Stories with Tag Columnar Storage

• Preview: Amazon S3 Tables and Lakehouse in DuckDB

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  Posted: 2025-03-18 16:36:20

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  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.

  This DuckDB blog post announces and details a preview release of a highly anticipated feature: the ability to query data directly in Amazon S3 using DuckDB, effectively turning S3 into a data lakehouse. The post emphasizes the performance and cost benefits of this approach, eliminating the need for complex and expensive data warehousing solutions in many scenarios.

  The core of the new functionality revolves around treating S3 buckets as if they were local file systems. Users can now create DuckDB tables directly on top of Parquet files stored in S3, querying the data without needing to download it first. This direct access is made possible through the integration of the s3fs file system library, enabling seamless interaction with S3 objects. The blog post highlights the simplicity of this integration, demonstrating the creation of a table from S3 data with a single SQL command. This streamlined process eliminates the data movement and transformation steps often required when using traditional data warehouses.

  Performance is a key focus of the announcement. The post explains how DuckDB leverages its internal query engine optimizations to achieve efficient querying of S3-based data. These optimizations include parallel processing, columnar storage, and intelligent filtering, all contributing to fast query execution even on large datasets. The post provides comparative performance benchmarks, showcasing the speed advantages of DuckDB compared to other query engines when accessing data in S3.

  Cost-effectiveness is another significant benefit highlighted in the blog post. By eliminating the need to move and store data in intermediate systems, DuckDB reduces both storage costs associated with data duplication and compute costs related to data transfer and processing. The pay-per-use nature of S3, combined with DuckDB's efficient querying capabilities, results in a more cost-effective solution for many analytical workloads.

  The post also discusses the preview nature of this release. While core functionalities are already implemented and demonstrably performant, ongoing development is focused on expanding format support beyond Parquet, enhancing SQL compliance, and further optimizing performance. The authors actively encourage community feedback to guide the development and ensure a robust and feature-rich final release. They detail how users can try out the preview version, providing instructions for installation and configuration. The post concludes by inviting users to explore the new S3 integration and contribute to its development through feedback and contributions.

  • DuckDB
  • Amazon S3
  • Data Lakes
  • Lakehouse
  • Data Warehousing
  • Cloud Storage
  • Query Engine
  • Analytics
  • Data Engineering
  • SQL
  • Columnar Storage
  • parquet
  • data formats
  • Preview Release
  • big data

  Summary of Comments ( 33 )
  https://news.ycombinator.com/item?id=43401421

  Verbose tl;dr

  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.

  The Hacker News post "Preview: Amazon S3 Tables and Lakehouse in DuckDB" generated a moderate number of comments discussing the announcement of DuckDB's ability to query data directly in Amazon S3, functioning similarly to a lakehouse. Several commenters expressed excitement and approval for this development.

  A recurring theme in the comments is the praise for DuckDB's impressive speed and efficiency. Users shared anecdotal experiences of DuckDB outperforming other database solutions, particularly for analytical queries on parquet files. Some specifically highlighted its superiority over Presto and Athena in certain scenarios, mentioning significantly faster query times. This performance advantage seems to be a key driver of the positive reception towards the S3 integration.

  Another point of discussion revolves around the practical implications of this feature. Commenters discussed the benefits of being able to analyze data directly in S3 without needing to move or transform it. This is seen as a major advantage for data exploration, prototyping, and ad-hoc analysis. The convenience and cost-effectiveness of querying data in-place were emphasized by several users.

  Several comments delve into technical aspects, comparing DuckDB's approach to other lakehouse solutions like Databricks and Apache Iceberg. The discussion touched upon the differences in architecture and the trade-offs between performance and features. Some commenters speculated about the potential use cases for DuckDB's S3 integration, mentioning applications in data science, analytics, and log processing.

  While the overall sentiment is positive, some comments also raised questions and concerns. One commenter inquired about the maturity and stability of the S3 integration, as it is still in preview. Another user pointed out the limitations of DuckDB in handling highly concurrent workloads compared to distributed query engines. Furthermore, discussions emerged around the security implications of accessing S3 data directly and the need for proper authentication and authorization mechanisms.

  Finally, some comments explored the potential impact of this feature on the data warehousing and lakehouse landscape. The ability of DuckDB to query S3 data efficiently could potentially disrupt existing solutions and offer a more streamlined and cost-effective approach to data analytics. Some speculated on the future development of DuckDB and its potential to become a major player in the cloud data ecosystem.

• Sparrow, a modern C++ implementation of the Apache Arrow columnar format

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  Posted: 2025-01-31 23:44:00

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  Sparrow is a new C++ library designed for efficiently working with the Apache Arrow columnar format. It prioritizes compile times and runtime performance by minimizing dependencies and utilizing modern C++ features like compile-time reflection. Sparrow offers zero-copy reads and writes, enabling high-throughput data processing. It differs from other Arrow C++ implementations by focusing on a minimal and performant core, intentionally omitting features like computation kernels to reduce complexity and compile times. This approach aims to make Sparrow a building block for higher-level libraries and applications that require efficient data manipulation based on the Arrow format.

