Amazon EKS Construct Library

--- ![cfn-resources: Stable](https://img.shields.io/badge/cfn--resources-stable-success.svg?style=for-the-badge) > All classes with the `Cfn` prefix in this module ([CFN Resources](https://docs.aws.amazon.com/cdk/latest/guide/constructs.html#constructs_lib)) are always stable and safe to use. ![cdk-constructs: Experimental](https://img.shields.io/badge/cdk--constructs-experimental-important.svg?style=for-the-badge) > The APIs of higher level constructs in this module are experimental and under active development. They are subject to non-backward compatible changes or removal in any future version. These are not subject to the [Semantic Versioning](https://semver.org/) model and breaking changes will be announced in the release notes. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package. ---

This construct library allows you to define Amazon Elastic Container Service for Kubernetes (EKS) clusters programmatically. This library also supports programmatically defining Kubernetes resource manifests within EKS clusters.

This example defines an Amazon EKS cluster with the following configuration:

  • Managed nodegroup with 2x m5.large instances (this instance type suits most common use-cases, and is good value for money)

  • Dedicated VPC with default configuration (see ec2.Vpc)

  • A Kubernetes pod with a container based on the paulbouwer/hello-kubernetes image.

# Example automatically generated. See https://github.com/aws/jsii/issues/826
cluster = eks.Cluster(self, "hello-eks",
    version=eks.KubernetesVersion.V1_16
)

# apply a kubernetes manifest to the cluster
cluster.add_manifest("mypod", {
    "api_version": "v1",
    "kind": "Pod",
    "metadata": {"name": "mypod"},
    "spec": {
        "containers": [{
            "name": "hello",
            "image": "paulbouwer/hello-kubernetes:1.5",
            "ports": [{"container_port": 8080}]
        }
        ]
    }
})

In order to interact with your cluster through kubectl, you can use the aws eks update-kubeconfig AWS CLI command to configure your local kubeconfig.

The EKS module will define a CloudFormation output in your stack which contains the command to run. For example:

Outputs:
ClusterConfigCommand43AAE40F = aws eks update-kubeconfig --name cluster-xxxxx --role-arn arn:aws:iam::112233445566:role/yyyyy

The IAM role specified in this command is called the “masters role“. This is an IAM role that is associated with the system:masters RBAC group and has super-user access to the cluster.

You can specify this role using the mastersRole option, or otherwise a role will be automatically created for you. This role can be assumed by anyone in the account with sts:AssumeRole permissions for this role.

Execute the aws eks update-kubeconfig ... command in your terminal to create a local kubeconfig:

$ aws eks update-kubeconfig --name cluster-xxxxx --role-arn arn:aws:iam::112233445566:role/yyyyy
Added new context arn:aws:eks:rrrrr:112233445566:cluster/cluster-xxxxx to /home/boom/.kube/config

And now you can simply use kubectl:

$ kubectl get all -n kube-system
NAME                           READY   STATUS    RESTARTS   AGE
pod/aws-node-fpmwv             1/1     Running   0          21m
pod/aws-node-m9htf             1/1     Running   0          21m
pod/coredns-5cb4fb54c7-q222j   1/1     Running   0          23m
pod/coredns-5cb4fb54c7-v9nxx   1/1     Running   0          23m
...

Endpoint Access

You can configure the cluster endpoint access by using the endpointAccess property:

# Example automatically generated. See https://github.com/aws/jsii/issues/826
cluster = eks.Cluster(self, "hello-eks",
    version=eks.KubernetesVersion.V1_16,
    endpoint_access=eks.EndpointAccess.PRIVATE
)

The default value is eks.EndpointAccess.PUBLIC_AND_PRIVATE. Which means the cluster endpoint is accessible from outside of your VPC, and worker node traffic to the endpoint will stay within your VPC.

Capacity

By default, eks.Cluster is created with a managed nodegroup with x2 m5.large instances. You must specify the kubernetes version for the cluster with the version property.

