239 lines
12 KiB
Markdown
239 lines
12 KiB
Markdown
---
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stage: none
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group: unassigned
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comments: false
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description: 'Next Runner Auto-scaling Architecture'
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---
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# Next Runner Auto-scaling Architecture
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## Summary
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GitLab Runner is a core component of GitLab CI/CD. It makes it possible to run
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CI/CD jobs in a reliable and concurrent environment. It has been initially
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introduced by Kamil Trzciński in early 2015 to replace a Ruby version of the
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same service. GitLab Runner written in Go turned out to be easier to use by the
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wider community, it was more efficient and reliable than the previous,
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Ruby-based, version.
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In February 2016 Kamil Trzciński [implemented an auto-scaling feature](https://gitlab.com/gitlab-org/gitlab-runner/-/merge_requests/53)
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to leverage cloud infrastructure to run many CI/CD jobs in parallel. This
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feature has become a foundation supporting CI/CD adoption on GitLab.com over
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the years, where we now run around 4 million builds per day at peak.
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During the initial implementation a decision was made to use Docker Machine:
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> Is easy to use. Is well documented. Is well supported and constantly
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> extended. It supports almost any cloud provider or virtualization
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> infrastructure. We need minimal amount of changes to support Docker Machine:
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> machine enumeration and inspection. We don't need to implement any "cloud
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> specific" features.
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This design choice was crucial for the GitLab Runner success. Since that time
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the auto-scaling feature has been used by many users and customers and enabled
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rapid growth of CI/CD adoption on GitLab.com.
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We can not, however, continue using Docker Machine. Work on that project [was
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paused in July 2018](https://github.com/docker/machine/issues/4537) and there
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was no development made since that time (except for some highly important
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security fixes). In 2018, after Docker Machine entered the “maintenance mode”,
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we decided to create [our own fork](https://gitlab.com/gitlab-org/ci-cd/docker-machine)
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to be able to keep using this and ship fixes and updates needed for our use case.
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[On September 26th, 2021 the project got archived](https://github.com/docker/docker.github.io/commit/2dc8b49dcbe85686cc7230e17aff8e9944cb47a5)
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and the documentation for it has been removed from the official page. This
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means that the original reason to use Docker Machine is no longer valid too.
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To keep supporting our customers and the wider community we need to design a
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new mechanism for GitLab Runner autoscaling. It not only needs to support
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auto-scaling, but it also needs to do that in the way to enable us to build on
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top of it to improve efficiency, reliability and availability.
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We call this new mechanism the “next GitLab Runner Scaling architecture”.
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_Disclaimer The following contain information related to upcoming products,
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features, and functionality._
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_It is important to note that the information presented is for informational
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purposes only. Please do not rely on this information for purchasing or
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planning purposes._
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_As with all projects, the items mentioned in this document and linked pages are
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subject to change or delay. The development, release and timing of any
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products, features, or functionality remain at the sole discretion of GitLab
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Inc._
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## Proposal
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Currently, GitLab Runner auto-scaling can be configured in a few ways. Some
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customers are successfully using an auto-scaled environment in Kubernetes. We
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know that a custom and unofficial GitLab Runner version has been built to make
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auto-scaling on Kubernetes more reliable. We recognize the importance of having
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a really good Kubernetes solution for running multiple jobs in parallel, but
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refinements in this area are out of scope for this architectural initiative.
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We want to focus on resolving problems with Docker Machine and replacing this
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mechanism with a reliable and flexible mechanism. We might be unable to build a
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drop-in replacement for Docker Machine, as there are presumably many reasons
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why it has been deprecated. It is very difficult to maintain compatibility with
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so many cloud providers, and it seems that Docker Machine has been deprecated
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in favor of Docker Desktop, which is not a viable replacement for us. [This
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issue](https://github.com/docker/roadmap/issues/245) contains a discussion
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about how people are using Docker Machine right now, and it seems that GitLab
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CI is one of the most frequent reasons for people to keep using Docker Machine.
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There is also an opportunity in being able to optionally run multiple jobs in a
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single, larger virtual machine. We can’t do that today, but we know that this
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can potentially significantly improve efficiency. We might want to build a new
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architecture that makes it easier and allows us to test how efficient it is
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with PoCs. Running multiple jobs on a single machine can also make it possible
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to reuse what we call a “sticky context” - a space for build artifacts / user
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data that can be shared between job runs.
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### 💡 Design a simple abstraction that users will be able to build on top of
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Because there is no viable replacement and we might be unable to support all
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cloud providers that Docker Machine used to support, the key design requirement
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is to make it really simple and easy for the wider community to write a custom
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GitLab auto-scaling plugin, whatever cloud provider they might be using. We
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want to design a simple abstraction that users will be able to build on top, as
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will we to support existing workflows on GitLab.com.
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The designed mechanism should abstract what Docker Machine executor has been doing:
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providing a way to create an external Docker environment, waiting to execute
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jobs by provisioning this environment and returning credentials required to
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perform these operations.
