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2017-11-10 |
Autoscaling GitLab Runner on AWS
One of the biggest advantages of GitLab Runner is its ability to automatically spin up and down VMs to make sure your builds get processed immediately. It's a great feature, and if used correctly, it can be extremely useful in situations where you don't use your Runners 24/7 and want to have a cost-effective and scalable solution.
Introduction
In this tutorial, we'll explore how to properly configure a GitLab Runner in AWS that will serve as the bastion where it will spawn new Docker machines on demand.
In addition, we'll make use of Amazon's EC2 Spot instances which will greatly reduce the costs of the Runner instances while still using quite powerful autoscaling machines.
Prerequisites
Your GitLab instance is going to need to talk to the Runners over the network, so consider the sensitivity of your projects and communication between nodes when moving forward. This has implications on the AWS security groups you'll need, your DNS configuration, and other networking factors.
For example, you can keep the EC2 resources segmented away from public traffic in a different VPC. Your environment is likely different, so consider what works best for your situation.
AWS security groups
Docker Machine will attempt to use a default security group with rules for port 2376, which is required for communication with the Docker daemon. Instead of relying on Docker, you can create a security group with the rules you need and provide that in the Runner options as we will see below. This way, you can customize it to your liking ahead of time based on your networking environment.
AWS credentials
You'll need an AWS Access Key tied to a user with permission to scale (EC2) and update the cache (via S3). Create a new user with policies for EC2 (AmazonEC2FullAccess) and S3 (AmazonS3FullAccess). To be more secure, you can disable console login for that user. Grab the security credentials as we'll use them later during the Runner configuration.
Prepare the bastion instance
The first step is to install GitLab Runner in an EC2 instance that will serve
as the bastion that spawns new machines. This doesn't have to be a powerful
machine since it will not run any jobs itself, a t2.micro
instance will do.
This machine will be a dedicated host since we need it always up and running,
thus it will be the only standard cost.
NOTE: Note: For the bastion instance, choose a distribution that both Docker and GitLab Runner support, for example either Ubuntu, Debian, CentOS or RHEL will work fine.
Install the prerequisites:
- Log in to your server
- Install GitLab Runner from the official GitLab repository
- Install Docker
- Install Docker Machine
Now that the Runner is installed, it's time to register it.
Registering GitLab Runner
TIP: Tip: If you want every user in your instance to be able to use the autoscaled Runners, register the Runner as a shared one.
Before configuring the GitLab Runner, you need to first register it, so that it connects with your GitLab instance:
- Obtain a Runner token
- Register the Runner
- When asked the executor type, enter
docker+machine
You can now move on to the most important part, configuring GitLab Runner.
Configuring GitLab Runner to use the AWS machine driver
Now that the Runner is registered, you need to edit its configuration file and add the required options for the AWS machine driver.
Here's a full example of /etc/gitlab-runner/config.toml
. Open it with your
editor and edit as you see fit:
concurrent = 10
check_interval = 0
[[runners]]
name = "gitlab-aws-autoscaler"
url = "<URL of your GitLab instance>"
token = "<Runner's token>"
executor = "docker+machine"
limit = 20
[runners.docker]
image = "alpine"
privileged = true
disable_cache = true
[runners.cache]
Type = "s3"
ServerAddress = "s3.amazonaws.com"
AccessKey = "<your AWS Access Key ID>"
SecretKey = "<your AWS Secret Access Key>"
BucketName = "<the bucket where your cache should be kept>"
BucketLocation = "us-east-1"
Shared = true
[runners.machine]
IdleCount = 1
IdleTime = 1800
MaxBuilds = 100
OffPeakPeriods = [
"* * 0-9,18-23 * * mon-fri *",
"* * * * * sat,sun *"
]
OffPeakIdleCount = 0
OffPeakIdleTime = 1200
MachineDriver = "amazonec2"
MachineName = "gitlab-docker-machine-%s"
MachineOptions = [
"amazonec2-access-key=XXXX",
"amazonec2-secret-key=XXXX",
"amazonec2-region=us-east-1",
"amazonec2-vpc-id=vpc-xxxxx",
"amazonec2-subnet-id=subnet-xxxxx",
"amazonec2-use-private-address=true",
"amazonec2-tags=runner-manager-name,GitLab Runner autoscale,gitlab,true,gitlab-runner-autoscale,true",
"amazonec2-security-group=docker-machine-scaler",
"amazonec2-instance-type=m4.2xlarge",
]
Let's break it down to pieces.
