Stories with Tag disk i/o

• LumoSQL

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  Posted: 2025-05-27 10:39:30

  Verbose tl;dr

  LumoSQL is an experimental project aiming to improve SQLite performance and extensibility by rewriting it in a modular fashion using the Lua programming language. It leverages Lua's JIT compiler and flexible nature to potentially surpass SQLite's speed while maintaining compatibility. This modular architecture allows for easier experimentation with different storage engines, virtual table implementations, and other components. LumoSQL emphasizes careful benchmarking and measurement to ensure performance gains are real and significant. The project's current focus is demonstrating performance improvements, after which features like improved concurrency and new functionality will be explored.

  LumoSQL is a project with the ambitious goal of building a new, high-performance implementation of the industry-standard SQL database language, leveraging the speed and security advantages of the SQLite database engine. It aims to be a drop-in replacement for existing SQLite deployments, providing significant performance improvements without requiring application code changes. The project's core strategy involves reimplementing the SQL processing layer, including the parser, planner, and optimizer, while retaining the highly optimized storage engine and virtual machine components of SQLite. This approach allows LumoSQL to capitalize on SQLite's strengths while addressing performance bottlenecks in the SQL processing pipeline.

  A key aspect of LumoSQL is its modular design, which encourages experimentation and allows for pluggable components. This modularity facilitates the development of new features and optimizations without impacting the stability of the core engine. The project explicitly focuses on improving performance in specific areas, such as query parsing, planning, and execution. This targeted approach, combined with rigorous benchmarking and profiling, allows developers to measure progress and identify areas for further optimization.

  LumoSQL is being developed with a strong emphasis on testability and maintainability. Comprehensive test suites are used to ensure correctness and prevent regressions. The project also prioritizes clear documentation and a well-defined development process to promote community involvement and long-term sustainability. While still under active development, LumoSQL represents a promising effort to enhance SQL database performance by building upon the solid foundation of SQLite. The project invites contributions and collaborations from the broader open-source community, encouraging developers to participate in testing, benchmarking, and feature development. Ultimately, LumoSQL aims to deliver a robust, high-performance, and easily deployable SQL database solution suitable for a wide range of applications.

  • sqlite
  • Database
  • fork
  • performance
  • scalability
  • concurrency
  • Transactions
  • Durability
  • ACID
  • Storage
  • Embedded Database
  • disk i/o
  • optimization
  • C
  • Open Source
  • data management
  • Relational Database
  • SQL

  Summary of Comments ( 77 )
  https://news.ycombinator.com/item?id=44105619

  Verbose tl;dr

  Hacker News users discussed LumoSQL's approach of compiling SQL to native code via LLVM, expressing interest in its potential performance benefits, particularly for read-heavy workloads. Some questioned the practical advantages over existing optimized databases and raised concerns about the complexity of the compilation process and debugging. Others noted the project's early stage and the need for more benchmarks to validate performance claims. Several commenters were curious about how LumoSQL handles schema changes and concurrency control, with some suggesting comparisons to SQLite's approach. The tight integration with SQLite was also a topic of discussion, with some seeing it as a strength for leveraging existing tooling while others wondered about potential limitations.

  The Hacker News post titled "LumoSQL" (https://news.ycombinator.com/item?id=44105619) has a modest number of comments, discussing the project's approach, potential benefits, and some concerns.

  Several commenters express interest in the project's goal of building a more reliable and verifiable SQLite. One commenter praises the project's focus on stability and the removal of legacy code, viewing it as a valuable contribution. They specifically mention that the careful approach to backwards compatibility is a wise decision. Another commenter highlights the potential of LumoSQL to serve as a reliable foundation for other projects. The use of SQLite as a base is seen as a strength due to its wide usage and established reputation.

  There's a discussion around the use of Lua for extensions. One commenter points out the potential security implications of using Lua, particularly concerning untrusted inputs. They emphasize the importance of careful sandboxing to mitigate these risks. Another commenter acknowledges the security concerns but also mentions Lua's speed and ease of integration as potential benefits.

  The licensing of LumoSQL also comes up. One commenter questions the specific terms of the license and its implications for commercial use. Another clarifies that the project uses the same license as SQLite, addressing the initial concern.

  One commenter expresses skepticism about the long-term viability of the project, questioning whether it will gain enough traction to sustain itself. They also mention the challenge of attracting contributors and maintaining momentum.

  Performance is also a topic of discussion, with one commenter inquiring about any performance benchmarks comparing LumoSQL to SQLite. This comment, however, remains unanswered.

  Finally, there are comments focusing on the technical aspects of the project. One commenter asks about the project's approach to compilation, particularly regarding static versus dynamic linking. Another commenter inquires about the rationale behind specific architectural choices. These technical questions generally receive responses from individuals involved with the LumoSQL project, providing further clarification and insights.

• Disk I/O bottlenecks in GitHub Actions

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  Posted: 2025-03-28 15:22:36

  Verbose tl;dr

  GitHub Actions workflows, especially those involving Node.js projects, can suffer from significant disk I/O bottlenecks, primarily during dependency installation (npm install). These bottlenecks stem from the limited I/O performance of the virtual machines used by GitHub Actions runners. This leads to dramatically slower execution times compared to local machines with faster disks. The blog post explores this issue by benchmarking npm install operations across various runner types and demonstrates substantial performance improvements when using self-hosted runners or alternative CI/CD platforms with better I/O capabilities. Ultimately, developers should be aware of these potential bottlenecks and consider optimizing their workflows, exploring different runner options, or utilizing caching strategies to mitigate the performance impact.

