Modern data management systems extensively use parallelism to speed up query processing over massive volumes of data. This trend has inspired a rich line of research on how to formally reason about the parallel complexity of join computation. In this paper, we go beyond joins and study the parallel evaluation of recursive queries. We introduce a novel framework to reason about multi-round evaluation of Datalog programs, which combines implicit predicate restriction with distribution policies to allow expressing a combination of data-parallel and query-parallel evaluation strategies. Using our framework, we reason about key properties of distributed Datalog evaluation, including parallel-correctness of the evaluation strategy, disjointness of the computation effort, and bounds on the number of communication rounds.

中文翻译：

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数据记录的分发策略

现代数据管理系统广泛使用并行性来加快对大量数据的查询处理。这种趋势激发了关于如何正式推理联接计算的并行复杂性的大量研究。在本文中，我们超越了联接，研究了递归查询的并行评估。我们引入了一个新颖的框架来推理Datalog程序的多轮评估，该框架结合了隐式谓词限制和分发策略允许表达数据并行和查询并行评估策略的组合。使用我们的框架，我们可以推断出分布式Datalog评估的关键属性，包括评估策略的并行正确性，计算工作的脱节性以及通信回合数的界限。