Xuan Zhou

In-memory databases rely on DDR memory for performance, yet capacity scaling is impeded by physical constraints and the high cost of high-density DIMMs. Compute Express Link (CXL) provides a practical path for memory expansion through a DDR-compatible interface. However, CXL memory exhibits inferior latency and bandwidth, thus naïve expansion with CXL can lead to significant performance degradation. In hybrid DDR–CXL systems, traditional caching policies (e.g., LRU, S3-FIFO) yield limited reductions in execution latency while incurring substantial metadata management and tuple migration overheads. Our analysis reveals a fundamental misalignment between traditional design assumptions and heterogeneous memory characteristics. To address this, we propose Nephele, a lightweight, benefit-aware caching policy for hybrid DDR–CXL databases. Nephele shifts the focus from eviction selection to effective admission, prioritizing data that are truly worth caching. It also features a low-overhead architecture that supports randomized eviction without relying on global metadata. By integrating epoch-based hotness estimation, Nephele enables probabilistic, benefit-aware admission, reducing cross-tier data movement and mitigating cache pollution. Evaluated on a hybrid DDR–CXL database prototype using YCSB and TPC-C, Nephele achieves up to 1.58× higher throughput than CXL-Expansion and up to 3.2× over existing caching policies, while retaining up to 97% of DDR-only performance. Additional integration with RocksDB further confirms its practical feasibility in a production system. These results demonstrate that lightweight, benefit-aware caching is critical for fully exploiting CXL-enabled memory expansion.

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.

Previous learning-based parameter query optimization (PQO) methods excel in static workloads by precisely selecting optimal plans in a cache with a fixed set of representative plans. However, these methods struggle in dynamic workloads because they cannot predict over dynamically changed plan caches. These queries that fall outside the previously observed query parameter distribution have the risk of reusing suboptimal plans. Unlike traditional PQO methods that learn mappings from parametric query parameters to a fixed set of execution plans, our adaptive PQO framework ( APQO ) takes both query parameters and the plans themselves as model inputs, thereby handling variable numbers of plans in dynamic workloads. By embedding plan representations through representation learning, we pre-train a foundation model offline, enabling APQO to acquire a generalizable plan performance prediction model. Leveraging the foundation model's predictive capability along with a hybrid data augmentation strategy, we train an online calibration model with minimal training data for distribution-shifted new queries, rapidly adapting knowledge for reusing new plans. APQO is natively designed to handle the characteristics of dynamic workloads. Experimental results show that APQO outperforms existing PQO methods in dynamic workloads, achieving a higher cache hit ratio and significantly reducing query latency.

Developing distributed systems presents significant challenges, primarily due to the complexity introduced by non-deterministic concurrency and faults. To address these, we propose a specification-driven development framework. Our method encompasses three key stages. The first stage defines system specifications and invariants using TLA${^+}$. It allows us to perform model checking on the algorithm's correctness and generate test cases for subsequent development phases. In the second stage, based on the established specifications, we write code to ensure consistency and accuracy in the implementation. Finally, after completing the coding process, we rigorously test the system using the test cases generated in the initial stage. This process ensures system quality by maintaining a strong connection between the abstract design and the concrete implementation through continuous verification.

This study proposes a novel storage engine, SynchroStore, designed to address the inefficiency of update operations in columnar storage systems based on Log-Structured Merge Trees (LSM-Trees) under hybrid workload scenarios. While columnar storage formats demonstrate significant query performance advantages when handling large-scale datasets, traditional columnar storage systems face challenges such as high update complexity and poor real-time performance in data-intensive applications. SynchroStore introduces an incremental row storage mechanism and a fine-grained row-to-column transformation and compaction strategy, effectively balancing data update efficiency and query performance. The storage system employs an in-memory row storage structure to support efficient update operations, and the data is converted to a columnar format after freezing to support high-performance read operations. The core innovations of SynchroStore are reflected in the following aspects:(1) the organic combination of incremental row storage and columnar storage; (2) a fine-grained row-to-column transformation and compaction mechanism; (3) a cost-based scheduling strategy. These innovative features allow SynchroStore to leverage background computational resources for row-to-column transformation and compaction operations, while ensuring query performance is unaffected, thus effectively solving the update performance bottleneck of columnar storage under hybrid workloads. Experimental evaluation results show that, compared to existing columnar storage systems like DuckDB, SynchroStore exhibits significant advantages in update performance under hybrid workloads.