Stories with Tag Distributed File Systems

• An Intro to DeepSeek's Distributed File System

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  Posted: 2025-04-17 12:50:37

  Verbose tl;dr

  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.

  • Distributed File Systems
  • deepseek
  • 3FS
  • file storage
  • scalability
  • performance
  • data storage
  • Cloud Storage
  • distributed systems
  • Introduction
  • Overview

  Summary of Comments ( 35 )
  https://news.ycombinator.com/item?id=43716058

  Verbose tl;dr

  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.

• File Systems Unfit as Distributed Storage Back Ends (2019)

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  Posted: 2025-03-30 19:03:42

  Verbose tl;dr

  The paper "File Systems Unfit as Distributed Storage Back Ends" argues that relying on traditional file systems for distributed storage systems leads to significant performance and scalability bottlenecks. It identifies fundamental limitations in file systems' metadata management, consistency models, and single points of failure, particularly in large-scale deployments. The authors propose that purpose-built storage systems designed with distributed principles from the ground up, rather than layered on top of existing file systems, are necessary for achieving optimal performance and reliability in modern cloud environments. They highlight how issues like metadata scalability, consistency guarantees, and failure handling are better addressed by specialized distributed storage architectures.

  The paper "File Systems Unfit as Distributed Storage Back Ends" argues that traditional file systems, while suitable for single-node storage, are fundamentally ill-suited to serve as the foundation for distributed storage systems. It contends that the inherent design principles and architectural characteristics of file systems create significant challenges in scalability, performance, and manageability when deployed in distributed environments.

  The authors meticulously dissect several key shortcomings of file systems in this context. Firstly, they highlight the impedance mismatch between the POSIX semantics, which govern file system operations, and the requirements of distributed systems. POSIX focuses on strong consistency and linearizability, which are difficult and expensive to maintain across a distributed cluster. This often leads to performance bottlenecks and complexities in data replication and consistency management.

  Secondly, the paper emphasizes the limitations of file systems in metadata management within distributed environments. Traditional file systems maintain metadata, such as file names, directories, and access permissions, in a centralized or hierarchical structure. This becomes a significant bottleneck when dealing with the massive scale and dynamic nature of data in distributed systems, hindering performance and scalability. The paper argues that distributed systems require decentralized and scalable metadata management mechanisms, which are not readily provided by conventional file systems.

  Furthermore, the paper points to the challenges of data placement and load balancing. File systems typically lack sophisticated mechanisms for intelligent data distribution and workload management across a cluster. This can result in uneven data distribution, hot spots, and suboptimal resource utilization in a distributed setting.

  The authors also address the complexities of failure management in distributed systems built on file systems. Maintaining data integrity and availability in the face of node failures becomes significantly more challenging due to the inherent limitations of file system semantics. The paper argues that more robust and flexible failure recovery mechanisms are required, which go beyond the capabilities of traditional file systems.

  Finally, the authors explore the difficulties in evolving and adapting file systems to meet the ever-changing demands of distributed storage. The tight coupling between the file system and the underlying operating system makes it challenging to introduce new features, optimize performance, and support new storage technologies without significant disruption. The paper advocates for a more modular and flexible approach to distributed storage architecture, where the storage back end is decoupled from the file system interface.

  In conclusion, the paper makes a compelling case against using traditional file systems as the foundation for distributed storage systems. It highlights the inherent limitations of file systems in addressing the scalability, performance, metadata management, data placement, failure recovery, and evolvability challenges posed by distributed environments. The authors suggest exploring alternative approaches that are specifically designed for the unique requirements of distributed storage, paving the way for more efficient, robust, and scalable solutions.

  • file systems
  • distributed systems
  • Storage Systems
  • Cloud Storage
  • scalability
  • performance
  • data storage
  • Distributed File Systems
  • 2019
  • ACM
  • Computer Science
  • data management
  • big data
  • Database Systems

  Summary of Comments ( 7 )
  https://news.ycombinator.com/item?id=43526621

  Verbose tl;dr

  HN commenters generally agree with the paper's premise that traditional file systems are poorly suited for distributed storage backends. Several highlighted the impedance mismatch between POSIX semantics and distributed systems, citing issues with consistency, metadata management, and performance bottlenecks. Some questioned the novelty of the paper's findings, arguing these limitations are well-known. Others discussed alternative approaches like object storage and databases, emphasizing the importance of choosing the right tool for the job. A few commenters offered anecdotal experiences supporting the paper's claims, while others debated the practicality of replacing existing file system-based infrastructure. One compelling comment suggested that the paper's true contribution lies in quantifying the performance overhead, rather than merely identifying the issues. Another interesting discussion revolved around whether "cloud-native" storage solutions truly address these problems or merely abstract them away.

  The Hacker News post titled "File Systems Unfit as Distributed Storage Back Ends (2019)" with the ID 43526621 has several comments discussing the linked ACM article. The discussion generally agrees with the premise of the paper, highlighting the inherent limitations of traditional file systems when used as the foundation for distributed storage systems.

  Several commenters point out that using file systems in this way often leads to performance bottlenecks. One commenter specifically mentions the challenges of managing metadata at scale, noting that operations like listing directories or checking file existence become significantly slower as the number of files grows. They suggest that specialized distributed storage systems are designed to handle these metadata operations more efficiently.

  Another commenter expands on this idea by describing the inherent trade-offs file systems make. They explain that file systems prioritize data consistency and durability, which are crucial for single-machine use cases. However, these guarantees come at the cost of performance and scalability in distributed environments, where eventual consistency and other relaxed guarantees are often more suitable.

  One compelling comment argues that the issue isn't with file systems themselves, but rather with the mismatch between their design goals and the requirements of distributed storage. They propose that file systems are optimized for local storage on a single machine, where factors like latency and bandwidth are relatively predictable. In contrast, distributed systems must contend with network partitions, varying node performance, and other complexities that make traditional file system semantics difficult to maintain efficiently.

  Another interesting perspective is offered by a commenter who suggests that the paper's title is slightly misleading. They argue that file systems can be used effectively in distributed storage, but only with careful consideration and significant modifications. They mention specific examples like GlusterFS and Ceph, which are distributed file systems designed to address the limitations of traditional file systems in distributed environments.

  A couple of comments mention alternative approaches to building distributed storage, including key-value stores and object storage. These systems, they argue, are better suited to the demands of large-scale data management because they offer simpler interfaces and more flexible consistency models.

  Finally, one commenter highlights the importance of understanding the trade-offs involved in choosing a storage back end. They emphasize that there is no one-size-fits-all solution and that the best choice depends on the specific requirements of the application. They advise considering factors like data volume, access patterns, and consistency requirements when making a decision.