The blog post details a performance optimization for Nix's evaluation process. By pre-resolving store paths for built-in functions, specifically fetchers, Nix can avoid redundant computations during evaluation, leading to significant speed improvements. This is achieved by introducing a new builtins attribute in the Nix expression language containing pre-computed hashes for commonly used fetchers. This change eliminates the need to repeatedly calculate these hashes during each evaluation, resulting in faster build times, particularly noticeable in projects with many dependencies. The post demonstrates benchmark results showing a substantial reduction in evaluation time with this optimization, highlighting its potential to improve the overall Nix user experience.
This blog post details how to use Nix to manage persistent software installations on a Steam Deck, separate from the read-only SteamOS filesystem. The author leverages a separate ext4 partition formatted and mounted at /opt, where Nix stores its packages. This setup allows users to install and manage software without affecting the integrity of the core system, offering a robust and reproducible environment. The guide covers partitioning, mounting, installing Nix, configuring the system to recognize the Nix store, and provides practical examples for installing and running applications like Discord and installing desktop environments like KDE Plasma. This approach offers a significant advantage for users seeking a more flexible and powerful software management solution on their Steam Deck.
Several commenters on Hacker News expressed skepticism about the practicality of using Nix on the Steam Deck, citing complexity, limited storage space, and potential performance impacts. Some suggested alternative solutions like using Flatpak or simply managing game installations through Steam directly. Others questioned the need for persistent packages at all for gaming. However, a few commenters found the approach interesting and appreciated the author's exploration of Nix on a non-traditional platform, showcasing its flexibility. Some acknowledged the potential benefits of reproducible environments, especially for development or modding. The discussion also touched on the steep learning curve of Nix and the need for better documentation and tooling to make it more accessible.
Several Linux distributions, including Arch Linux, Debian, Fedora, and NixOS, are collaborating to improve reproducible builds. This means ensuring that compiling source code results in identical binary packages, regardless of the build environment or timing. This joint effort aims to increase security by allowing independent verification that binaries haven't been tampered with and simplifies debugging by guaranteeing consistent build outputs. The project involves sharing tools and best practices across distributions, improving build reproducibility across different architectures, and working upstream with software developers to address issues that hinder reproducibility.
Hacker News commenters generally expressed support for the reproducible builds initiative, viewing it as a crucial step towards improved security and trustworthiness. Some highlighted the potential to identify malicious code injections, while others emphasized the benefits for debugging and verifying software integrity. A few commenters discussed the practical challenges of achieving reproducible builds across different distributions, citing variations in build environments and dependencies as potential obstacles. One commenter questioned the feasibility of guaranteeing bit-for-bit reproducibility across all architectures, prompting a discussion about the nuances of the goal and the acceptability of minor, non-functional differences. There was also some discussion of existing tooling and the importance of community involvement in driving the project forward.
Laurie Tratt's blog post explores the tension between the convenience of transitive dependencies in software development and the security risks they introduce. Transitive dependencies, where a project relies on libraries that themselves have dependencies, simplify development but create a sprawling attack surface. The post argues that while completely eliminating transitive dependencies is impractical, mitigating their risks is crucial. Proposed solutions include tools for visualizing and understanding the dependency tree, stricter version pinning, vulnerability scanning, and possibly leveraging WebAssembly or similar technologies to isolate dependencies. The ultimate goal is to find a balance, retaining the efficiency gains of transitive dependencies while minimizing the potential for security breaches via deeply nested, often unvetted, code.
HN commenters largely agree with the author's premise that transitive dependencies pose a significant security risk. Several highlight the difficulty of auditing even direct dependencies, let alone the exponentially increasing number of transitive ones. Some suggest exploring alternative dependency management strategies like vendoring or stricter version pinning. A few commenters discuss the tradeoff between convenience and security, with one pointing out the parallels to the "DLL hell" problem of the past. Another emphasizes the importance of verifying dependencies through various methods like checksumming and code review. A recurring theme is the need for better tooling to manage the complexity of dependencies and improve security in the software supply chain.
This blog post explains how to visualize a Python project's dependencies to better understand its structure and potential issues. It recommends several tools, including pipdeptree for a simple text-based dependency tree, pip-graph for a visual graph output in various formats (including SVG and PNG), and dependency-graph for generating an interactive HTML visualization. The post also briefly touches on using conda's conda-tree utility within Conda environments. By visualizing project dependencies, developers can identify circular dependencies, conflicts, and outdated packages, leading to a healthier and more manageable codebase.
Hacker News users discussed various tools for visualizing Python dependencies beyond the one presented in the article (Gauge). Several commenters recommended pipdeptree for its simplicity and effectiveness, while others pointed out more advanced options like dephell and the Poetry package manager's built-in visualization capabilities. Some highlighted the importance of understanding not just direct but also transitive dependencies, and the challenges of managing complex dependency graphs in larger projects. One user shared a personal anecdote about using Gephi to visualize and analyze a particularly convoluted dependency graph, ultimately opting to refactor the project for simplicity. The discussion also touched on tools for other languages, like cargo-tree for Rust, emphasizing a broader interest in dependency management and visualization across different ecosystems.
