Large Language Models (LLMs) have become instrumental across various applications, with the customization of these models to specific scenarios becoming increasingly critical. System message, a fundamental component of LLMs, is consist of carefully crafted instructions that guide the behavior of model to meet intended goals. Despite the recognized potential of system messages to optimize AI-driven solutions, there is a notable absence of a comprehensive benchmark for evaluating how well LLMs follow system messages. To fill this gap, we introduce SysBench, a benchmark that systematically analyzes system message following ability in terms of three limitations of existing LLMs: constraint violation, instruction misjudgement and multi-turn instability. Specifically, we manually construct evaluation dataset based on six prevalent types of constraints, including 500 tailor-designed system messages and multi-turn user conversations covering various interaction relationships. Additionally, we develop a comprehensive evaluation protocol to measure model performance. Finally, we conduct extensive evaluation across various existing LLMs, measuring their ability to follow specified constraints given in system messages. The results highlight both the strengths and weaknesses of existing models, offering key insights and directions for future research.

Indexes play a pivotal role in enhancing database performance. However, index selection remains one of the most challenging problems in relational database management systems, as it demands a careful equilibrium: the search procedure needs to efficiently navigate through a multitude of potential configu-rations, while the evaluation method needs to precisely assess the performance impact of index configurations. Specifically, prohibitively high costs can arise from frequent index creation and workload execution for evaluation, whereas over-reliance on cost estimations can yield suboptimal performance due to potential inaccuracies. In this paper, we present a multi-fidelity index advisor, MFIX, designed to reconcile search efficiency and solution quality. To balance evaluation accuracy and efficiency, MFIX coordinates a range of low-fidelity cost estimates as cheap-to-evaluate ap-proximations, with a select few precise high-fidelity workload executions for refinement. To optimize search efficiency, MFIX employs a data-efficient Bayesian optimization method, paired with a condensed tree-structured index space that eliminates redundant configurations. Furthermore, MFIX incorporates his-torical tasks as auxiliary information with variable fidelity, using an adaptive weighting mechanism that considers task similarity to expedite the search process. Extensive experiments with diverse analytical workloads show that MFIX consistently out-performs state-of-the-art single-fidelity methods, achieving up to a 10.2% increase in performance improvement in actual execution cost over the leading estimation-based approach. Furthermore, through its multi-fidelity Bayesian optimization over conditional space, MFIX significantly enhances the search efficiency and ensures a sustainable search cost.

Cardinality estimation (CardEst) plays a significant role in generating high-quality query plans for a query optimizer in DBMS. In the last decade, an increasing number of advanced CardEst methods (especially ML-based) have been proposed with outstanding estimation accuracy and inference latency. However, there exists no study that systematically evaluates the quality of these methods and answer the fundamental problem: to what extent can these methods improve the performance of query optimizer in real-world settings, which is the ultimate goal of a CardEst method. In this paper, we comprehensively and systematically compare the effectiveness of CardEst methods in a real DBMS. We establish a new benchmark for CardEst, which contains a new complex real-world dataset STATS and a diverse query workload STATS-CEB. We integrate multiple most representative CardEst methods into an open-source DBMS PostgreSQL, and comprehensively evaluate their true effectiveness in improving query plan quality, and other important aspects affecting their applicability. We obtain a number of key findings under different data and query settings. Furthermore, we find that the widely used estimation accuracy metric (Q-Error) cannot distinguish the importance of different sub-plan queries during query optimization and thus cannot truly reflect the generated query plan quality. Therefore, we propose a new metric P-Error to evaluate the performance of CardEst methods, which overcomes the limitation of Q-Error and is able to reflect the overall end-to-end performance of CardEst methods. It could serve as a better optimization objective for future CardEst methods.