Distribution Constraints: The Chase for Distributed Data

Authors Gaetano Geck , Frank Neven , Thomas Schwentick

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Gaetano Geck
  • TU Dortmund University, Germany
Frank Neven
  • Hasselt University and transnational University of Limburg, Belgium
Thomas Schwentick
  • TU Dortmund University, Germany


We thank Michael Benedikt, Bas Ketsman, Andreas Pieris, Phokion Kolaitis, Christopher Spinrath, Brecht Vandevoort, and Thomas Zeume for helpful discussions on various aspects of this work.

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Gaetano Geck, Frank Neven, and Thomas Schwentick. Distribution Constraints: The Chase for Distributed Data. In 23rd International Conference on Database Theory (ICDT 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 155, pp. 13:1-13:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


This paper introduces a declarative framework to specify and reason about distributions of data over computing nodes in a distributed setting. More specifically, it proposes distribution constraints which are tuple and equality generating dependencies (tgds and egds) extended with node variables ranging over computing nodes. In particular, they can express co-partitioning constraints and constraints about range-based data distributions by using comparison atoms. The main technical contribution is the study of the implication problem of distribution constraints. While implication is undecidable in general, relevant fragments of so-called data-full constraints are exhibited for which the corresponding implication problems are complete for EXPTIME, PSPACE and NP. These results yield bounds on deciding parallel-correctness for conjunctive queries in the presence of distribution constraints.

Subject Classification

ACM Subject Classification
  • Theory of computation → Database constraints theory
  • Theory of computation → Logic and databases
  • Information systems → Parallel and distributed DBMSs
  • tuple-generating dependencies
  • chase
  • conjunctive queries
  • distributed evaluation


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