This Probot application can be deployed as an Azure Web App (via docker container) to automatically forward workflow log data from a GitHub org to a variety of data stores. In general the application listens for a workflow_run event in a GitHub repository and then

• Downloads the logs for the workflow run using the GitHub REST API
• Forwards the logs to an Azure Event hub and optionally a PostgreSQL database
• Azure Stream Analytics is used to send Event Hub messages to a Power BI streaming dataset

Connection strings (and other credentials used for authentication) are stored and retrieved from Configuration Settings (e.g., environment variables) of a given Azure Web App, enabling the application to be easily configured for different environments.

Different environments support the process of shipping and vizualizing workflow log data

• GitHub hosts the Beaver application (this repository), the REST API, as well as the Organization that the GitHub App is installed on
• The infrasturcure required to recieve the GitHub App webhooks and obtain workflow logs exists in Azure and is managed with Terraform
• Data storage requirements and visualization tools can exist leveraging the Office 365 ecosystem or Open Source components

Note In order for the managed identity used by the Stream Analytics job to be able to successfully write data to the Power BI Streaming dataset (as shown above), the group ID that hosts the dataset must belong to the same Azure AD Tenant as the managed identity

It is now easier than ever to gather insights from data given the improvements in modern data visualization tools. However, it has historically been complex to gather, manipulate, and store large amounts of data. Especially streaming data sources, such as webhooks and CI/CD job logs. This, along with there not yet being rich features supporting the displaying of metrics and insights for Actions usage on GitHub sets the stage for a problem that is

• Large in scale
• Neglected by others
• A feasible area to make progress in

1. A GitHub App must be installed on the Organization that you wish to collect log data for

   • The GitHub App name must be supplied with a name (e.g., my-org-beaver)
   • The Homepage URL must be provided (e.g., https://github.com/octodemo/beaver )
   • The initial Webhook URL must be a temporary one (e.g., https://example.com)
   • A Webhook secret must be generated and used
   • It must have Read-only to Actions as a Repository permission
   • It must be subscribed to the Workflow run event
   • It should be installed Only on this account (i.e., not on Any account)

2. Generate a Private key for the GitHub App and Base64 encode the associated .pem file

   foo@bar:~$ base64 -i oodles-noodles-beaver.YYYY-MM-DD.private-key.pem
   LS0tLS1CRUdJTiBSU0EgUFJJVkFURSBLRVktLS0tLQpNSUlFb3dJQkFBS0NBUUVBa2xwaVlUdEZQbG5kdWdySDNOcGlvaGNZN1ZwNTlYMkhGTjJXM
   jZKdHkzYkRJWTJCClpJc20rRGN5dEZNb0kxbUg3UGUvUk1CN0xuOXZLS2N5Sk1kNVRuakxwUTBZWGdCOFRlQzdTa2tHNFB3alZKWlEKK1RlN3hiQU
   ...
   ...
   
   foo@bar:~$

3. Install the GitHub App on all the repositories in the Organization

4. Create a repository to store needed configuration items and deploy required infrastructure

5. Create values the following as Repository secrets in the repository created in Step 3

   • CLIENT_ID: The client ID of the Azure App Registration used to deploy infrastructure
   • TENANT_ID: The tenant ID of the Azure App Registration used to deploy infrastructure
   • SUBSCRIPTION_ID: The subscription ID of the Azure subscription that infrastructure is to be deployed to
   • APP_ID: The GitHub App ID
   • WEBHOOK_SECRET: The Webhook secret specified when creating the GitHub App
   • PRIVATE_KEY: The Base64 string associated with the GitHub Apps Private key .pem file

Infrastructure is required to process webhook events, as well as gather and transform GitHub Actions log data. A variety of Azure services are used to provide the needed runtimes, configurations, and storage that allows for easy reporting layers to be integrated.

Note In this case it is assumed that a Federated credential for GitHub Actions has been correctly configured.

Use GitHub Actions 🚀 to execute Terraform CLI commands

1. Copy the .github/workflows/deploy_to_azure.yml into the repository created during Step 4 of the Requirements section
2. Copy the contents of terraform/ into the repository created during Step 4 of the Requirements section
   1. Optionally edit the default values in the variables.tf file copied in Step 1
3. Upon committing the files Step 2 observe the deploy_to_azure.yml workflow execute

Note If using the default values in terraform/variables.tf, resources will be deployed in the East US region

Code is included in the .github/workflows/deploy_to_azure.yml to boostrap and maintain the needed Azure infrastructure for Terraform state files. The workflow creates an Azure Storage Account beaverstate<GITHUB_ORG> (omitting - characters, limiting the name to 24 characters), as well as a storage container named <GITHUB_ORG>-tfstate if they are not present. This Azure Storage Account is then referenced as part of a backend configuration for Terraform state when initializing with the terraform CLI. If these values create a collision or are not up to the desired naming standards, change them before executing the workflow.

...
...

terraform init -backend-config="resource_group_name=$($storageAccount.ResourceGroupName)" `
               -backend-config="storage_account_name=$($storageAccount.StorageAccountName)" `
               -backend-config="container_name=$env:GITHUB_REPOSITORY_OWNER-tfstate" `
               -backend-config="key=prod.terraform.tfstate" `
               -backend-config="use_oidc=true" `
               -backend-config="subscription_id=$env:TF_VAR_subscription_id" `
               -backend-config="tenant_id=$env:TF_VAR_tenant_id" `
               -backend-config="client_id=$env:TF_VAR_client_id" && terraform plan -out out.tfplan && terraform apply -auto-approve out.tfplan

...
...

A PowerBI template file (.pbit) exists as a starting point to quickly begin dashboarding data set to a PowerBI streaming data set.

Download and open the .pbit file and open the Data source settings with the Transform data section of the Home ribbon.

Click Change Source to adjust the source to the Power BI streaming data set created by Beaver

To install this Probot application, follow these steps:

1. Clone this repository to your development environment

2. Install dependencies by running npm install in the root directory of the repository

3. Create a .env file in the root directory of the repository with the following content, and replace the values in angle brackets with your own values:

   APP_ID=<your GitHub App ID> 
   PRIVATE_KEY=<your Github App private key>
   GITHUB_CLIENT_ID=<your Github App client id> 
   GITHUB_CLIENT_SECRET=<your Github app client secret>
   WEBHOOK_SECRET=<your GitHub App webhook secret>
   
   # Optional environment variables for the Azure Event Hub
   AZURE_EVENT_HUB_CONNECTION_STRING=
   AZURE_EVENT_HUB_NAME=
   
   # Optional environment variables for the postgresql database
   POSTGRESQL_USER=
   POSTGRESQL_PASSWORD=
   POSTGRESQL_HOST=
   POSTGRESQL_PORT=
   POSTGRESQL_DATABASE=
   

4. On a terminal run the following command: npm start

The code for this application was originally drafted by @enyil and @decyjphr