Abstract
Real time business intelligence demands to execute OLAP queries on a current, up-to-date state of the transactional OLTP data. The currently exercised separation of transaction processing on the OLTP database and BI query processing on the data warehouse that is only periodically refreshed violates this goal. We propose to enhance the transactional database with highly effective query processing capabilities. We contrast different architectures proposed for achieving the real-time BI goal: versioning of the data and thereby separating the query from the transactions workload, continuous DW refreshing, heterogeneous workload management, update staging by periodically merging the update delta into the queryable main database, update and query batching, and our newly developed virtual memory snapshot mechanism based on hardware-supported shadowed pages. In our HyPer main memory database management system, the OLAP queries are executed on an arbitrarily current database snapshot that is created by forking the OLTP process. This snapshot is efficiently maintained by the operating system’s copy on write mechanism that is supported by the MMU hardware. To assess the performance of such hybrid DBMSs we propose a new OLTP&OLAP benchmark that combines the transaction processing functionality of the TPC-C benchmark with the query workload of the TPC-H benchmark in one mixed workload. Based on this benchmark we substantiate the claim that it is indeed possible to architect a hybrid system that can achieve the transaction throughput of dedicated in-memory OLTP systems and, in parallel, execute a BI workload on the same data at the same performance as dedicated OLAP systems, such as in-memory column stores.
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Kemper, A., Neumann, T. (2011). One Size Fits all, Again! The Architecture of the Hybrid OLTP&OLAP Database Management System HyPer. In: Castellanos, M., Dayal, U., Markl, V. (eds) Enabling Real-Time Business Intelligence. BIRTE 2010. Lecture Notes in Business Information Processing, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22970-1_2
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DOI: https://doi.org/10.1007/978-3-642-22970-1_2
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