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.
XPipe is a command-line tool designed to simplify and streamline connections to various remote environments like SSH servers, Docker containers, Kubernetes clusters, and virtual machines. It acts as a central hub, allowing users to define and manage connections with descriptive names and easily switch between them using simple commands. XPipe aims to improve workflow efficiency by reducing the need for complex commands and remembering connection details, offering features like automatic port forwarding, SSH agent forwarding, and seamless integration with existing SSH configurations. This effectively provides a unified interface for interacting with diverse environments, boosting productivity for developers and system administrators.
Hacker News users generally expressed interest in XPipe, praising its potential for streamlining complex workflows involving various connection types. Several commenters appreciated the consolidated approach to managing different access methods, finding value in a single tool for SSH, Docker, Kubernetes, and VMs. Some questioned its advantages over existing solutions like sshuttle, while others raised concerns about security implications, particularly around storing credentials. The discussion also touched upon the project's open-source nature and potential integration with tools like Tailscale. A few users requested clarification on specific features, such as container access and the handling of jump hosts.
This paper explores how Just-In-Time (JIT) compilers have evolved, aiming to provide a comprehensive overview for both newcomers and experienced practitioners. It covers the fundamental concepts of JIT compilation, tracing its development from early techniques like tracing JITs and method-based JITs to more modern approaches involving tiered compilation and adaptive optimization. The authors discuss key optimization techniques employed by JIT compilers, such as inlining, escape analysis, and register allocation, and analyze the trade-offs inherent in different JIT designs. Finally, the paper looks towards the future of JIT compilation, considering emerging challenges and research directions like hardware specialization, speculation, and the integration of machine learning techniques.
HN commenters generally express skepticism about the claims made in the linked paper attempting to make interpreters competitive with JIT compilers. Several doubt the benchmarks are representative of real-world workloads, suggesting they're too micro and don't capture the dynamic nature of typical programs where JITs excel. Some point out that the "interpreter" described leverages techniques like speculative execution and adaptive optimization, blurring the lines between interpretation and JIT compilation. Others note the overhead introduced by the proposed approach, particularly in terms of memory usage, might negate any performance gains. A few highlight the potential value in exploring alternative execution models but caution against overstating the current results. The lack of open-source code for the presented system also draws criticism, hindering independent verification and further exploration.
ForeverVM allows users to run AI-generated code persistently in isolated, stateful sandboxes called "Forever VMs." These VMs provide a dedicated execution environment that retains data and state between runs, enabling continuous operation and the development of dynamic, long-running AI agents. The platform simplifies the deployment and management of AI agents by abstracting away infrastructure complexities, offering a web interface for control, and providing features like scheduling, background execution, and API access. This allows developers to focus on building and interacting with their agents rather than managing server infrastructure.
HN commenters are generally skeptical of ForeverVM's practicality and security. Several question the feasibility and utility of "forever" VMs, citing the inevitable need for updates, dependency management, and the accumulation of technical debt. Concerns around sandboxing and security vulnerabilities are prevalent, with users pointing to the potential for exploits within the sandboxed environment, especially when dealing with AI-generated code. Others question the target audience and use cases, wondering if the complexity outweighs the benefits compared to existing serverless solutions. Some suggest that ForeverVM's current implementation is too focused on a specific niche and might struggle to gain wider adoption. The claim of VMs running "forever" is met with significant doubt, viewed as more of a marketing gimmick than a realistic feature.
The author explores several programming language design ideas centered around improving developer experience and code clarity. They propose a system for automatically managing borrowed references with implicit borrowing and optional explicit lifetimes, aiming to simplify memory management. Additionally, they suggest enhancing type inference and allowing for more flexible function signatures by enabling optional and named arguments with default values, along with improved error messages for type mismatches. Finally, they discuss the possibility of incorporating traits similar to Rust but with a focus on runtime behavior and reflection, potentially enabling more dynamic code generation and introspection.
Hacker News users generally reacted positively to the author's programming language ideas. Several commenters appreciated the focus on simplicity and the exploration of alternative approaches to common language features. The discussion centered on the trade-offs between conciseness, readability, and performance. Some expressed skepticism about the practicality of certain proposals, particularly the elimination of loops and reliance on recursion, citing potential performance issues. Others questioned the proposed module system's reliance on global mutable state. Despite some reservations, the overall sentiment leaned towards encouragement and interest in seeing further development of these ideas. Several commenters suggested exploring existing languages like Factor and Joy, which share some similarities with the author's vision.
Lume is a lightweight command-line interface (CLI) tool designed specifically for managing macOS and Linux virtual machines (VMs) on Apple Silicon Macs. It simplifies the creation, control, and configuration of VMs, offering a streamlined alternative to more complex virtualization solutions. Lume aims for a user-friendly experience, focusing on essential VM operations with an intuitive command set and minimal dependencies.
HN commenters generally expressed interest in Lume, praising its lightweight nature and simple approach to managing VMs. Several users appreciated the focus on CLI usage and its speed compared to other solutions like UTM. Some questioned the choice of using Alpine Linux for the host environment and suggested alternatives like NixOS. Others pointed out potential improvements, such as better documentation and ARM support for the host itself. The project's novelty and its potential as a faster, more streamlined alternative to existing VM managers were highlighted as key strengths. Some users also expressed interest in contributing to the project.
Austrian cloud provider Anexia has migrated 12,000 virtual machines from VMware to its own internally developed KVM-based platform, saving millions of euros annually in licensing costs. Driven by the desire for greater control, flexibility, and cost savings, Anexia spent three years developing its own orchestration, storage, and networking solutions to underpin the new platform. While acknowledging the complexity and effort involved, the company claims the migration has resulted in improved performance and stability, along with the substantial financial benefits.
Hacker News commenters generally praised Anexia's move away from VMware, citing cost savings and increased flexibility as primary motivators. Some expressed skepticism about the "homebrew" aspect of the new KVM platform, questioning its long-term maintainability and the potential for unforeseen issues. Others pointed out the complexities and potential downsides of such a large migration, including the risk of downtime and the significant engineering effort required. A few commenters shared their own experiences with similar migrations, offering both warnings and encouragement. The discussion also touched on the broader trend of moving away from proprietary virtualization solutions towards open-source alternatives like KVM. Several users questioned the wisdom of relying on a single vendor for such a critical part of their infrastructure, regardless of whether it's VMware or a custom solution.
Summary of Comments ( 16 )
https://news.ycombinator.com/item?id=43506574
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
ddfor benchmarking. They argue thatddmight 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.