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Pelorus Development Guide

We appreciate your interest in contributing to Pelorus! Use this guide to help you get up and running.

There are three main tracks of Pelorus development to consider.

  1. Deployment Automation
    This track mostly involves testing, fixing, and updating our Helm chart(s) to streamline the installation and configuration experience. Knowledge of Helm, OpenShift, Operators and Prometheus configuration is assumed for this work.
  2. Dashboard Development
    This is where we take the raw data we've collected and turn it into actionable visual representations that will help IT organizations make important decisions. Knowledge of Grafana and PromQL is required for contribution here.
  3. Exporter Development
    This track is focused around the development of custom Prometheus exporters to gather the information we need in order to calculate our core metrics. Python development experience is assumed.

Contributing to Deployment Automation

We use Helm to provide an automated deployment and configuration experience for Pelorus. We are always doing work to cover more and more complex use cases with our helm charts. In order to be able to effectively contribute to these charts, you'll need a cluster that satisfies all of the installation prerequisites for Pelorus.

See the Install guide for more details on that.

Currently we have two charts:

  1. The operators chart installs the community operators on which Pelorus depends.
  2. The pelorus chart manages the Pelorus stack, which includes:
    • Prometheus
    • Thanos
    • Grafana
    • A set of Grafana Dashboards and Datasources
    • The Pelorus exporters, managed in an exporter subchart.

We use Helm's chart-testing tool to ensure quality and consistency in the chart. When making updates to one of the charts, ensure that the chart still passes lint testing using make chart-lint. The most common linting failure is forgetting to bump the version field in the Chart.yaml. See below for instructions on updating the version.

Updating the chart versions

When any of our Helm charts are updated, we need to bump the version number. This allows for a seemless upgrade experience. We have provided scripts that can test when a version bump is needed and do the bumping for you.

  1. Ensure the development environment is set up with make dev-env.
  2. Run make chart-lint to lint the charts, including checking the version number.

You can check all chart versions and bump them if needed with a script that compares upstream pelorus repository with the changes in a fork. To do so ensure your upstream repository is added to your fork by:

$ git remote add upstream
$ git pull
$ make chart-check-bump

or bump specific charts with shell script:

$ ./scripts/bump-version CHART_PATH [ CHART_PATH ...]

Dashboard Development

We are continually doing work to enhance and bugfix the Pelorus dashboards. Doing so requires a complete Pelorus stack, including all exporters required to populate a given dashboard. See the Dashboards user guide for that information.

To effectively do dashboard development, you'll likely need at least two browser windows open, one with Grafana, and another with Prometheus for testing queries. Since our dashboards are imported to Grafana via the Grafana Operator, they get imported in read-only mode. Because of this, you'll need to make a copy of it for development purposes.

The following outlines a workflow for working on a dashboard:

  1. Sign in to Grafana via the Grafana route. To check the route:

    $ oc get route grafana-route -n pelorus
  2. Once signed in, sign as an administrator

    1. Click the signin button in the bottom right corner:
      Signin button
    2. The admin credentials can be pulled from the following commands:

      $ oc get secrets -n pelorus grafana-admin-credentials -o jsonpath='{.data.GF_SECURITY_ADMIN_USER}' | base64 -d
      $ oc get secrets -n pelorus grafana-admin-credentials -o jsonpath='{.data.GF_SECURITY_ADMIN_PASSWORD}' | base64 -d
      1. Export the dashboard JSON.
        1. Open the dashboard, and select the Share... button.
        2. Select the Export tab.
        3. Click View JSON.
        4. Click Copy to Clipboard.
      2. Import as a new dashboard
        1. Click Create -> Import.
        2. Paste your JSON code in the box and click Load.
        3. Change the Name and Unique Identifier fields, and click Import.
      3. Make changes to the live dashboard. You can do this by clicking the dropdown by the panel names, and selecting Edit.
      4. Once you are happy with your changes, export your updated dashboard, and replace the existing content in the GrafanaDashbaord CR.
        1. Open the dashboard, and select the Share... button.
        2. Select the Export tab.
        3. Click View JSON.
        4. Click Copy to Clipboard.
        5. Open the appropriate GrafanaDashboard CR file, and paste the new dashboard JSON over the existing.


