IceDust

Incremental and eventual computation of derived values in persistent object graphs

Conference Paper (2016)
Author(s)

Daco Harkes (TU Delft - Programming Languages)

D.M. Groenewegen (TU Delft - Programming Languages)

Eelco Visser (TU Delft - Programming Languages)

Research Group
Programming Languages
Copyright
© 2016 D.C. Harkes, D.M. Groenewegen, Eelco Visser
DOI related publication
https://doi.org/10.4230/LIPIcs.ECOOP.2016.11
More Info
expand_more
Publication Year
2016
Language
English
Copyright
© 2016 D.C. Harkes, D.M. Groenewegen, Eelco Visser
Research Group
Programming Languages
Pages (from-to)
1-26
ISBN (electronic)
978-3-95977-014-9
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Derived values are values calculated from base values. They can be expressed in object-oriented languages by means of getters calculating the derived value, and in relational or logic databases by means of (materialized) views. However, switching to a different calculation strategy (for example caching) in object-oriented programming requires invasive code changes, and the databases limit expressiveness by disallowing recursive aggregation. In this paper, we present IceDust, a data modeling language for expressing derived attribute values without committing to a calculation strategy. IceDust provides three strategies for calculating derived values in persistent object graphs: Calculate-on-Read, Calculate-on-Write, and Calculate-Eventually. We have developed a path-based abstract interpretation that provides static dependency analysis to generate code for these strategies. Benchmarks show that different strategies perform better in different scenarios. In addition we have conducted a case study that suggests that derived value calculations of systems used in practice can be expressed in IceDust.