The rising hardware heterogeneity in modern systems emphasizes new dimensions of optimizing task execution for data processing frameworks. Specialized hardware is often expected to be the exclusive executor of some particular workload because it was designed for it or is simply the fastest option. In heterogeneous database systems, almost always, the entire operation offloading is considered. However, little attention was given to database systems with horizontal cross-device pipeline parallelization. We argue that such an approach can be applied to systems with morsel-driven parallelism and improve performance. We apply our parallelization strategy to an existing system and accelerate aggregations using two devices by up to 1.5x compared to the fastest exclusive device executor.

Steered query optimizers address the planning mistakes of traditional query optimizers by providing them with hints on a per-query basis, thereby guiding them in the right direction. This paper introduces QO-Insight, a visual tool designed for exploring query execution traces of such steered query optimizers. Although steered query optimizers are typically perceived as black boxes, QO-Insight empowers database administrators and experts to gain qualitative insights and enhance their performance through visual inspection and analysis.

This paper presents AutoSteer, a learning-based solution that automatically drives query optimization in any SQL database that exposes tunable optimizer knobs. AutoSteer builds on the Bandit optimizer (Bao) and extends it with new capabilities (e.g., automated hint-set discovery) to minimize integration effort and facilitate usability in both monolithic and disaggregated SQL systems. We successfully applied AutoSteer on PostgreSQL, PrestoDB, Spark-SQL, MySQL, and DuckDB - five popular open-source database engines with diverse query optimizers. We then conducted a detailed experimental evaluation with public benchmarks (JOB, Stackoverflow, TPC-DS) and a production workload from Meta's PrestoDB deployments. Our evaluation shows that AutoSteer can not only outperform these engines' native query optimizers (e.g., up to 40% improvements for PrestoDB) but can also match the performance of Bao-for-PostgreSQL with reduced human supervision and increased adaptivity, as it replaces Bao's static, expert-picked hint-sets with those that are automatically discovered. We also provide an open-source implementation of AutoSteer together with a visual tool for interactive use by query optimization experts.

While index structures are crucial components in high-performance query processing systems, they occupy a large fraction of the available memory. Recently-proposed compact indexes reduce this space overhead and thus speed up queries by allowing the database to keep larger working sets in memory. These compact indexes, however, are slower than performance-optimized in-memory indexes because they adopt encodings that trade performance for memory efficiency. Applying different encodings within a single index might allow optimizing both dimensions at the same time - however, it is not clear which encodings should be applied to which index parts at build-time.

Connected mobility applications rely heavily on geospatial joins that associate point data, such as locations of Uber cars, to static polygonal regions, such as city neighborhoods. These joins typically involve expensive geometric computations, which makes it hard to provide an interactive user experience. In this paper, we propose an adaptive polygon index that leverages true hit fltering to avoid expensive geometric computations in most cases. In particular, our approach closely approximates polygons by combining quadtrees with true hit filtering, and stores these approximations in a query-effcient radix tree. Based on this index, we introduce two geospatial join algorithms: an approximate one that guarantees a user-defined precision, and an exact one that adapts to the expected point distribution. In summary, our technique outperforms existing CPU-based joins by up to two orders of magnitude and is competitive with state-of-the-art GPU implementations.