Approximating StreamingWindow Joins Under CPU Limitations

Abstract

Data streaming systems face the possibility of having to shed load in the case of CPU or memory resource limitations. We study the CPU limited scenario in detail. First, we propose a new model for the CPU cost. Then we formally state the problem of shedding load for the goal of obtaining the maximum possible subset of the complete answer, and propose an online strategy for semantic load shedding. Moving on to random load shedding, we discuss random load shedding strategies that decouple the window maintenance and tuple production operations of the symmetric hash join, and prove that one of them — Probe-No-Insert — always dominates the previously proposed coin flipping strategy.