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.
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 ( 45 )
https://news.ycombinator.com/item?id=43200572
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.
The Hacker News post titled "Fire-Flyer File System from DeepSeek," linking to the GitHub repository for 3FS (https://github.com/deepseek-ai/3FS), has a moderate number of comments discussing various aspects of the file system.
Several commenters focused on the niche nature of 3FS, designed specifically for AI workloads and large language models (LLMs). They questioned the practical applicability beyond this specific use case, particularly given the existing mature file systems like S3 and Ceph. Some expressed skepticism about the need for a specialized file system for AI, suggesting that existing solutions could be adapted or optimized sufficiently.
Performance claims made by 3FS were also a subject of discussion. Some commenters expressed interest in seeing more detailed benchmarks and comparisons against established file systems, especially in real-world scenarios. The lack of readily available performance data led to some reservations about the claimed benefits.
The closed-source nature of 3FS drew criticism. Several commenters lamented the lack of transparency and community involvement that open-source projects typically enjoy. This closed nature was seen as a potential barrier to wider adoption and scrutiny. Concerns were also raised regarding potential vendor lock-in.
A few commenters pointed out the potential conflicts arising from DeepSeek's business model, which centers around providing AI infrastructure. They questioned whether 3FS was truly a general-purpose file system or primarily a tool to drive customers towards their platform.
The focus on flash storage optimization within 3FS was acknowledged as a positive aspect, but some commenters wondered about its suitability for other storage tiers, like hard drives or cloud storage. The discussion touched upon the specific hardware dependencies and whether 3FS could function effectively in a more heterogeneous storage environment.
Overall, the comments reflected a mix of curiosity, skepticism, and calls for greater transparency. While the potential benefits of a specialized file system for AI were acknowledged, many commenters emphasized the need for more concrete evidence and open development to justify its existence alongside existing solutions.