Database technology for the masses

Abstract

A wealth of technology has evolved around relational databases over decades that has been successfully tried and tested in many settings and use cases. Yet, the majority of it remains overlooked in the pursuit of performance (e.g., NoSQL) or new functionality (e.g., graph data or machine learning). In this paper, we argue that a wide range of techniques readily available in databases are crucial to tackling the challenges the IT industry faces in terms of hardware trends management, growing workloads, and the overall complexity of a rapidly changing application and platform landscape. However, to be truly useful, these techniques must be freed from the legacy component of database engines: relational operators. Therefore, we argue that to make databases more flexible as platforms and to extend their functionality to new data types and operations requires exposing a lower level of abstraction: instead of working with SQL it would be desirable for database engines to compile, optimize, and run a collection of sub-operators for manipulating and managing data, offering them as an external interface. In this paper, we discuss the advantages of this, provide an initial list of such sub-operators, and show how they can be used in practice.