Types from data: Making structured data first-class citizens in F#

TL;DR

The paper introduces F# Data, a library that integrates external structured data into F# by inferring data shapes from samples and embedding them into the type system, enhancing safety and reducing code complexity.

Contribution

It develops a shape inference algorithm for external data and formalizes its integration into F#'s type system using type providers, with proven soundness.

Findings

Reduces data access code significantly

Provides safety guarantees over weakly typed methods

Formalizes shape inference with a soundness proof

Abstract

Most modern applications interact with external services and access data in structured formats such as XML, JSON and CSV. Static type systems do not understand such formats, often making data access more cumbersome. Should we give up and leave the messy world of external data to dynamic typing and runtime checks? Of course, not! We present F# Data, a library that integrates external structured data into F#. As most real-world data does not come with an explicit schema, we develop a shape inference algorithm that infers a shape from representative sample documents. We then integrate the inferred shape into the F# type system using type providers. We formalize the process and prove a relative type soundness theorem. Our library significantly reduces the amount of data access code and it provides additional safety guarantees when contrasted with the widely used weakly typed techniques.