Using Helm and Pulumi to define cloud native infrastructure as code

Posted by Alex Clemmer on Oct 31, 2018 1:21:54 PM
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The Helm community is one of the brightest spots in the infrastructure ecosystem: collectively, it has accumulated person-decades of operational expertise to produce Kubernetes manifests that “just work.”

But for many users, it is not feasible to run everything in Kubernetes, and the community is just starting to develop answers to questions like: what happens when a Helm Chart needs to interface with, for example, a managed database like AWS RDS or Azure CosmosDB?

 Pulumi is a cloud native development platform designed to be able to express any cloud native infrastructure as code in a natural, intentional manner using real languages. The most natural way to solve this challenge would be to stand up an instance of AWS RDS, populate a Kubernetes Secret with the connection details, and then simply let my application use these newly available resources. Pulumi gives users the primitives they need in order to achieve tasks like this most effectively.

How to connect a Kubernetes app with CosmosDB

In the following Pulumi program, we can manage both Azure and Kubernetes resources, including the interconnected dependencies between the two. Specifically, we

  • create an AKS cluster,
  • create a MongoDB-flavored instance of Azure’s CosmosDB,
  • create a Kubernetes Secret from the connection string exported by CosmosDB
  • deploy a Node.js Helm Chart that references it.

If you have the Azure command line, try running the example with pulumi up!

import * as k8s from "@pulumi/kubernetes";
import * as azure from "@pulumi/azure";
import * as mongoHelpers from "./mongoHelpers";
import * as config from "./config";

// Create an AKS cluster.
import { k8sCluster, k8sProvider } from "./cluster";

// Create a MongoDB-flavored instance of CosmosDB.
const cosmosdb = new azure.cosmosdb.Account("cosmosDb", {
   kind: "MongoDB",
   resourceGroupName: config.resourceGroup.name,
   location: config.location,
   consistencyPolicy: {
       consistencyLevel: "BoundedStaleness",
       maxIntervalInSeconds: 10,
       maxStalenessPrefix: 200
   },
   offerType: "Standard",
   enableAutomaticFailover: true,
   geoLocations: [
       { location: config.location, failoverPriority: 0 },
       { location: config.failoverLocation, failoverPriority: 1 }
   ]);

// Create secret from MongoDB connection string.
const mongoConnStrings = new k8s.core.v1.Secret(
   "mongo-secrets",
   { data: mongoHelpers.parseConnString(cosmosdb.connectionStrings) },
   { provider: k8sProvider }
);

// Boot up nodejs Helm chart example using CosmosDB in place of in-cluster MongoDB.
const node = new k8s.helm.v2.Chart(
   "node",
   {
       repo: "bitnami",
       chart: "node",
       version: "4.0.1",
       values: {
           serviceType: "LoadBalancer",
           mongodb: { install: false },
           externaldb: { ssl: true, secretName: mongoConnStrings.metadata.apply(m => m.name) }
       }
   },
   { providers: { kubernetes: k8sProvider }, dependsOn: mongoConnStrings }
);

Pulumi supports deploying resources to all the major cloud vendors, as well as Kubernetes. Using the Pulumi programming model, it is possible to define and deploy apps and infrastructure with an arbitrary mix of resources from any combination of cloud providers. And, because Pulumi uses normal $KUBECONFIG files, it is compatible anywhere you would use Helm or kubectl.

The Pulumi CLI provides rich insight into the progress a deployment makes. When you run pulumi up, the CLI will provide detailed information about the progress we’re making as we try to deploy the Chart. To get a sense of what this looks like, take a look at the progress reported as we deploy the Wordpress Chart:

helm-pulumi-deploy

Toward Cloud Native Infrastructure as Code

As cloud native architectures mature, Pulumi can reduce the complexity in choosing and using the available services from cloud vendors, and combine those choices using a single, consistent programming model. This can make the best use of existing tools such as Helm, and also reduce the friction caused by multiple deployment tools and models across complex architectures.

Topics: Kubernetes, Azure

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