Nick Janetakis's blog post explores the maximum number of Alpine Linux packages installable at once. He systematically tested installation limits, encountering various errors related to package database size, memory usage, and filesystem capacity. Ultimately, he managed to install around 7,800 packages simultaneously before hitting unavoidable resource constraints, demonstrating that while Alpine's package manager can technically handle a vast number of packages, practical limitations arise from system resources. His experiment highlights the balance between package manager capabilities and the realistic constraints of a system's available memory and storage.
Hacker News users generally agree with the article's premise that Alpine Linux's package manager allows for installing a remarkably high number of packages simultaneously, far exceeding other distributions. Some commenters point out that this isn't necessarily a practical metric, arguing it's more of a fun experiment than a reflection of real-world usage. A few suggest the high number is likely due to Alpine's smaller package size and its minimalist approach. Others discuss the potential implications for dependency management and the possibility of conflicts arising from installing so many packages. One commenter questions the significance of the experiment, suggesting a focus on package quality and usability is more important than sheer quantity.
The Hacker News post discusses whether any programming languages allow specifying package dependencies directly within import or include statements, rather than separately in a dedicated dependency management file. The original poster highlights the potential benefits of this approach, such as improved clarity and ease of understanding dependencies for individual files. They suggest a syntax where version numbers or constraints could be incorporated into the import statement itself. While no existing mainstream languages seem to offer this feature, some commenters mention related concepts like import maps in JavaScript and conditional imports in some languages. The core idea is to make dependency management more localized and transparent at the file level.
The Hacker News comments discuss the pros and cons of specifying package requirements directly within import statements. Several commenters appreciate the clarity and explicitness this would bring, as it makes dependencies immediately obvious and reduces the need for separate dependency management files. Others argue against it, citing potential drawbacks like redundancy, increased code verbosity, and difficulties managing complex dependency graphs. Some propose alternative solutions, like embedding version requirements in comments or using language-specific mechanisms for dependency specification. A few commenters mention existing languages or tools that offer similar functionality, such as Nix and Dhall, pointing to these as potential examples or inspiration for how such a system could work. The discussion also touches on the practical implications for tooling and build systems, with commenters considering the impact on IDE integration and compilation processes.
Summary of Comments ( 30 )
https://news.ycombinator.com/item?id=43026071
Hacker News users generally praised the technique described in the article for improving Nix evaluation performance. Several commenters highlighted the cleverness of pre-computing store paths, noting that it bypasses a significant bottleneck in Nix's evaluation process. Some expressed surprise that this optimization wasn't already implemented, while others discussed potential downsides, like the added complexity to the tooling and the risk of invalidating the cache if the store path changes. A few users also shared their own experiences with Nix performance issues and suggested alternative optimization strategies. One commenter questioned the significance of the improvement in practical scenarios, arguing that derivation evaluation is often not the dominant factor in overall build time.
The Hacker News post "Improved evaluation times with pre-resolved Nix store paths" discussing the linked blog post about optimizing Nix evaluation times has generated a moderate number of comments, mostly focusing on the technical aspects and implications of the proposed optimization.
Several commenters express interest and appreciation for the performance improvements achieved by pre-resolving Nix store paths. One commenter specifically mentions how significant the improvements are, particularly for larger projects where evaluation time can be a bottleneck. Another highlights the potential benefits this optimization could bring to projects using Nix flakes, which often involve numerous dependencies and complex evaluation graphs.
A significant portion of the discussion revolves around the intricacies of Nix's evaluation model and how this optimization interacts with it. One commenter delves into the technical details of how Nix resolves paths and how pre-resolution can avoid redundant work, leading to faster evaluation times. Another discusses the trade-offs involved in pre-computing these paths, noting that while it improves evaluation speed, it might introduce complexity in other areas. There's also a comment exploring the potential implications of this change for Nix's caching mechanisms.
Some commenters also raise questions about the implementation and practical applications of this optimization. One inquires about the feasibility of integrating this technique into Nix itself, while another asks about potential compatibility issues with existing Nix projects. A user questions the overall impact on real-world usage, wondering if the improvement is noticeable in typical development workflows. There is further discussion around specific aspects of the implementation, including the use of SHA256 hashes and the handling of dynamic dependencies.
Finally, there are a few comments that offer alternative perspectives or suggestions. One commenter proposes a different approach to optimizing Nix evaluation, suggesting that focusing on reducing the number of dependencies might be more effective. Another mentions related work in other build systems, drawing parallels and highlighting potential areas for cross-pollination.