Jun-Peng Zhu

The SQL-based exploratory data analysis has garnered significant attention within the data analysis community. The emergence of large language models (LLMs) has facilitated the paradigm shift from manual to automated data exploration. However, existing methods generally lack the ability for cross-domain analysis, and the exploration of LLMs capabilities remains insufficient. This paper presents TiInsight, a SQL-based automated cross-domain exploratory data analysis system. First, TiInsight offers a user-friendly GUI enabling users to explore data using natural language queries. Second, TiInsight offers a robust cross-domain exploratory data analysis pipeline: hierarchical data context (i.e., HDC) generation, question clarification and decomposition, text-to-SQL (i.e., TiSQL), and data visualization (i.e., TiChart). Third, we have implemented and deployed TiInsight in the production environment of PingCAP and demonstrated its capabilities using representative datasets.

Existing database knob tuning methods evaluate the actual performance of each configuration by fully executing the entire workload. However, our experimental analysis reveals that this exhaustive execution approach significantly limits tuning efficiency, particularly when dealing with underperforming configurations. To address this issue, we propose ESTune, which is designed to early-stop the execution of poorly performing configurations. ESTune approximates the actual performance of these configurations using high-confidence predicted values generated from partially executed workload data and configuration knob settings. This strategy significantly reduces the evaluation time for underperforming configurations while maintaining the overall tuning effectiveness. The high-confidence predicted values are produced by a Hybrid Bayesian Neural Network (HBNN), which models the performance distribution with respect to different knob configurations. To address the challenge of limited training data commonly encountered in database knob tuning, ESTune integrates a Model-Agnostic Meta-Learning (MAML), thereby enhancing the few-shot learning capability of the HBNN. Extensive evaluations on a wide range of workloads consistently demonstrate that ESTune improves the tuning efficiency of existing methods.