The upcoming release of version 3.0 will represent a major milestone for TypeDB: it will bring about fundamental improvements to the architecture and feel, and incorporate pivotal insights from our research and user feedback.

Unlike existing database paradigms, TypeDB is designed based on modern ideas from type theory. If you want to learn how this works in detail, you may be interested in our article on type-theoretic databases in our learning section. In this post, we will discuss a crucial difference between TypeDB and other database paradigms, which is a direct result of these type-theoretic foundations of TypeDB. But don’t worry, we won’t assume any familiarity with type theory and will keep our discussion as high level as possible!

In this article, we’ll explain how a new revolution in databases overcomes the limitations of relational, document and graph databases by using subtyping and polymorphism — core object-oriented (OO) programming principles — to create higher abstractions and achieve greater expressivity.

Databases excel at handling traditional data types such as numbers and strings, but they lack robust mechanisms for working with complex data structures like collections (e.g., lists and sets). In this blog post, we explore the difficulties encountered when storing data as collections in databases.

Ever since Google popularised the term in 2012, knowledge graphs have seen massive amounts of public interest. From Fortune 500 companies to universities, organisations all over the world are investing large resources into knowledge graphs.

The Wikipedia interpretation of inference is “moving from premises to logical consequences”. This definition is so broad that we can actually identify two distinct types of inference TypeDB does: Rule inference and type inference. With TypeQL, we’ve combined both types of inference into a powerful query language. This forms the basis of a paradigm-shifting database that puts elegant modelling at your fingertips.