How do cloud-native storage solutions ensure high throughput and low latency?

Cloud-native storage ensures high throughput and low latency primarily through optimized architectures and protocols. It is crucial in application scenarios requiring high-performance data processing, such as real-time analytics, AI/ML training, and high-frequency trading, meeting the need for fast data access in containerized applications.

Core mechanisms include horizontally scalable architectures, local NVMe SSD storage, RDMA network support, and parallel I/O path design. By reducing network hops, enabling kernel bypass technologies (e.g., SPDK), and utilizing Container Storage Interface (CSI) for dynamic storage policy binding, I/O latency is significantly reduced, while the distributed architecture allows linear scalability of throughput.

During implementation, solutions supporting local storage or low-latency cloud disks should be selected and configured as CSI drivers. Combined with Kubernetes topology-aware scheduling, Pods are scheduled to data-proximate nodes. Business value is reflected in accelerating data processing pipelines, enhancing real-time decision-making capabilities, and optimizing user experience, such as improved order processing efficiency in flash sale scenarios of e-commerce platforms.