As database systems continue to evolve toward distributed architectures, cloud-native deployments, hybrid transactional/analytical processing workloads, multimodal data management, and agent-driven access, their evaluation frameworks—including target objects, workload characteristics, and quality metrics—have changed significantly. Conventional evaluation methodologies are primarily designed for relational databases with fixed query patterns, stable workloads, and single-dimensional performance metrics, thus rendering them insufficient for comprehensively characterizing modern database systems operating under complex functionalities, hybrid workloads, abnormal operating conditions, ecosystem migration, and security constraints. Hence, this paper presents a systematic taxonomy of database-evaluation techniques across five key quality dimensions: functional correctness, performance, availability, compatibility, and security. Furthermore, the research progress, application scenarios, and limitations of representative evaluation approaches are reviewed. Subsequently, challenges are introduced by large language models and agent-driven database interactions. We argue that future database evaluation must shift from isolated functionality testing to end-to-end quality validation, and from static, benchmark-based assessment to dynamic scenario simulation. Furthermore, evaluation frameworks must encompass emerging evaluation targets, such as multimodal queries, agent interfaces, structured result generation, and end-to-end security verification. This survey aims to provide a comprehensive reference for academic researchers and engineering practitioners in modern database evaluation.
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