The reliable pattern is simple: run the job on a Linux runner, check out the repository, start the Compose project with docker compose up --build --wait, execute tests with a one-off container, print Compose diagnostics if anything fails, and tear the stack down in an always() step. The details that make this dependable are the runner/network choice, explicit health checks, correct service hostnames, and deliberate cleanup.
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What Docker Compose is doing inside a GitHub Actions job
A Compose file is a repeatable definition of the services your application needs: web servers, databases, caches, queues, and supporting workers. Docker documents Compose as suitable for development, testing, staging, production, and CI. In Actions, that means the same service topology can be started for every run instead of asking the workflow to install and configure each dependency separately.
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The workflow still runs on a GitHub-hosted or self-hosted machine. Compose creates the project network and containers on that machine, builds images when requested, starts the services, and leaves them available while the test steps run. Your test command can therefore exercise the same multi-container boundaries used by the application rather than replacing the database or cache with a local mock.
A normal CI lifecycle has six phases:
- Choose a Linux runner with Docker available.
- Check out the repository.
- Build and start the Compose services.
- Wait for health checks, not an arbitrary sleep.
- Run tests or migrations in a disposable container.
- Capture status and logs on failure, then remove the stack.
Prerequisites and project layout
Your repository should contain a Compose file (for example, compose.yaml or docker-compose.yml) and the Dockerfiles or image references it uses. The workflow must run on Linux. GitHub states that workflows using Docker container actions, job containers, or service containers must use a Linux runner; GitHub-hosted jobs should use an Ubuntu runner, while a self-hosted runner must be Linux and have Docker installed.
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Before adding Actions, verify the same commands locally from the repository root:
docker compose configparses the file and resolves its configuration.docker compose up --build --waitcan build and start the stack.docker compose run --rm web pytest(replacewebandpytestwith your project values) returns the test result.docker compose downremoves the containers cleanly.
Keeping these commands identical between a laptop and CI makes a failed run easier to reproduce. The service name used in a command is the Compose service key, not necessarily the image name or container ID.
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Runner job or container job?
Decide where the test process itself runs before choosing hostnames. The two arrangements have different networking rules.
| Choice | Where the test process runs | How it reaches a service | Best fit | Important constraint |
|---|---|---|---|---|
| Runner-machine job | Directly on the Ubuntu runner | Map a Compose port and use localhost:port or 127.0.0.1:port |
Projects that need host shell tools, Docker commands, or straightforward port access | Every port used by the test process must be published, and the application configuration must use localhost addresses |
| Container job | Inside the job’s container | On GitHub’s user-defined bridge network, a configured service label is the hostname; a service labeled redis is reached as redis |
A fully containerized test environment with label-based service discovery | Use Linux and ensure the job has the Docker access required to run Compose itself; do not mix service-label addresses with localhost settings |
For a Compose project that must build images and create its own containers, a runner-machine job is usually the least surprising starting point. A container job can be useful when the test toolchain belongs in a fixed image, but its Docker access and network layout must be designed deliberately. Whichever arrangement you select, keep the address in application configuration consistent with it.
Make readiness explicit in the Compose file
Starting a container is not the same as making an application ready. A database may still be replaying logs, a cache may still be accepting connections, or a web process may have started before its dependency. Compose supports --wait and --wait-timeout; those options become useful when each service has a health check that describes actual readiness.
A minimal pattern looks like this (adapt the image, command, and probe to your application):
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services:
db:
image: postgres
healthcheck:
test: ['CMD-SHELL', 'your-database-readiness-command']
interval: 5s
timeout: 3s
retries: 20
web:
build: .
depends_on:
db:
condition: service_healthy
environment:
DATABASE_HOST: db
The probe should test the capability your tests need, such as accepting database connections or returning a successful health endpoint. A process check that only confirms that a PID exists can report healthy while the service is still unusable. If a service cannot expose a meaningful health check, use a project-specific polling command after up and fail with a useful message rather than inserting a fixed sleep whose duration may be too short on a busy runner.
docker compose up --wait waits for services to be running or healthy. Add --wait-timeout when the normal startup window needs an explicit upper bound. The right timeout depends on your images and runner; it is not a universal delay. A timeout should be long enough for a clean build and cold startup, but finite enough that a broken service does not consume the entire job.
