Stories with Tag parquet

• 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.

• The best way to use text embeddings portably is with Parquet and Polars

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  Posted: 2025-02-24 18:27:49

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  Storing and utilizing text embeddings efficiently for machine learning tasks can be challenging due to their large size and the need for portability across different systems. This post advocates for using Parquet files in conjunction with the Polars DataFrame library as a superior solution. Parquet's columnar storage format enables efficient filtering and retrieval of specific embeddings, while Polars provides fast data manipulation in Python. This combination outperforms traditional methods like storing embeddings in CSV or JSON, especially when dealing with millions of embeddings, by significantly reducing file size and processing time, leading to faster model training and inference. The author demonstrates this advantage by showcasing a practical example of similarity search within a large embedding dataset, highlighting the significant performance gains achieved with the Parquet/Polars approach.

  Max Woolf, the author of the blog post "The best way to use text embeddings portably is with Parquet and Polars," argues that storing and utilizing text embeddings is most effectively achieved through a combination of the Parquet file format and the Polars data processing library, especially when portability and performance are paramount. He begins by explaining the increasing prevalence of embedding models like Sentence Transformers, which convert textual data into numerical vectors capturing semantic meaning. These embeddings are crucial for various tasks like semantic search, clustering, and classification.

  Woolf highlights the limitations of current common practices for storing embeddings. Storing them within databases, while offering structured querying, often suffers from performance issues, especially as the dataset grows. Saving embeddings as simple CSV or JSON files, while straightforward, lacks efficiency in both storage space and access speed, primarily due to their text-based nature. These formats are also less interoperable with data analysis tools optimized for columnar data.

  The blog post then introduces Parquet as a superior alternative. Parquet, a columnar storage format, offers significant advantages. Its columnar structure enables efficient filtering and retrieval of specific embeddings or associated metadata without reading the entire file. This results in substantial performance gains, especially for large datasets. Additionally, Parquet's binary format compresses data effectively, reducing storage requirements compared to text-based formats. Furthermore, Parquet enjoys broad support across diverse programming languages and data processing frameworks, ensuring excellent portability.

  To further enhance performance and usability, Woolf advocates for using the Polars library in conjunction with Parquet. Polars, a DataFrame library built in Rust, is known for its speed and memory efficiency. It provides a convenient and performant way to load, process, and manipulate the embedding data stored in Parquet files. This combination allows for rapid filtering and querying of embeddings, making it ideal for tasks like similarity search where quick access to specific embeddings is crucial.

  Woolf provides concrete examples demonstrating the process of saving and loading embeddings with Parquet and Polars, using Python code snippets. He emphasizes the simplicity and efficiency of this approach, particularly when dealing with millions of embeddings. The post also touches upon the importance of storing metadata alongside embeddings, which Parquet readily accommodates. This metadata, such as text associated with the embeddings, is essential for interpreting and utilizing the embedding data effectively. The post concludes by reiterating the combined power of Parquet and Polars as a robust and efficient solution for managing text embeddings, facilitating portability and scalability for various embedding-driven applications.

  • text embeddings
  • parquet
  • polars
  • portable
  • data formats
  • vector databases
  • data storage
  • big data
  • Data Science
  • machine learning
  • natural language processing
  • NLP
  • performance
  • optimization
  • Python

  Summary of Comments ( 27 )
  https://news.ycombinator.com/item?id=43162995

  Verbose tl;dr

  Hacker News users discussed the benefits of using Parquet and Polars for storing and accessing text embeddings. Several commenters praised the combination, highlighting Parquet's efficiency for storing vector data and Polars' speed for querying and manipulating it. One commenter mentioned the ease of integration with tools like DuckDB for analytical queries. Others pointed out potential downsides, including Parquet's columnar storage being less ideal for retrieving entire embeddings and the relative immaturity of the Polars ecosystem compared to Pandas. The discussion also touched on alternative approaches like FAISS and LanceDB, acknowledging their strengths for similarity searches but emphasizing the advantages of Parquet/Polars for general-purpose data manipulation and analysis of embeddings. A few users questioned the focus on "portability," suggesting that cloud-based vector databases offer superior performance for most use cases.

  The Hacker News post titled "The best way to use text embeddings portably is with Parquet and Polars" generated a moderate amount of discussion with a focus on the practicalities and alternatives to the proposed approach.

  Several commenters questioned the necessity of Parquet for smaller datasets, suggesting that simpler formats like JSON or even CSV could suffice and offer faster processing, especially when the embedding dimensionality is relatively low. The added complexity of Parquet was seen as unnecessary overhead in such cases. One commenter specifically mentioned that for their use case of fewer than 100,000 embeddings, JSON proved to be significantly faster, highlighting the importance of considering dataset size when choosing a storage format.

  The discussion also explored alternative tools and approaches. One commenter proposed using DuckDB and its native ability to query JSON and CSV files directly, potentially offering a simpler and faster solution than loading into Polars. Another mentioned the potential of vaex, a Python library for memory mapping and lazy computations, as a suitable tool for managing large numerical datasets like embeddings.

  Performance considerations were a recurring theme. Commenters discussed the trade-offs between memory usage and speed, and how tools like parquet-tools can be used to optimize Parquet files for different access patterns. The choice between row-oriented and column-oriented storage was also touched upon, with implications for different types of queries.

  While the original post advocated for Parquet and Polars, the comments presented a more nuanced perspective, highlighting the importance of evaluating different options based on the specific needs of the project. Factors like dataset size, query patterns, and performance requirements were all considered in the discussion, offering valuable insights into the practical considerations of working with text embeddings. No single solution emerged as universally superior, reinforcing the idea that the "best" approach is context-dependent.