stargz-snapshotter

stargz-snapshotter

容器延迟拉取技术提升启动速度

Stargz Snapshotter是containerd的快照插件,利用延迟拉取技术加快容器启动。它支持与OCI/Docker兼容的eStargz镜像格式,可存储于标准容器仓库。该技术允许容器在镜像完全下载前启动,并按需获取所需数据。通过运行时优化和内容验证等功能,Stargz Snapshotter有效缩短容器启动时间,对大型镜像尤其有效。

Stargz Snapshotter容器懒加载eStargzcontainerdGithub开源项目

[⬇️ Download] [📔Browse images] [☸Quick Start (Kubernetes)] [🤓Quick Start (nerdctl)] [🔆Install]

Stargz Snapshotter

Tests Status Benchmarking Nightly

Read also introductory blog: Startup Containers in Lightning Speed with Lazy Image Distribution on Containerd

Pulling image is one of the time-consuming steps in the container lifecycle. Research shows that time to take for pull operation accounts for 76% of container startup time[FAST '16]. Stargz Snapshotter is an implementation of snapshotter which aims to solve this problem by lazy pulling. Lazy pulling here means a container can run without waiting for the pull completion of the image and necessary chunks of the image are fetched on-demand.

eStargz is a lazily-pullable image format proposed by this project. This is compatible to OCI/Docker images so this can be pushed to standard container registries (e.g. ghcr.io) as well as this is still runnable even on eStargz-agnostic runtimes including Docker. eStargz format is based on stargz image format by CRFS but comes with additional features like runtime optimization and content verification.

The following histogram is the benchmarking result for startup time of several containers measured on Github Actions, using GitHub Container Registry.

<img src="docs/images/benchmarking-result-ecdb227.png" width="600" alt="The benchmarking result on ecdb227">

legacy shows the startup performance when we use containerd's default snapshotter (overlayfs) with images copied from docker.io/library without optimization. For this configuration, containerd pulls entire image contents and pull operation takes accordingly. When we use stargz snapshotter with eStargz-converted images but without any optimization (estargz-noopt) we are seeing performance improvement on the pull operation because containerd can start the container without waiting for the pull completion and fetch necessary chunks of the image on-demand. But at the same time, we see the performance drawback for run operation because each access to files takes extra time for fetching them from the registry. When we use eStargz with optimization (estargz), we can mitigate the performance drawback observed in estargz-noopt images. This is because stargz snapshotter prefetches and caches likely accessed files during running the container. On the first container creation, stargz snapshotter waits for the prefetch completion so create sometimes takes longer than other types of image. But it's still shorter than waiting for downloading all files of all layers.

The above histogram is the benchmarking result on the commit ecdb227. We are constantly measuring the performance of this snapshotter so you can get the latest one through the badge shown top of this doc. Please note that we sometimes see dispersion among the results because of the NW condition on the internet and the location of the instance in the Github Actions, etc. Our benchmarking method is based on HelloBench.

:nerd_face: You can also run containers on IPFS with lazy pulling. This is an experimental feature. See ./docs/ipfs.md for more details.

Stargz Snapshotter is a non-core sub-project of containerd.

Quick Start with Kubernetes

For using stargz snapshotter on kubernetes nodes, you need the following configuration to containerd as well as run stargz snapshotter daemon on the node. We assume that you are using containerd (> v1.4.2) as a CRI runtime.

version = 2 # Plug stargz snapshotter into containerd # Containerd recognizes stargz snapshotter through specified socket address. # The specified address below is the default which stargz snapshotter listen to. [proxy_plugins] [proxy_plugins.stargz] type = "snapshot" address = "/run/containerd-stargz-grpc/containerd-stargz-grpc.sock" # Use stargz snapshotter through CRI [plugins."io.containerd.grpc.v1.cri".containerd] snapshotter = "stargz" disable_snapshot_annotations = false

Note that disable_snapshot_annotations = false is required since containerd > v1.4.2

You can try our prebuilt KinD node image that contains the above configuration.

$ kind create cluster --name stargz-demo --image ghcr.io/containerd/stargz-snapshotter:0.12.1-kind

:information_source: kind binary v0.16.x or newer is recommended for ghcr.io/containerd/stargz-snapshotter:0.12.1-kind.

:information_source: You can get latest node images from ghcr.io/containerd/stargz-snapshotter:${VERSION}-kind namespace.