A complete runner-based workflow
The following workflow uses the runner-machine arrangement. Replace web and pytest with the service and command used by your project:
name: Compose tests
on:
push:
pull_request:
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Start services
run: docker compose up --build --wait
- name: Run tests
run: docker compose run --rm web pytest
- name: Show status and logs on failure
if: failure()
run: |
docker compose ps
docker compose logs --no-color
- name: Tear down
if: always()
run: docker compose down
This ordering is intentional. Checkout makes the Compose file and build context available. The start command builds images, recreates containers when their configuration has changed, starts the stack, and waits for readiness. The test command then runs a one-off container. The failure step executes before teardown so that the containers and their logs still exist. The final step runs regardless of whether startup, testing, or diagnostics failed.
Why use --build in CI?
--build tells Compose to build images before starting services. It avoids accidentally testing an old local image and ensures that a change in the checked-out source is represented in the run. If your Compose file intentionally pulls all images from a registry, omit --build and make the pull policy part of the workflow instead. The important choice is to make the image source explicit: build from the checked-out commit or pull a known registry image.
What up does and does not do
Docker’s reference describes docker compose up as the command that builds, recreates, starts, and attaches to services. Detached mode leaves them running while later steps execute. The --wait option provides the readiness gate; the workflow should not proceed merely because the command was able to create containers.
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Run tests, migrations, and administrative commands
docker compose run --rm is designed for one-off work such as tests, database migrations, linters, and administrative scripts. It creates a new container from the selected service configuration and removes that container when the command exits. In the example, web is the service whose image and environment contain the test toolchain.
By default, docker compose run does not create the ports declared by the service. That prevents a one-off test container from colliding with the long-running web container. If the command genuinely needs the service’s published ports, request them explicitly with the service-port option rather than assuming they exist.
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docker compose run --rm web pytest
# A migration before tests
docker compose run --rm web ./manage.py migrate
# A one-off command that must publish the service ports
docker compose run --rm --service-ports web your-command
Use docker compose exec when you need to run a command inside an already-running service container, for example to inspect a live application process. Use run for a disposable test or migration so its exit status is the status your workflow evaluates.
Propagate the right exit code
When a service itself is the test runner, --exit-code-from can make that selected service determine the Compose command’s exit status. This is useful for a test service that starts, runs its suite, and then exits. For the one-off pattern, the exit code from docker compose run already represents the test command, so an additional wrapper is unnecessary.
Connect to databases, caches, and queues
The hostname depends on job placement, not on where the YAML file sits.
Tests running on the runner machine
Publish only the ports your host-side test command needs:
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services:
db:
image: postgres
ports:
- '5432:5432'
Configure a host-side test process with 127.0.0.1 or localhost and the published host port. If the host port is changed to avoid a collision, update the test configuration to match. A container started by Compose should not use localhost to reach a different Compose service; inside that container, localhost refers to itself.
Tests running in a Compose container
Containers in the same Compose project use the service name as the DNS hostname. If the database service key is db, set the application database host to db and use the container port, not the host-mapped port. The same rule applies to a cache service named redis: the client address is redis on its container port.
Tests in a GitHub container job
GitHub’s container-job networking gives the job and its configured service containers a shared user-defined bridge network. GitHub documents that the configured service label becomes the hostname, so a service labeled redis is reachable as redis. This is different from a process running directly on the runner, which uses mapped ports and localhost.
Do not copy a container-job configuration into a runner job without changing the addresses. A test that works with redis inside a network can fail on the host unless a Redis port is published and the host configuration points to localhost. Conversely, using localhost inside a container usually targets the test container itself.
Capture useful diagnostics before teardown
A red test result without container state is difficult to investigate. Put diagnostics in a step guarded by if: failure(), before the unconditional teardown:
- name: Show status and logs on failure
if: failure()
run: |
docker compose ps
docker compose logs --no-color
docker compose ps shows whether services exited, are still starting, or failed their health checks. docker compose logs --no-color keeps the output readable in the Actions log and includes messages from every service. If the output is very large, target the failing service after identifying it with ps, for example docker compose logs --no-color db.
