A Load Shedding Framework and Optimizations for M-way Windowed Stream Joins

Tuple dropping, though commonly used for load shedding in most stream operations, is inadequate for m-way, windowed stream joins. The join output rate can be overly reduced because it fails to exploit the time correlations likely to exist among interrelated streams. In this paper, we introduce GrubJoin; an adaptive, m-way, windowed stream join that effectively performs time correlation-aware CPU load shedding. GrubJoin maximizes the output rate by achieving near-optimal window harvesting, which picks only the most profitable window segments for the join. Due to combinatorial explosion of possible m-way join sequences involving window segments, m-way, windowed stream joins pose several unique challenges. We focus on addressing two of them: (1) How can we quickly determine the optimal window harvesting configuration for any m-way, windowed stream join? (2) How can we monitor and learn the time correlations among the streams with high accuracy and minimal overhead? To tackle these challenges, we formalize window harvesting as an optimization problem, develop greedy heuristics to determine near-optimal window harvesting configurations and use approximation techniques to capture the time correlations. Our experimental results show that GrubJoin is vastly superior to tuple dropping when time correlations exist and is equally effective when time correlations are nonexistent.