Stories with Tag 3FS

• Smallpond – A lightweight data processing framework built on DuckDB and 3FS

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  Posted: 2025-02-28 01:56:35

  Verbose tl;dr

  Smallpond is a lightweight Python framework designed for efficient data processing using DuckDB and the Apache Arrow-based filesystem 3FS. It simplifies common data tasks like loading, transforming, and analyzing datasets by leveraging the performance of DuckDB for querying and the flexibility of 3FS for storage. Smallpond aims to provide a convenient and scalable solution for working with various data formats, including Parquet, CSV, and JSON, while abstracting away the complexities of data management and enabling users to focus on their analysis. It offers a Pandas-like API for familiarity and ease of use, promoting a more streamlined workflow for data scientists and engineers.

  The GitHub repository introduces Smallpond, a novel data processing framework meticulously designed for efficiency and ease of use, especially when dealing with medium-sized datasets (ranging from gigabytes to terabytes). It leverages the strengths of two core technologies: DuckDB, an in-process analytical SQL database, and 3FS, a file system abstraction layer optimized for object storage services like AWS S3.

  Smallpond aims to bridge the gap between simplistic single-machine processing and the complexities of distributed computing frameworks like Spark. It avoids the operational overhead of a distributed system while still providing substantial performance improvements over naive single-machine approaches, particularly when working with cloud-stored data.

  The framework's architecture centers around the concept of "ponds," which represent logical units of data. These ponds are essentially directories residing on a compatible file system (typically 3FS for cloud storage access or the local file system). Within a pond, data is stored as Parquet files, a columnar storage format well-suited for analytical queries.

  Smallpond facilitates data processing by providing a Python API that seamlessly integrates with DuckDB. Users can define data transformations using SQL queries directly within their Python code. Smallpond then orchestrates the execution of these queries against the data stored in the designated pond, leveraging DuckDB's efficient query engine and optimized Parquet handling. This tight integration allows users to leverage the familiarity and expressiveness of SQL while benefiting from the performance advantages of DuckDB and the scalability afforded by cloud storage via 3FS.

  The framework further enhances efficiency by enabling parallel processing of multiple ponds. This allows users to distribute their workload across multiple cores or machines, significantly accelerating processing time for large datasets. This parallelism is managed transparently by Smallpond, simplifying the process for the user.

  Smallpond emphasizes simplicity and ease of use as core design principles. The Python API is designed to be intuitive and easy to learn, even for users without prior experience with distributed computing frameworks. The framework handles the complexities of data partitioning, query execution, and result aggregation, freeing the user to focus on the logic of their data transformations. Furthermore, the reliance on SQL allows users to leverage their existing SQL skills and readily adapt existing SQL-based workflows.

  In summary, Smallpond offers a streamlined and efficient approach to processing medium-sized datasets, combining the power of DuckDB and 3FS to provide a user-friendly and performant alternative to both simplistic single-machine processing and complex distributed systems. Its focus on SQL-based transformations, efficient Parquet handling, and transparent parallelism simplifies the data processing pipeline and allows users to effectively analyze data stored in cloud storage or locally without the overhead of managing a distributed computing cluster.

  • data processing
  • Framework
  • DuckDB
  • 3FS
  • lightweight
  • Data Analysis
  • SQL
  • Python
  • Data Engineering
  • Database
  • file system
  • Open Source
  • Data Science
  • Data Lake
  • parquet
  • CSV
  • Data Warehousing

  Summary of Comments ( 42 )
  https://news.ycombinator.com/item?id=43200793

  Verbose tl;dr

  Hacker News commenters generally expressed interest in Smallpond, praising its simplicity and the potential combination of DuckDB and fsspec. Several noted the clever use of these existing tools to create a lightweight yet powerful framework. Some questioned the long-term viability of relying solely on DuckDB for complex ETL pipelines, citing performance limitations for very large datasets or specific transformation tasks. Others discussed the benefits of using Polars or DataFusion as alternative processing engines. A few commenters also suggested potential improvements, like adding support for streaming data ingestion and more sophisticated data validation features. Overall, the sentiment was positive, with many seeing Smallpond as a useful tool for certain data processing scenarios.

