Pulumi templates for building Kubernetes clusters

Are you tired of manually setting up your Kubernetes clusters every time you need to deploy a new application? Do you want to automate this process and save yourself some time and effort? Look no further than Pulumi templates for building Kubernetes clusters!

Pulumi is a modern infrastructure as code platform that allows you to write code in your favorite programming language and deploy it to any cloud provider. With Pulumi, you can create reusable templates that define your infrastructure and automate the deployment process.

In this article, we will explore how to use Pulumi templates to build Kubernetes clusters. We will cover the basics of Pulumi, how to set up a Kubernetes cluster using Pulumi, and how to customize your cluster to meet your specific needs.

What is Pulumi?

Pulumi is a modern infrastructure as code platform that allows you to write code in your favorite programming language and deploy it to any cloud provider. With Pulumi, you can create reusable templates that define your infrastructure and automate the deployment process.

Pulumi supports a wide range of programming languages, including JavaScript, TypeScript, Python, Go, and .NET. This means that you can write your infrastructure code in the language you are most comfortable with.

Pulumi also provides a powerful CLI that allows you to manage your infrastructure from the command line. You can use the CLI to create, update, and delete your infrastructure resources.

Setting up a Kubernetes cluster with Pulumi

To set up a Kubernetes cluster with Pulumi, you will need to create a new Pulumi project and install the necessary dependencies. You will also need to configure your cloud provider credentials.

Once you have set up your project, you can create a new stack and define your Kubernetes cluster resources. You can use Pulumi's Kubernetes provider to create your cluster resources, including the cluster itself, nodes, and services.

Here is an example of a Pulumi program that creates a Kubernetes cluster:

import * as k8s from "@pulumi/kubernetes";
import * as pulumi from "@pulumi/pulumi";

const name = "my-cluster";
const nodeCount = 3;

const provider = new k8s.Provider("provider", {
    kubeconfig: pulumi.output(k8s.config.loadKubeConfig()),
});

const cluster = new k8s.apiextensions.CustomResource("cluster", {
    apiVersion: "kubermatic.k8s.io/v1",
    kind: "Cluster",
    metadata: {
        name: name,
    },
    spec: {
        version: "1.19.0",
        cloud: {
            dc: "my-dc",
            provider: "aws",
        },
        exposeStrategy: "NodePort",
        nodeportRange: "30000-32767",
        etcd: {
            backupConfig: {
                s3: {
                    bucket: "my-bucket",
                    endpoint: "s3.amazonaws.com",
                    region: "us-west-2",
                },
            },
        },
        components: {
            kubelet: {
                version: "1.19.0",
            },
            kubeProxy: {
                version: "1.19.0",
            },
            kubeControllerManager: {
                version: "1.19.0",
            },
            kubeScheduler: {
                version: "1.19.0",
            },
            etcd: {
                version: "3.4.13-0",
            },
            apiserver: {
                version: "1.19.0",
            },
        },
        nodes: {
            count: nodeCount,
            machineType: "t2.medium",
            diskSize: "100",
            image: "ami-0c55b159cbfafe1f0",
            labels: {
                "node-role.kubernetes.io/master": "",
            },
        },
    },
}, { provider: provider });

const nodeGroup = new k8s.apiextensions.CustomResource("nodeGroup", {
    apiVersion: "kubermatic.k8s.io/v1",
    kind: "NodeGroup",
    metadata: {
        name: `${name}-nodegroup`,
    },
    spec: {
        clusterName: name,
        nodeCount: nodeCount,
        cloud: {
            dc: "my-dc",
            provider: "aws",
        },
        nodeTemplate: {
            spec: {
                machineType: "t2.medium",
                diskSize: "100",
                image: "ami-0c55b159cbfafe1f0",
                labels: {
                    "node-role.kubernetes.io/worker": "",
                },
            },
        },
    },
}, { provider: provider });

const service = new k8s.core.v1.Service("service", {
    metadata: {
        name: "my-service",
    },
    spec: {
        type: "NodePort",
        ports: [
            {
                name: "http",
                port: 80,
                targetPort: 8080,
            },
        ],
        selector: {
            app: "my-app",
        },
    },
}, { provider: provider });

In this example, we define a Kubernetes cluster with three nodes running on AWS. We also define a service that exposes port 80 on the nodes.

Customizing your Kubernetes cluster with Pulumi

One of the benefits of using Pulumi to build your Kubernetes cluster is that you can customize your cluster to meet your specific needs. You can define your cluster resources using your favorite programming language and take advantage of Pulumi's rich library of providers and modules.

For example, you can use Pulumi's AWS provider to create your cluster nodes and take advantage of AWS features such as auto-scaling groups and load balancers. You can also use Pulumi's Helm provider to install and configure Kubernetes applications on your cluster.

