Abstract
Data Warehouses (DW) store valuable information not only for strategic business decisions, but also for operational daily decisions. As a consequence, a large number of queries are concurrently submitted, stressing the database engine ability to handle such query workloads without severely degrading query response times. The query-at-time model of common database engines, where each query is independently executed and competes for the same resources, is inefficient for handling large DWs and does not provides the expected performance and scalability when processing large numbers of concurrent queries. Related work shows that there’s a performance advantage on sharing data and processing, but the proposed solutions suffer from memory limitations, reduced scalability and unpredictable execution times when applied to large DWs, particularly those with large dimensions. SPIN proposes an approach to share computation and data among concurrent queries that delivers scale-up, even in the presence of massive query workloads. In this paper we describe the mechanisms used by SPIN to embed data and queries into a shared query processing pipeline tree and how SPIN dynamically reorganizes the processing tree. We also provide experimental validation of the approach.
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Costa, J.P., Furtado, P. (2015). Data Warehouse Processing Scale-Up for Massive Concurrent Queries with SPIN. In: Hameurlain, A., Küng, J., Wagner, R., Bellatreche, L., Mohania, M. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XVII. Lecture Notes in Computer Science(), vol 8970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46335-2_1
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DOI: https://doi.org/10.1007/978-3-662-46335-2_1
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