# Example automatically generated. See https://github.com/aws/jsii/issues/826
eks.Cluster(self, "cluster-two-m5-large",
    version=eks.KubernetesVersion.V1_16
)

To use the traditional self-managed Amazon EC2 instances instead, set defaultCapacityType to DefaultCapacityType.EC2

# Example automatically generated. See https://github.com/aws/jsii/issues/826
cluster = eks.Cluster(self, "cluster-self-managed-ec2",
    default_capacity_type=eks.DefaultCapacityType.EC2,
    version=eks.KubernetesVersion.V1_16
)

The quantity and instance type for the default capacity can be specified through the defaultCapacity and defaultCapacityInstance props:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
eks.Cluster(self, "cluster",
    default_capacity=10,
    default_capacity_instance=ec2.InstanceType("m2.xlarge"),
    version=eks.KubernetesVersion.V1_16
)

To disable the default capacity, simply set defaultCapacity to 0:

# Example automatically generated. See https://github.com/aws/jsii/issues/826
eks.Cluster(self, "cluster-with-no-capacity",
    default_capacity=0,
    version=eks.KubernetesVersion.V1_16
)

The cluster.defaultCapacity property will reference the AutoScalingGroup resource for the default capacity. It will be undefined if defaultCapacity is set to 0 or defaultCapacityType is either NODEGROUP or undefined.

And the cluster.defaultNodegroup property will reference the Nodegroup resource for the default capacity. It will be undefined if defaultCapacity is set to 0 or defaultCapacityType is EC2.

You can add AutoScalingGroup resource as customized capacity through cluster.addCapacity() or cluster.addAutoScalingGroup():

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster.add_capacity("frontend-nodes",
    instance_type=ec2.InstanceType("t2.medium"),
    min_capacity=3,
    vpc_subnets={"subnet_type": ec2.SubnetType.PUBLIC}
)

Managed Node Groups

Amazon EKS managed node groups automate the provisioning and lifecycle management of nodes (Amazon EC2 instances) for Amazon EKS Kubernetes clusters. By default, eks.Nodegroup create a nodegroup with x2 t3.medium instances.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
eks.Nodegroup(stack, "nodegroup", cluster=cluster)

You can add customized node group through cluster.addNodegroup():

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster.add_nodegroup("nodegroup",
    instance_type=ec2.InstanceType("m5.large"),
    min_size=4
)

Custom AMI and Launch Template support

Specify the launch template for the nodegroup with your custom AMI. When using a custom AMI, Amazon EKS doesn’t merge any user data. Rather, You are responsible for supplying the required bootstrap commands for nodes to join the cluster. In the following sample, /ect/eks/bootstrap.sh from the AMI will be used to bootstrap the node. See Using a custom AMI for more details.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
user_data = ec2.UserData.for_linux()
user_data.add_commands("set -o xtrace", f"/etc/eks/bootstrap.sh {this.cluster.clusterName}")
lt = ec2.CfnLaunchTemplate(self, "LaunchTemplate",
    launch_template_data={
        # specify your custom AMI below
        "image_id": image_id,
        "instance_type": ec2.InstanceType("t3.small").to_string(),
        "user_data": Fn.base64(user_data.render())
    }
)
self.cluster.add_nodegroup("extra-ng",
    launch_template={
        "id": lt.ref,
        "version": lt.attr_default_version_number
    }
)

ARM64 Support

Instance types with ARM64 architecture are supported in both managed nodegroup and self-managed capacity. Simply specify an ARM64 instanceType (such as m6g.medium), and the latest Amazon Linux 2 AMI for ARM64 will be automatically selected.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# create a cluster with a default managed nodegroup
cluster = eks.Cluster(self, "Cluster",
    vpc=vpc,
    masters_role=masters_role,
    version=eks.KubernetesVersion.V1_17
)

# add a managed ARM64 nodegroup
cluster.add_nodegroup("extra-ng-arm",
    instance_type=ec2.InstanceType("m6g.medium"),
    min_size=2
)

# add a self-managed ARM64 nodegroup
cluster.add_capacity("self-ng-arm",
    instance_type=ec2.InstanceType("m6g.medium"),
    min_capacity=2
)

Fargate

AWS Fargate is a technology that provides on-demand, right-sized compute capacity for containers. With AWS Fargate, you no longer have to provision, configure, or scale groups of virtual machines to run containers. This removes the need to choose server types, decide when to scale your node groups, or optimize cluster packing.

