We try to keep an up to date list of all our publications. If you are interested in a PDF that we have not uploaded yet, feel free to send us an email to get a copy. All recent publications you will find below. For older, please click appropriate year.
Gévay, G.E., Rabl, T., Breß, S., Madai-Tahy, L., Quiané-Ruiz, J.-A., Markl, V.: Efficient Control Flow in Dataflow Systems: When Ease-of-Use Meets High Performance.37th IEEE International Conference on Data Engineering. (2021).
Daase, B., Bollmeier, L.J., Benson, L., Rabl, T.: Maximizing Persistent Memory Bandwidth Utilization for OLAP Workloads.Proceedings of the 2021 International Conference on Management of Data (SIGMOD '21), June 20--25, 2021, Virtual Event, China. ACM (2021).
Modern database systems for online analytical processing (OLAP) typically rely on in-memory processing. Keeping all active data in DRAM severely limits the data capacity and makes larger deployments much more expensive than disk-based alternatives. Byte-addressable persistent memory (PMEM) is an emerging storage technology that bridges the gap between slow-but-cheap SSDs and fast-but-expensive DRAM. Thus, research and industry have identified it as a promising alternative to pure in-memory data warehouses. However, recent work shows that PMEM's performance is strongly dependent on access patterns and does not always yield good results when simply treated like DRAM. To characterize PMEM's behavior in OLAP workloads, we systematically evaluate PMEM on a large, multi-socket server commonly used for OLAP workloads. Our evaluation shows that PMEM can be treated like DRAM for most read access but must be used differently when writing. To support our findings, we run the Star Schema Benchmark on PMEM and DRAM. We show that PMEM is suitable for large, read-heavy OLAP workloads with an average query runtime slowdown of 1.66x compared to DRAM. Following our evaluation, we present 7 best practices on how to maximize PMEM's bandwidth utilization in future system designs.
Menon, P., M. Qadah, T., Rabl, T., Sadoghi, M., Jacobsen, H.-A.: LogStore: A Workload-aware, Adaptable Key-Value Store on Hybrid Storage Systems.37th IEEE International Conference on Data Engineering. (2021).