The core reasoning task for datalog engines is materialization, the
evaluation of a datalog program over a database alongside its physical
incorporation into the database itself. The de-facto method of computing it, is
through the recursive application of inference rules. Due to it being a costly
operation, it is a must for datalog engines to provide incremental
materialization, that is, to adjust the computation to new data, instead of
restarting from scratch. One of the major caveats, is that deleting data is
notoriously more involved than adding, since one has to take into account all
possible data that has been entailed from what is being deleted. Differential
Dataflow is a computational model that provides efficient incremental
maintenance, notoriously with equal performance between additions and
deletions, and work distribution, of iterative dataflows. In this paper we
investigate the performance of materialization with three reference datalog
implementations, out of which one is built on top of a lightweight relational
engine, and the two others are differential-dataflow and non-differential
versions of the same rewrite algorithm, with the same optimizations.

研究表明，对于 datalog 引擎来说，材料化是核心推理任务，本文通过比较不同实现方法的性能，尤其是基于轻量级关系型引擎的一种方法和基于 Differential Dataflow 的两种方法，探讨了材料化的性能问题。