You can control which pods start on Fargate and how they run with Fargate Profiles, which are defined as part of your Amazon EKS cluster.

See Fargate Considerations in the AWS EKS User Guide.

You can add Fargate Profiles to any EKS cluster defined in your CDK app through the addFargateProfile() method. The following example adds a profile that will match all pods from the “default” namespace:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster.add_fargate_profile("MyProfile",
    selectors=[{"namespace": "default"}]
)

To create an EKS cluster that only uses Fargate capacity, you can use FargateCluster.

The following code defines an Amazon EKS cluster without EC2 capacity and a default Fargate Profile that matches all pods from the “kube-system” and “default” namespaces. It is also configured to run CoreDNS on Fargate through the coreDnsComputeType cluster option.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster = eks.FargateCluster(self, "MyCluster",
    version=eks.KubernetesVersion.V1_16
)

# apply k8s resources on this cluster
cluster.add_manifest(...)

NOTE: Classic Load Balancers and Network Load Balancers are not supported on pods running on Fargate. For ingress, we recommend that you use the ALB Ingress Controller on Amazon EKS (minimum version v1.1.4).

Spot Capacity

If spotPrice is specified, the capacity will be purchased from spot instances:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster.add_capacity("spot",
    spot_price="0.1094",
    instance_type=ec2.InstanceType("t3.large"),
    max_capacity=10
)

Spot instance nodes will be labeled with lifecycle=Ec2Spot and tainted with PreferNoSchedule.

The AWS Node Termination Handler DaemonSet will be installed from ` Amazon EKS Helm chart repository <https://github.com/aws/eks-charts/tree/master/stable/aws-node-termination-handler>`_ on these nodes. The termination handler ensures that the Kubernetes control plane responds appropriately to events that can cause your EC2 instance to become unavailable, such as EC2 maintenance events and EC2 Spot interruptions and helps gracefully stop all pods running on spot nodes that are about to be terminated.

Current version:

name

version

Helm Chart

0.9.5

App

1.7.0

Bootstrapping

When adding capacity, you can specify options for /etc/eks/boostrap.sh which is responsible for associating the node to the EKS cluster. For example, you can use kubeletExtraArgs to add custom node labels or taints.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# up to ten spot instances
cluster.add_capacity("spot",
    instance_type=ec2.InstanceType("t3.large"),
    min_capacity=2,
    bootstrap_options={
        "kubelet_extra_args": "--node-labels foo=bar,goo=far",
        "aws_api_retry_attempts": 5
    }
)

To disable bootstrapping altogether (i.e. to fully customize user-data), set bootstrapEnabled to false when you add the capacity.

Kubernetes Resources

The KubernetesManifest construct or cluster.addManifest method can be used to apply Kubernetes resource manifests to this cluster.

When using cluster.addManifest, the manifest construct is defined within the cluster’s stack scope. If the manifest contains attributes from a different stack which depend on the cluster stack, a circular dependency will be created and you will get a synth time error. To avoid this, directly use new KubernetesManifest to create the manifest in the scope of the other stack.

The following examples will deploy the paulbouwer/hello-kubernetes service on the cluster:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
app_label = {"app": "hello-kubernetes"}

deployment = {
    "api_version": "apps/v1",
    "kind": "Deployment",
    "metadata": {"name": "hello-kubernetes"},
    "spec": {
        "replicas": 3,
        "selector": {"match_labels": app_label},
        "template": {
            "metadata": {"labels": app_label},
            "spec": {
                "containers": [{
                    "name": "hello-kubernetes",
                    "image": "paulbouwer/hello-kubernetes:1.5",
                    "ports": [{"container_port": 8080}]
                }
                ]
            }
        }
    }
}

service = {
    "api_version": "v1",
    "kind": "Service",
    "metadata": {"name": "hello-kubernetes"},
    "spec": {
        "type": "LoadBalancer",
        "ports": [{"port": 80, "target_port": 8080}],
        "selector": app_label
    }
}

# option 1: use a construct
KubernetesManifest(self, "hello-kub",
    cluster=cluster,
    manifest=[deployment, service]
)

