- 💡 Design a simple abstraction that users will be able to build on top of
- 💡 Migrate existing Docker Machine solution to a plugin
- 💡 Build plugins for AWS, Google Cloud Platform and Azure
- 💡 Write a solid documentation about how to build your own plugin
- 💡 Build a PoC to run multiple builds on a single machine
- Plugin boundary proposals
GitLab Runner is a core component of GitLab CI/CD. It makes it possible to run CI/CD jobs in a reliable and concurrent environment. It has been initially introduced by Kamil Trzciński in early 2015 to replace a Ruby version of the same service. GitLab Runner written in Go turned out to be easier to use by the wider community, it was more efficient and reliable than the previous, Ruby-based, version.
In February 2016 Kamil Trzciński implemented an auto-scaling feature to leverage cloud infrastructure to run many CI/CD jobs in parallel. This feature has become a foundation supporting CI/CD adoption on GitLab.com over the years, where we now run around 4 million builds per day at peak.
During the initial implementation a decision was made to use Docker Machine:
Is easy to use. Is well documented. Is well supported and constantly extended. It supports almost any cloud provider or virtualization infrastructure. We need minimal amount of changes to support Docker Machine: machine enumeration and inspection. We don’t need to implement any “cloud specific” features.
This design choice was crucial for the GitLab Runner success. Since that time the auto-scaling feature has been used by many users and customers and enabled rapid growth of CI/CD adoption on GitLab.com.
We can not, however, continue using Docker Machine. Work on that project was paused in July 2018 and there was no development made since that time (except for some highly important security fixes). In 2018, after Docker Machine entered the “maintenance mode”, we decided to create our own fork to be able to keep using this and ship fixes and updates needed for our use case. On September 26th, 2021 the project got archived and the documentation for it has been removed from the official page. This means that the original reason to use Docker Machine is no longer valid too.
To keep supporting our customers and the wider community we need to design a new mechanism for GitLab Runner auto-scaling. It not only needs to support auto-scaling, but it also needs to do that in the way to enable us to build on top of it to improve efficiency, reliability and availability.
We call this new mechanism the “next GitLab Runner Scaling architecture”.
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Currently, GitLab Runner auto-scaling can be configured in a few ways. Some customers are successfully using an auto-scaled environment in Kubernetes. We know that a custom and unofficial GitLab Runner version has been built to make auto-scaling on Kubernetes more reliable. We recognize the importance of having a really good Kubernetes solution for running multiple jobs in parallel, but refinements in this area are out of scope for this architectural initiative.
We want to focus on resolving problems with Docker Machine and replacing this mechanism with a reliable and flexible mechanism. We might be unable to build a drop-in replacement for Docker Machine, as there are presumably many reasons why it has been deprecated. It is very difficult to maintain compatibility with so many cloud providers, and it seems that Docker Machine has been deprecated in favor of Docker Desktop, which is not a viable replacement for us. This issue contains a discussion about how people are using Docker Machine right now, and it seems that GitLab CI is one of the most frequent reasons for people to keep using Docker Machine.
There is also an opportunity in being able to optionally run multiple jobs in a single, larger virtual machine. We can’t do that today, but we know that this can potentially significantly improve efficiency. We might want to build a new architecture that makes it easier and allows us to test how efficient it is with PoCs. Running multiple jobs on a single machine can also make it possible to reuse what we call a “sticky context” - a space for build artifacts / user data that can be shared between job runs.
Because there is no viable replacement and we might be unable to support all cloud providers that Docker Machine used to support, the key design requirement is to make it really simple and easy for the wider community to write a custom GitLab plugin for whatever cloud provider they might be using. We want to design a simple abstraction that users will be able to build on top, as will we to support existing workflows on GitLab.com.
The designed mechanism should abstract what Docker Machine executor has been doing: providing a way to create an external Docker environment, waiting to execute jobs by provisioning this environment and returning credentials required to perform these operations.
The new plugin system should be available for all major platforms: Linux, Windows, MacOS.
Once we design and implement the new abstraction, we should be able to migrate existing Docker Machine mechanisms to a plugin. This will make it possible for users and customers to immediately start using the new architecture, but still keep their existing workflows and configuration for Docker Machine. This will give everyone time to migrate to the new architecture before we drop support for the legacy auto-scaling entirely.
