evaluating window joins over punctuated streams
We explore join optimizations in the presence of both time-based constraints (sliding windows) and value-based constraints (punctuations). We present the first join solution named PWJoin that exploits such combined constraints to shrink the runtime join state and to propagate punctuations to benefit downstream operators. We design a state structure for PWJoin that facilitates the exploitation of both constraint types. We also explore optimizations enabled by the interactions between window and punctuation, e.g., early punctuation propagation. The costs of the PWJoin are analyzed using a cost model. We also conduct an experimental study using CAPE continuous query system. The experimental results show that in most cases, by exploiting punctuations, PWJoin outperforms the pure window join with regard to both memory overhead and throughput. Our technique complements the joins in the literature, such as symmetric hash join or window join, to now require less runtime resources without compromising the accuracy of the result.
punctuation sliding window join algorithm streaming data processing