  This blog post by Depot details the author's experience troubleshooting and resolving performance bottlenecks stemming from disk I/O limitations within their GitHub Actions CI/CD pipelines. The author initially observed inexplicably slow build times for their Rust project, specifically during the cargo build phase. Suspecting resource constraints within the GitHub Actions virtual environment, they began investigating various possibilities, including CPU, memory, and network limitations. However, through systematic experimentation and profiling using tools like iostat, they pinpointed the root cause to be sluggish disk I/O performance.

  The author meticulously describes their investigation process, showcasing the data they collected and the reasoning behind their conclusions. They initially ruled out CPU and memory bottlenecks as the primary culprits due to consistently low utilization during the slow builds. Network limitations were also discounted after observing consistent network performance. This led them to focus on disk I/O, where iostat revealed exceptionally high "await" times, indicating that processes were spending significant time waiting for disk operations to complete.

  Having identified disk I/O as the bottleneck, the author explored several mitigation strategies. They experimented with utilizing tmpfs, a RAM-based file system, to hold parts of the build process, effectively bypassing the slower physical disk. Mounting the project's target directory (where build artifacts are stored) within tmpfs yielded significant performance improvements, drastically reducing build times.

  Further investigation revealed that the performance discrepancy was primarily due to the differing I/O characteristics between the self-hosted runner used for local testing and the GitHub-hosted runner used for CI. The self-hosted runner likely utilized an SSD, providing significantly faster random read/write speeds compared to the potentially slower storage used by the GitHub-hosted runner. The author emphasizes the importance of considering these environmental differences when optimizing CI pipelines.

  The blog post concludes with a recommendation to consider tmpfs as a valuable tool for addressing I/O bottlenecks in CI environments, particularly for scenarios involving frequent disk access, such as compilation processes. It emphasizes the importance of profiling and understanding resource utilization to pinpoint performance bottlenecks accurately. The author also acknowledges that tmpfs may not be a universal solution, particularly for very large projects where RAM capacity might become a limiting factor. However, they suggest it as a valuable optimization technique for many projects running in constrained CI environments.

  • GitHub Actions
  • disk i/o
  • bottlenecks
  • CI/CD
  • performance
  • optimization
  • Workflows
  • Continuous Integration
  • continuous delivery
  • Virtual Machines
  • io performance
  • disk performance

  Summary of Comments ( 16 )
  https://news.ycombinator.com/item?id=43506574

  Verbose tl;dr

  HN users discussed the surprising performance disparity between GitHub-hosted and self-hosted runners, with several suggesting network latency as a significant factor beyond raw disk I/O. Some pointed out the potential impact of ephemeral runner environments and the overhead of network file systems. Others highlighted the benefits of using actions/cache or alternative CI providers with better I/O performance for specific workloads. A few users shared their experiences, with one noting significant improvements from self-hosting and another mentioning the challenges of optimizing build processes within GitHub Actions. The general consensus leaned towards self-hosting for I/O-bound tasks, while acknowledging the convenience of GitHub's hosted runners for less demanding workflows.

  The Hacker News post titled "Disk I/O bottlenecks in GitHub Actions" (https://news.ycombinator.com/item?id=43506574) has generated a moderate number of comments, discussing various aspects of the linked blog post about disk I/O performance issues in GitHub Actions.

  Several commenters corroborate the author's findings, sharing their own experiences with slow disk I/O in GitHub Actions. One user mentions observing significantly improved performance after switching to self-hosted runners, highlighting the potential benefits of having more control over the execution environment. They specifically mention the use of tmpfs for build directories as a contributing factor to the improved speeds.

  Another commenter points out that the observed I/O bottlenecks are likely not unique to GitHub Actions, suggesting that similar issues might exist in other CI/CD environments that rely on virtualized or containerized runners. They argue that understanding the underlying hardware and storage configurations is crucial for optimizing performance in any CI/CD pipeline.

  A more technically inclined commenter discusses the potential impact of different filesystem layers and virtualization technologies on I/O performance. They suggest that the choice of filesystem within the runner's container, as well as the virtualization technology used by the underlying infrastructure, could play a significant role in the observed performance differences.

  One commenter questions the methodology used in the original blog post, specifically regarding the use of dd for benchmarking. They argue that dd might not accurately reflect real-world I/O patterns encountered in typical CI/CD workloads. They propose alternative benchmarking tools and techniques that might provide more relevant insights into the performance characteristics of the storage system.

  Finally, some commenters discuss potential workarounds and mitigation strategies for dealing with slow disk I/O in GitHub Actions, including using RAM disks, optimizing build processes to minimize disk access, and leveraging caching mechanisms to reduce the amount of data that needs to be read from or written to disk. They also discuss the trade-offs associated with each of these approaches, such as the limited size of RAM disks and the potential complexity of implementing custom caching solutions.