Prometheus DORA Metrics Exporter

This project configures and exposes DORA metrics for a GitHub repository using Prometheus. The metrics include Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Recovery (MTTR).

Prerequisites

• Python 3.x
• A GitHub repository
• A GitHub token with appropriate permissions
• Prometheus

Setup

Clone the Repository

Clone the repository to your local machine:

git clone https://github.com/olat-nji/prometheus-dora-metrics.git
cd dora-metrics

Install Dependencies

Install the required Python dependencies:

pip install -r requirements.txt

Configuration

1. Create a .env File

   Create a .env file in the project root with the following content:

   GITHUB_TOKEN=your_github_token
   GITHUB_OWNER=github_username_or_organization
   GITHUB_REPO=your_github_repo
   PORT=your_desired_port
   

Script Details

The script fetches commits and workflow runs from a specified GitHub repository and exposes metrics useful for calculating various DORA metrics. Here’s a breakdown:

1. Deployment Frequency:

   • Definition: Measures how often deployments occur over a specific time period.

   • Calculation:

     Deployment Frequency = Σ (Total Number of Deployments) in Number of Days

   • Script Detail: Counts the total number of successful and failed deployments and divides by the number of days in the analysis period.

2. Lead Time for Changes:

   • Definition: The average time it takes from when a deployment process starts to when it is deployed.

   • Calculation:

     Average Lead Time for Changes = (Sum of Lead Times) / (Number of Lead Times)

     where Lead is the time difference between the commit time and deployment time.

   • Script Detail: Calculates lead time for each commit with a corresponding deployment and computes the average lead time.

3. Change Failure Rate:

   • Definition: The percentage of deployments that fail compared to the total number of deployments.

   • Calculation:

     Change Failure Rate = (Number of Failed Deployments / Total Number of Deployments) × 100

   • Script Detail: Counts the number of failed deployments, divides by the total number of deployments, and multiplies by 100 to get the percentage.

4. Mean Time to Recovery (MTTR):

   • Definition: The average time taken to recover from a failed deployment.

   • Calculation:

     MTTR = Σ(Recovery Times) / Number of Recovery Times

     where Recovery Times is the duration between a failure being resolved and the next successful deployment.

   • Script Detail: Calculates recovery times for each successful deployment following a failure and computes the average.

Running the Script

To start the Prometheus metrics server and update the metrics periodically, run:

python main.py

The server will start on port 5555 (or the specified port) and update the metrics every 10 minutes.

Prometheus Configuration

Add the following job to your Prometheus configuration file (prometheus.yml):

scrape_configs:
  - job_name: 'dora-metrics'
    static_configs:
      - targets: ['localhost:5555']

main.py Script

The main.py script is responsible for fetching data from GitHub, calculating metrics, and exposing them via a Prometheus server. View the script.

Conclusion

This README provides step-by-step instructions to set up and configure DORA metrics for a GitHub repository, including fetching data from GitHub, calculating metrics, and exposing them via a Prometheus server. Ensure you replace placeholder values with your actual data and credentials.