3FS (Three Files System), pronounced as “Thrifty Files System,” is a high-performance distributed file system designed specifically to address challenges faced in AI training and inference workloads. It aims to optimize data access latency, scalability, fault tolerance, and overall efficiency for large datasets commonly used in machine learning applications.
The project combines elements from various existing technologies such as Google’s FUSE-based file system called GFS (Google File System), Facebook’s open source distributed storage platform Apache Hadoop’s MapReduce framework, Intel® Optane™ persistent memory technology, and other innovative techniques to achieve its goals.
3FS offers several unique features that differentiate it from traditional file systems:
– **Distributed architecture**: It distributes data across multiple nodes in a cluster for improved scalability and fault tolerance. This design allows the system to handle massive amounts of data efficiently while maintaining low latency access times even when dealing with large datasets typical in AI applications.
– **Data striping**: 3FS uses an advanced data striping technique that breaks down files into smaller chunks called “stripes.” These stripes are then distributed across multiple storage devices within the cluster, enabling parallel processing and faster read/write operations. This approach also enhances fault tolerance by ensuring redundancy in case of hardware failures.
– **Incremental metadata updates**: Instead of rewriting entire file metadata when only a small portion changes (as traditional systems do), 3FS employs incremental update techniques to minimize unnecessary overhead during data manipulation operations such as write or delete actions. This feature contributes significantly towards reducing overall latency and improving system performance.
– **Intelligent load balancing**: The file system dynamically adjusts the distribution of workloads among available resources based on real-time monitoring information, ensuring optimal resource utilization across all nodes in the cluster while maintaining high throughput rates for both read/write operations.
– **Support for various storage media**: 3FS can seamlessly integrate with different types of storage devices including SSDs (solid state drives), HDDs (hard disk drives), NVMe SSDs, Intel® Optane™ persistent memory modules, and even cloud storage solutions like Amazon AWS’ S3 or Google Cloud Storage. This flexibility allows users to choose the most cost-effective combination according to their specific needs while still benefiting from high performance characteristics.
To run a test cluster for 3FS or deploy it in production environments, follow instructions provided in the [setup guide](https://github.com/deepseek-ai/3FS/blob/main/deploy/README.md). If you encounter any issues while using this project, please report them via GitHub Issues page at https://github.com/deepseek-ai/3FS/.
License: MIT License (See LICENSE file for details)
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