DeepSeek's 3FS is a distributed file system designed for large language models (LLMs) and AI training, prioritizing throughput over latency. It achieves this by utilizing a custom kernel bypass network stack and RDMA to minimize overhead. 3FS employs a metadata service for file discovery and a scale-out object storage approach with configurable redundancy. Preliminary benchmarks demonstrate significantly higher throughput compared to NFS and Ceph, particularly for large files and sequential reads, making it suitable for the demanding I/O requirements of large-scale AI workloads.
This blog post, titled "An Intro to DeepSeek's Distributed File System," introduces and analyzes the performance of 3FS, a novel distributed file system designed by DeepSeek for AI workloads. The author emphasizes the specific challenges posed by these workloads, such as the need to manage massive datasets, support high throughput for both sequential and random access patterns, and minimize latency, especially for metadata operations. Traditional file systems often struggle to meet these demands, prompting the development of 3FS.
The blog post dives into the architectural design of 3FS, highlighting several key features. A core component is its reliance on RDMA (Remote Direct Memory Access) for data transfer. This bypasses the CPU and kernel, allowing for significantly faster and more efficient communication between nodes. Further enhancing performance is the utilization of SPDK (Storage Performance Development Kit), a library specifically optimized for NVMe drives, which are common in high-performance storage systems. SPDK further reduces overhead and maximizes the potential of the underlying hardware.
The author also elaborates on the implementation details of 3FS's metadata management. A crucial design choice is the adoption of a hierarchical metadata structure, which aims to alleviate performance bottlenecks often associated with metadata access. This structure likely distributes metadata across multiple nodes, allowing for parallel access and reducing contention. The post explicitly mentions the importance of minimizing metadata access latency, particularly for small files, a common characteristic of AI workloads.
A significant portion of the blog post is dedicated to showcasing performance benchmarks of 3FS. The author presents results demonstrating superior throughput and significantly lower latency compared to Ceph, a popular distributed file system often used for large-scale storage. These benchmarks cover various access patterns, including sequential reads and writes, as well as random reads and writes, highlighting the versatility of 3FS. The author is careful to specify the hardware configuration used during testing, allowing for better context and replicability of the results. While specific numbers are provided, the author focuses more on the relative performance gains achieved by 3FS over Ceph, demonstrating orders of magnitude improvement in certain scenarios.
Finally, the blog post concludes with a brief outlook on the future development of 3FS. The author mentions planned features and improvements, indicating ongoing work and commitment to refining and enhancing the file system. This suggests that 3FS is not a static project but an evolving solution designed to meet the dynamic demands of AI workloads. The overall tone suggests optimism about the potential of 3FS to address the storage challenges faced by AI practitioners and researchers.
Summary of Comments ( 35 )
https://news.ycombinator.com/item?id=43716058
Hacker News users discuss DeepSeek's new distributed file system, focusing on its performance and design choices. Several commenters question the need for a new distributed file system given existing solutions like Ceph and GlusterFS, prompting discussion around DeepSeek's specific niche targeting AI workloads. Performance claims are met with skepticism, with users requesting more detailed benchmarks and comparisons to established systems. The decision to use Rust is praised by some for its performance and safety features, while others express concerns about the relatively small community and potential debugging challenges. Some commenters also delve into the technical details of the system, particularly its metadata management and consistency guarantees. Overall, the discussion highlights a cautious interest in DeepSeek's offering, with a desire for more data and comparisons to validate its purported advantages.
The Hacker News post titled "An Intro to DeepSeek's Distributed File System" (linking to https://maknee.github.io/blog/2025/3FS-Performance-Journal-1/) has generated several comments discussing various aspects of the presented file system.
One commenter questions the choice of Go for implementing the file system, expressing concerns about Go's garbage collection potentially impacting tail latency for critical operations. They suggest Rust or C++ as alternatives that might offer more predictable performance. This sparked a small discussion, with another commenter suggesting that while Go's GC might be a concern in some high-performance scenarios, optimizations and careful tuning could mitigate its impact, especially given the focus on throughput over latency in this particular file system.
Another thread of discussion focuses on the architectural decisions of 3FS, particularly the claimed efficiency advantages of shared-nothing and avoiding POSIX compliance. A commenter praises the approach of eschewing POSIX for a cleaner, more performant design, contrasting it with the complexities and overhead often associated with POSIX compliance. Another user chimes in, expressing skepticism about the ability to completely avoid POSIX compatibility in practice, especially if broader adoption is a goal, suggesting that the eventual need to interact with POSIX-compliant tools and workflows might necessitate some level of integration down the line.
The author of the blog post (and presumably the file system) engages in the comments, responding to several inquiries. They clarify specific design choices, providing context around the target workloads and performance goals. They also address the POSIX compatibility concerns, acknowledging the potential need for a translation layer in the future while emphasizing the current focus on optimizing for their specific use case.
Furthermore, a commenter raises questions about the availability and resilience of the system, particularly in the face of hardware failures. They inquire about the mechanisms in place for data replication and recovery, emphasizing the importance of robust failure handling in a distributed file system.
Overall, the comments section demonstrates a mix of curiosity, skepticism, and praise for the presented file system. The commenters delve into technical details, offering informed opinions on the design choices and potential tradeoffs. The author's active participation adds valuable context and clarifies several aspects of the system.