  The Hacker News post titled "Smallpond – A lightweight data processing framework built on DuckDB and 3FS" has a modest number of comments, generating a brief discussion around the project. Several commenters express initial interest and curiosity about Smallpond, noting the appealing combination of DuckDB and fsspec/3FS.

  One commenter questions the need for another data processing framework given the existing landscape, prompting a response from the project author (seemingly u/tmokmss) clarifying that Smallpond aims to address a specific niche: providing an easy-to-use, Python-native framework tailored for data exploration and analysis on medium-sized datasets that fit comfortably in memory. They emphasize that Smallpond isn't intended to compete with larger-scale distributed processing frameworks like Spark or Dask, but rather offers a streamlined, lightweight alternative for simpler tasks. The author further explains the project's focus on leveraging DuckDB's efficient in-memory processing capabilities, combined with the flexibility of accessing data from various sources via fsspec/3FS.

  Another commenter raises a point about the project's early stage of development and the limited documentation, to which the author acknowledges the current state and expresses their commitment to improving documentation as the project matures. They also invite contributions and feedback from the community.

  The discussion also briefly touches upon alternative approaches, with one commenter suggesting exploring Polars as another potential tool in this space. However, there's no extended debate or comparison between Smallpond and other frameworks. The overall tone of the comments remains generally positive and inquisitive, with users expressing interest in the project's potential while recognizing its early stage of development.

• Fire-Flyer File System from DeepSeek

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  Posted: 2025-02-28 01:26:26

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  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.

  DeepSeek has introduced 3FS (Fire-Flyer File System), a novel file system meticulously engineered for the efficient storage and retrieval of AI data, specifically catering to the demanding requirements of large language models (LLMs) and vector databases. The core design principle of 3FS revolves around optimizing data access patterns typical in AI workloads, where small files are frequently read and written at high speeds, often concurrently. Traditional file systems, designed for larger files and different access patterns, become bottlenecks in these scenarios.

  3FS tackles this challenge through several key innovations. Firstly, it employs a log-structured merge-tree (LSM-tree) architecture for managing metadata, offering significant performance improvements for metadata-intensive operations like file creation, deletion, and listing, which are common in AI workflows involving numerous small files. This approach contrasts with traditional file systems that often rely on less efficient data structures for metadata management.

  Furthermore, 3FS incorporates a novel technique called "Tail-Trim," which optimizes the storage and retrieval of the latest versions of files. This feature is especially advantageous in AI training scenarios where models are constantly iterated upon, requiring frequent updates and access to the most recent versions of data. Tail-Trim likely allows for efficient management of these updates without incurring the overhead of traditional file system update mechanisms.

  The system is also designed with a focus on horizontal scalability. This allows 3FS to handle the ever-growing datasets used in AI by distributing data and metadata across multiple storage devices, ensuring that performance remains consistent even as the data volume increases. This distributed nature is essential for large-scale AI training and deployment.

  Finally, DeepSeek emphasizes 3FS's compatibility with existing tools and workflows. The file system supports the POSIX standard, meaning that it behaves like a typical file system from the perspective of applications, enabling seamless integration with existing AI frameworks and software without requiring significant code modifications. This compatibility minimizes the friction of adopting 3FS and allows developers to leverage its performance benefits without disrupting their existing pipelines. In summary, 3FS aims to address the specific storage challenges posed by AI workloads by combining an LSM-tree-based metadata management system, the Tail-Trim optimization for versioned data, a horizontally scalable architecture, and POSIX compatibility.

  • file system
  • distributed file system
  • Cloud Storage
  • High Performance
  • scalability
  • deepseek
  • 3FS
  • Fire-Flyer
  • AI
  • artificial intelligence
  • machine learning
  • big data
  • data storage
  • Open Source

  Summary of Comments ( 45 )
  https://news.ycombinator.com/item?id=43200572

  Verbose tl;dr

  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.