Continuously Adaptive Continuous Queries Over Streams

Abstract

We present a continuously adaptive, continuous query (CACQ) implementation based on the eddy query processing framework. We show that our design provides significant performance benefits over existing approaches to evaluating continuous queries, not only because of its adaptivity, but also because of the aggressive cross-query sharing of work and space that it enables. By breaking the abstraction of shared relational algebra expressions, our Telegraph CACQ implementation is able to share physical operators --- both selections and join state --- at a very fine grain. We augment these features with a grouped-filter index to simultaneously evaluate multiple selection predicates. We include measurements of the performance of our core system, along with a comparison to existing continuous query approaches.