We are happy to announce that our group has two demonstrations and a poster at BTW 2025 in Bamberg, Germany on 3rd of March - 7th of March, 2025.
1) A Demonstration of Skyrise: A Serverless Query Processor by Thomas Bodner and Tilmann Rabl
Abstract:
Data processing systems are increasingly being deployed in the cloud, because of the cost-effectiveness of short-term resource provisioning. In recent years, serverless cloud computing has been extended with highly elastic resource pools. This elasticity has the potential to make cloud-based systems more cost-efficient, preventing resource over- and under-provisioning. In this demonstration, we present Skyrise, a serverless SQL query processor for in-situ analytics on data in cloud storage. We highlight Skyrise's capabilities to run entirely on serverless compute resources and to complement them with virtual servers and cloud object storage.
Poster
2) Compression in Main Memory Database Systems: Cost and Performance Trade-Offs of Workload-Driven Data Encoding by Martin Boissier, Marcel Weisgut, and Tilmann Rabl
Abstract:
Automating physical design optimizations of database systems is challenging. Recent work on index selection or data compression has shown significant advantages of automated approaches. However, the impact on running systems is often hard to predict. Moreover, automated systems often lack the capabilities to help users understand the decisions taken. In this demonstration, we study the impact of optimal encoding configurations for in-memory database systems. We allow the user to set varying main memory budgets for which optimal encoding configurations are applied. Effects on runtime performance and memory consumption can be directly observed. The user can further analyze the impact compression has on overall memory consumption and how compression ratios affect performance when the memory bandwidth is saturated.
Poster
3) Offset-Value Coding using SIMD Intrinsics by Florian Schmeller, Tilmann Rabl and Goetz Graefe at the NoDMC workshop
Abstract:
Core operations in database systems are based on sorting, e.g., creating a new B-tree index, merge joins, or grouped aggregations. The required comparisons can be costly due to many or large columns. In order to reuse previous comparison effort, it can be encoded in form of offset-value codes. While hash values can guarantee that two keys are not equal, offset-value codes can also guarantee equality of keys and indicate their sort order, making them usable in sorting algorithms. Modern CPUs provide specialized functional units that enable data parallel execution within a single core. In this paper, we report on our initial experiences and measurements for comparisons using SIMD intrinsics. Our techniques are portable to many architectures based on architecture-agnostic vector types and instructions. Our results demonstrate that hardware-accelerated sorting and merging
are available on any CPU with SIMD intrinsics, i.e., practically any modern CPU.
Poster