  Johan Mabille's Medium post introduces Sparrow, a nascent C++ implementation of the Apache Arrow columnar memory format. Mabille emphasizes Sparrow's focus on performance, aiming to surpass the speed of existing Arrow implementations. He outlines several key strategies employed to achieve this goal.

  One primary strategy is the extensive use of expression templates, a C++ technique allowing for compile-time optimization of complex arithmetic operations on data columns. This avoids unnecessary temporary object creation and function call overhead, resulting in faster execution. Mabille illustrates this with an example of adding two columns, where Sparrow's expression template approach compiles down to a single loop, minimizing overhead compared to traditional virtual function calls or dynamic dispatch.

  Another performance-enhancing technique is the utilization of SIMD (Single Instruction, Multiple Data) instructions. Sparrow leverages these instructions to perform operations on multiple data elements concurrently, exploiting the parallel processing capabilities of modern CPUs. This vectorization significantly accelerates computations, particularly for numerical data.

  Mabille also highlights Sparrow's adoption of lazy evaluation. Instead of immediately executing operations, Sparrow builds an execution graph representing the sequence of computations. This allows for global optimization of the entire computation pipeline before execution, potentially leading to further performance gains. For example, filtering operations can be applied early in the pipeline, reducing the amount of data processed by subsequent operations.

  Furthermore, Sparrow integrates seamlessly with other C++ libraries, promoting interoperability and code reuse. Specifically, it works well with the popular range-v3 library, simplifying the development of complex data processing pipelines. This integration allows developers to leverage the powerful algorithms and data structures provided by range-v3 in conjunction with Sparrow's optimized columnar data representation.

  The post underscores that Sparrow is still in its early stages of development. While core components like numerical and boolean data types are functional, support for other data types like strings and dictionaries is still under development. Mabille emphasizes the project's open-source nature and invites contributions from the community. He expresses his ambition for Sparrow to eventually become a highly competitive, performant alternative in the landscape of Arrow implementations. He also mentions that while initially targeting x86 architectures with AVX2 support, future plans include expanding support to other architectures like ARM.

  • C++
  • Apache Arrow
  • Columnar Storage
  • data format
  • In-Memory Analytics
  • data processing
  • High Performance Computing
  • Sparrow
  • serialization
  • deserialization
  • data structures
  • Open Source

  Summary of Comments ( 21 )
  https://news.ycombinator.com/item?id=42893844

  Verbose tl;dr

  Hacker News users generally expressed enthusiasm for Sparrow's performance improvements over Apache Arrow's C++ implementation. Several commenters highlighted the importance of memory management and zero-copy operations in achieving these gains. Some discussed the potential benefits for data-intensive applications and integration with other libraries like Pandas. One commenter raised a question about SIMD utilization, while others praised the project's clear benchmarks and documentation. Several users expressed interest in contributing to or experimenting with Sparrow. A few comments also touched on the broader implications for C++ development and the evolution of data processing frameworks.

  The Hacker News post discussing Sparrow, a modern C++ implementation of the Apache Arrow columnar format, has generated a moderate amount of discussion. Several commenters express interest and appreciation for the project.

  One commenter highlights the importance of columnar formats for analytical workloads, pointing out their efficiency for accessing only necessary columns and applying vectorized operations. They see Sparrow as a valuable addition to the C++ ecosystem for such tasks.

  Another commenter questions the performance comparison presented in the Sparrow blog post, specifically the choice of benchmarks and the lack of comparison with Parquet, a popular columnar storage format. They suggest that a broader range of benchmarks, including comparisons to established solutions, would provide a more comprehensive performance picture. This comment spurred a brief discussion about the purpose of benchmarks and the complexities of comparing different technologies fairly.

  Further discussion revolves around the complexities of memory management in C++ and the potential advantages of using a language like Rust for such projects. A commenter raises concerns about the potential for memory leaks or segmentation faults in C++ and suggests that Rust's ownership model and borrow checker offer stronger safety guarantees. However, another commenter points out that modern C++ techniques, like smart pointers and RAII (Resource Acquisition Is Initialization), can effectively mitigate these risks.

  Several commenters inquire about specific features of Sparrow, such as support for nested data structures and integration with other C++ libraries. They also discuss the potential use cases of Sparrow in different domains, including data science, machine learning, and high-performance computing.

  Overall, the comments indicate a generally positive reception of Sparrow, with commenters recognizing its potential value in the C++ ecosystem. However, some commenters also raise important questions regarding performance comparisons, memory management, and specific features, prompting further discussion and suggesting areas for potential improvement or clarification.