Then you can create eStargz pods on the cluster. In this example, we create a stargz-converted Node.js pod (ghcr.io/stargz-containers/node:17.8.0-esgz) as a demo.

apiVersion: v1 kind: Pod metadata: name: nodejs spec: containers: - name: nodejs-stargz image: ghcr.io/stargz-containers/node:17.8.0-esgz command: ["node"] args: - -e - var http = require('http'); http.createServer(function(req, res) { res.writeHead(200); res.end('Hello World!\n'); }).listen(80); ports: - containerPort: 80

The following command lazily pulls ghcr.io/stargz-containers/node:17.8.0-esgz from Github Container Registry and creates the pod so the time to take for it is shorter than the original image library/node:13.13.

$ kubectl --context kind-stargz-demo apply -f stargz-pod.yaml && kubectl --context kind-stargz-demo get po nodejs -w $ kubectl --context kind-stargz-demo port-forward nodejs 8080:80 & $ curl 127.0.0.1:8080 Hello World!

Stargz snapshotter also supports further configuration including private registry authentication, mirror registries, etc.

Getting eStargz images

For lazy pulling images, you need to prepare eStargz images first. There are several ways to achieve that. This section describes some of them.

Trying pre-built eStargz images

You can try our pre-converted eStargz images on ghcr.io listed in Trying pre-converted images.

Building eStargz images using BuildKit

BuildKit supports building eStargz image since v0.10.

You can try it using Docker Buildx. The following command builds an eStargz image and push it to ghcr.io/ktock/hello:esgz. Flags oci-mediatypes=true,compression=estargz enable to build eStargz.

$ docker buildx build -t ghcr.io/ktock/hello:esgz \
    -o type=registry,oci-mediatypes=true,compression=estargz,force-compression=true \
    /tmp/buildctx/

NOTE1: force-compression=true isn't needed if the base image is already eStargz.

NOTE2: Docker still does not support lazy pulling of eStargz.

eStargz-enabled BuildKit (v0.10) will be included to Docker v22.XX however you can build eStargz images with the prior version using Buildx driver feature. You can enable the specific version of BuildKit using docker buildx create (this example specifies v0.10.3).

$ docker buildx create --use --name v0.10.3 --driver docker-container --driver-opt image=moby/buildkit:v0.10.3
$ docker buildx inspect --bootstrap v0.10.3

Building eStargz images using Kaniko

Kaniko is an image builder runnable in containers and Kubernetes. Since v1.5.0, it experimentally supports building eStargz. GGCR_EXPERIMENT_ESTARGZ=1 is needed.

$ docker run --rm -e GGCR_EXPERIMENT_ESTARGZ=1 \ -v /tmp/buildctx:/workspace -v ~/.docker/config.json:/kaniko/.docker/config.json:ro \ gcr.io/kaniko-project/executor:v1.8.1 --destination ghcr.io/ktock/hello:esgz

Building eStargz images using nerdctl

nerdctl, Docker-compatible CLI of containerd, supports building eStargz images.

$ nerdctl build -t ghcr.io/ktock/hello:1 /tmp/buildctx $ nerdctl image convert --estargz --oci ghcr.io/ktock/hello:1 ghcr.io/ktock/hello:esgz $ nerdctl push ghcr.io/ktock/hello:esgz

NOTE: --estargz should be specified in conjunction with --oci

Please refer to nerdctl document for details for further information (e.g. lazy pulling): https://github.com/containerd/nerdctl/blob/master/docs/stargz.md

Creating eStargz images using ctr-remote

ctr-remote allows converting an image into eStargz with optimizing it. As shown in the above benchmarking result, on-demand lazy pulling improves the performance of pull but causes runtime performance penalty because reading files induce remotely downloading contents. For solving this, ctr-remote has workload-based optimization for images.

For trying the examples described in this section, you can also use the docker-compose-based demo environment. You can setup this environment as the following commands (put this repo on ${GOPATH}/src/github.com/containerd/stargz-snapshotter). Note that this runs privileged containers on your host.

$ cd ${GOPATH}/src/github.com/containerd/stargz-snapshotter/script/demo $ docker-compose build containerd_demo $ docker-compose up -d $ docker exec -it containerd_demo /bin/bash (inside container) # ./script/demo/run.sh

Generally, container images are built with purpose and the workloads are defined in the Dockerfile with some parameters (e.g. entrypoint, envvars and user). By default, ctr-remote optimizes the performance of reading files that are most likely accessed in the workload defined in the Dockerfile. You can also specify the custom workload using options if needed.

The following example converts the legacy library/ubuntu:20.04 image into eStargz. The command also optimizes the image for the workload of executing ls on /bin/bash. The thing actually done is it runs the specified workload in a temporary container and profiles all file accesses with marking them as likely accessed also during runtime. The converted image is still docker-compatible so you can run it with eStargz-agnostic runtimes (e.g. Docker).

