The developer experience engineering team at GitHub works on creating safe, delightful, and inclusive solutions for GitHub engineers to efficiently code, ship, and operate software–setting an example for the world on how to build software with GitHub. To achieve this we provide our developers with a paved path–a comprehensive suite of automated tools and applications to streamline our runtime platforms, deployment, and hosting that helps power some of the microservices on the GitHub.com platform and many internal tools. Let’s take a deeper look at how one of our main paved paths works.
GitHub’s main paved path covers everything that’s needed for running software–creating, deploying, scaling, debugging, and running applications. It is an ecosystem of tools like Kubernetes, Docker, load balancers, and many custom apps that work together to create a cohesive experience for our engineers. It isn’t just infrastructure and isn’t just Kubernetes. Kubernetes is our base layer, and the paved path is a mix of conventions, tools, and settings built on top of it.
The kind of services that we typically run using the paved path include web apps, computation pipelines, batch processors, and monitoring systems.
Kubernetes, which is the base layer of the paved path, runs in a multi-cluster, multi-region topology.
There are hundreds of services at GitHub–from a small internal tool to an external API supporting production workloads. For a variety of reasons, it would be inefficient to spin up virtual machines for each service.
- Planning and capacity usage across all services wouldn’t be efficient. We would encounter significant overhead in managing both physical and Kubernetes infrastructure on an ongoing basis.
- Teams would need to build deep expertise in managing their own Kubernetes clusters and would have less time to focus on their application’s unique needs.
- We would have less central visibility of applications.
- Security and compliance would be difficult to standardize and enforce.
With the paved path based on Kubernetes and other runtime apps, we’re instead able to:
- Plan capacity centrally and only for the Kubernetes nodes, so we can optimally use capacity across nodes, as small workloads and large workloads coexist on the same machines.
- Scale rapidly thanks to central capacity planning.
- Easily manage configuration and deployments across services in one central control plane.
- Consistently provide insights into app and deployment performance for individual services.
Onboarding a service with the code living in its own repository has been made easy with our ChatOps command service, called Hubot, and GitHub Apps. Service owners can easily generate some basic scaffolding needed to deploy the service by running a command like:
hubot gh-platform app scaffold monalisa-app
A custom GitHub App installed on the service’s GitHub repository will then automatically generate a pull request to add the necessary configurations, which includes:
deployment.yamlfile that defines the service’s deployment environments.
- Kubernetes manifests that define
Serviceobjects for deploying the service.
- A Debian Dockerfile that runs a trivial web server to start off with, which will be used by the Kubernetes manifests.
- Setting up CI builds as GitHub Checks that build the Docker images on every push, and store in a container registry ready for deployment.
Each service that is onboarded to the paved path has its unique Kubernetes namespace that is defined by
<app-name>-<environment> and generally has a staging and production environment. This helps separate the workloads of multiple services, and also multiple environments for the same service since each environment gets its own Kubernetes namespace.
At GitHub, we deploy branches and perform deployments through Hubot ChatOps commands. To deploy a branch named
bug-fixes in the
monalisa-app repository to the
staging environment, a developer would run a ChatOps command like:
hubot deploy monalisa-app/bug-fixes to staging
This triggers a deployment that fetches the Docker image associated with the latest commit in the
bug-fixes branch, updates the Kubernetes manifests, and applies those manifests to the clusters in the runtime platform relevant to that environment.
Typically, the Docker image would be deployed to multiple Kubernetes clusters across multiple geographical sites in a region that forms a part of the runtime platform.
To automate pull request merges into the busiest branches and orchestrate the rollout across environments we’re also using merge queue and deployment pipelines, which our engineers can observe and interact with during their deployment.
For any company, the security of the platform itself, along with services running within it, is critical. In addition to our engineering-wide practices, such as requiring two-person reviews on every pull request, we also have Security and Platform teams automating security measures, such as:
- Pre-built Docker images to be used as base images for the Dockerfiles. These base images contain only the necessary packages/dependencies with security updates, a set of installed software that is auditable and curated according to shared needs.
- Build-time and periodic scanning of all packages and running container images, for any vulnerabilities or dependencies needing a patch, powered by our own products for software supply chain security like Dependabot.
- Build time and periodic scanning of GitHub repositories of services for exposed secrets and vulnerabilities, using GitHub’s native security features for advanced security like code scanning and secret scanning.
- Multiple authentication and authorization mechanisms that allow only the relevant individuals to directly access underlying Kubernetes resources.
- Comprehensive telemetry for threat detection.
- Services running in the platform are by default accessible only within GitHub’s internal networks and not exposed on the public internet.
- Branch protection policies are enforced on all production repositories. These policies prevent merging a pull request until designated automated tests pass and the change has been reviewed by a different developer from the one who proposed the change.
Another key aspect of security for an application is how secrets like keys and tokens are managed. At GitHub, we use a centralized secret store to manage secrets. Each service and each environment within the service has its own vault to store secrets. These secrets are then injected into the relevant pods in Kubernetes, which are then exposed to the containers.
The whole deployment process would look something like this:
- A GitHub engineer merges a pull request to a branch in a repository. In the above example, it is the
bug-fixesbranch in the
monalisa-apprepository. This repository would also contain the Kubernetes manifest template files for deploying the application.
- The pull request merge triggers relevant CI workflows. One of them is a build of the Docker image, which builds the container image based on the Dockerfile specified in the repository, and pushes the image to an internal artifact registry.
- Once all the CI workflows have completed successfully, the engineer initiates a deployment by running a ChatOps command like
hubot deploy monalisa-app/bug-fixes to staging. This triggers a deployment to our environments such as
- The build systems fetch the Kubernetes manifest files from the repository branch, replaces the latest image to be deployed from the artifact registry, injects app secrets from the secret store, and runs some custom operations. At the end of this stage, a ready-to-deploy Kubernetes manifest is available.
- Our deployment systems then apply the Kubernetes manifest to relevant clusters and monitor the rollout status of new changes.
GitHub’s internal paved path helps developers at GitHub focus on building services and delivering value to our users, with minimal focus on the infrastructure. We accomplish this by providing a streamlined path to our GitHub engineers that uses the power of containers and Kubernetes; scalable security, authentication, and authorization mechanisms; and the GitHub.com platform itself.
Want to try some of these for yourself? Learn more about all of GitHub’s features on github.com/features. If you have adopted any of our practices for your own development, give us a shout on Twitter!