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.
Google Cloud has expanded its AI infrastructure with new offerings focused on speed and scale. The A3 VMs, based on Nvidia H100 GPUs, are designed for large language models and generative AI training and inference, providing significantly improved performance compared to previous generations. Google is also improving networking infrastructure with the introduction of Cross-Cloud Network platform, allowing easier and more secure connections between Google Cloud and on-premises environments. Furthermore, Google Cloud is enhancing data and storage capabilities with updates to Cloud Storage and Dataproc Spark, boosting data access speeds and enabling faster processing for AI workloads.
HN commenters are skeptical of Google's "AI hypercomputer" announcement, viewing it more as a marketing push than a substantial technical advancement. They question the vagueness of the term "hypercomputer" and the lack of concrete details on its architecture and capabilities. Several point out that Google is simply catching up to existing offerings from competitors like AWS and Azure in terms of interconnected GPUs and high-speed networking. Others express cynicism about Google's track record of abandoning cloud projects. There's also discussion about the actual cost-effectiveness and accessibility of such infrastructure for smaller research teams, with doubts raised about whether the benefits will trickle down beyond large, well-funded organizations.
Firebase Studio is a visual development environment built for Firebase, offering a low-code approach to building web and mobile applications. It simplifies backend development with pre-built UI components and integrations for various Firebase services like Authentication, Firestore, Storage, and Cloud Functions. Developers can visually design UI layouts, connect them to data sources, and implement logic without extensive coding. This allows for faster prototyping and development, particularly for frontend developers who may be less familiar with backend complexities. Firebase Studio aims to streamline the entire Firebase development workflow, from building and deploying apps to monitoring performance and user engagement.
HN commenters generally expressed skepticism and disappointment with Firebase Studio. Several pointed out that it seemed like a rebranded version of FlutterFlow, offering little new functionality. Some questioned the value proposition, especially given FlutterFlow's existing presence and the perception of Firebase Studio as a closed-source, vendor-locked solution. Others were critical of the pricing model, considering it expensive compared to alternatives. A few commenters expressed interest in trying it out, but the overall sentiment was one of cautious negativity, with many feeling that it didn't address existing pain points in Firebase development.
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.
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.
DuckDB now offers preview support for querying data directly in Amazon S3 via a new extension. This allows users to create and query tables stored as Parquet, CSV, or JSON files on S3 without downloading data, leveraging S3's scalability and DuckDB's analytical capabilities. The extension utilizes the httpfs extension for access and supports various S3-specific features like AWS credentials and different regions. While still experimental, this functionality opens the door to building efficient "lakehouse" architectures directly on S3 using DuckDB.
Hacker News commenters generally expressed excitement about DuckDB's new S3 integration, praising its speed, simplicity, and potential to disrupt the data lakehouse space. Several users shared their positive experiences using DuckDB, highlighting its performance advantages compared to other query engines like Presto and Athena. Some raised concerns about the potential vendor lock-in with S3, suggesting that supporting alternative storage solutions would be beneficial. Others discussed the limitations of Parquet files for analytical workloads, and how DuckDB might address those issues. A few commenters pointed out the importance of robust schema evolution and data governance features for enterprise adoption. The overall sentiment was very positive, with many seeing this as a significant step forward for data analysis on cloud storage.
Werner Vogels argues that while Amazon S3's simplicity was initially a key differentiator and driver of its widespread adoption, maintaining that simplicity in the face of ever-increasing scale and feature requests is an ongoing challenge. He emphasizes that adding features doesn't equate to improving the customer experience and that preserving S3's core simplicity—its fundamental object storage model—is paramount. This involves thoughtful API design, backwards compatibility, and a focus on essential functionality rather than succumbing to the pressure of adding complexity for its own sake. S3's continued success hinges on keeping the service easy to use and understand, even as the underlying technology evolves dramatically.
