Skaffold

Running the project with skaffold

Prerequisites

Before you can get going with skaffold you need to edit one file first to add your bot token

base64 encode the token so it can be saved as a k8s secret

python3 script/base64string.py --string supersecretvalue

From the root of this repo, edit the following file: script/k8s/errbot/secret.yaml.example
- Add your base64 encoded bot token to the file like so: CHAT_SERVICE_TOKEN: "base64encodedvaluehere=="
- Rename the file to secret.yaml
- ⚠️ Ensure you don't commit this file as it has your secret token!

Start your minikube cluster, configure skaffold, and set your Docker context:

minikube start --profile custom

skaffold config set --global local-cluster true

eval $(minikube -p custom docker-env)

Start your cluster with skaffold:
```
skaffold dev
```
Optionally, run skaffold dev --tail=true to attach to your running containers to view log output while they run

The first thing you may want to do is connect to your minikube instance via the dashboard UI.

Enable the k8s dashboard by running the following command in a seperate terminal window:

minikube dashboard --profile custom

Now that your cluster is up and running, you can get to your Grafana instance for metrics, logs, etc by doing the following:

Obtain your Grafana password (the username will be admin):

kubectl get secret --namespace observability grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo

Port forward to your grafana instance:
```
kubectl port-forward --namespace observability service/grafana 3000:80
```
If your IDE does not automatically direct you, simply go to http://127.0.0.1:3000/login after port forwarding with kubectl
Sign in with the credentials obtained in step two and navigate to the explore tab in Grafana
Run a query like the one below to view your errbot container logs in real time!