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Supported Computing Services

For every task Cirrus CI starts a new Virtual Machine or a new Docker Container on a given compute service. Using a new VM or a new Docker Container each time for running tasks has many benefits:

  • Atomic changes to an environment where tasks are executed. Everything about a task is configured in .cirrus.yml file including VM image version and Docker Container image version. After commiting changes to .cirrus.yml not only new tasks will use the new environment but also outdated branches will continue using the old configuration.
  • Reproducibility. Fresh environment guarantees no corrupted artifacts or caches are presented from the previous tasks.
  • Cost efficiency. Most compute services are offering per-second pricing which makes them ideal for using with Cirrus CI. Also each task for repository can define ideal amount of CPUs and Memory specific for a nature of the task. No need to manage pools of similar VMs or try to fit workloads within limits of a given Continuous Integration systems.

To be fair there are of course some disadvantages of starting a new VM or a container for every task:

  • Virtual Machine Startup Speed. Starting a VM can take from a few dozen seconds to a minute or two depending on a cloud provider and a particular VM image. Starting a container on the other hand just takes a few hundred milliseconds! But even a minute on average for starting up VMs is not a big inconvenience in favor of more stable, reliable and more reproducible CI.
  • Cold local caches for every task execution. Many tools tend to store some caches like downloaded dependencies locally to avoid downloading them again in future. Since Cirrus CI always uses fresh VMs and containers such local caches will always be empty. Performance implication of empty local caches can be avoided by using Cirrus CI features like built-in caching mechanism. Some tools like Gradle can even take advantages of built-in HTTP cache!

Please check the list of currently supported cloud compute services below and please see what's coming next.

Community Cluster

Community Cluster is a Kubernetes cluster running on Google Kubernetes Engine that is available free of charge for Open Source community. Paying customers can also use Community Cluster for personal private repositories.

Community Cluster is configured the same way as anyone can configure a personal GKE cluster as described below.

By default a container is given 2 CPUs and 4 Gb of memory but it can be configured in .cirrus.yml:

container:
  image: openjdk:8-jdk
  cpu: 4
  memory: 12

Containers on Community Cluster can use maximum 8.0 CPUs and up to 24 Gb of memory. Custom GKE clusters don't have that limitation though.

Scheduling Times on Community Cluster

Since Community Cluster is shared, scheduling times for containers can vary from time to time. Also the smaller a container require resources faster it will be scheduled.

Google Cloud

Cirrus CI can schedule tasks on several Google Cloud Compute services. In order to interact with Google Cloud APIs Cirrus CI needs permissions. Creating a service account is a common way to safely give granular access to parts of Google Cloud Projects.

Isolation

We do recommend to create a separate Google Cloud project for running CI builds to make sure tests are isolated from production data. Having a separate project also will show how much money is spent on CI and how efficient Cirrus CI is 😉

Once you have a Google Cloud project for Cirrus CI please create a service account by running the following command: 

gcloud iam service-accounts create cirrus-ci \
    --project $PROJECT_ID

Depending on a compute service Cirrus CI will need different roles assigned to the service account. But Cirrus CI will always need permissions to Google Cloud Storage to store logs and caches. In order to give Google Cloud Storage permissions to the service account please run:

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member serviceAccount:cirrus-ci@$PROJECT_ID.iam.gserviceaccount.com \
    --role roles/storage.admin

Default Logs Retentions Period

By default Cirrus CI will store logs and caches for 30 days but it can be changed by manually configuring a lifecycle rule for a Google Cloud Storage bucket that Cirrus CI is using.

Now we have a service account that Cirrus CI can use! It's time to let Cirrus CI know about that fact by securely providing a private key for the service account. A private key can be created by running the following command:

gcloud iam service-accounts keys create service-account-credentials.json \
  --iam-account cirrus-ci@$PROJECT_ID.iam.gserviceaccount.com

At last create an encrypted variable from contents of service-account-credentials.json file and add it to the top of .cirrus.yml file:

gcp_credentials: ENCRYPTED[qwerty239abc]

Now Cirrus CI can store logs and caches for scheduled tasks in Google Cloud Storage. Please check following sections with additional instructions about Compute Engine or Kubernetes Engine.