Although we might be unable to add support for all the cloud providers that Docker Machine used to support, it seems to be important to provide GitLab-maintained plugins for the major cloud providers like AWS, Google Cloud Platform and Azure.
We should build them, presumably in separate repositories, in a way that they are easy to contribute to, fork, modify for certain needs the wider community team members might have. It should be also easy to install a new plugin without the need of rebuilding GitLab Runner whenever it happens.
It is important to show users how to build a plugin, so that they can implement support for their own cloud infrastructure.
Building new plugins should be simple and supported with great documentation. We want to design the plugin system in a way that the entry barrier for contributing new plugins is very low.
We want to better understand what kind of efficiency can running multiple jobs on a single machine bring. It is difficult to predict that, so ideally we should build a PoC that will help us to better understand what we can expect from this.
To run this experiment we most likely we will need to build an experimental plugin, that not only allows us to schedule running multiple builds on a single machine, but also has a set of comprehensive metrics built into it, to make it easier to understand how it performs.
How the abstraction for the custom provider will look exactly is something that we will need to prototype, PoC and decide in a data-informed way. There are a few proposals that we should describe in detail, develop requirements for, PoC and score. We will choose the solution that seems to support our goals the most.
In order to describe the proposals we first need to better explain what part of the GitLab Runner needs to be abstracted away. To make this easier to grasp these concepts, let’s take a look at the current auto-scaling architecture and sequence diagram.
On the diagrams above we see that currently a GitLab Runner Manager runs on a machine that has access to a cloud provider’s API. It is using Docker Machine to provision new Virtual Machines with Docker Engine installed and it configures the Docker daemon there to allow external authenticated requests. It stores credentials to such ephemeral Docker environments on disk. Once a machine has been provisioned and made available for GitLab Runner Manager to run builds, it is using one of the existing executors to run a user-provided script. In auto-scaling, this is typically done using the Docker executor.
There are several concerns represented in the current architecture. They are coupled in the current implementation so we will break them out here to consider them each separately.
- Virtual Machine (VM) shape. The underlying provider of a VM requires configuration to know what kind of machine to create. E.g. Cores, memory, failure domain, etc… This information is very provider specific.
- VM lifecycle management. Multiple machines will be created and a system must keep track of which machines belong to this executor. Typically a cloud provider will have a way to manage a set of homogenous machines. E.g. GCE Instance Group. The basic operations are increase, decrease and usually delete a specific machine.
- VM autoscaling. In addition to low-level lifecycle management, job-aware capacity decisions must be made to the set of machines to provide capacity when it is needed but not maintain excess capacity for cost reasons.
- Job to VM mapping (routing). Currently the system assigns only one job to a given a machine. A machine may be reused based on the specific executor configuration.
- In-VM job execution. Within each VM a job must be driven through various pre-defined stages and results and trace information returned to the Runner system. These details are highly dependent on the VM architecture and operating system as well as Executor type.
The current architecture has several points of coupling between concerns. Coupling reduces opportunities for abstraction (e.g. community supported plugins) and increases complexity, making the code harder to understand, test, maintain and extend.
A primary design decision will be which concerns to externalize to the plugin and which should remain with the runner system. The current implementation has several abstractions internally which could be used as cut points for a new abstraction.
For example the
type uses the
function to get an executor provider based on a dispatching executor string.
Various executor types register with the system by being imported and calling
during initialization. Here the abstractions are the
docker+autoscaling executor the
type has a
interface which it uses to aquire a VM during the common
phase. This abstraction primarily creates, accesses and deletes VMs.
There is no current abstraction for the VM autoscaling logic. It is tightly
coupled with the VM lifecycle and job routing logic. Creating idle capacity
happens as a side-effect of calling
Acquire on the
machineProvider while binding a job to a VM.
There is also no current abstraction for in-VM job execution. VM-specific
commands are generated by the Runner Manager using the
function and injected
into the VM as the manager drives the job execution stages.
Our goal is to design a GitLab Runner plugin system interface that is flexible and simple for the wider community to consume. As we cannot build plugins for all cloud platforms, we want to ensure a low entry barrier for anyone who needs to develop a plugin. We want to allow everyone to contribute.
