Abstract
Multistores are data management systems that enable query processing across different database management systems (DBMSs); besides the distribution of data, complexity factors like schema heterogeneity and data replication must be resolved through integration and data fusion activities. In a recent work [2], we have proposed a multistore solution that relies on a dataspace to provide the user with an integrated view of the available data and enables the formulation and execution of GPSJ (generalized projection, selection and join) queries. In this paper, we propose a technique to optimize the execution of GPSJ queries by finding the most efficient execution plan on the multistore. In particular, we devise three different strategies to carry out joins and data fusion, and we build a cost model to enable the evaluation of different execution plans. Through the experimental evaluation, we are able to profile the suitability of each strategy to different multistore configurations, thus validating our multi-strategy approach and motivating further research on this topic.
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Notes
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Although the level of parallelism in Spark is given in terms of CPU cores, we consider the number of machines because the cost model is focused on disk IO rather than on CPU computation.
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Forresi, C., Francia, M., Gallinucci, E., Golfarelli, M. (2021). Optimizing Execution Plans in a Multistore. In: Bellatreche, L., Dumas, M., Karras, P., Matulevičius, R. (eds) Advances in Databases and Information Systems. ADBIS 2021. Lecture Notes in Computer Science(), vol 12843. Springer, Cham. https://doi.org/10.1007/978-3-030-82472-3_11
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