Grafana Dashboards#

Each 2i2c Hub is set up with a Prometheus server to generate metrics and information about activity on the hub, and each cluster of hubs has a Grafana deployment to ingest and visualize this data.

This section describes how to use these dashboards for a cluster.

Access Hub Grafana Dashboards#

The Grafana for each cluster can be accessed at grafana.<cluster-name>.2i2c.cloud. For example, the Grafana for community hubs running on our GCP project is accessible at grafana.pilot.2i2c.cloud.

To access the Grafana dashboards you’ll need a username and password. These can be accessed using sops (see sops overview for how to set up sops on your machine). See Log in to the cluster-spcific Grafana dashboard for how to find the credentials information.

The Central Grafana#

The Grafana deployment in the 2i2c cluster ingests data from all the 2i2c clusters and will soon be able to be used as “the central Grafana”.

Note

TODO: should add more info once this is ready to use.

Set up Grafana Dashboards for a cluster#

This guide will walk through the steps required to setup a suite of Grafana dashboards for a cluster.

Deploy the support chart#

The support chart is a helm chart maintained by the 2i2c Engineers that consists of common tools used to support JupyterHub deployments in the cloud. These tools are ingress-nginx, for controlling ingresses and load balancing; cert-manager, for automatically provisioning TLS certificates from Let’s Encrypt; Prometheus, for scraping and storing metrics from the cluster and hub; and Grafana, for visualising the metrics retreived by Prometheus.

Create a support.values.yaml file in your chosen cluster folder#

In the infrastructure repo, the full filepath should be: config/clusters/<cluster_name>/support.values.yaml.

Add the following helm chart values to your support.values.yaml file. <grafana-domain> should follow the pattern grafana.<cluster_name>.2i2c.cloud, and <prometheus-domain> should follow the pattern prometheus.<cluster_name>.2i2c.cloud.

prometheusIngressAuthSecret:
  enabled: true

grafana:
  ingress:
    hosts:
      - <grafana-domain>
    tls:
      - secretName: grafana-tls
        hosts:
          - <grafana-domain>

prometheus:
  server:
    ingress:
      enabled: true
      hosts:
        - <prometheus-domain>
      tls:
        - secretName: prometheus-tls
          hosts:
            - <prometheus-domain>

Create a enc-support.secret.values.yaml file#

Only 2i2c staff + our centralized grafana should be able to access the prometheus data on a cluster from outside the cluster. The basic auth feature of nginx-ingress is used to restrict this. A enc-support.secret.values.yaml file is used to provide these secret credentials.

prometheusIngressAuthSecret:
  username: <output of pwgen -s 64 1>
  password: <output of pwgen -s 64 1>

Note

We use the pwgen program, commonly installed by default in many operating systems, to generate the password.

Once you create the file, encrypt it in-place with sops --in-place --encrypt <file-name>.

Edit your cluster.yaml file#

Add the following config as a top-level key to your cluster.yaml file. Note this filepath is relative to the location of your cluster.yaml file.

support:
  helm_chart_values_files:
    - support.values.yaml
    - enc-support.secret.values.yaml

Deploy the support chart via the deployer#

Use the deployer tool to deploy the support chart to the cluster. See Manually deploy a config change for details on how to setup the tool locally.

python3 deployer deploy-support CLUSTER_NAME

Setting DNS records#

Once the support chart has been successfully deployed, retrieve the external IP address for the ingress-nginx load balancer.

kubectl --namespace support get svc support-ingress-nginx-controller

Add the following DNS records via Namecheap.com:

  1. <cluster-name>.2i2c.cloud, used for the primary hub (if it exists).

  2. *.<cluster-name>.2i2c.cloud, for all other hubs, grafana and prometheus instances.

The DNS records should be A records if using GCP or Azure (where external IP is an IPv4 address), or CNAME records if using AWS (where external IP is a domain name).

Wait a while for the DNS to propagate!

Log in to the cluster-spcific Grafana dashboard#

Eventually, visiting GRAFANA_URL will present you with a login page. Here are the credentials for logging in:

  • username: admin

  • password: located in helm-charts/support/enc-support.secret.values.yaml (sops encrypted).

Register the cluster’s Prometheus Server with the central Grafana#

Once you have deployed the support chart, you must also register this cluster as a datasource for the central Grafana dashboard. This will allow you to visualize cluster statistics not only from the cluster-specific Grafana deployement but also from the central dashboard, that aggregates data from all the clusters.

Run the update_central_grafana_datasources.py script in the deployer to let the central Grafana know about this new prometheus server:

$ python3 deployer/update_central_grafana_datasources.py <grafana-cluster-name>

Where:

  • is the name of the cluster where the central Grafana lives. Right now, this defaults to “2i2c”.

Setting up Grafana Dashboards#

Once you have logged into grafana as the admin user, create a new API key. You can do this by selecting the gear icon from the left-hand menu, and then selecting API keys. The key you create needs admin permissions.

Keep this key safe as you won’t be able to retrieve it!

Create the file config/clusters/<cluster>/grafana-token.secret.yaml with the following content.

grafana_token: PASTE_YOUR_API KEY HERE

Then encrypt this file using sops like so:

sops --output config/clusters/<cluster>/enc-grafana-token.secret.yaml --encrypt config/clusters/<cluster>/grafana-token.secret.yaml

The encrypted file can now be committed to the repository.

This key will be used by the deploy-grafana-dashboards workflow to deploy some default grafana dashboards for JupyterHub using jupyterhub/grafana-dashboards.

Once you’ve pushed the encrypted grafana_token to the GitHub repository, manually trigger the deploy-grafana-dashboards workflow using the “Run workflow” button from here to deploy the dashboards.

Note

The workflow only runs when manually triggered.

Any re-triggering of the workflow after the initial deployment will overwrite any dashboard created from the Grafana UI and not stored in the jupyterhub/grafana-dashboards repository.