Saggitarius: A DSL for Specifying Grammatical Domains

TL;DR

Saggitarius is a new language and system that enables reasoning about data formats by describing sets of context-free grammars, facilitating analysis and inference of data representations from examples.

Contribution

It introduces a novel language and algorithm for inferring data grammars from examples, with applications in data validation and dialect detection.

Findings

Typically infers a satisfying grammar within a few seconds

Achieves 84% success rate in CSV dialect detection within 60 seconds

Offers comparable accuracy to specialized tools for data format inference

Abstract

Common data types like dates, addresses, phone numbers and tables can have multiple textual representations, and many heavily-used languages, such as SQL, come in several dialects. These variations can cause data to be misinterpreted, leading to silent data corruption, failure of data processing systems, or even security vulnerabilities. Saggitarius is a new language and system designed to help programmers reason about the format of data, by describing grammatical domains -- that is, sets of context-free grammars that describe the many possible representations of a datatype. We describe the design of Saggitarius via example and provide a relational semantics. We show how Saggitarius may be used to analyze a data set: given example data, it uses an algorithm based on semi-ring parsing and MaxSAT to infer which grammar in a given domain best matches that data. We evaluate the effectiveness of the algorithm on a benchmark suite of 110 example problems, and we demonstrate that our system typically returns a satisfying grammar within a few seconds with only a small number of examples. We also delve deeper into a more extensive case study on using Saggitarius for CSV dialect detection. Despite being general-purpose, we find that Saggitarius offers comparable results to hand-tuned, specialized tools; in the case of CSV, it infers grammars for 84% of benchmarks within 60 seconds, and has comparable accuracy to custom-built dialect detection tools.