Recent studies have made it possible to integrate learning techniques into database systems for practical utilization. In particular, the state-of-the-art studies hook the conventional query optimizer to explore multiple execution plan candidates, then choose the optimal one with a learned model. This framework simplifies the integration of learning techniques into the database system. However, these methods still have room for improvement due to their limited plan exploration space and ineffective learning from execution plans. In this work, we propose Athena, an effective learning-based framework of query optimizer enhancer. It consists of three key components: (i) an order-centric plan explorer, (ii) a Tree-Mamba plan comparator and (iii) a time-weighted loss function. We implement Athena on top of the open-source database PostgreSQL and demonstrate its superiority via extensive experiments. Specifically, We achieve 1.75x, 1.95x, 5.69x, and 2.74x speedups over the vanilla PostgreSQL on the JOB, STATS-CEB, TPC-DS, and DSB benchmarks, respectively. Athena is 1.74x, 1.87x, 1.66x, and 2.28x faster than the state-of-the-art competitor Lero on these benchmarks. Additionally, Athena is open-sourced and it can be easily adapted to other relational database systems as all these proposed techniques in Athena are generic.

It is an open challenge for cloud database service providers to guarantee tenants' service-level objectives (SLOs) and enjoy high resource utilization simultaneously. In this work, we propose a novel system Tao to overcome it. Tao consists of three key components: (i) tasklet-based DAG generator, (ii) tasklet-based DAG executor, and (iii) SLO-guaranteed scheduler. The core concept in Tao is tasklet, a coroutine-based lightweight execution unit of the physical execution plan. In particular, we first convert each SQL operator in the traditional physical execution plan into a set of fine-grained tasklets by the tasklet-based DAG generator. Then, we abstract the tasklet-based DAG execution procedure and implement the tasklet-based DAG executor using C++20 coroutines. Finally, we introduce the SLO-guaranteed scheduler for scheduling tenants' tasklets across CPU cores. This scheduler guarantees tenants' SLOs with a token bucket model and improves resource utilization with an on-demand core adjustment strategy. We build Tao on an open-sourced relational database, Hyrise, and conduct extensive experimental studies to demonstrate its superiority over existing solutions.