Git Product home page Git Product logo

little-vm-helper's Introduction

little-vm-helper

little-vm-helper (lvh) is a VM management tool, aimed for testing and development of features that depend on the kernel, such as BPF. It is used in cilium, tetragon, and pwru. It can also be used for kernel development. It is not meant, and should not be used for running production VMs. Fast booting and image building, as well as being storage efficient are the main goals.

It uses qemu and libguestfs tools.

Configurations for specific images used in the Cilium project can be found in: https://github.com/cilium/little-vm-helper-images.

Usage

For an example script, see scripts/example.sh.

LVH can be used to:

  • build root images for VMs
  • build kernels
  • boot VMs using above

Root images

Build example images:

$ mkdir _data
$ go run ./cmd/lvh images example-config > _data/images.json
$ go run ./cmd/lvh images build --dir _data # this may require sudo as relies on /dev/kvm

The first command will create a configuration file:

jq . < _data/images.json
[
  {
    "name": "base",
    "packages": [
      "less",
      "vim",
      "sudo",
      "openssh-server",
      "curl"
    ],
    "actions": [
      {
        "comment": "disable password for root",
        "op": {
          "Cmd": "passwd -d root"
        },
        "type": "run-command"
      }
    ]
  },
  {
    "name": "k8s",
    "parent": "base",
    "image_size": "20G",
    "packages": [
      "docker.io"
    ]
  }
]

The configuration file includes:

  • a set of packages for the image
  • an optional parent image
  • a set of actions to be performed after the installation of the packets. There are multiple actions supported, see pkg/images/actions.go.

Once the build-images command completes, the two images described in the configuration file will be present in the images directory. ote that the images are stored as sparse files so they take less space:

$ ls -sh1 _data/images/*.img
856M _data/images/base.img
1.7G _data/images/k8s.img

Kernels

$ mkdir -p _data/kernels
$ go run ./cmd/lvh kernels --dir _data init
$ go run ./cmd/lvh kernels --dir _data add bpf-next git://git.kernel.org/pub/scm/linux/kernel/git/bpf/bpf-next.git --fetch
$ go run ./cmd/lvh kernels --dir _data build bpf-next

The configuration file keeps the url for a kernel, together with its configuration options:

$ jq . < _data/kernel.json
{
  "kernels": [
    {
      "name": "bpf-next",
      "url": "git://git.kernel.org/pub/scm/linux/kernel/git/bpf/bpf-next.git"
    }
  ],
  "common_opts": [
    [
      "--enable",
      "CONFIG_LOCALVERSION_AUTO"
    ],
     ... more options ...
  ]
}

There are options that are applied to all kernels (common_opts) as well as kernel-specific options.

The kernels are kept in worktrees. Specifically, there is a git bare directory (git) that holds all the objects, and one worktree per kernel. This allows efficient fetching and, also, having each kernel on its own separate directory.

For example:

$ ls -1 _data/kernels
5.18/
bpf-next/
git/

Currently, kernels are built using the bzImage and dir-pkg targets (see pkg/kernels/conf.go).

Booting images

You can use the run subcommand to start images.

For example:

go run ./cmd/lvh run --image _data/images/base.qcow2 --kernel _data/kernels/bpf-next/arch/x86_64/boot/bzImage

Or, to with the kernel installed in the image:

go run ./cmd/lvh run --image _data/images/base.qcow2

Note: Building images and kernels is only supported on Linux. On the other hand, images and kernels already build on Linux can be booted in MacOS (both x86 and Arm). The only requirement is qemu-system-x86_64. As MacOS does not support KVM, the commands to boot images are:

go run ./cmd/lvh run --image _data/images/base.qcow2 --qemu-disable-kvm

FAQ

Why not use packer to build images?

Existing packer builders (e.g,.https://github.com/cilium/packer-ci-build/blob/710ad61e7d5b0b6872770729a30bcdade2ee1acb/cilium-ubuntu.json#L19, https://www.packer.io/plugins/builders/qemu) are meant to manage VMs with longer lifetimes than a single use, and use facilities that introduce unnecessary overhead for our use-case.

Also, packer does not seem to have a way to provision images without booting a machine. There is an outdated chroot package https://github.com/summerwind/packer-builder-qemu-chroot, and cloud chroot builders (e.g., https://www.packer.io/plugins/builders/amazon/chroot that uses https://github.com/hashicorp/packer-plugin-sdk/tree/main/chroot).

That being said, if we need packer functionality we can create a packer plugin (https://www.packer.io/docs/plugins/creation#developing-plugins).

Why not use vagrant (or libvirt-based tools)?

These tools also target production VMs with lifetime streching beyond a single use. As a result, they introduce overhead in booting time, provisioning time, and storage.

TODO

Notes

little-vm-helper's People

Contributors

aanm avatar brb avatar dependabot[bot] avatar dhawton avatar dramirez-qb avatar dylandreimerink avatar joestringer avatar kkourt avatar learnitall avatar markpash avatar mhofstetter avatar renovate[bot] avatar tklauser avatar tpapagian avatar willfindlay avatar yutarohayakawa avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.