Here is an example of a Pulumi program that creates a Kubernetes cluster with auto-scaling nodes and a load balancer:

import * as k8s from "@pulumi/kubernetes";
import * as aws from "@pulumi/aws";
import * as pulumi from "@pulumi/pulumi";

const name = "my-cluster";
const nodeCount = 3;

const provider = new k8s.Provider("provider", {
    kubeconfig: pulumi.output(k8s.config.loadKubeConfig()),
});

const vpc = new aws.ec2.Vpc("vpc", {
    cidrBlock: "10.0.0.0/16",
});

const subnet = new aws.ec2.Subnet("subnet", {
    cidrBlock: "10.0.0.0/24",
    vpcId: vpc.id,
});

const securityGroup = new aws.ec2.SecurityGroup("securityGroup", {
    vpcId: vpc.id,
});

const cluster = new k8s.apiextensions.CustomResource("cluster", {
    apiVersion: "kubermatic.k8s.io/v1",
    kind: "Cluster",
    metadata: {
        name: name,
    },
    spec: {
        version: "1.19.0",
        cloud: {
            dc: "my-dc",
            provider: "aws",
        },
        exposeStrategy: "LoadBalancer",
        nodeportRange: "30000-32767",
        etcd: {
            backupConfig: {
                s3: {
                    bucket: "my-bucket",
                    endpoint: "s3.amazonaws.com",
                    region: "us-west-2",
                },
            },
        },
        components: {
            kubelet: {
                version: "1.19.0",
            },
            kubeProxy: {
                version: "1.19.0",
            },
            kubeControllerManager: {
                version: "1.19.0",
            },
            kubeScheduler: {
                version: "1.19.0",
            },
            etcd: {
                version: "3.4.13-0",
            },
            apiserver: {
                version: "1.19.0",
            },
        },
        nodes: {
            count: nodeCount,
            machineType: "t2.medium",
            diskSize: "100",
            image: "ami-0c55b159cbfafe1f0",
            labels: {
                "node-role.kubernetes.io/worker": "",
            },
            autoscaling: {
                minSize: 3,
                maxSize: 10,
            },
            cloudProvider: {
                aws: {
                    securityGroupIds: [securityGroup.id],
                    subnetIds: [subnet.id],
                },
            },
        },
    },
}, { provider: provider });

const service = new k8s.core.v1.Service("service", {
    metadata: {
        name: "my-service",
    },
    spec: {
        type: "LoadBalancer",
        ports: [
            {
                name: "http",
                port: 80,
                targetPort: 8080,
            },
        ],
        selector: {
            app: "my-app",
        },
    },
}, { provider: provider });

const lb = new aws.lb.LoadBalancer("lb", {
    internal: false,
    subnets: [subnet.id],
    securityGroups: [securityGroup.id],
});

const tg = new aws.lb.TargetGroup("tg", {
    port: 80,
    protocol: "HTTP",
    vpcId: vpc.id,
});

const listener = new aws.lb.Listener("listener", {
    loadBalancerArn: lb.arn,
    port: 80,
    protocol: "HTTP",
    defaultActions: [
        {
            type: "forward",
            targetGroupArn: tg.arn,
        },
    ],
});

const asg = new aws.autoscaling.Group("asg", {
    vpcZoneIdentifier: subnet.id,
    minSize: 3,
    maxSize: 10,
    launchConfiguration: {
        imageId: "ami-0c55b159cbfafe1f0",
        instanceType: "t2.medium",
        securityGroups: [securityGroup.id],
    },
});

const scaleOutPolicy = new aws.autoscaling.Policy("scaleOutPolicy", {
    autoscalingGroupName: asg.name,
    policyType: "TargetTrackingScaling",
    targetTrackingConfiguration: {
        predefinedMetricSpecification: {
            predefinedMetricType: "ASGAverageCPUUtilization",
        },
        targetValue: 50,
    },
});

const scaleInPolicy = new aws.autoscaling.Policy("scaleInPolicy", {
    autoscalingGroupName: asg.name,
    policyType: "TargetTrackingScaling",
    targetTrackingConfiguration: {
        predefinedMetricSpecification: {
            predefinedMetricType: "ASGAverageCPUUtilization",
        },
        targetValue: 20,
    },
});

const scaleOutAlarm = new aws.cloudwatch.MetricAlarm("scaleOutAlarm", {
    alarmName: "scale-out",
    comparisonOperator: "GreaterThanOrEqualToThreshold",
    evaluationPeriods: 1,
    metricName: "CPUUtilization",
    namespace: "AWS/EC2",
    period: 60,
    statistic: "Average",
    threshold: 50,
    alarmActions: [scaleOutPolicy.arn],
    dimensions: {
        AutoScalingGroupName: asg.name,
    },
});

const scaleInAlarm = new aws.cloudwatch.MetricAlarm("scaleInAlarm", {
    alarmName: "scale-in",
    comparisonOperator: "LessThanOrEqualToThreshold",
    evaluationPeriods: 1,
    metricName: "CPUUtilization",
    namespace: "AWS/EC2",
    period: 60,
    statistic: "Average",
    threshold: 20,
    alarmActions: [scaleInPolicy.arn],
    dimensions: {
        AutoScalingGroupName: asg.name,
    },
});

In this example, we define a Kubernetes cluster with auto-scaling nodes running on AWS. We also define a load balancer that distributes traffic to the nodes. We use Pulumi's AWS provider to create the necessary AWS resources, including the VPC, subnet, security group, load balancer, target group, listener, and auto-scaling group.

Conclusion

Pulumi templates for building Kubernetes clusters provide a powerful and flexible way to automate the deployment of your Kubernetes infrastructure. With Pulumi, you can define your infrastructure using your favorite programming language and take advantage of Pulumi's rich library of providers and modules.

In this article, we covered the basics of Pulumi, how to set up a Kubernetes cluster using Pulumi, and how to customize your cluster to meet your specific needs. We hope that this article has given you a good understanding of how to use Pulumi to build your Kubernetes clusters.

If you want to learn more about Pulumi and how to use it to automate your infrastructure, be sure to check out the Pulumi documentation and join the Pulumi community. Happy coding!

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