databases

Contemporary relational database management systems (RDBMSs) suffer from insufficient logical data independence, reducing them to passive storage layers incapable of supporting modern architectural innovation. This paper argues that the Entity-Relationship (ER) model must serve as the native abstraction layer of RDBMSs to overcome this limitation, and it provides the first systematic theoretical justification and empirical validation of the ER model’s necessity and feasibility for ensuring logical independence. Based on this insight, we design and implement ErbiumDB—a prototype system integrating metadata-driven schema management, declarative relational semantic modeling, and runtime relationship evolution. Experimental evaluation demonstrates that ER-based abstraction significantly enhances decoupling between application and storage layers, enabling flexible, semantics-aware data management. ErbiumDB establishes a novel paradigm for intelligent database architectures and delivers a rigorously validated, extensible prototype foundation for future research and development.

Document-oriented NoSQL databases, which typically adopt eventual consistency models, struggle to support highly reliable transaction processing. This work proposes a four-phase transaction management framework that achieves conflict-serializable consistency while preserving system scalability. By integrating transaction lifecycle management, operation classification, pre-execution conflict detection, and an adaptive locking strategy, the approach effectively eliminates deadlocks and significantly reduces both transaction abort rates and latency variance. Experimental results demonstrate that the abort rate decreases from 8.3% to 4.7%, and latency variance is reduced by 34.2%. Under high concurrency, throughput improves by 6.3%–18.4%, with a 15.2% increase observed in a 9-node cluster, accompanied by a 53% reduction in abort rate.

This work addresses the high memory access latency and performance bottlenecks arising from data-computation separation in NVM-main-memory database systems. To tackle these challenges, we propose a co-optimization architecture comprising two synergistic components: (1) a deep redesign of the PostgreSQL storage engine to fully exploit NVM’s byte-addressability, low latency, and persistence; and (2) a lightweight, general-purpose asynchronous prefetching library that mitigates NVM access bottlenecks via multi-threaded prefetching and low-overhead API abstractions. Experimental evaluation shows that the engine redesign alone reduces query latency by 19% (vs. disk baseline) and 4% (vs. NVM baseline) on average; integrating the prefetching library further improves reductions to 23% and 8%, respectively—with peak speedups reaching 54% over disk. This work pioneers the “engine refactoring + system-level prefetching” co-design paradigm, delivering a reusable architectural blueprint and practical implementation pathway for NVM-native DBMS development.

To address the challenge of jointly optimizing efficient storage, historical preservation, and global query performance for dynamically evolving graph data in NoSQL systems, this paper proposes a space-efficient, enhanced vertex-centric storage model implemented atop MongoDB. The model innovatively integrates dual-mode historical representation—combining snapshots with time intervals—and leverages MongoDB’s native query capabilities to eliminate storage redundancy. It is the first approach to jointly optimize memory footprint, client-side query load, and server-side computational overhead in NoSQL-based graph storage. Extensive experiments, conducted on LDBC Social Network Benchmark (SNB) synthetic datasets under both single-node and distributed deployments, demonstrate that our method reduces average global query latency by 32.7% and storage overhead by 28.4% compared to baseline approaches, significantly improving the efficiency of resource-intensive, history-aware graph queries.

To address replication latency, race conditions, and high network overhead induced by pointer-based designs in cross-region, high-throughput NoSQL database storage, this paper proposes the “Chunked Object” pattern. It decomposes oversized objects into ordered data chunks and stores them directly within a multi-region replicated key-value store (e.g., DynamoDB Global Tables), collocating data and metadata within a single consistency domain. This eliminates asynchronous replication between the database and external object storage, thereby significantly reducing cross-region p99 consistency attainment time. Evaluated in production under heavy load (>200K TPH, 1 MB/transaction), the approach demonstrates robust effectiveness and generalizability. It is applicable to any key-value store supporting per-item size limits and cross-region strong synchronization, offering a practical, system-agnostic solution for scalable, low-latency global data management.