Global section
In the global section, you can define the limit of the jobs that can be run
concurrently across all Runners (concurrent
). This heavily depends on your
needs, like how many users your Runners will accommodate, how much time your
builds take, etc. You can start with something low, and increase its value going
forward.
The check_interval
setting defines in seconds how often the Runner should
check GitLab for new jobs.
Example:
concurrent = 10
check_interval = 0
Read more about all the options you can use.
[[runners]]
section
From the [[runners]]
section, the most important part is the executor
which
must be set to docker+machine
. Most of those settings are taken care of when
you register the Runner for the first time.
limit
sets the maximum number of machines (running and idle) that this Runner
will start. For more info check the relationship between limit
, concurrent
and IdleCount
.
Example:
[[runners]]
name = "gitlab-aws-autoscaler"
url = "<URL of your GitLab instance>"
token = "<Runner's token>"
executor = "docker+machine"
limit = 20
Read more
about all the options you can use under [[runners]]
.
[runners.docker]
section
In the [runners.docker]
section you can define the default Docker image to
be used by the child Runners if it's not defined in .gitlab-ci.yml
.
Using privileged = true
, all Runners will be able to run Docker in Docker
which is useful if you plan to build your own Docker images via GitLab CI/CD.
Next, we use disable_cache = true
to disable the Docker executor's inner
cache mechanism since we will use the distributed cache mode as described
below.
Example:
[runners.docker]
image = "alpine"
privileged = true
disable_cache = true
Read more
about all the options you can use under [runners.docker]
.
[runners.cache]
section
To speed up your jobs, GitLab Runner provides a cache mechanism where selected directories and/or files are saved and shared between subsequent jobs. While not required for this setup, it is recommended to use the distributed cache mechanism that GitLab Runner provides. Since new instances will be created on demand, it is essential to have a common place where cache is stored.
In the following example, we use Amazon S3:
[runners.cache]
Type = "s3"
ServerAddress = "s3.amazonaws.com"
AccessKey = "<your AWS Access Key ID>"
SecretKey = "<your AWS Secret Access Key>"
BucketName = "<the bucket where your cache should be kept>"
BucketLocation = "us-east-1"
Shared = true
Here's some more info to get you started:
- The
[runners.cache]
section reference - Deploying and using a cache server for GitLab Runner
- How cache works
[runners.machine]
section
This is the most important part of the configuration and it's the one that tells GitLab Runner how and when to spawn new or remove old Docker Machine instances.
We will focus on the AWS machine options, for the rest of the settings read about the:
- autoscaling algorithm and the parameters it's based on - depends on the needs of your organization
- off peak time configuration - useful when there are regular time periods in your organization when no work is done, for example weekends
Example:
[runners.machine]
IdleCount = 1
IdleTime = 1800
MaxBuilds = 10
OffPeakPeriods = [
"* * 0-9,18-23 * * mon-fri *",
"* * * * * sat,sun *"
]
OffPeakIdleCount = 0
OffPeakIdleTime = 1200
MachineDriver = "amazonec2"
MachineName = "gitlab-docker-machine-%s"
MachineOptions = [
"amazonec2-access-key=XXXX",
"amazonec2-secret-key=XXXX",
"amazonec2-region=us-east-1",
"amazonec2-vpc-id=vpc-xxxxx",
"amazonec2-subnet-id=subnet-xxxxx",
"amazonec2-use-private-address=true",
"amazonec2-tags=runner-manager-name,GitLab Runner autoscale,gitlab,true,gitlab-runner-autoscale,true",
"amazonec2-security-group=docker-machine-scaler",
"amazonec2-instance-type=m4.2xlarge",
]
The Docker Machine driver is set to amazonec2
and the machine name has a
standard prefix followed by %s
(required) that is replaced by the ID of the
child Runner: gitlab-docker-machine-%s
.
