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Parallelizing Intra-Window Join on Multicores

Shuhao Zhang, Yancan Mao, Jiong He, Philipp M. Grulich, Steffen Zeuch, Bingsheng He, T. B. Richard, Volker Markl

202115 citationsDOI

Abstract

The intra-window join (IaWJ), i.e., joining two input streams over a single window, is a core operation in modern stream processing applications. This paper presents the first comprehensive study on parallelizing the IaWJ on modern multicore architectures. In particular, we classify IaWJ algorithms into lazy and eager execution approaches. For each approach, there are further design aspects to consider, including different join methods and partitioning schemes, leading to a large design space. Our results show that none of the algorithms always performs the best, and the choice of the most performant algorithm depends on: (i) workload characteristics, (ii) application requirements, and (iii) hardware architectures. Based on the evaluation results, we propose a decision tree that can guide the selection of an appropriate algorithm.

Topics & Concepts

Computer scienceJoin (topology)WorkloadWindow (computing)Multi-core processorParallel computingSelection (genetic algorithm)Core (optical fiber)Tree (set theory)Distributed computingArtificial intelligenceOperating systemMathematical analysisMathematicsTelecommunicationsCombinatoricsAdvanced Database Systems and QueriesCloud Computing and Resource ManagementPeer-to-Peer Network Technologies