Compute Engine

In order to schedule tasks on Google Compute Engine a service account that Cirrus CI operates via should have a necessary role assigned. It can be done by running a gcloud command:

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member serviceAccount:cirrus-ci@$PROJECT_ID.iam.gserviceaccount.com \
    --role roles/compute.admin

Now tasks can be scheduled on Compute Engine within $PROJECT_ID project by configuring gce_instance something like this:

gce_instance:
  image_project: ubuntu-os-cloud
  image_name: ubuntu-1604-xenial-v20171121a
  zone: us-central1-a
  cpu: 8
  memory: 40Gb
  disk: 20

task:
  script: ./run-ci.sh

Custom VM images

Building an immutable VM image with all necessary software pre-configured is a known best practice with many benefits. It makes sure environment where a task is executed is always the same and that no time is spent on useless work like installing a package over and over again for every single task.

There are many ways how one can create a custom image for Google Compute Engine. Please refer to the official documentation. At Cirrus Labs we are using Packer to automate building such images. An example of how we use it can be found in our public GitHub repository.

Windows Support

Google Compute Engine support Windows images and Cirrus CI can take full advantages of it by just explicitly specifying platform of an image like this:

gce_instance:
  image_project: windows-cloud
  image_name: windows-server-2016-dc-core-v20170913
  platform: windows
  zone: us-central1-a
  cpu: 8
  memory: 40Gb
  disk: 20

task:
  script: run-ci.bat

Instance Scopes

By default Cirrus CI will create Google Compute instances without any scopes so an instance can't access Google Cloud Storage for example. But sometimes it can be useful to give some permissions to an instance by using scopes key of gce_instance. For example if a particular task builds Docker images and then pushes them to Container Registry it's configuration file can look something like:

gcp_credentials: ENCRYPTED[qwerty239abc]

gce_instance:
  image_project: my-project
  image_name: my-custom-image-with-docker
  zone: us-central1-a
  cpu: 8
  memory: 40Gb
  disk: 20

test_task:
  test_script: ./scripts/test.sh

push_docker_task:
  depends_on: test
  only_if: $CIRRUS_BRANCH == "master"
  gce_instance:
    scopes: cloud-platform
  push_script: ./scripts/push_docker.sh

Preemptible Instances

Cirrus CI can schedule preemptible instances with all price benefits and stability risks. But sometimes risks of an instance being preempted at any time can be tolerated. For example gce_instance can be configured to schedule preemptible instance for non master branches like this:

gce_instance:
  image_project: my-project
  image_name: my-custom-image-with-docker
  zone: us-central1-a
  preemptible: $CIRRUS_BRANCH != "master"

Kubernetes Engine

Scheduling tasks on Compute Engine has one big disadvantage of waiting for an instance to start which usually takes around a minute. One minute is not that long but can't compete with hundreds of milliseconds that takes a container cluster on GKE to start a container.

To start scheduling tasks on a container cluster we first need to create one using gcloud. Here is a command to create an auto-scalable cluster that will scale down to zero nodes when there is no load for some time and quickly scale up with the load during pick hours:

gcloud container clusters create cirrus-ci-cluster \
  --project cirruslabs-ci \
  --zone us-central1-a \
  --num-nodes 1 --machine-type n1-standard-8 \
  --enable-autoscaling --min-nodes=0 --max-nodes=10

A service account that Cirrus CI operates via should be assigned with container.admin role that allows to administrate GKE clusters:

gcloud projects add-iam-policy-binding $PROJECT_ID \
    --member serviceAccount:cirrus-ci@$PROJECT_ID.iam.gserviceaccount.com \
    --role roles/container.admin

Done! Now after creating cirrus-ci-cluster cluster and having gcp_credentials configured tasks can be scheduled on the newly created cluster like this:

gcp_credentials: ENCRYPTED[qwerty239abc]

gke_container:
  image: gradle:4.3.0-jdk8
  cluster_name: cirrus-ci-cluster
  zone: us-central1-a
  namespace: default
  cpu: 6
  memory: 24Gb

Coming Soon

We are actively working on supporting AWS and Azure and planning to add support for them in the end of Q1 2018.