol it is a drop-in replacement for a manually-deployed instance.

In this talk, he discusses why distributed relational data is difficult, why the current solutions fall short, and the mathematical background behind a new theory of relations which allows highly-performant distributed systems to be built.