Azure DevOps Services
If your pipeline requires the support of one or more services, in many cases you'll want to create, connect to, and clean up each service on a per-job basis. For instance, a pipeline may run integration tests that require access to a database and a memory cache. The database and memory cache need to be freshly created for each job in the pipeline.
A container provides a simple and portable way to run a service that your pipeline depends on. A service container enables you to automatically create, network, and manage the lifecycle of your containerized service. Each service container is accessible by only the job that requires it. Service containers work with any kind of job, but they're most commonly used with container jobs.
Service containers must define a
The pipeline will run
docker run for the provided container without additional arguments.
Azure Pipelines can run Linux or Windows Containers. Use either hosted Ubuntu for Linux containers, or the Hosted Windows Container pool for Windows containers. (The Hosted macOS pool doesn't support running containers.)
Single container job
A simple example of using container jobs:
resources: containers: - container: my_container image: buildpack-deps:focal - container: nginx image: nginx pool: vmImage: 'ubuntu-latest' container: my_container services: nginx: nginx steps: - script: | curl nginx displayName: Show that nginx is running
This pipeline fetches the
buildpack-deps containers from Docker Hub
and then starts the containers. The containers are networked together so that they can reach each other
From inside this job container, the
nginx host name resolves to the correct services using Docker networking.
All containers on the network automatically expose all ports to each other.
You can also use service containers without a job container. A simple example:
resources: containers: - container: nginx image: nginx ports: - 8080:80 env: NGINX_PORT: 80 - container: redis image: redis ports: - 6379 pool: vmImage: 'ubuntu-latest' services: nginx: nginx redis: redis steps: - script: | curl localhost:8080 echo $AGENT_SERVICES_REDIS_PORTS_6379
This pipeline starts the latest
nginx containers. Since the job isn't running in a container, there's no automatic name resolution.
This example shows how you can instead reach services by using
In the above example, we provide the port explicitly (for example,
An alternative approach is to let a random
port get assigned dynamically at runtime. You can then access these dynamic ports by using variables.
In a Bash script, you can access a variable by using the process environment. These variables take the form:
In the above example,
redis is assigned a random available port on the host.
agent.services.redis.ports.6379 variable contains the port number.
Service containers are also useful for running the same steps against multiple versions of the same service. In the following example, the same steps run against multiple versions of PostgreSQL.
resources: containers: - container: my_container image: ubuntu:22.04 - container: pg15 image: postgres:15 - container: pg14 image: postgres:14 pool: vmImage: 'ubuntu-latest' strategy: matrix: postgres15: postgresService: pg15 postgres14: postgresService: pg14 container: my_container services: postgres: $[ variables['postgresService'] ] steps: - script: printenv
When specifying a container resource or an inline container, you can specify an array of
ports to expose on the container.
resources: containers: - container: my_service image: my_service:latest ports: - 8080:80 - 5432 services: redis: image: redis ports: - 6379/tcp
ports is not required if your job is running in a container because containers on the same Docker network automatically expose all ports
to each other by default.
If your job is running on the host, then
ports are required to access the service. A port takes the form
<hostPort>:<containerPort> or just
with an optional
/<protocol> at the end, for example
6379/tcp to expose
tcp over port
6379, bound to a random port on the host machine.
For ports bound to a random port on the host machine, the pipeline creates a variable of the form
agent.services.<serviceName>.ports.<port> so that it can be accessed
by the job. For example,
agent.services.redis.ports.6379 resolves to the randomly assigned port on the host machine.
Volumes are useful for sharing data between services, or for persisting data between multiple runs of a job.
You can specify volume mounts as an array of
volumes. Volumes can either be named Docker volumes, anonymous Docker volumes, or bind mounts on the host.
services: my_service: image: myservice:latest volumes: - mydockervolume:/data/dir - /data/dir - /src/dir:/dst/dir
Volumes take the form
<source> can be a named volume or an absolute path on the host machine, and
an absolute path in the container.
If you use our hosted pools, then your volumes will not be persisted between jobs because the host machine is cleaned up after the job is completed.
Service containers share the same container resources as container jobs. This means that you can use the same additional options.
Optionally, if any service container specifies a HEALTHCHECK, the agent waits until the container is healthy before running the job.
Multiple containers with services example
In this example, there's a Django Python web container connected to two database containers - PostgreSQL and MySQL. The PostgreSQL database is the primary database and its container has the name
db container uses volume
/data/db:/var/lib/postgresql/data and there are three database variables passed to the container via
mysql container uses port
3306:3306 and there are also database variables passed via
web container is open with port
8000. In the steps,
pip installs dependencies and then Django test are run. If you'd like to get a working example set up, you'll need a Django site set up with two databases. This example assumes your
manage.py file is in the root directory and your Django project is within that directory. You may need to update the
/__w/1/s/ path in
resources: containers: - container: db image: postgres volumes: - '/data/db:/var/lib/postgresql/data' env: POSTGRES_DB: postgres POSTGRES_USER: postgres POSTGRES_PASSWORD: postgres - container: mysql image: 'mysql:5.7' ports: - '3306:3306' env: MYSQL_DATABASE: users MYSQL_USER: mysql MYSQL_PASSWORD: mysql MYSQL_ROOT_PASSWORD: mysql - container: web image: python volumes: - '/code' ports: - '8000:8000' pool: vmImage: 'ubuntu-latest' container: web services: db: db mysql: mysql steps: - script: | pip install django pip install psycopg2 pip install mysqlclient displayName: set up django - script: | python /__w/1/s/manage.py test