Learned indexes, emerging as a promising alternative to traditional indexes like B+Tree, utilize machine learning models to enhance query performance and reduce memory usage. However, the widespread adoption of learned indexes is limited by their expensive training cost and the need for high accuracy of internal models. Although some studies attempt to optimize the building process of these learned indexes, existing methods are restrictive in scope and applicability. They are usually tailored to specific index types and heavily rely on pre-trained model knowledge, making deployment a challenging task. In this work, we introduce the Learned Index Optimization Framework (LIOF), a general and easily integrated solution aimed at expediting the training process and improving the accuracy of index model for one-dimensional and multi-dimensional learned indexes. The optimization of LIOF for the learned indexes is intuitive, directly providing optimized parameters for index models based on the distribution of node data. By leveraging the correlation between key distribution and node model parameters, LIOF significantly reduces the training epochs required for each node model. Initially, we introduce an optimization strategy inspired by optimization-based meta-learning to train the LIOF to generate optimized initial parameters for index node models. Subsequently, we present a data-driven encoder and a parameter-centric decoder network, which adaptively translate key distribution into a latent variable representation and decode it into optimized node model initialization. Additionally, to further utilize characteristics of key distribution, we propose a monotonic regularizer and focal loss, guiding LIOF training towards efficiency and precision. Through extensive experimentation on real-world and synthetic datasets, we demonstrate that LIOF provides substantial enhancements in both training efficiency and the predictive accuracy for learned indexes.

Vertical partitioning is a crucial physical design strategy in databases that enhances data management and retrieval through optimal data placement. However, current research often overlooks the use of query predicates for effective data block allocation, resulting in potential performance bottlenecks. Moreover, selecting an appropriate partitioning technique based solely on historical experimental results from research articles is challenging due to variability in storage devices, evaluation metrics, and database schemas. We propose PARS to address these issues by offering end-to-end input/output, customizable database configurations, and prioritized optimization objectives to aid database administrators (DBAs) in making informed partitioning decisions. Additionally, PARS introduces a novel algorithm that leverages both numeric and non-numeric query predicates to partition the tablespace into finer data blocks, reducing query latency by 36.1% when benchmarked against the state-of-the-art (SOTA) method.

Various works have utilized deep learning to address the query optimization problem in database system. They either learn to construct plans from scratch in a bottom-up manner or steer the plan generation behavior of traditional optimizer using hints. While these methods have achieved some success, they face challenges in either low training efficiency or limited plan search space. To address these challenges, we introduce FOSS, a novel framework for query optimization based on deep reinforcement learning. FOSS initiates optimization from the original plan generated by a traditional optimizer and incrementally refines suboptimal nodes of the plan through a sequence of actions. Additionally, we devise an asymmetric advantage model to evaluate the advantage between two plans. We integrate it with a traditional optimizer to form a simulated environment. Leveraging this simulated environment, FOSS can bootstrap itself to rapidly generate a large amount of high-quality simulated experiences. FOSS then learns from these experiences to improve its optimization capability. We evaluate the performance of FOSS on Join Order Benchmark, TPC-DS, and Stack Overflow. The experimental results demonstrate that FOSS outperforms the state-of-the-art methods in terms of latency performance. Compared to PostgreSQL, FOSS achieves speedup ranging from 1.15x to 8.33x in total latency across different benchmarks.