Project Icon

nixery

基于Nix的按需Docker容器镜像构建服务

Nixery是一个Docker兼容的容器注册中心,可根据镜像名称自动构建和提供容器镜像。它通过镜像名称的路径组件指定包含的软件包,采用优化的分层策略,支持私有包集成,并利用Google Cloud Storage高效提供镜像层。Nixery提供灵活的配置选项,适用于多种部署场景。


Build status

Nixery is a Docker-compatible container registry that is capable of transparently building and serving container images using Nix.

Images are built on-demand based on the image name. Every package that the user intends to include in the image is specified as a path component of the image name.

The path components refer to top-level keys in nixpkgs and are used to build a container image using a layering strategy that optimises for caching popular and/or large dependencies.

A public instance as well as additional documentation is available at nixery.dev.

You can watch the NixCon 2019 talk about Nixery for more information about the project and its use-cases.

The canonical location of the Nixery source code is //tools/nixery in the TVL monorepository. If cloning the entire repository is not desirable, the Nixery subtree can be cloned like this:

git clone https://code.tvl.fyi/depot.git:/tools/nixery.git

The subtree is infrequently mirrored to tazjin/nixery on Github.

Demo

Click the image to see an example in which an image containing an interactive shell and GNU hello is downloaded.

asciicast

To try it yourself, head to nixery.dev!

The special meta-package shell provides an image base with many core components (such as bash and coreutils) that users commonly expect in interactive images.

Feature overview

  • Serve container images on-demand using image names as content specifications

    Specify package names as path components and Nixery will create images, using the most efficient caching strategy it can to share data between different images.

  • Use private package sets from various sources

    In addition to building images from the publicly available Nix/NixOS channels, a private Nixery instance can be configured to serve images built from a package set hosted in a custom git repository or filesystem path.

    When using this feature with custom git repositories, Nixery will forward the specified image tags as git references.

    For example, if a company used a custom repository overlaying their packages on the Nix package set, images could be built from a git tag release-v2:

    docker pull nixery.thecompany.website/custom-service:release-v2

  • Efficient serving of image layers from Google Cloud Storage

    After building an image, Nixery stores all of its layers in a GCS bucket and forwards requests to retrieve layers to the bucket. This enables efficient serving of layers, as well as sharing of image layers between redundant instances.

Configuration

Nixery supports the following configuration options, provided via environment variables:

  • PORT: HTTP port on which Nixery should listen

  • NIXERY_CHANNEL: The name of a Nix/NixOS channel to use for building

  • NIXERY_PKGS_REPO: URL of a git repository containing a package set (uses locally configured SSH/git credentials)

  • NIXERY_PKGS_PATH: A local filesystem path containing a Nix package set to use for building

  • NIXERY_STORAGE_BACKEND: The type of backend storage to use, currently supported values are gcs (Google Cloud Storage) and filesystem.

    For each of these additional backend configuration is necessary, see the storage section for details.

  • NIX_TIMEOUT: Number of seconds that any Nix builder is allowed to run (defaults to 60)

  • NIX_POPULARITY_URL: URL to a file containing popularity data for the package set (see popcount/)

If the GOOGLE_APPLICATION_CREDENTIALS environment variable is set to a service account key, Nixery will also use this key to create [signed URLs][] for layers in the storage bucket. This makes it possible to serve layers from a bucket without having to make them publicly available.

In case the GOOGLE_APPLICATION_CREDENTIALS environment variable is not set, a redirect to storage.googleapis.com is issued, which means the underlying bucket objects need to be publicly accessible.

Storage

Nixery supports multiple different storage backends in which its build cache and image layers are kept, and from which they are served.

Currently the available storage backends are Google Cloud Storage and the local file system.

In the GCS case, images are served by redirecting clients to the storage bucket. Layers stored on the filesystem are served straight from the local disk.

These extra configuration variables must be set to configure storage backends:

  • GCS_BUCKET: Name of the Google Cloud Storage bucket to use (required for gcs)
  • GOOGLE_APPLICATION_CREDENTIALS: Path to a GCP service account JSON key (optional for gcs)
  • STORAGE_PATH: Path to a folder in which to store and from which to serve data (required for filesystem)

Background

The project started out inspired by the buildLayeredImage blog post with the intention of becoming a Kubernetes controller that can serve declarative image specifications specified in CRDs as container images. The design for this was outlined in a public gist.

Roadmap

Kubernetes integration

It should be trivial to deploy Nixery inside of a Kubernetes cluster with correct caching behaviour, addressing and so on.

See issue #4.

Nix-native builder

The image building and layering functionality of Nixery will be extracted into a separate Nix function, which will make it possible to build images directly in Nix builds.

项目侧边栏1项目侧边栏2
推荐项目
Project Cover

豆包MarsCode

豆包 MarsCode 是一款革命性的编程助手,通过AI技术提供代码补全、单测生成、代码解释和智能问答等功能,支持100+编程语言,与主流编辑器无缝集成,显著提升开发效率和代码质量。

Project Cover

AI写歌

Suno AI是一个革命性的AI音乐创作平台,能在短短30秒内帮助用户创作出一首完整的歌曲。无论是寻找创作灵感还是需要快速制作音乐,Suno AI都是音乐爱好者和专业人士的理想选择。

Project Cover

有言AI

有言平台提供一站式AIGC视频创作解决方案,通过智能技术简化视频制作流程。无论是企业宣传还是个人分享,有言都能帮助用户快速、轻松地制作出专业级别的视频内容。

Project Cover

Kimi

Kimi AI助手提供多语言对话支持,能够阅读和理解用户上传的文件内容,解析网页信息,并结合搜索结果为用户提供详尽的答案。无论是日常咨询还是专业问题,Kimi都能以友好、专业的方式提供帮助。

Project Cover

阿里绘蛙

绘蛙是阿里巴巴集团推出的革命性AI电商营销平台。利用尖端人工智能技术,为商家提供一键生成商品图和营销文案的服务,显著提升内容创作效率和营销效果。适用于淘宝、天猫等电商平台,让商品第一时间被种草。

Project Cover

吐司

探索Tensor.Art平台的独特AI模型,免费访问各种图像生成与AI训练工具,从Stable Diffusion等基础模型开始,轻松实现创新图像生成。体验前沿的AI技术,推动个人和企业的创新发展。

Project Cover

SubCat字幕猫

SubCat字幕猫APP是一款创新的视频播放器,它将改变您观看视频的方式!SubCat结合了先进的人工智能技术,为您提供即时视频字幕翻译,无论是本地视频还是网络流媒体,让您轻松享受各种语言的内容。

Project Cover

美间AI

美间AI创意设计平台,利用前沿AI技术,为设计师和营销人员提供一站式设计解决方案。从智能海报到3D效果图,再到文案生成,美间让创意设计更简单、更高效。

Project Cover

AIWritePaper论文写作

AIWritePaper论文写作是一站式AI论文写作辅助工具,简化了选题、文献检索至论文撰写的整个过程。通过简单设定,平台可快速生成高质量论文大纲和全文,配合图表、参考文献等一应俱全,同时提供开题报告和答辩PPT等增值服务,保障数据安全,有效提升写作效率和论文质量。

投诉举报邮箱: service@vectorlightyear.com
@2024 懂AI·鲁ICP备2024100362号-6·鲁公网安备37021002001498号