忆联 Docker+MySQL 流控方案：打造安全高效存储底座，释放 AI 极致性能

Core Insights - The article emphasizes the importance of efficient data storage and processing capabilities in seizing technological advantages in the AI era, highlighting Docker-based MySQL as a preferred database solution for AI applications [1][3]. Group 1: Background and Technology Overview - The acceleration of digital transformation has made AI technology a core driver of economic and social development, necessitating robust data processing and storage capabilities to handle vast amounts of data generated during AI model training and operation [3]. - MySQL, as one of the most widely used open-source relational databases, is favored for its stability and efficiency, but traditional deployment methods have limitations in flexibility and resource utilization [3]. - Docker's containerization technology offers a new deployment solution for MySQL, enhancing resource efficiency and isolating environments to avoid conflicts, making it suitable for rapid iteration and parallel development in AI projects [3][4]. Group 2: Testing Configuration and Results - The testing environment for Docker-based MySQL included high-performance hardware, such as Intel® Xeon® Gold 6430 processors and PCIe Gen5 ESSD UH812a storage [5]. - Tests were conducted in three scenarios: read-only, write-only, and mixed read/write, with QoS thresholds set for IOPS and bandwidth to evaluate performance [9][14][18]. - In the read-only scenario, IOPS and bandwidth performance remained stable, with deviations from QoS thresholds controlled within 1% [9]. - In the write-only scenario, IOPS and bandwidth performance also showed minimal deviation from QoS thresholds, ensuring robust performance under high load [14]. - The mixed read/write scenario demonstrated exceptional resource management and performance, with deviations from QoS thresholds remaining below 1% [18]. Group 3: Benefits of Docker-based MySQL Solution - The Docker-based MySQL solution enhances storage utilization efficiency and reduces total cost of ownership (TCO) by allowing elastic resource allocation based on AI project needs [20]. - Data security is improved through independent storage units for different projects, preventing resource contention and ensuring stable operation of critical tasks [20]. - The solution simplifies data lifecycle management, reducing operational complexity and labor costs through quick data classification and backup operations [20]. Group 4: Conclusion - The company positions itself as a key player in driving data ecosystem innovation, focusing on high-efficiency storage solutions to support various AI applications and accelerate data value release [21]. - Future developments will continue to enhance storage solutions, enabling AI applications to reach new heights and fostering collaborative growth in data storage and intelligent applications [21].