Developing Lagoon#
Development of Lagoon locally can now be performed on a local Kubernetes cluster, or via Docker Compose (as a fallback).
Note
The full Lagoon stack relies on a range of upstream projects which are currently incompatible with ARM-based architectures, such as the the M1/M2 Apple Silicon-based machines. For this reason, running or developing lagoon-core
or lagoon-remote
locally on these architectures is not currently supported. See https://github.com/uselagoon/lagoon/issues/3189 for more information.
Docker#
Docker must be installed to build and run Lagoon locally.
Install Docker and Docker Compose#
Please check the official docs for how to install Docker.
Docker Compose is included in Docker for Mac installations. For Linux installations see the directions here.
Configure Docker#
You will need to update your insecure registries in Docker. Read the instructions here on how to do that. We suggest adding the entire local IPv4 Private Address Spaces to avoid unnecessary reconfiguration between Kubernetes and Docker Compose. e.g. "insecure-registries" : ["172.16.0.0/12","192.168.0.0/16"],
Allocate Enough Docker Resources#
Running a Lagoon, Kubernetes, or Docker cluster on your local machine consumes a lot of resources. We recommend that you give your Docker host a minimum of 8 CPU cores and 12GB RAM.
Build Lagoon Locally#
Warning
Only consider building Lagoon this way if you intend to develop features or functionality for it, or want to debug internal processes. We will also be providing instruction to install Lagoon without building it (i.e. by using the published releases).
We're using make
(see the Makefile) in order to build the needed Docker images, configure Kubernetes and run tests.
We have provided a number of routines in the Makefile to cover most local development scenarios. Here we will run through a complete process.
Build images#
- Here
-j8
tells make to run 8 tasks in parallel to speed the build up. Adjust as necessary. - We have set
SCAN_IMAGES=false
as a default to not scan the built images for vulnerabilities. If set to true, ascan.txt
file will be created in the project root with the scan output.
- Start Lagoon test routine using the defaults in the Makefile (all tests).
Warning
There are a lot of tests configured to run by default - please consider only testing locally the minimum that you need to ensure functionality. This can be done by specifying or removing tests from the TESTS
variable in the Makefile.
This process will:
- Download the correct versions of the local development tools if not installed -
kind
,kubectl
,helm
,jq
. - Update the necessary Helm repositories for Lagoon to function.
- Ensure all of the correct images have been built in the previous step.
- Create a local KinD cluster, which provisions an entire running Kubernetes cluster in a local Docker container. This cluster has been configured to talk to a provisioned image registry that we will be pushing the built Lagoon images to. It has also been configured to allow access to the host filesystem for local development.
- Clone Lagoon from https://github.com/uselagoon/lagoon-charts (use the
CHARTS_TREEISH
variable in the Makefile to control which branch if needed). - Install the Harbor Image registry into the KinD cluster and configure its ingress and access properly.
- Docker will push the built images for Lagoon into the Harbor image registry.
- It then uses the Makefile from lagoon-charts to perform the rest of the setup steps.
- A suitable ingress controller is installed - we use the NGINX Ingress Controller.
- A local NFS server provisioner is installed to handle specific volume requests - we use one that handles Read-Write-Many operations (RWX).
- Lagoon Core is then installed, using the locally built images pushed to the cluster-local Image Registry, and using the default configuration, which may exclude some services not needed for local testing. The installation will wait for the API and Keycloak to come online.
- The DBaaS providers are installed - MariaDB, PostgreSQL and MongoDB. This step provisions standalone databases to be used by projects running locally, and emulates the managed services available via cloud providers (e.g. Cloud SQL, RDS or Azure Database).
- Lagoon Remote is then installed, and configured to talk to the Lagoon Core, databases and local storage. The installation will wait for this to complete before continuing.
- To provision the tests, the Lagoon Test chart is then installed, which provisions a local Git server to host the test repositories, and pre-configures the Lagoon API database with the default test users, accounts and configuration. It then performs readiness checks before starting tests.
- Lagoon will run all the tests specified in the TESTS variable in the Makefile. Each test creates its own project & environments, performs the tests, and then removes the environments & projects. The test runs are output to the console log in the
lagoon-test-suite-*
pod, and can be accessed one test per container.
Ideally, all of the tests pass and it's all done!
View the test progress and your local cluster#
The test routine creates a local Kubeconfig file (called kubeconfig.kind.lagoon
in the root of the project, that can be used with a Kubernetes dashboard, viewer or CLI tool to access the local cluster. We use tools like Lens, Octant, kubectl or Portainer in our workflows. Lagoon Core, Remote and Tests all build in the Lagoon
namespace, and each environment creates its own namespace to run, so make sure to use the correct context when inspecting.
In order to use kubectl with the local cluster, you will need to use the correct Kubeconfig. This can be done for every command or it can be added to your preferred tool:
The Helm charts used to build the local Lagoon are cloned into a local folder and symlinked to lagoon-charts.kind.lagoon
where you can see the configuration. We'll cover how to make easy modifications later in this documentation.
Interact with your local Lagoon cluster#
The Makefile includes a few simple routines that will make interacting with the installed Lagoon simpler:
This will create local ports to expose the UI (6060), API (7070) and Keycloak (8080). Note that this logs to stdout
, so it should be performed in a secondary terminal/window.
