存算分离+AI驱动，金融业数据库升维

Core Viewpoint - The transformation of database architecture is crucial for the efficiency of financial institutions, with a shift from traditional integrated storage-computing architecture to a distributed architecture being emphasized as essential for digital transformation [1][2]. Group 1: Challenges of Traditional Architecture - Traditional integrated storage-computing architecture has significant limitations, including low resource utilization, high failure rates, and increased operational complexity [2][3]. - Resource utilization in integrated architecture can be as low as 5% for CPU and disk [2]. - The annual failure rate of local hard drives can reach 1%, leading to time-consuming data recovery processes that affect business continuity [2][3]. Group 2: Advantages of Decoupled Architecture - Decoupled storage-computing architecture allows for flexible resource expansion and higher system stability, making it a necessary trend in financial technology evolution [3]. - The reliability of decoupled architecture effectively isolates hard drive failures, maintaining database stability [3]. - The transition to virtual machines in decoupled architecture allows for rapid recovery from hardware failures, significantly enhancing business continuity [3][4]. Group 3: Economic Benefits - Decoupled architecture reduces server costs, particularly benefiting small and medium-sized financial institutions that face cost pressures [4]. Group 4: AI Integration - The integration of AI into database architecture represents a future direction, focusing on enhancing database efficiency and optimizing databases for AI applications [5][6]. - AI can automate database management tasks, which were previously reliant on manual operations by database administrators [5][6]. - Future databases are expected to possess self-learning capabilities, automatically optimizing performance based on operational data [6]. Group 5: Evolving Data Interaction - The interaction with databases is shifting from SQL to natural language, indicating a need for databases to adapt to new data consumption patterns in the AI era [6][7]. - The rise of agent technology will increase the complexity of machine-to-machine data interactions, necessitating databases that can support new interaction models [6][7].