      Be sure to match the indentation of the previous dashboard JSON. Your git diffs should still show only the lines changed like the example below.

       $ git diff charts/deploy/templates/metrics-dashboard.yaml
       diff --git a/charts/deploy/templates/metrics-dashboard.yaml b/charts/deploy/templates/metrics-dashboard.yaml
       index 73151ad..c470afc 100644
       --- a/charts/deploy/templates/metrics-dashboard.yaml
       +++ b/charts/deploy/templates/metrics-dashboard.yaml
       @@ -25,7 +25,7 @@ spec:
                   "editable": true,
                   "gnetId": null,
                   "graphTooltip": 0,
       -            "id": 2,
       +            "id": 3,
                   "links": [],
                   "panels": [
       @@ -323,7 +323,7 @@ spec:
                       "tableColumn": "",
                       "targets": [
       -                    "expr": "count (deploy_timestamp)",
       +                    "expr": "count (count_over_time (deploy_timestamp [$__range]) )",
                           "format": "time_series",
                           "instant": true,
                           "intervalFactor": 1,
       @@ -410,7 +410,7 @@ spec:

You're done! Commit your changes and open a PR!

Exporter Development

A Pelorus exporter is simply a Prometheus exporter. While they can technically be written in many languages, we've written ours in Python using the Prometheus python client library. We chose Python because it seems to be the most popular programming language for operations teams.

Exporter directory layout

The following is a recommended directory structure for a Pelorus exporter <NAME>.

├── charts
│   └── pelorus
│       ├── configmaps
│       │   └── <NAME>.yaml
│       └── values.yaml
└── exporters
    ├── <NAME>
    │   ├──
    │   └──
    └── tests
        └── test_<NAME>.py

Dev Environment Setup

Python & Repo Setup

After cloning the repo, you'll need a python version that's >= 3.9 but < 3.11.

Running make dev-env should be enough to get you started.

This will:

  • check for the right version of python
  • set up a virtual environment
  • install required CLI tools such as helm, oc, tkn and ct, promtool, conftest (inside .venv/bin)
  • install required python runtime and test dependencies
  • install the exporters package
  • set up git hooks for formatting and linting checks
  • configure git blame to ignore large revisions that just changed formatting

IDE Setup (VSCode)

Most of us use Visual Studio Code to do our python development. The following extensions for VSCode are useful. Each can be installed by hitting Ctrl+P and pasting the commands below.

The python extension can activate your virtualenv automatically.

Code also comes with a nice debugger feature. Here is a starter configuration to use with our exporters. Just create a file called .vscode/launch.json in your pelorus/ project directory with the following content.

    // Use IntelliSense to learn about possible attributes.
    // Hover to view descriptions of existing attributes.
    // For more information, visit:
    "version": "0.2.0",
    "configurations": [
            "name": "Commit Time Exporter",
            "type": "python",
            "request": "launch",
            "program": "${workspaceFolder}/exporters/committime/",
            "console": "integratedTerminal",
            "env": {
                "API_USER": "<github username here>",
                "GITHUB_TOKEN": "<personal access token here>",
                "LOG_LEVEL": "INFO",
                "APP_LABEL": ""
            "name": "Deploy Time Exporter",
            "type": "python",
            "request": "launch",
            "program": "${workspaceFolder}/exporters/deploytime/",
            "console": "integratedTerminal",
            "env": {
                "LOG_LEVEL": "INFO",
                "APP_LABEL": ""
            }        },
            "name": "Deploy Time Exporter",
            "type": "python",
            "request": "launch",
            "program": "${workspaceFolder}/exporters/failure/",
            "console": "integratedTerminal",
            "env": {
                "SERVER": "<Jira server url>",
                "PROJECT": "<Jira project ID>",
                "API_USER": "<Jira username>",
                "TOKEN": "<Jira personal access token>",
                "LOG_LEVEL": "INFO",
                "APP_LABEL": ""

For more information, see the Debugging doc in VS Code.