This will retrieve the necessary credentials to interact with the Lagoon.
- The JWT is an admin-scoped token for use as a bearer token with your local GraphQL client. See more in our GraphQL documentation.
- There is a token for use with the "admin" user in Keycloak, who can access all users, groups, roles, etc.
- There is also a token for use with the "lagoonadmin" user in Lagoon, which can be allocated default groups, permissions, etc.
This will re-push the images listed in KIND_SERVICES
with the correct tag, and redeploy the lagoon-core chart. This is useful for testing small changes to Lagoon services, but does not support "live" development. You will need to rebuild these images locally first, e.g rm build/api && make build/api
.
This will build the typescript services, using your locally installed Node.js (it should be >16.0). It will then:
- Mount the "dist" folders from the Lagoon services into the correct lagoon-core pods in Kubernetes
- Redeploy the lagoon-core chart with the services running with
nodemon
watching the code for changes - This will facilitate "live" development on Lagoon.
- Note that occasionally the pod in Kubernetes may require redeployment for a change to show. Clean any build artifacts from those services if you're rebuilding different branches with
git clean -dfx
as the dist folders are ignored by Git.
This will create a standalone OpenDistro for Elasticsearch cluster in your local Docker, and configure Lagoon to dispatch all logs (Lagoon and project) to it, using the configuration in lagoon-logging.
This will re-run a suite of tests (defined in the TESTS
variable) against the existing cluster. It will re-push the images needed for tests (tests, local-git, and the data-watcher-pusher). You can specify tests to run by passing the TESTS variable inline.
If updating a test configuration, the tests image will need to be rebuilt and pushed, e.g rm build/tests && make build/tests && make kind/push-images IMAGES='tests' && make kind/retest TESTS='[api]'
This will push all the images up to the image registry. Specifying IMAGES
will tag and push specific images.
This will remove the KinD Lagoon cluster from your local Docker.
Ansible#
The Lagoon test uses Ansible to run the test suite. Each range of tests for a specific function has been split into its own routine. If you are performing development work locally, select which tests to run, and update the $TESTS
variable in the Makefile to reduce the concurrent tests running.
The configuration for these tests is held in three services:
tests
is the Ansible test services themselves. The local testing routine runs each individual test as a separate container within a test-suite pod. These are listed below.local-git
is a Git server hosted in the cluster that holds the source files for the tests. Ansible pulls and pushes to this repository throughout the testsapi-data-watcher-pusher
is a set of GraphQL mutations that pre-populates local Lagoon with the necessary Kubernetes configuration, test user accounts and SSH keys, and the necessary groups and notifications. Note that this will wipe local projects and environments on each run.
The individual routines relevant to Kubernetes are:
active-standby-kubernetes
runs tests to check active/standby in Kubernetes.api
runs tests for the API - branch/PR deployment, promotion.bitbucket
,gitlab
andgithub
run tests for the specific SCM providers.drupal-php74
runs a single-pod MariaDB, MariaDB DBaaS and a Drush-specific test for a Drupal 8/9 project (drupal-php73
doesn't do the Drush test).drupal-postgres
runs a single-pod PostgreSQL and a PostgreSQL DBaaS test for a Drupal 8 project.elasticsearch
runs a simple NGINX proxy to an Elasticsearch single-pod.features-variables
runs tests that utilize variables in Lagoon.features-kubernetes
runs a range of standard Lagoon tests, specific to Kubernetes.features-kubernetes-2
runs more advanced kubernetes-specific tests - covering multi-project and subfolder configurations.nginx
,node
andpython
run basic tests against those project types.node-mongodb
runs a single-pod MongoDB test and a MongoDB DBaaS test against a Node.js app.
Local Development#
Most services are written in Node.js. As many of these services share similar Node.js code and Node.js packages, we're using a feature of Yarn, called Yarn workspaces. Yarn workspaces need a package.json
in the project's root directory that defines the workspaces.
The development of the services can happen directly within Docker. Each container for each service is set up in a way that its source code is mounted into the running container (see docker-compose.yml
). Node.js itself is watching the code via nodemon
, and restarts the Node.js process automatically on a change.
lagoon-commons#
The services not only share many Node.js packages, but also share actual custom code. This code is within node-packages/lagoon-commons
. It will be automatically symlinked by Yarn workspaces. Additionally, the nodemon
of the services is set up in a way that it checks for changes in node-packages
and will restart the node process automatically.
Troubleshooting#
I can't build a Docker image for any Node.js based service#
Rebuild the images via:
I get errors about missing node_modules
content when I try to build / run a Node.js based image#
Make sure to run yarn
in Lagoon's root directory, since some services have common dependencies managed by yarn
workspaces.
I get an error resolving the nip.io
domains#
Error response from daemon: Get https://registry.172.18.0.2.nip.io:32080/v2/: dial tcp: lookup registry.172.18.0.2.nip.io: no such host
This can happen if your local resolver filters private IPs from results. You can work around this by editing /etc/resolv.conf
and adding a line like nameserver 8.8.8.8
at the top to use a public resolver that doesn't filter results.
Example workflows#
Here are some development scenarios and useful workflows for getting things done.
Add tests#
- Repeat the first step above.
- Edit
tests/tests/features-variables.yaml
and add a test case. - Rebuild the
tests
image.
- Push the new
tests
image into the cluster registry.
- Rerun the tests.