Hacker News users largely agreed with the premise of the article, emphasizing that S3's simplicity is its greatest strength, while also acknowledging areas where improvements could be made. Several commenters pointed out the hidden complexities of S3, such as eventual consistency and subtle performance gotchas. The discussion also touched on the trade-offs between simplicity and more powerful features, with some arguing that S3's simplicity forces users to build solutions on top of it, leading to more robust architectures. The lack of a true directory structure and efficient renaming operations were also highlighted as pain points. Some users suggested potential improvements like native support for symbolic links or atomic renaming, but the general consensus was that any added features should be carefully considered to avoid compromising S3's core simplicity. A few comments compared S3 to other storage solutions, noting that while some offer more advanced features, none have matched S3's simplicity and ubiquity.
DeepSeek's Fire-Flyer File System (3FS) is a high-performance, distributed file system designed for AI workloads. It boasts significantly faster performance than existing solutions like HDFS and Ceph, particularly for small files and random access patterns common in AI training. 3FS leverages RDMA and kernel bypass techniques for low latency and high throughput, while maintaining POSIX compatibility for ease of integration with existing applications. Its architecture emphasizes scalability and fault tolerance, allowing it to handle the massive datasets and demanding requirements of modern AI.
Hacker News users discussed the potential advantages and disadvantages of 3FS, DeepSeek's Fire-Flyer File System. Several commenters questioned the claimed performance benefits, particularly the "10x faster" assertion, asking for clarification on the specific benchmarks used and comparing it to existing solutions like Ceph and GlusterFS. Some expressed skepticism about the focus on NVMe over other storage technologies and the lack of detail regarding data consistency and durability. Others appreciated the open-sourcing of the project and the potential for innovation in the distributed file system space, but stressed the importance of rigorous testing and community feedback for wider adoption. Several commenters also pointed out the difficulty in evaluating the system without more readily available performance data and the lack of clear documentation on certain features.
This blog post demonstrates how to build a flexible and cost-effective data lakehouse using AWS S3 for storage and leveraging the open-source Apache Iceberg table format. It walks through using Python and various open-source query engines like DuckDB, DataFusion, and Polars to interact with data directly on S3, bypassing the need for expensive data warehousing solutions. The post emphasizes the advantages of this approach, including open table formats, engine interchangeability, schema evolution, and cost optimization by separating compute and storage. It provides practical examples of data ingestion, querying, and schema management, showcasing the power and flexibility of this architecture for data analysis and exploration.
Hacker News users generally expressed skepticism towards the proposed "open" data lakehouse solution. Several commenters pointed out that while using open file formats like Parquet is a step in the right direction, true openness requires avoiding vendor lock-in with specific query engines like DuckDB. The reliance on custom Python tooling was also seen as a potential barrier to adoption and maintainability compared to established solutions. Some users questioned the overall benefit of this approach, particularly regarding cost-effectiveness and operational overhead compared to managed services. The perceived complexity and lack of clear advantages led to discussions about the practical applicability of this architecture for most users. A few commenters offered alternative approaches, including using managed services or simpler open-source tools.
Apache Iceberg is an open table format for massive analytic datasets. It brings modern data management capabilities like ACID transactions, schema evolution, hidden partitioning, and time travel to big data, while remaining performant on petabyte scale. Iceberg supports various data file formats like Parquet, Avro, and ORC, and integrates with popular big data engines including Spark, Trino, Presto, Flink, and Hive. This allows users to access and manage their data consistently across different tools and provides a unified, high-performance data lakehouse experience. It simplifies complex data operations and ensures data reliability and correctness for large-scale analytical workloads.
Hacker News users discuss Apache Iceberg's utility and compare it to other data lake table formats. Several commenters praise Iceberg's schema evolution features, particularly its handling of schema changes without rewriting the entire dataset. Some express concern about the complexity of implementing Iceberg, while others highlight the benefits of its open-source nature and active community. Performance comparisons with Hudi and Delta Lake are also brought up, with some users claiming Iceberg offers better performance for certain workloads while others argue it lags behind in features like time travel. A few users also discuss Iceberg's integration with various query engines and data warehousing solutions. Finally, the conversation touches on the potential for Iceberg to become a standard table format for data lakes.
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.