Abstract
In the world of enterprise computing, single applications are often classified either as transactional or analytical. From a data management perspective, both application classes issue a database workload with commonly agreed characteristics. However, traditional database management systems (DBMS) are typically optimized for one or the other. Today, we see two trends in enterprise applications that require bridging these two workload categories: (1) enterprise applications of both classes access a single database instance and (2) longer-running, analytical-style queries issued by transactional applications. As a reaction to this change, in-memory DBMS on multi-core CPUs have been proposed to handle the mix of transactional and analytical queries in a single database instance. However, running heterogeneous queries potentially causes situations where longer running queries block shorter running queries from execution. A task-based query execution model with priority-based scheduling allows for an effective prioritization of query classes. This paper discusses the impact of task granularity on responsiveness and throughput of an in-memory DBMS. We show that a larger task size for long running operators negatively affects the response time of short running queries. Based on this observation, we propose a solution to limit the maximum task size with the objective of controlling the mutual performance impact of query classes.
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Wust, J., Grund, M., Hoewelmeyer, K., Schwalb, D., Plattner, H. (2014). Concurrent Execution of Mixed Enterprise Workloads on In-Memory Databases. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8421. Springer, Cham. https://doi.org/10.1007/978-3-319-05810-8_9
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