IceDust: Incremental and Eventual Computation of Derived Values in Persistent Object Graphs

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.