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Diagnostics must precede docker compose down. Once containers are removed, commands that inspect those containers cannot show their startup error. The always() condition on teardown is equally important: a failed test must not leave the runner’s Docker resources behind for a later step or job.
Choose cleanup and persistence deliberately
docker compose down removes the project containers and their writable-layer data. That is normally the correct CI behavior because each run should start from an isolated state. docker compose stop only stops containers and preserves them, which can be useful for an interactive debugging session but can leak state between jobs.
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Container-job considerations
A container job can provide a fixed userland for the test process and label-based service discovery, but it changes more than the base image. Docker commands must have access to a Docker daemon, and the job image must contain the Docker CLI or another deliberate way to invoke Compose. If those pieces are not already part of your runner design, a direct Ubuntu runner avoids an extra layer of Docker configuration.
When using GitHub service containers rather than Compose-managed services, use the service labels and network behavior documented by GitHub. When using Compose to create the stack, keep all Compose commands and the test process on the network you designed. Do not assume that a port published by a Compose container automatically becomes available as localhost inside an unrelated job container.
Build locally or pull from a registry
Compose supports both an image built from the checked-out repository and an image pulled from a registry. Build locally when the workflow is validating the commit’s Dockerfile and application source. Pull when the workflow is intended to test a previously published image. These are different tests and should be visible in the workflow rather than mixed implicitly.
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|---|---|---|
| Build in the job | Use docker compose up --build --wait; the checkout is part of the build context |
A Dockerfile, build context, or dependency change breaks startup |
| Pull from a registry | Use the registry image reference and authenticate through the runner’s configured mechanism | The requested tag is unavailable or contains a different service configuration than expected |
Whichever strategy you choose, keep the Compose file and the workflow aligned. A workflow that builds one service but silently reuses an unrelated image can produce misleading test results.
Troubleshoot the common failure modes
| Symptom | Likely cause | Correction |
|---|---|---|
--wait never completes |
A health check is missing, inaccurate, or the service cannot reach a dependency | Inspect docker compose ps and docker compose logs; make the health check test real readiness and set an appropriate --wait-timeout |
| Tests report connection refused immediately | The test starts before the dependency is ready, or the address uses the wrong network model | Use health checks and --wait; use a Compose service name from a container and localhost only from the runner host |
| The test container cannot reach a published port | docker compose run does not create service ports by default |
Add the service-port option only when the one-off command needs those ports |
| Logs are missing after a failed run | Teardown ran before diagnostics | Place the failure() diagnostics step before an always() teardown step |
| A later run sees old data | Stopped containers or volumes were retained | Use docker compose down for isolation and explicitly choose a volume or external service when persistence is required |
| The workflow fails before Compose starts | The runner is not Linux or Docker is unavailable | Use ubuntu-latest on GitHub-hosted runners, or a Linux self-hosted runner with Docker installed |
The host test command cannot resolve db |
db is a container-network hostname, not a host-machine hostname |
Publish the database port and configure the host test process with localhost, or run the test inside the Compose network |
A practical checklist for every Compose workflow
- The job uses a Linux runner and Docker is available.
- The Compose file parses successfully from the repository root.
- Every dependency that gates tests has a meaningful health check.
- The start step uses
docker compose up --build --wait, with a project-appropriate wait timeout when needed. - The test command uses the correct service key and runs as a disposable
docker compose run --rmcontainer, unless a dedicated test service and--exit-code-fromare more suitable. - Host tests use mapped ports and localhost; container tests use service-label hostnames.
- Failure diagnostics print
docker compose psanddocker compose logs --no-colorbefore teardown. - Teardown is guarded with
if: always()and usesdownwhen isolation matters. - The workflow clearly chooses between building images from the commit and pulling images from a registry.
With those decisions encoded in the Compose file and workflow, each Actions run gets the same service topology, an explicit readiness gate, a test result that propagates correctly, and enough retained information to explain a failure. The resulting pipeline is repeatable without relying on timing luck or leftover containers.
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