# or, option2: use `addManifest`
cluster.add_manifest("hello-kub", service, deployment)

Kubectl Layer and Environment

The resources are created in the cluster by running kubectl apply from a python lambda function. You can configure the environment of this function by specifying it at cluster instantiation. For example, this can useful in order to configure an http proxy:

# Example automatically generated. See https://github.com/aws/jsii/issues/826
cluster = eks.Cluster(self, "hello-eks",
    version=eks.KubernetesVersion.V1_16,
    kubectl_environment={
        "http_proxy": "http://proxy.myproxy.com"
    }
)

By default, the kubectl, helm and aws commands used to operate the cluster are provided by an AWS Lambda Layer from the AWS Serverless Application in aws-lambda-layer-kubectl. In most cases this should be sufficient.

You can provide a custom layer in case the default layer does not meet your needs or if the SAR app is not available in your region.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# custom build:
layer = lambda_.LayerVersion(self, "KubectlLayer",
    code=lambda_.Code.from_asset(f"{__dirname}/layer.zip")
)
compatible_runtimes = ;

Pass it to kubectlLayer when you create or import a cluster:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster = eks.Cluster(self, "MyCluster",
    kubectl_layer=layer
)

# or
cluster = eks.Cluster.from_cluster_attributes(self, "MyCluster",
    kubectl_layer=layer
)

Instructions on how to build layer.zip can be found here.

Adding resources from a URL

The following example will deploy the resource manifest hosting on remote server:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import js_yaml as yaml
import sync_request as request

manifest_url = "https://url/of/manifest.yaml"
manifest = yaml.safe_load_all(request("GET", manifest_url).get_body())
cluster.add_manifest("my-resource", (SpreadElement ...manifest
  manifest))

Since Kubernetes resources are implemented as CloudFormation resources in the CDK. This means that if the resource is deleted from your code (or the stack is deleted), the next cdk deploy will issue a kubectl delete command and the Kubernetes resources will be deleted.

Dependencies

There are cases where Kubernetes resources must be deployed in a specific order. For example, you cannot define a resource in a Kubernetes namespace before the namespace was created.

You can represent dependencies between KubernetesManifests using resource.node.addDependency():

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
namespace = cluster.add_manifest("my-namespace",
    api_version="v1",
    kind="Namespace",
    metadata={"name": "my-app"}
)

service = cluster.add_manifest("my-service",
    metadata={
        "name": "myservice",
        "namespace": "my-app"
    },
    spec=
)

service.node.add_dependency(namespace)

NOTE: when a KubernetesManifest includes multiple resources (either directly or through cluster.addManifest()) (e.g. cluster.addManifest('foo', r1, r2, r3,...))), these resources will be applied as a single manifest via kubectl and will be applied sequentially (the standard behavior in kubectl).

Patching Kubernetes Resources

The KubernetesPatch construct can be used to update existing kubernetes resources. The following example can be used to patch the hello-kubernetes deployment from the example above with 5 replicas.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
KubernetesPatch(self, "hello-kub-deployment-label",
    cluster=cluster,
    resource_name="deployment/hello-kubernetes",
    apply_patch={"spec": {"replicas": 5}},
    restore_patch={"spec": {"replicas": 3}}
)

Querying Kubernetes Object Values

The KubernetesObjectValue construct can be used to query for information about kubernetes objects, and use that as part of your CDK application.

For example, you can fetch the address of a ``LoadBalancer` <https://kubernetes.io/docs/concepts/services-networking/service/#loadbalancer>`_ type service:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# query the load balancer address
my_service_address = KubernetesObjectValue(self, "LoadBalancerAttribute",
    cluster=cluster,
    resource_type="service",
    resource_name="my-service",
    json_path=".status.loadBalancer.ingress[0].hostname"
)

# pass the address to a lambda function
proxy_function = lambda_.Function(self, "ProxyFunction", {
    (SpreadAssignment ...
      environment
      environment)
},
    my_service_address=my_service_address.value
)

Specifically, since the above use-case is quite common, there is an easier way to access that information:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
load_balancer_address = cluster.get_service_load_balancer_address("my-service")

Kubernetes Resources in Existing Clusters

The Amazon EKS library allows defining Kubernetes resources such as Kubernetes manifests and Helm charts on clusters that are not defined as part of your CDK app.

