Enterprise data explosion and the urgent industry demand for realtime complex multidimensional analytics require OLAP databases to be highly scalable, efficient, and cost-effective. Though diverse solutions (shared-nothing MPP databases, cloud-native decoupled systems, in-process analytical engines) exist with respective strengths, they all have critical inherent flaws.
In response, this paper presents TDSQL, a distributed OLAP database system developed by Tencent. We leverage its native architectural advantages, analyze the merits and drawbacks of state-of-the-art systems, and elaborate on the rationale behind our technical solution selection and proprietary innovations tailored for TDSQL. Specifically, built upon the traditional MPP execution framework, TDSQL incorporates the Forward Node mechanism to enhance scalability. By optimizing parallel execution, runtime filter strategies, and designing and implementing a vectorized execution engine, TDSQL achieves rapid response to large-scale complex queries. Experimental results based on the TPC-DS benchmark demonstrate that TDSQL ranks first among publicly reported systems. In a cluster configuration for 10,000 GB data size, TDSQL achieves a score of 72.6 million QphDS, which is 1.81 times and 3.82 times the scores of the second- and third-highest-performing database systems in public TPC-DS results, respectively, while offering a 79% and 37% lower cost per 1000 QphDS.
Yi-Teng Chu, Jie Jiang, Yuxing Chen et al.· Proceedings of the VLDB Endo...· 0 citations
This work proposes BiVCoder, a diagnosis-driven multi-agent framework featuring a novel bidirectional code-test diagnosis mechanism, and introduces BiVCoder-SFT, a role-specific instruction fine-tuning scheme.
Xiaoyang Li, Jin-Hao Dong, Wenhang Shi et al.· Proceedings of the 32nd ACM...· 0 citations
Slow queries frequently cause severe performance bottlenecks in database management systems. Diagnosing their root causes online risks exacerbating resource contention, while data privacy regulations often prohibit copying production data to test environments. Synthesizing a proxy database from non-intrusive metadata that induces the query optimizer to generate the same physical execution plans is therefore critical for offline diagnosis. High-fidelity reproduction requires preserving global statistical distributions while enforcing exact local cardinalities. Existing data-driven and workload-aware approaches cannot satisfy both requirements simultaneously. We present DBRepro, an automated end-to-end framework that formulates database generation as a constrained distribution synthesis problem. DBRepro initializes a global distribution from lightweight column statistics, extracts execution constraints from target queries, and progressively adjusts the distribution to satisfy these constraints while preserving the global distribution. Experiments on TPC-H and SSB show that DBRepro reduces cardinality error by up to 20.3% over a data-driven baseline while maintaining identical plan consistency. Compared with a workload-aware baseline, it reproduces 15% more consistent execution plans and reduces latency proportion error by 21.5%. We further validate DBRepro on a nearly 1 TB real-world dataset managed by KingbaseES, where it reproduces the execution performance of complex slow queries with high fidelity.
Zhao-Yang Zhang, Shuang Liu, Deng-Feng Xu et al.· 0 citations
Large Language Models (LLMs) have demonstrated remarkable potential in automated code generation. However, existing test-driven code generation and refinement frameworks are often hindered by the tests' quality: they typically treat self-generated tests as ground truth, leading to ineffective debugging loops where code is modified to satisfy erroneous tests. To address this, we propose BiVCoder, a diagnosis-driven multi-agent framework featuring a novel bidirectional code-test diagnosis mechanism. BiVCoder coordinates three specialized agents—Coding Agent, Test Agent, and Review Agent. Central to this architecture is the Review Agent, which serves as a diagnosis and decision-making hub. By integrating an MCP-based code-test execution tool, the Review Agent rigorously executes programs to diagnose failure root causes, distinguishing between implementation bugs and test case deficiencies, and subsequently triggers targeted repairs (Coding Agent or Test Agent). Furthermore, we introduce BiVCoder-SFT, a role-specific instruction fine-tuning scheme. We construct high-quality datasets to fine-tune the Qwen3-4B base model into specialized agents for coding, testing, and reviewing. Extensive experiments on HumanEval, MBPP, and their rigorous ''ET'' variants demonstrate the superiority of our approach. With GPT-3.5, BiVCoder achieves a Pass@1 of 77.3%. Notably, the specialized BiVCoder-SFT achieves an average score of 79.5% across the four datasets, not only outperforming its base model (69.7%) but also surpassing larger general-purpose models such as Qwen3-8B (77.8%). Additionally, BiVCoder boosts the performance of more powerful models, improving DeepSeek-V3.2 from 87.2% to 92.1% on HumanEval.
Xiaoyang Li, Jinhao Dong, Wenhang Shi et al.· Proceedings of the 32nd ACM...· 0 citations
DBcover is proposed, an LLM-driven database test generation framework that performs white-box, code-aware SQL test generation through contextual reasoning, and substantially outperforms existing fuzzers.
Yan-Kai Rong, Shuang Liu, Jin-Hao Dong et al.· Proceedings of the 2026 IEEE...· 0 citations
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