Adaptive Dataflow: Eddies, SteMs and FLuX

该项目提出了一个议程，详细研究了三个关键的运营商设计的自适应查询处理：涡服务于做连续的，自适应的重新排序的运营商在查询计划面对不断变化的数据统计和系统行为。 Stem将查询处理中固有的状态管理暴露为物理代数中的第一类运算符，允许查询处理替代方案之间的自适应竞争和杂交。FLuXen提供跨并行机的自适应负载平衡和容错。使用电报系统基础设施，该提案的议程是在传统和新的查询处理挑战的背景下研究高效，强大的漩涡，Stem和FluXen的机制和政策。最终目标是提供一个并行查询处理架构，它可以在完美的环境中运行得几乎和传统架构一样好，更重要的是，它可以在面对大规模环境中常见的各种缺陷和变化时自动适应，以有效和健壮地运行。

This project sets out an agenda to study in detail three key operators designed for adaptivity in query processing: Eddies serve to do continuous, adaptive reordering of operators in query plans in the face of changing data statistics and system behavior. SteMs expose the state management inherent in query processing as a first class operator in a physical algebra, allowing for adaptive competition and hybridization among query processing alternatives. FLuXen provide adaptive load balancing and fault tolerance across parallel machines. Using the Telegraph system infrastructure, this proposal's agenda is to investigate mechanisms and policies for efficient, robust eddies, SteMs and FluXen in the context of traditional and new query processing challenges. The end goal is to provide a parallel query processing architecture that can run nearly as well in perfect circumstances as a traditiona architecture, and -- more importantly -- that can automatically adapt to run efficiently and robustly when faced with the kinds of imperfections and variations commonly seen in large-scale environments.