1月18日，第五届OceanBase数据库大赛落幕。此前该赛事已在2023年纳入全国大学生计算机系统能力大 赛体系，成为教育部认定的A类学科竞赛。教育体系对人才培养的关注，又一次凸显数据库在AI时代的 关键地位。 当大模型以狂飙之势席卷各行各业，一场更为基础的变革正在算力与数据的底层悄然发生。 据悉，本届赛事吸引全国高校的1223支队伍、2620名学生参赛，他们在决赛中面临两项任务：一是优 化"全文检索+结构化过滤"的混合查询性能；二是基于同一数据库内核，构建可溯源的多模态RAG系 统。 上述赛题直面当前AI落地产业时的真实瓶颈：再聪明的模型，若缺乏高质量、高效率、可治理的数据 支撑，就如同空中楼阁。 而竞赛的风向也揭示了一个趋势：AI不是孤立的技术革命，而是一场系统性重构。在AI加速变革、深 入重塑各行业生产力的阶段，基础软件非但不会被淹没，反而正走向前所未有的关键位置。 AI越热，数据库越关键 在传统认知中，数据库如同一个数字仓库，确保数据的准确、一致与持久化，核心职能是"记录"与"保 管"。但AI时代的需求远不止于此。 2020年，数据首次与土地、劳动力、资本、技术并列，被定义为第五大生产要素，标志着数 ...

Core Insights - The AI industry continues to evolve despite macroeconomic disturbances and debates about "bubble theories," with companies like OceanBase focusing on foundational infrastructure through the release of seekdb, an AI-native database product [1][2] - OceanBase has surpassed 4,000 customers in five years, with an average annual growth rate of over 100% in customer numbers, indicating strong market demand across various sectors [1][3] Industry Challenges - The explosion of data is a core issue for the new era, with predictions indicating that by 2025, the global volume of newly created data will exceed 175 ZB, posing unprecedented challenges for database infrastructure [3] - Traditional databases struggle with scalability, high costs, and fragmented ecosystems, leading to a need for a balance between storage and computing costs while ensuring high concurrency and efficiency [3][4] OceanBase's Solutions - OceanBase's seekdb allows developers to build AI applications with just three lines of code, enabling efficient handling of multi-modal data retrieval at scale [3][4] - The product supports unified mixed search capabilities across various data types and is compatible with over 30 mainstream AI frameworks, enhancing its integration into existing ecosystems [4][5] Market Position and Growth - OceanBase is the only database to break records in the "database World Cup" tests and has been a reliable backbone for Ant Group's core systems for over a decade [8][10] - The domestic database market is projected to reach 43.6 billion yuan by Q3 2025, with OceanBase leading in the financial sector, covering over 100 major banks and numerous key business systems [8][10] Case Studies - Successful migrations to OceanBase's distributed architecture have been completed for major clients like ICBC and招商证券, demonstrating its capability to handle massive data volumes and complex systems [10][11] - Companies like 泡泡玛特 have benefited from OceanBase's database in managing rapid growth and high traffic during peak sales events, showcasing its reliability and performance [10][11] Global Expansion and Compliance - OceanBase supports multi-cloud environments and has obtained numerous regional compliance certifications, addressing the challenges of data localization and regulatory requirements for global enterprises [13][14] - The company has successfully entered overseas markets, including Japan, and has implemented its database in international banking systems, marking significant milestones in its global strategy [13][14] Future Outlook - OceanBase aims to enhance its core capabilities in storage, computation, intelligence, governance, and accessibility to maintain its competitive edge in the evolving database landscape [18][21] - The shift from passive data recording systems to proactive business innovation platforms represents a strategic opportunity for Chinese enterprises in the global market [21][22]

AI Agent & Database Evolution - AI agents are challenging the traditional database model designed for human interaction [1] - The industry recognizes the need for databases to adapt to the requirements of AI agents, which differ significantly from human users [1] Agentic Postgres Features - TimescaleDB introduces Agentic Postgres, an agent-ready version of Postgres designed to address the challenges posed by AI agents [2] - Agentic Postgres enables instant database branching, facilitating parallel agent evaluations, safe experiments, migrations, and isolated testing with minimal cost and time [2] - It includes a built-in MCP server, offering schema guidance, best practices, and secure, structured access to Postgres for agents, aiding in informed migrations [3] - Hybrid search (vector search and BM25) is integrated, allowing agents to directly retrieve data within the database [3] - The database is memory-native, providing a persistent context for agent evolution [3] AI Agent Requirements - AI agents require the ability to branch endlessly, run multiple experiments concurrently, and operate within isolated, contextualized, and secure sandboxes [4]

Database & AI - AI agents are redefining the traditional role of databases [1] - Traditional databases were designed for human interaction, a model now challenged by AI agents [1] - AI agents require features like isolation, context, memory, and structured data [1] Postgres & AI - This is a significant moment for Postgres in the context of AI development [1] AI Agent Behavior - AI agents exhibit branching behavior [1] - AI agents conduct multiple experiments simultaneously [1]

Big moment for Postgres!AI agents broke the idea of what a database is supposed to do.Traditional databases were built for humans, and Agents broke that model.- They branch endlessly.- They run ten experiments at once.- They need isolation, context, memory, structured reasoning, and safe sandboxes.Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior.Agentic Postgres is an agent-ready version of Postgres by @TimescaleDB that solves ...

It's been a wild year of growth and fundraising for vibe-coding database of choice, Supabase. https://t.co/grk9RGomcv ...

Blockchain Database Technology - Most blockchain databases utilize a Merkle–Patricia trie on top of a key–value store, such as LevelDB or RocksDB [1] - This design introduces extra read lookups, leading to a read amplification problem as the blockchain database expands [1] - SonicDB eliminates the key-value store layer by directly indexing the world state in binary files [1] - This approach avoids read amplification and significantly enhances access time [1]

Technology & Performance - SonicDB is optimized for speed by directly writing state to disk [1] - SonicDB achieves 620% (6.2x) faster performance compared to Geth [2] - SonicDB utilizes direct binary storage [2] - SonicDB achieves zero read amplification [2] Database Architecture - Traditional chains often use key-value stores, which can slow down database performance [1] - Sonic Labs avoids key-value stores in favor of a faster approach [1]