Using kubernetes monitoring.grafana.com with milvus.io

Question

Pulumi · Accepted Answer

To set up monitoring for a Kubernetes cluster with Grafana and integrate it with Milvus, an open-source vector database, you'll need to perform several steps. You'd typically deploy Grafana, set up data sources that point to your Kubernetes metrics, and then configure dashboards to visualize the metrics. Milvus itself doesn't emit metrics that Grafana would consume directly, but if it's running in Kubernetes, you can monitor the Kubernetes resources it uses (like pods, services, etc.)

The program below will use Pulumi to deploy a Grafana instance on a Kubernetes cluster and set up the necessary configurations for monitoring. Here's a step-by-step guide of what we are going to achieve with the program:

1. Deploy Grafana to the Kubernetes cluster.
2. Configure Grafana with a data source for Kubernetes metrics.
3. Create a dashboard in Grafana to visualize the metrics.

We'll start by writing the Pulumi program in TypeScript. Before running the program, make sure you have a Kubernetes cluster running and `kubectl` configured to interact with the cluster. This program assumes you have Pulumi installed and the Pulumi CLI is logged in.

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

// Create a Kubernetes Namespace
const ns = new k8s.core.v1.Namespace("monitoring-ns", {
    metadata: {
        name: "monitoring",
    },
}, { provider: k8sProvider });

// Deploy Grafana to your Kubernetes cluster
const grafana = new k8s.helm.v3.Chart("grafana", {
    namespace: ns.metadata.name,
    chart: "grafana",
    version: "6.1.17",
    fetchOpts: {
        repo: "https://grafana.github.io/helm-charts",
    },
    values: {
        persistence: {
            enabled: true,
            storageClassName: "standard", // change to your storage class
            accessModes: ["ReadWriteOnce"],
            size: "10Gi",
        },
        adminPassword: "admin", // replace with a secure password
        ingress: {
            enabled: true,
            annotations: {
                // Use the annotations for the Ingress controller you use
                "kubernetes.io/ingress.class": "nginx",
            },
            hosts: ["grafana.yourdomain.com"], // replace with your domain
        },
        // Define Grafana data sources
        datasources: {
            "datasources.yaml": {
                apiVersion: 1,
                datasources: [
                    {
                        name: "Kubernetes",
                        type: "prometheus",
                        url: "http://prometheus-server.monitoring.svc.cluster.local", // adjust this if using different namespace or service name
                        access: "proxy",
                        isDefault: true,
                    },
                ],
            },
        },
    },
}, { provider: k8sProvider });

// Export the Grafana ingress endpoint to access the Grafana UI
export const grafanaUrl = grafana.getResourceProperty("v1/Service", "grafana-grafana", "status").apply(s => `http://${s.loadBalancer.ingress[0].ip}:3000`);
```

### Explanation of the Program

- We create a Kubernetes namespace where all our monitoring services will reside.
- We deploy Grafana using the Helm chart from the official Grafana repository. This is done using Pulumi's Helm chart resource which simplifies Kubernetes deployments.
- Persistence is enabled for Grafana, so you'll need to specify the appropriate storage class for your cluster.
- We set up an ingress controller for Grafana, which requires you to have an Ingress controller such as nginx installed on your Kubernetes cluster. Replace `grafana.yourdomain.com` with the domain where you want to access Grafana.
- We configure Grafana with the Prometheus data source, which will be used to scrape Kubernetes metrics. The URL `http://prometheus-server.monitoring.svc.cluster.local` should be replaced with the URL where Prometheus is available in your cluster.
- Finally, we export the URL of the Grafana service, which will be the endpoint through which you can access the Grafana dashboard.

After deploying this code with Pulumi, you will need to access the Grafana UI at the exported URL, log in with the admin credentials, and set up dashboards according to your requirements. If you're monitoring Milvus, you would focus on creating a dashboard that visualizes metrics related to the Kubernetes resources that Milvus is using, such as Pods, Deployments, StatefulSets, Services, etc., all of which will be available through the Prometheus data source.

Remember that the security setup here, especially with regards to the Grafana admin password and exposing the service through Ingress is minimal and meant for demonstration purposes. You should follow best practices for securing your Grafana instance in a production environment, including changing the default password, setting up TLS for your Ingress, and restricting access through network policies or authentication mechanisms.