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The new plugin system should be available for all major platforms: Linux,
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Windows, MacOS.
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### 💡 Migrate existing Docker Machine solution to a plugin
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Once we design and implement the new abstraction, we should be able to migrate
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existing Docker Machine mechanisms to a plugin. This will make it possible for
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users and customers to immediately start using the new architecture, but still
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keep their existing workflows and configuration for Docker Machine. This will
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give everyone time to migrate to the new architecture before we drop support
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for the legacy auto-scaling entirely.
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### 💡 Build plugins for AWS, Google Cloud Platform and Azure
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Although we might be unable to add support for all the cloud providers that
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Docker Machine used to support, it seems to be important to provide
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GitLab-maintained plugins for the major cloud providers like AWS, Google Cloud
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Platform and Azure.
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We should build them, presumably in separate repositories, in a way that they
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are easy to contribute to, fork, modify for certain needs the wider community
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team members might have. It should be also easy to install a new plugin without
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the need of rebuilding GitLab Runner whenever it happens.
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### 💡 Write a solid documentation about how to build your own plugin
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It is important to show users how to build an auto-scaling plugin, so that they
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can implement support for their own cloud infrastructure.
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Building new plugins should be simple, and with the support of great
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documentation it should not require advanced skills, like understanding how
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gRPC works. We want to design the plugin system in a way that the entry barrier
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for contributing new plugins is very low.
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### 💡 Build a PoC to run multiple builds on a single machine
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We want to better understand what kind of efficiency can running multiple jobs
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on a single machine bring. It is difficult to predict that, so ideally we
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should build a PoC that will help us to better understand what we can expect
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from this.
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To run this experiement we most likely we will need to build an experimental
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plugin, that not only allows us to schedule running multiple builds on a single
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machine, but also has a set of comprehensive metrics built into it, to make it
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easier to understand how it performs.
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## Details
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How the abstraction for the custom provider will look exactly is something that
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we will need to prototype, PoC and decide in a data-informed way. There are a
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few proposals that we should describe in detail, develop requirements for, PoC
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and score. We will choose the solution that seems to support our goals the
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most.
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In order to describe the proposals we first need to better explain what part of
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the GitLab Runner needs to be abstracted away. To make this easier to grasp
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these concepts, let's take a look at the current auto-scaling architecture and
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sequence diagram.
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On the diagrams above we see that currently a GitLab Runner Manager runs on a
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machine that has access to a cloud provider’s API. It is using Docker Machine
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to provision new Virtual Machines with Docker Engine installed and it
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configures the Docker daemon there to allow external authenticated requests. It
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stores credentials to such ephemeral Docker environments on disk. Once a
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machine has been provisioned and made available for GitLab Runner Manager to
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run builds, it is using one of the existing executors to run a user-provided
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script. In auto-scaling, this is typically done using Docker executor.
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### Custom provider
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In order to reduce the scope of work, we only want to introduce the new
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abstraction layer in one place.
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A few years ago we introduced the [Custom Executor](https://docs.gitlab.com/runner/executors/custom.html)
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feature in GitLab Runner. It allows users to design custom build execution
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methods. The custom executor driver can be implemented in any way - from a
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simple shell script to a dedicated binary - that is then used by a Runner
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through os/exec system calls.
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Thanks to the custom executor abstraction there is no more need to implement
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new executors internally in Runner. Users who have specific needs can implement
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their own drivers and don’t need to wait for us to make their work part of the
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“official” GitLab Runner. As each driver is a separate project, it also makes
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it easier to create communities around them, where interested people can
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collaborate together on improvements and bug fixes.
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We want to design the new Custom Provider to replicate the success of the
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Custom Executor. It will make it easier for users to build their own ways to
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provide a context and an environment in which a build will be executed by one
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of the Custom Executors.
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There are multiple solutions to implementing a custom provider abstraction. We
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can use raw Go plugins, Hashcorp’s Go Plugin, HTTP interface or gRPC based
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facade service. There are many solutions, and we want to choose the most
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optimal one. In order to do that, we will describe the solutions in a separate
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document, define requirements and score the solution accordingly. This will
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allow us to choose a solution that will work best for us and the wider
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community.
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## Status
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Status: RFC.
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## Who
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Proposal:
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<!-- vale gitlab.Spelling = NO -->
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| Role | Who
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|------------------------------|------------------------------------------|
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| Authors | Grzegorz Bizon, Tomasz Maczukin |
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| Architecture Evolution Coach | Kamil Trzciński |
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| Engineering Leader | Elliot Rushton, Cheryl Li |
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| Product Manager | Darren Eastman, Jackie Porter |
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| Domain Expert / Runner | Arran Walker |
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DRIs:
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| Role | Who
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|------------------------------|------------------------|
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| Leadership | Elliot Rushton |
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| Product | Darren Eastman |
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| Engineering | Tomasz Maczukin |
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Domain experts:
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| Area | Who
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|------------------------------|------------------------|
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| Domain Expert / Runner | Arran Walker |
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<!-- vale gitlab.Spelling = YES -->
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