First, you’ll need to “import” a cluster to your CDK app. To do that, use the eks.Cluster.fromClusterAttributes() static method:

# Example automatically generated. See https://github.com/aws/jsii/issues/826
cluster = eks.Cluster.from_cluster_attributes(self, "MyCluster",
    cluster_name="my-cluster-name",
    kubectl_role_arn="arn:aws:iam::1111111:role/iam-role-that-has-masters-access"
)

Then, you can use addManifest or addHelmChart to define resources inside your Kubernetes cluster. For example:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster.add_manifest("Test",
    api_version="v1",
    kind="ConfigMap",
    metadata={
        "name": "myconfigmap"
    },
    data={
        "Key": "value",
        "Another": "123454"
    }
)

At the minimum, when importing clusters for kubectl management, you will need to specify:

  • clusterName - the name of the cluster.

  • kubectlRoleArn - the ARN of an IAM role mapped to the system:masters RBAC role. If the cluster you are importing was created using the AWS CDK, the CloudFormation stack has an output that includes an IAM role that can be used. Otherwise, you can create an IAM role and map it to system:masters manually. The trust policy of this role should include the the arn:aws::iam::${accountId}:root principal in order to allow the execution role of the kubectl resource to assume it.

If the cluster is configured with private-only or private and restricted public Kubernetes endpoint access, you must also specify:

  • kubectlSecurityGroupId - the ID of an EC2 security group that is allowed connections to the cluster’s control security group.

  • kubectlPrivateSubnetIds - a list of private VPC subnets IDs that will be used to access the Kubernetes endpoint.

AWS IAM Mapping

As described in the Amazon EKS User Guide, you can map AWS IAM users and roles to Kubernetes Role-based access control (RBAC).

The Amazon EKS construct manages the aws-auth ConfigMap Kubernetes resource on your behalf and exposes an API through the cluster.awsAuth for mapping users, roles and accounts.

Furthermore, when auto-scaling capacity is added to the cluster (through cluster.addCapacity or cluster.addAutoScalingGroup), the IAM instance role of the auto-scaling group will be automatically mapped to RBAC so nodes can connect to the cluster. No manual mapping is required any longer.

For example, let’s say you want to grant an IAM user administrative privileges on your cluster:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
admin_user = iam.User(self, "Admin")
cluster.aws_auth.add_user_mapping(admin_user, groups=["system:masters"])

A convenience method for mapping a role to the system:masters group is also available:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster.aws_auth.add_masters_role(role)

Cluster Security Group

When you create an Amazon EKS cluster, a cluster security group is automatically created as well. This security group is designed to allow all traffic from the control plane and managed node groups to flow freely between each other.

The ID for that security group can be retrieved after creating the cluster.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster_security_group_id = cluster.cluster_security_group_id

Cluster Encryption Configuration

When you create an Amazon EKS cluster, envelope encryption of Kubernetes secrets using the AWS Key Management Service (AWS KMS) can be enabled. The documentation on creating a cluster can provide more details about the customer master key (CMK) that can be used for the encryption.

You can use the secretsEncryptionKey to configure which key the cluster will use to encrypt Kubernetes secrets. By default, an AWS Managed key will be used.

This setting can only be specified when the cluster is created and cannot be updated.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
secrets_key = kms.Key(self, "SecretsKey")
cluster = eks.Cluster(self, "MyCluster",
    secrets_encryption_key=secrets_key
)

The Amazon Resource Name (ARN) for that CMK can be retrieved.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster_encryption_config_key_arn = cluster.cluster_encryption_config_key_arn

Node ssh Access

If you want to be able to SSH into your worker nodes, you must already have an SSH key in the region you’re connecting to and pass it, and you must be able to connect to the hosts (meaning they must have a public IP and you should be allowed to connect to them on port 22):

# Example automatically generated. See https://github.com/aws/jsii/issues/826
asg = cluster.add_capacity("Nodes",
    instance_type=ec2.InstanceType("t2.medium"),
    vpc_subnets=SubnetSelection(subnet_type=ec2.SubnetType.PUBLIC),
    key_name="my-key-name"
)

# Replace with desired IP
asg.connections.allow_from(ec2.Peer.ipv4("1.2.3.4/32"), ec2.Port.tcp(22))

If you want to SSH into nodes in a private subnet, you should set up a bastion host in a public subnet. That setup is recommended, but is unfortunately beyond the scope of this documentation.

