Data centers produce a significant and increasing amount of CO 2 emissions. In the past, these have been predominantly due to energy generation for powering data centers. With the transition to energy sources with lower carbon production, the embodied carbon (i.e., CO 2 and other greenhouse gas emissions during production, transport, and end-of-life) plays an increasing role when planning server lifecycles. While replacing an old server with newer hardware will typically reduce the power consumption of individual tasks, due to better efficiency of modern CPUs, offsetting the embodied carbon of new hardware can take months to tens of years, depending on the grid carbon intensity. In this demo, we invite attendees to interactively analyze the ecological lifecycles of modern database servers for different workloads and grid carbon intensities. Attendees can compare servers with different CPU architectures and estimate ecological deployment cycles for database servers.

Join ordering and query optimization are crucial for query performance but remain challenging due to unknown or changing characteristics of query intermediates, especially for complex queries with many joins. Over the past two decades, a spectrum of techniques for adaptive query processing (AQP)---including inter-/intra-operator adaptivity and tuple routing---have been proposed to address these challenges. However, commercial database systems in practice do not implement holistic AQP techniques because they increase the system complexity (e.g., intertwined planning and execution) and thus, complicate debugging and testing. Additionally, existing approaches may incur large overheads, leading to problematic performance regressions. In this paper, we introduce POLAR, a simple yet very effective technique for a self-regulating selection of alternative join orderings with bounded overhead. We enhance left-deep join pipelines with alternative join orders, perform regret-bounded tuple routing to find and validate "plans of least resistance", and then process the majority of tuple batches through these plans. We study different join order selection techniques, different routing strategies, and a variety of workload characteristics. Our experiments with a POLAR prototype in DuckDB show runtime improvements of up to 9x and less than 7% overhead for all benchmark queries, while outperforming state-of-the-art AQP systems by up to 15x.

We present an overview of our work on the SAP HANA Scale-out Extension, a novel distributed database architecture designed to support large scale analytics over real-time data. This platform permits high performance OLAP with massive scale-out capabilities, while concurrently allowing OLTP workloads. This dual capability enables analytics over real-time changing data and allows fine grained user-specified service level agreements (SLAs) on data freshness. We advocate the decoupling of core database components such as query processing, concurrency control, and persistence, a design choice made possible by advances in high-throughput low-latency networks and storage devices. We provide full ACID guarantees and build on a logical timestamp mechanism to provide MVCC-based snapshot isolation, while not requiring synchronous updates of replicas. Instead, we use asynchronous update propagation guaranteeing consistency with timestamp validation. We provide a view into the design and development of a large scale data management platform for real-time analytics, driven by the needs of modern enterprise customers.