Running locally

Running an exporter on your local machine should follow this process:

  1. Set up your local dev environment:

    make dev-env
  2. Activate your virtual environment

    . .venv/bin/activate
  3. Set any environment variables required (or desired) for the given exporter (see Configuring Exporters to see supported variables).

    export LOG_LEVEL=debug
    export TOKEN=xxxx
    export API_USER=xxxx
  4. Log in to your OpenShift cluster OR export KUBECONFIG environment variable

    oc login --token=<token> --server=
    # OR
    export KUBECONFIG=/path/to/kubeconfig_file
  5. (Optional) To avoid certificate warnings and some possible errors, you need to set up your local machine to trust your cluster certificate

    1. Download your cluster ca.crt file
    2. Add cert to system trust bundle
    3. Pass cert bundle with your login command
      oc login --token=<token> --server=  --certificate-authority=/etc/pki/tls/certs/ca-bundle.crt
  6. Start the exporter

    python exporters/committime/

At this point, your exporter should be available at http://localhost:8080

curl http://localhost:8080

Testing Pull Requests

The following are notes and general steps for testing Pull Requests for specific types of changes.

To checkout PR we recommend using GitHub CLI, which simplifies process of pulling PRs.

Ensure you have Pelorus GitHub project Forked into your GitHub user space.

Checkout the PR on top of your fork.

git clone<your_github_username>/pelorus.git
cd pelorus
gh pr checkout 535

# If asked:
# ? Which should be the base repository, select:
# > konveyor/pelorus

Dashboard Changes

  1. Checkout the PR on top of your fork.

  2. Install Pelorus from checked out fork/branch.


    In most cases you can deploy changes to an existing deployment to retain existing data.

  3. Log into Grafana via the grafana route.

    oc get route grafana-route -n pelorus
  4. Click on the dashboard containing changes, and visually validate the behavior change described in the PR.


    Eventually we'd like to have some Selenium tests in place to validate dashboards. If you have skills in this area let us know!

Exporter Changes

Each PR runs exporter tests in the CI systems, however those changes can be tested locally in a very similar way they run in the CI.

  1. Checkout the PR on top of your fork.

  2. Set up the dev environment

    make dev-env
  3. Activate your virtual environment

    . .venv/bin/activate
  4. Check what type of tests you can run

    make help
  5. As an example run unit tests using make unit-tests.

    1. You can also run coverage reports with the following:
      coverage run -m pytest -rap -m "not integration and not mockoon"
      coverage report
  6. Gather necessary configuration information.

  7. Run exporter locally. You can do this either via the command line, or use the provided VSCode debug confuration to run it in your IDE Debugger.
  8. Once exporter is running, you can test it via a simple curl localhost:8080. You should be validating that:
    1. You get a valid response with metrics.
    2. Confirm the format of expected metrics.

Helm Install changes

For testing changes to the helm chart, you should just follow the standard install process, then verify that:

  • All expected pods are running and healthy
  • Any expected behavior changes mentioned in the PR can be observed.

A different way is to simply run e2e-tests against your cluster, to do so, ENSURE your pelorus namespace is either not existing or no resources are within that namespace.

​export KUBECONFIG=/path/to/kubeconfig_file
# DANGEROUS as this will remove your previously deployed pelorus instance
oc delete namespace pelorus
make e2e-tests
# Check if command ran without failures
echo $?

You can do some rudimentary linting with make chart-lint.

We are in the process of refactoring our helm charts such that they can be tested more automatically using helm chart-testing. Some general guidelines are outlined in the CoP Helm testing strategy. More to come soon.

Release Management Process

The following is a walkthrough of the process we follow to create and manage versioned releases of Pelorus. Pelorus release versions follow SemVer versioning conventions. Change of the version is managed via Makefile.

  1. Create Pelorus Pull Request with the release you're about to make.

    For PATCH version bump use:

    make release

    For minor-release version:

    make minor-release

    For major-release version:

    make major-release
  2. Propose Pull Request to the project github repository. Ensure that the PR is labeled with "minor" or "major" if one was created.

  3. After PR is merged on the Pelorus releases page, click edit on the latest Draft.

    • Click Publish Release.

Testing the Docs

Our documentation gets published via readthedocs, via the mkdocs framework. Mkdocs can be run locally for testing the rendering of the markdown files. If you followed the local setup instructions above, you should already have mkdocs installed.

Stand up the local server by running:

mkdocs serve

The mkdocs config is controlled by the mkdocs.yml file in the root of this project. All of the documents that will be served are in the /docs folder.