Author: Changjian Gao

As technology advances and data continues to explode, traditional disk file systems have revealed their limitations. To address the growing storage demands, distributed file systems have emerged as dynamic and scalable solutions. In this article, we explore the design principles, innovations, and challenges addressed by three representative distributed file systems: Google File System (GFS), Tectonic, and JuiceFS.

• GFS pioneered commodity hardware use and influenced systems like Hadoop Distributed File System (HDFS) in big data.
• Tectonic introduced layered metadata and storage/compute separation, improving scalability and performance.
• JuiceFS, designed for the cloud-native era, uses object storage and a versatile metadata engine for scalable file storage in the cloud.

By exploring the architectures of these three systems, you will gain valuable insights into designing distributed file systems. This understanding can guide enterprises in choosing suitable file systems. We aim to inspire professionals and researchers in big data, distributed system design, and cloud-native technologies with the knowledge to optimize data storage, stay informed about industry trends, and explore practical applications.

Traditional machine-learning models can be stored on standalone machines or local hard drives when working with small datasets and simple algorithms. However, as deep learning advances, teams increasingly encounter storage bottlenecks when handling larger datasets and more complex algorithms.

This highlights the importance of distributed storage in the artificial intelligence (AI) field. JuiceFS, an open-source, high-performance distributed file system, provides a solution to this problem.