A Data Partitioning Method Using Dynamic Data Dependence Graphs

While data partitioning significantly improves the scalability of multithreaded and clustered transaction processing (TP) systems, selecting a correct and effective partitioning criterion (i.e. how to partition data) requires deep insight into the target TP systems. In this paper, we propose a novel analysis method to find even non-intuitive partitioning criteria for TP systems using our tracing tool that generates dynamic data dependence graphs (dynamic DDGs). Analyzing the exact behavior of a TP system from its dynamic DDG, our method can also generate a routing function of transaction requests for each partitioning criterion. We have demonstrated that our method could find the candidates of partitioning criteria and their routing functions, with a non-trivial TP system scenario.

By: Mikio Takeuchi; Ryoh Neyama

Published in: RT0709 in 2007


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