Existing research lacks systematic empirical analysis of database selection and co-management strategies in microservice architectures. Method: We conducted a large-scale empirical study of 1,000 open-source GitHub projects (2008–2023), covering 14 database categories and 180 distinct database systems, employing open-data collection, multi-dimensional classification, and statistical modeling. Contribution/Results: This is the first study to empirically characterize the evolutionary patterns of database usage in microservices: 52% of systems adopt cross-category database combinations, with architectural complexity increasing significantly with the number of databases; newer systems prefer key-value and document databases, whereas legacy systems predominantly rely on relational databases; mainstream database types (relational, key-value, document, search) are frequently deployed alongside niche databases. The study yields 18 empirically grounded findings and 9 actionable engineering recommendations, providing both theoretical foundations and practical guidance for heterogeneous distributed data management in microservice environments.

To address the performance bottlenecks in insertion and random lookup throughput of key-value (KV) stores on flash-based SSDs under high-load workloads, this paper proposes a high-performance KV storage system co-designed with flash hardware characteristics. Our approach integrates log-structured storage, fine-grained hierarchical indexing aligned with flash page/block boundaries, asynchronous I/O scheduling, and an SSD-optimized memory-mapping mechanism—collectively reducing write amplification and enhancing I/O concurrency. The key innovation lies in synchronizing index updates with data writes while aligning index granularity precisely with flash physical units, thereby fully exploiting SSD parallelism and low-latency capabilities. Evaluated on a single datacenter server, the system achieves 19.8 M insertions/sec and 23.8 M random lookups/sec—outperforming state-of-the-art KV stores by 2–5× in throughput. It is particularly suited for high-concurrency applications such as caching, session management, and distributed systems.

This work proposes OceanBase Mercury, a distributed near real-time analytical processing system built upon OceanBase, designed to deliver enterprise-grade analytical capabilities—including multi-tenancy, high availability, and elastic scalability—for petabyte-scale data. Traditional OLAP systems struggle to simultaneously support real-time transactions and efficient analytics at scale, often suffering from high data redundancy, complex cross-system synchronization, and poor timeliness. Mercury addresses these challenges through three key innovations: an adaptive columnar storage format with hybrid layout optimization, a materialized view differential refresh mechanism that ensures temporal consistency, and a polymorphic vectorized execution engine compatible with three distinct data formats. Experimental results on real-world workloads demonstrate that Mercury achieves 1.3–3.1× faster query latency than specialized OLAP engines while maintaining sub-second response times, effectively balancing analytical depth with operational agility.

This work addresses the longstanding challenge of reconciling correctness guarantees with high performance in database storage systems by proposing a compositional construction methodology grounded in formal specifications. The approach leverages formal specifications to guide a Java implementation and employs storage equivalence proofs to rigorously ensure correctness. Crucially, it decouples performance optimizations from functional logic, enabling flexible composition of components. Using this methodology, the authors reimplement RocksDB’s tiered storage architecture and develop CobbleDB, a prototype system that demonstrates strong real-world performance and practical applicability while maintaining strict correctness guarantees.

Traditional query optimizers—based on System R, Volcano, or Cascades—employ a monolithic, static, single-query architecture that exhibits performance instability, lacks global workload-level optimization, and suffers from architectural rigidity in cloud-native environments with massive-scale data and unified data platforms. To address these industrial challenges, this paper proposes three evolutionary directions: (1) extending optimization from individual queries to workload-aware collaborative optimization; (2) establishing an execution-feedback-driven closed-loop optimization framework; and (3) designing a composable, modular optimizer architecture enabling cross-engine reuse and agile iteration. The study distills three key trends—workload awareness, feedback closure, and architectural decoupling—providing a practical, implementable technical pathway and industrial paradigm for building next-generation query optimization systems that are dynamic, holistic, and self-adaptive.