With full resource disaggregation on the horizon, it is unclear what the most suitable programming model is that enables dataflow developers to fully harvest the potential that recent hardware developments offer. In our vision, we propose to raise the abstraction level to allow developers to primarily reason about their dataflow and the requirements that need to be met by the underlying system in a declarative fashion. Underneath, the system works with typed memory regions and uses the notion of ownership that allows for more flexible memory management across the different compute devices and the tasks mapped onto them. This requires a holistic approach that crosses multiple layers of the system stack, opening exciting systems research questions.

Current hardware development greatly influences the design decisions of modern database systems. For many modern performance-focused database systems, query compilation emerged as an integral part and different approaches for code generation evolved, making use of standard compilers, general-purpose compiler libraries, or domain-specific code generators. However, development primarily focused on the dominating x86-64 server architecture; but neglected current hardware developments towards other CPU architectures like ARM and other RISC architectures. Therefore, we explore the design space of code generation in database systems considering a variety of state-of-the-art compilation approaches with a set of qualitative and quantitative metrics. Based on our findings, we have developed a new code generator called FireARM for AArch64-based systems in our database system, Umbra. We identify general as well as architecture-specific challenges for custom code generation in databases and provide potential solutions to abstract or handle them. Furthermore, we present an extensive evaluation of different compilation approaches in Umbra on a wide variety of x86-64 and ARM machines. In particular, we compare quantitative performance characteristics such as compilation latency and query throughput. Our results show that using standard languages and compiler infrastructures reduces the barrier to employing query compilation and allows for high performance on big data sets, while domain-specific code generators can achieve a significantly lower compilation overhead and allow for better targeting of new architectures.