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Schlosser, R., Kossmann, J., Boissier, M.: Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 1238-1249. IEEE (2019).
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Kossmann, J., Schlosser, R.: A Framework for Self-Managing Database Systems. ICDE workshop (Self-Managing Databases). pp. 100-106 (2019).
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Dreseler, M., Kossmann, J., Boissier, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise Re-engineered: An Extensible Database System for Research in Relational In-Memory Data Management. 22nd International Conference on Extending Database Technology (EDBT). pp. 313-324 (2019).
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Kossmann, J.: Self-Driving: From General Purpose to Specialized DBMSs. Proceedings of the VLDB 2018 PhD Workshop co-located with the 44th International Conference on Very Large Databases (VLDB 2018), Rio de Janeiro, Brasil, Aug 27-31, 2018 (2018).
Large data sets, variable workloads of high complexity, and flexible cloud infrastructure make the administration of database systems at the same time more challenging and more important. In the future, self-driving database systems will utilize workload-driven optimization and machine learning techniques to generate forecasts of future workloads, decide upon which actions to take to process workloads most efficiently and to incorporate knowledge from past decisions into future ones without human intervention. But database systems are typically not designed having such capabilities in mind. We propose the architecture of a generalized framework that enables seamless integration of self-driving capabilities into database systems. Thereby, general-purpose database systems can transform themselves into systems tailored to a specific use case. Furthermore, we present our scalable approach to finding solutions for large problem instances of a physical design challenge, i.e., the index selection problem. Both concepts have been implemented and partly evaluated with real-world data on the research database system Hyrise.
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Dreseler, M., Kossmann, J., Frohnhofen, J., Uflacker, M., Plattner, H.: Fused Table Scans: Combining AVX-512 and JIT to Double the Performance of Multi-Predicate Scans. Joint Workshop of HardBD (International Workshop on Big Data Management on Emerging Hardware) and Active (Workshop on Data Management on Virtualized Active Systems), in conjunction with ICDE (2018).
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Kossmann, J., Dreseler, M., Gasda, T., Uflacker, M., Plattner, H.: Visual Evaluation of SQL Plan Cache Algorithms. Australasian Database Conference (ADC) (2018).
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Dreseler, M., Gasda, T., Kossmann, J., Uflacker, M., Plattner, H.: Adaptive Access Path Selection for Hardware-Accelerated DRAM Loads. Australasian Database Conference (ADC) (2018).
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Mueller, S., Fritzsche, M., Kossmann, J., Schneider, M., Striebel, J., Baudisch, P.: Scotty: Relocating Physical Objects Across Distances Using Destructive Scanning, Encryption, and 3D Printing. TEI '15 Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction. pp. 233-240 (2015).
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Schwalb, D., Kossmann, J., Faust, M., Klauck, S., Uflacker, M., Plattner, H.: Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications. Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics (IMDM), in conjunction with VLDB 2015 Kohala Coast, Hawaii (2015).
In-memory database systems are well-suited for enterprise workloads, consisting of transactional and analytical queries. A growing number of users and an increasing demand for enterprise applications can saturate or even overload single- node database systems at peak times. Better performance can be achieved by improving a single machine’s hardware but it is often cheaper and more practicable to follow a scale-out approach and replicate data by using additional machines. In this paper we present Hyrise-R, a lazy master replication system for the in-memory database Hyrise. By setting up a snapshot-based Hyrise cluster, we increase both performance by distributing queries over multiple instances and availability by utilizing the redundancy of the cluster structure. This paper describes the architecture of Hyrise- R and details of the implemented replication mechanisms. We set up Hyrise-R on instances of Amazon’s Elastic Compute Cloud and present a detailed performance evaluation of our system, including a linear query throughput increase for enterprise workloads.
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