To achieve this goal, we will follow a few critical design principles. These principles will guide our development process for the new plugin system abstraction.
- Design the new auto-scaling architecture aiming for having more choices and flexibility in the future, instead of imposing new constraints.
- Design the new auto-scaling architecture to experiment with running multiple jobs in parallel, on a single machine.
- Design the new provisioning architecture to replace Docker Machine in a way that the wider community can easily build on top of the new abstractions.
- Make the entry barrier for writing a new plugin low.
- Developing a new plugin should be simple and require only basic knowledge of a programming language and a cloud provider’s API.
- Strive for a balance between the plugin system’s simplicity and flexibility. These are not mutually exclusive.
- Abstract away as many technical details as possible but do not hide them completely.
- Build an abstraction that serves our community well but allows us to ship it quickly.
- Invest in a flexible solution, avoid one-way-door decisions, foster iteration.
- When in doubts err on the side of making things more simple for the wider community.
- Limit coupling between concerns in order to make the system more simple and extensible.
- Concerns should live on one side of the plug or the other–not both, which duplicates effort and increases coupling.
- Favor gRPC communication between a plugin and GitLab Runner.
- Make it possible to version communication interface and support many versions.
- Make Go a primary language for writing plugins but accept other languages too.
- Prefer a GitLab job-aware autoscaler to provider specific autoscalers. Cloud provider autoscalers don’t know which VM to delete when scaling down so they make sub-optimal decisions. Rather than teaching all autoscalers about GitLab jobs, we prefer to have one, GitLab-owned autoscaler (not in the plugin).
The following are proposals for where to draw the plugin boundary. We will evaluate these proposals and others by the design principles and technical constraints listed above.
In order to reduce the scope of work, we only want to introduce the new abstraction layer in one place.
A few years ago we introduced the Custom Executor feature in GitLab Runner. It allows users to design custom build execution methods. The custom executor driver can be implemented in any way - from a simple shell script to a dedicated binary - that is then used by a Runner through os/exec system calls.
Thanks to the custom executor abstraction there is no more need to implement new executors internally in Runner. Users who have specific needs can implement their own drivers and don’t need to wait for us to make their work part of the “official” GitLab Runner. As each driver is a separate project, it also makes it easier to create communities around them, where interested people can collaborate together on improvements and bug fixes.
We want to design the new Custom Provider to replicate the success of the Custom Executor. It will make it easier for users to build their own ways to provide a context and an environment in which a build will be executed by one of the Custom Executors.
There are multiple solutions to implementing a custom provider abstraction. We can use raw Go plugins, Hashcorp’s Go Plugin, HTTP interface or gRPC based facade service. There are many solutions, and we want to choose the most optimal one. In order to do that, we will describe the solutions in a separate document, define requirements and score the solution accordingly. This will allow us to choose a solution that will work best for us and the wider community.
This proposal places VM lifecycle and autoscaling concerns as well as job to VM mapping (routing) into the plugin. The build need only ask for a VM and it will get one with all aspects of lifecycle and routing already accounted for by the plugin.
We can introduce a more simple version of the
Machine abstraction in the
form of a “Fleeting” interface. Fleeting provides a low-level interface to
a homogenous VM group which allows increasing and decreasing the set size
as well as consuming a VM from within the set.
Plugins for cloud providers and other VM sources are implemented via the Hashicorp go-plugin library. This is in practice gRPC over STDIN/STDOUT but other wire protocols can be used also.
In order to make use of the new interface, the autoscaling logic is pulled out of the Docker Executor and placed into a new Taskscaler library.
This places the concerns of VM lifecycle, VM shape and job routing within
the plugin. It also places the conern of VM autoscaling into a separate
component so it can be used by multiple Runner Executors (not just
Rationale: Description of the InstanceGroup / Fleeting proposal POC: Merge request
|Authors||Grzegorz Bizon, Tomasz Maczukin, Joseph Burnett|
|Architecture Evolution Coach||Kamil Trzciński|
|Engineering Leader||Elliot Rushton, Cheryl Li|
|Product Manager||Darren Eastman, Jackie Porter|
|Domain Expert / Runner||Arran Walker|
|Domain Expert / Runner||Arran Walker|