# ctr-remote image pull docker.io/library/ubuntu:20.04 # ctr-remote image optimize --oci --entrypoint='[ "/bin/bash", "-c" ]' --args='[ "ls" ]' docker.io/library/ubuntu:20.04 registry2:5000/ubuntu:20.04 # ctr-remote image push --plain-http registry2:5000/ubuntu:20.04

Finally, the following commands clear the local cache then pull the eStargz image lazily. Stargz snapshotter prefetches files that are most likely accessed in the optimized workload, which hopefully increases the cache hit rate for that workload and mitigates runtime overheads as shown in the benchmarking result shown top of this doc.

# ctr-remote image rm --sync registry2:5000/ubuntu:20.04 # ctr-remote images rpull --plain-http registry2:5000/ubuntu:20.04 fetching sha256:610399d1... application/vnd.oci.image.index.v1+json fetching sha256:0b4a26b4... application/vnd.oci.image.manifest.v1+json fetching sha256:8d8d9dbe... application/vnd.oci.image.config.v1+json # ctr-remote run --rm -t --snapshotter=stargz registry2:5000/ubuntu:20.04 test /bin/bash root@8eabb871a9bd:/# ls bin boot dev etc home lib lib32 lib64 libx32 media mnt opt proc root run sbin srv sys tmp usr var

NOTE: You can perform lazy pulling from any OCI-compatible registries (e.g. docker.io, ghcr.io, etc) as long as the image is formatted as eStargz.

Importing Stargz Snapshotter as go module

Currently, Stargz Snapshotter repository contains two Go modules as the following and both of them need to be imported.

  • github.com/containerd/stargz-snapshotter
  • github.com/containerd/stargz-snapshotter/estargz

Please make sure you import the both of them and they point to the same commit version.

Project details

Stargz Snapshotter is a containerd non-core sub-project, licensed under the Apache 2.0 license. As a containerd non-core sub-project, you will find the:

information in our containerd/project

编辑推荐精选

iTerms

iTerms

企业专属的AI法律顾问

iTerms是法大大集团旗下法律子品牌,基于最先进的大语言模型(LLM)、专业的法律知识库和强大的智能体架构,帮助企业扫清合规障碍,筑牢风控防线,成为您企业专属的AI法律顾问。

SimilarWeb流量提升

SimilarWeb流量提升

稳定高效的流量提升解决方案,助力品牌曝光

稳定高效的流量提升解决方案,助力品牌曝光

Sora2视频免费生成

Sora2视频免费生成

最新版Sora2模型免费使用,一键生成无水印视频

最新版Sora2模型免费使用,一键生成无水印视频

Transly

Transly

实时语音翻译/同声传译工具

Transly是一个多场景的AI大语言模型驱动的同声传译、专业翻译助手,它拥有超精准的音频识别翻译能力,几乎零延迟的使用体验和支持多国语言可以让你带它走遍全球,无论你是留学生、商务人士、韩剧美剧爱好者,还是出国游玩、多国会议、跨国追星等等,都可以满足你所有需要同传的场景需求,线上线下通用,扫除语言障碍,让全世界的语言交流不再有国界。

讯飞绘文

讯飞绘文

选题、配图、成文,一站式创作,让内容运营更高效

讯飞绘文,一个AI集成平台,支持写作、选题、配图、排版和发布。高效生成适用于各类媒体的定制内容,加速品牌传播,提升内容营销效果。

热门AI辅助写作AI工具讯飞绘文内容运营AI创作个性化文章多平台分发AI助手
TRAE编程

TRAE编程

AI辅助编程,代码自动修复

Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。

AI工具TraeAI IDE协作生产力转型热门
商汤小浣熊

商汤小浣熊

最强AI数据分析助手

小浣熊家族Raccoon,您的AI智能助手,致力于通过先进的人工智能技术,为用户提供高效、便捷的智能服务。无论是日常咨询还是专业问题解答,小浣熊都能以快速、准确的响应满足您的需求,让您的生活更加智能便捷。

imini AI

imini AI

像人一样思考的AI智能体

imini 是一款超级AI智能体,能根据人类指令,自主思考、自主完成、并且交付结果的AI智能体。

Keevx

Keevx

AI数字人视频创作平台

Keevx 一款开箱即用的AI数字人视频创作平台,广泛适用于电商广告、企业培训与社媒宣传,让全球企业与个人创作者无需拍摄剪辑,就能快速生成多语言、高质量的专业视频。

即梦AI

即梦AI

一站式AI创作平台

提供 AI 驱动的图片、视频生成及数字人等功能,助力创意创作

下拉加载更多