Data driven business applications in logistics industry often issue prediction queries over relational databases to retrieve the newly generated transaction data for feature computations. In some cases even using slightly outdated data can result in significant inaccuracies in predictions. In another cases, we also observed the accuracy of model prediction is not sensitive to the data freshness. In the setting of primary-backup databases, one may choose to fetch the freshest data from the primary database to ensure model accuracy. However, this may hurt the performance of read-write transactions on the primary, especially when subjected to a high volume of prediction requests. In this work, we propose a Model-Accuracy Aware Service that facilitates a flexible trade-off between model prediction accuracy and primary database performance. This service implements an automated routing strategy aimed at minimizing the impact on the primary database's performance while meeting the requirements of model accuracy. It achieves this by leveraging the maintained database freshness information and the predictive results feedback from the model to learn the relationship between data discrepancy and prediction discrepancy in primary-backup scenarios. We report the experimental results on a real logistics application and also show its effectiveness on a public dataset.

Throughout index tuning, existing index advisors allocate tuning budget equally across all queries in the workload, even though a considerable portion of queries benefit negligible from index tuning, leading to high costs and inefficiency. This paper introduces a novel learning-based index advisor named GITEE, which increases tuning efficiency and effectiveness by intelligently guiding the exploration of the large search space on candidate index. Our solution consists of three components. First, we utilize execution plan and predicate information to accurately estimate the maximum improvement indexing can bring, which serves as preliminary knowledge for reasonable tuning budget allocation. Second, we filter out queries based on the impact of indexing on the individual queries and their influence on others, thereby reducing the number of candidate indexes. Third, we leverage a Monte Carlo Tree Search-based solution, guided by the knowledge, to accelerate the selection of high-quality index configurations within the valuable search space. Extensive experiments across various benchmarks demonstrate that GITEE achieves superior tuning performance compared to state-of-theart heuristic or learning-based index advisors, while reducing tuning overhead by 1-2 orders of magnitude.

Update-intensive workloads are prevalent in contemporary OLTP and AI/ML scenarios. An update operation typically involves deleting the old version of the target record and then inserting a new version. In this work, we demonstrate that an LSM-tree faces two issues when dealing with update-intensive workloads. Firstly, the deleted old versions are not promptly garbage collected until they merge with their new versions during compaction. This may lead to space waste and write amplification. Secondly, it is common for an update operation to modify only a small fraction of a data record, such as one of a hundred attributes. However, state-of-the-art LSM-trees fail to effectively utilize the incremental storage strategy, which involves storing only the updated fraction rather than the entire new version to enhance efficiency. In this paper, we propose two techniques, active and fast garbage collection, and adaptive incremental updating, to address these issues, respectively. Active and fast garbage collection probes the distribution of invalid data versions in an LSM-tree and performs garbage collection in a more promptly manner. Adaptive incremental updating applies different storage modes to the update operation to achieve balanced write and read amplification ratios as much as possible. Based on the techniques, we introduce SylphDB implemented based on the codebase of RocksDB and optimized for update-intensive workloads. Experimental results demonstrated that, compared to traditional LSM-tree based systems, SylphDB can improve the efficiency of garbage collection by 2× and reduce write amplification by 20%.

Distributed databases are widely used in various fields, such as financial services and e-commerce. These businesses generally exhibit characteristics of large-scale and rapid growth. However, these business systems often suffer from deadlocks that prevent them from operating normally for extended periods. Traditional deadlock detection methods face challenges in scalability and efficiency, especially as the number of nodes increases. Therefore, deadlock detection has always been a research area in distributed databases. In this paper, we introduce an efficient deadlock detection algorithm called HAWK, leveraging a H ierarchical A pproach based on W or K load modeling. Our algorithm addresses these issues by constructing a dynamic hierarchical detection tree that adapts to transaction patterns, significantly reducing time complexity and communication overhead. HAWK first models the workload and generates a predicted access graph (PAG), transforming the problem of partitioning detection task in the basic hierarchical detection into partition detection zone (DZ) in the PAG by a graph-cutting algorithm. Then, leveraging the properties of strongly connected components (SCCs) and deadlock cycles, the SCC-cut algorithm naturally partitions the system-wide deadlock detection into multiple non-intersecting detection zones, thereby enhancing detection efficiency. We used the greedy SCC-cut algorithm to perform a more fine-grained partitioning of the complex PAG. Finally, by periodically sampling and updating the hierarchical structure, the algorithm remains responsive to dynamic workload variations, ensuring efficient detection. Our approach outperforms both centralized and distributed methods, offering a more efficient and adaptive solution. Extensive experimental results demonstrate the effectiveness of the HAWK algorithm, showing significant reductions in the duration of the deadlock and improved system throughput.

As real-time analytics become increasingly important, more organizations are deploying Hybrid Transactional/An-alytical Processing (HTAP) systems. The HTAP systems, based on a primary/backup replication architecture, usually support real-time read-only queries on backup nodes for the data recently generated by OLTP applications on the primary node. This work is based on the observation that real-time analytical applications often require access to only a fraction of the latest modifications from OLTP applications. However, the state-of-the-art parallel log replay approaches treat all replicated transaction logs equally and replay the entire transaction logs with the same priority which does not take consideration into the OLAP query access pattern. This design can result in increased response latency for real-time applications. This paper presents AETS, an Adaptive Epoch-based Two-Stage log replay framework that implements epoch-based log replay and table group transaction commit. Simultaneously, AETS also takes full account of the table access priority in real-time HTAP workload log replay. It aims to make the data required by analytical queries visible more quickly. Furthermore, AETS includes a two-phase parallel log replay algorithm called TPLR, which achieves lower overhead compared to state-of-the-art algorithms through careful design. We also offer an adaptive fine-grained thread resource allocation method that considers changes in table access patterns over time under thread resource constraints. Our experimental results show that AETS significantly reduces visibility delay for real-time queries. And the results also show that AETS achieves significant replay throughput improvement.

In the realm of high‐dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer‐aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real‐time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three‐line hybrid rice, which is yet to be thoroughly explored in the context of solving high‐dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem‐oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high‐dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.

Distributed shared buffer (DSB) is a well-known solution to support multi-master database systems. DSB involves controlling access to shared data among different nodes through a distributed shared buffer and lock-based cache coherence protocols. Existing DSB implementations resolve conflicts at the page level and lack the flexibility required by modern cloud database systems. Authors present HyBuffer which mixes rows and pages in the distributed shared buffer. It enables multiple masters to independently modify different rows on the same page, thereby enhancing concurrency and performance. HyBuffer adopts the hybrid design to track the location information of cached data. A centralized node maintains page locations indicating which masters have cached which pages, and the record locations of a cached page are maintained by the master which has cached this page. This design eliminates the need to redistribute metadata when dynamically adding or removing masters. Experimental results demonstrate that HyBuffer achieves better scalability and performance compared to existing approaches.