Run Code Coverage Job using a Container and Service

In this tutorial, you’ll learn how to configure a GitHub Actions workflow that runs a code coverage job entirely inside a Docker container and connects to a MongoDB service container. This strategy isolates your test environment and streamlines networking between the job and its service.

Job Container Overview
Adding a MongoDB Service Container
Defining the Code Coverage Job
Workflow Execution and Logs
Links and References

1. Job Container Overview

Specifying a container at the job level ensures that all subsequent steps execute inside your chosen Docker image. You no longer need to manually install language runtimes or tools on the runner—the container already includes them. Basic syntax:

jobs:
  my-job:
    runs-on: ubuntu-latest
    container:
      image: node:18
    steps:
      # All steps run inside node:18
      - name: Run Node.js script
        run: node index.js

Define additional container properties under the container key:

Property	Description
`options`	Extra Docker run flags (e.g., `--user`)
`env`	Environment variables injected into the container
`ports`	Ports to expose from the container
`volumes`	Host paths or named volumes to mount

2. Adding a MongoDB Service Container

Service containers run alongside your job container on a shared network. Each service is reachable by its service name without specifying ports. Example with PostgreSQL:

services:
  postgres:
    image: postgres:latest
    env:
      POSTGRES_PASSWORD: mysecretpassword

For MongoDB, you’ll configure the service similarly:

services:
  mongo:
    image: mongo:6.0
    env:
      MONGO_INITDB_ROOT_USERNAME: admin
      MONGO_INITDB_ROOT_PASSWORD: secret

Service containers automatically join a user-defined network. You can ping them by service name (e.g., mongo) from within the job container without extra port mappings.

3. Defining the Code Coverage Job

Below is a complete code-coverage job that:

Runs inside the node:18 container
Spins up a MongoDB service container
Caches and installs dependencies
Executes the coverage script
Archives the test reports

name: CI
on:
  push:
    branches:
      - main

jobs:
  code-coverage:
    name: Code Coverage
    runs-on: ubuntu-latest
    container:
      image: node:18
    services:
      mongo:
        image: siddharth67/mongo-db:non-prod
        options: --name mongo
    env:
      MONGO_URI: 'mongodb://mongo:27017/superData'
      MONGO_USERNAME: non-prod-user
      MONGO_PASSWORD: non-prod-password

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4

      - name: Cache NPM dependencies
        uses: actions/cache@v3
        with:
          path: node_modules
          key: ${{ runner.os }}-node-modules-${{ hashFiles('package-lock.json') }}

      - name: Install dependencies
        run: npm install

      - name: Run coverage
        run: npm run coverage
        continue-on-error: true

      - name: Archive test results
        if: always()
        uses: actions/upload-artifact@v3
        with:
          name: Mocha-Test-Result
          path: test-results.xml

4. Workflow Execution and Logs

When this workflow runs, GitHub Actions will:

Pull and start the node:18 job container.
Pull and start the MongoDB service container (siddharth67/mongo-db:non-prod).
Automatically join both containers on a shared bridge network.

In the Run coverage step, your script can connect to MongoDB at mongodb://mongo:27017/superData using the provided credentials.

The image shows a GitHub Actions workflow interface with jobs for unit testing and code coverage, indicating successful completion of tasks like initializing containers and starting a MongoDB service container.

Behind the scenes, the containers use their service label (mongo) as the hostname, eliminating explicit port mappings.

Introduction

GitHub Actions - Core Concepts

Continuous Integration with GitHub Actions

Continuous Deployment with GitHub Actions

Reusable Workflows and Reporting

Custom Actions

Self Hosted Runner

Security Guide

GitHub Actions in the Enterprise Cloud

Run Code Coverage Job using a Container and Service

Table of Contents

1. Job Container Overview

2. Adding a MongoDB Service Container

3. Defining the Code Coverage Job

4. Workflow Execution and Logs

5. Links and References

Watch Video

​Table of Contents

​1. Job Container Overview

​2. Adding a MongoDB Service Container

​3. Defining the Code Coverage Job

​4. Workflow Execution and Logs

​5. Links and References

Watch Video

Table of Contents

1. Job Container Overview

2. Adding a MongoDB Service Container

3. Defining the Code Coverage Job

4. Workflow Execution and Logs

5. Links and References