Helm Charts

The HelmChart construct or cluster.addChart method can be used to add Kubernetes resources to this cluster using Helm.

When using cluster.addChart, the manifest construct is defined within the cluster’s stack scope. If the manifest contains attributes from a different stack which depend on the cluster stack, a circular dependency will be created and you will get a synth time error. To avoid this, directly use new HelmChart to create the chart in the scope of the other stack.

The following example will install the NGINX Ingress Controller to your cluster using Helm.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# option 1: use a construct
HelmChart(self, "NginxIngress",
    cluster=cluster,
    chart="nginx-ingress",
    repository="https://helm.nginx.com/stable",
    namespace="kube-system"
)

# or, option2: use `addChart`
cluster.add_chart("NginxIngress",
    chart="nginx-ingress",
    repository="https://helm.nginx.com/stable",
    namespace="kube-system"
)

Helm charts will be installed and updated using helm upgrade --install, where a few parameters are being passed down (such as repo, values, version, namespace, wait, timeout, etc). This means that if the chart is added to CDK with the same release name, it will try to update the chart in the cluster. The chart will exists as CloudFormation resource.

Helm charts are implemented as CloudFormation resources in CDK. This means that if the chart is deleted from your code (or the stack is deleted), the next cdk deploy will issue a helm uninstall command and the Helm chart will be deleted.

When there is no release defined, the chart will be installed using the node.uniqueId, which will be lower cased and truncated to the last 63 characters.

By default, all Helm charts will be installed concurrently. In some cases, this could cause race conditions where two Helm charts attempt to deploy the same resource or if Helm charts depend on each other. You can use chart.node.addDependency() in order to declare a dependency order between charts:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
chart1 = cluster.add_chart(...)
chart2 = cluster.add_chart(...)

chart2.node.add_dependency(chart1)

Bottlerocket

Bottlerocket is a Linux-based open-source operating system that is purpose-built by Amazon Web Services for running containers on virtual machines or bare metal hosts. At this moment the managed nodegroup only supports Amazon EKS-optimized AMI but it’s possible to create a capacity of self-managed AutoScalingGroup running with bottlerocket Linux AMI.

NOTICE: Bottlerocket is only available in some supported AWS regions.

The following example will create a capacity with self-managed Amazon EC2 capacity of 2 t3.small Linux instances running with Bottlerocket AMI.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# add bottlerocket nodes
cluster.add_capacity("BottlerocketNodes",
    instance_type=ec2.InstanceType("t3.small"),
    min_capacity=2,
    machine_image_type=eks.MachineImageType.BOTTLEROCKET
)

The Bottlerocket AMI will be auto selected with the variant of different k8s version for the x86_64 architecture. For example, if the Amazon EKS cluster version is 1.17, the Bottlerocket AMI variant will be auto selected as aws-k8s-1.17 behind the scene. See Variants for more details.

To define only Bottlerocket capacity in your cluster, set defaultCapacity to 0 when you define the cluster as described above.

Please note Bottlerocket does not allow to customize bootstrap options and bootstrapOptions properties is not supported when you create the Bottlerocket capacity.

Service Accounts

With services account you can provide Kubernetes Pods access to AWS resources.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# add service account
sa = cluster.add_service_account("MyServiceAccount")

bucket = Bucket(self, "Bucket")
bucket.grant_read_write(service_account)

mypod = cluster.add_manifest("mypod",
    api_version="v1",
    kind="Pod",
    metadata={"name": "mypod"},
    spec={
        "service_account_name": sa.service_account_name,
        "containers": [{
            "name": "hello",
            "image": "paulbouwer/hello-kubernetes:1.5",
            "ports": [{"container_port": 8080}]
        }
        ]
    }
)

# create the resource after the service account
mypod.node.add_dependency(sa)

# print the IAM role arn for this service account
cdk.CfnOutput(self, "ServiceAccountIamRole", value=sa.role.role_arn)