Now, depending on your AWS infrastructure, there are many options you can set up
under MachineOptions
. Let's see the most common ones:
amazonec2-access-key=XXXX
- The AWS access key of the user that has permissions to create EC2 instances, see AWS credentials.amazonec2-secret-key=XXXX
- The AWS secret key of the user that has permissions to create EC2 instances, see AWS credentials.amazonec2-region=eu-central-1
- The region to use when launching the instance. You can omit this entirely and the defaultus-east-1
will be used.amazonec2-vpc-id=vpc-xxxxx
- Your VPC ID to launch the instance in, read more in Docker docs about the VPC ID.amazonec2-subnet-id=subnet-xxxx
- AWS VPC subnet ID.amazonec2-use-private-address=true
- Use the private IP address for docker-machine, but still create a public IP address. Useful to keep the traffic internal and avoid extra costs.amazonec2-tags=runner-manager-name,GitLab Runner autoscale,gitlab,true,gitlab-runner-autoscale,true
- AWS extra tag key-value pairs, useful to identify the instances on the AWS console. Read more about using tags in AWS.amazonec2-security-group=docker-machine-scaler
- AWS VPC security group name, see AWS security groups.amazonec2-instance-type=m4.2xlarge
- The instance type that the child Runners will run on.
TIP: Tip:
Under MachineOptions
you can add anything that the AWS Docker Machine driver
supports. You are highly
encouraged to read Docker's docs as your infrastructure setup may warrant
different options to be applied.
NOTE: Note:
The child instances will use by default Ubuntu 16.04 unless you choose a
different AMI ID by setting amazonec2-ami
.
NOTE: Note:
If you specify amazonec2-private-address-only=true
as one of the machine
options, your EC2 instance won't get assigned a public IP. This is fine if your
VPC is configured correctly with an Internet Gateway (IGW) and routing is fine,
but it’s something to consider if you've got a more exotic configuration. Read
more in Docker docs about VPC connectivity.
Read more
about all the options you can use under [runners.machine]
.
Cutting down costs with Amazon EC2 Spot instances
As described by Amazon:
Amazon EC2 Spot instances allow you to bid on spare Amazon EC2 computing capacity. Since Spot instances are often available at a discount compared to On-Demand pricing, you can significantly reduce the cost of running your applications, grow your application’s compute capacity and throughput for the same budget, and enable new types of cloud computing applications.
In addition to the [runners.machine]
options
you picked above, in /etc/gitlab-runner/config.toml
under the MachineOptions
section, add the following:
MachineOptions = [
"amazonec2-request-spot-instance=true",
"amazonec2-spot-price=0.03",
"amazonec2-block-duration-minutes=60"
]
With this configuration, Docker Machines are created on Spot instances with a maximum bid price of $0.03 per hour and the duration of the Spot instance is capped at 60 minutes. Be sure to check on the current pricing based on the region you picked.
To learn more about Amazon EC2 Spot instances, visit the following links:
- https://aws.amazon.com/ec2/spot/
- https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/spot-requests.html
- https://aws.amazon.com/blogs/aws/focusing-on-spot-instances-lets-talk-about-best-practices/
Caveats of Spot instances
While Spot instances is a great way to use unused resources and minimize the costs of your infrastructure, you must be aware of the implications.
Running CI jobs on Spot instances may increase the failure rates because of the Spot instances pricing model. If the price exceeds your bid, the existing Spot instances will be immediately terminated and all your jobs on that host will fail.
As a consequence, the auto-scale Runner would fail to create new machines while it will continue to request new instances. This eventually will make 60 requests and then AWS won't accept any more. Then once the Spot price is acceptable, you are locked out for a bit because the call amount limit is exceeded.
If you encounter that case, you can use the following command in the bastion machine to see the Docker Machines state:
docker-machine ls -q --filter state=Error --format "{{.NAME}}"
NOTE: Note: In issue 2771 there is a discussion to make GitLab Runner gracefully handle Spot price changes.
Conclusion
Using the autoscale feature of GitLab Runner can save you both time and money. Using the Spot instances that AWS provides can save you even more, but you must be aware of the implications. As long as your bid is high enough, there won't be an issue.
You can read the following use cases from which this tutorial was influenced: