1. Tracing Microservices in AI Workflows with Jaeger on Kubernetes


    To set up tracing for microservices in AI workflows using Jaeger on Kubernetes, you’ll need to:

    1. Set up a Kubernetes cluster if you don't have one already.
    2. Deploy Jaeger as an all-in-one solution or as a production setup within your cluster for distributed tracing.
    3. Configure your microservices to send traces to the Jaeger agent or collector.

    For this example, I'll provide you with a Pulumi program to deploy a Jaeger all-in-one instance on Kubernetes, which is suited for testing and development environments. This setup includes the basic components you need: a Jaeger agent, a Jaeger collector, a query service, and a simple in-memory storage. In production, you'd likely separate these components and use persistent storage.

    The following program demonstrates how to create a Kubernetes Namespace for Jaeger and deploy Jaeger using the all-in-one configuration. We will be using the Pulumi Kubernetes provider, which allow us to deploy Kubernetes resources with Pulumi.

    Here is the Python program:

    import pulumi import pulumi_kubernetes as kubernetes # Create a new Kubernetes Namespace jaeger_namespace = kubernetes.core.v1.Namespace("jaeger-namespace", metadata={ "name": "jaeger" }) # Define the Jaeger all-in-one template jaeger_all_in_one_yaml = """ apiVersion: apps/v1 kind: Deployment metadata: name: jaeger namespace: jaeger spec: replicas: 1 selector: matchLabels: app: jaeger template: metadata: labels: app: jaeger spec: containers: - name: jaeger image: jaegertracing/all-in-one:1.21 ports: - containerPort: 5775 protocol: UDP - containerPort: 6831 protocol: UDP - containerPort: 6832 protocol: UDP - containerPort: 5778 - containerPort: 16686 - containerPort: 14268 - containerPort: 9411 env: - name: COLLECTOR_ZIPKIN_HTTP_PORT value: "9411" readinessProbe: httpGet: path: / port: 16686 livenessProbe: httpGet: path: / port: 16686 """ # Deploy Jaeger all-in-one using the Kubernetes provider jaeger_all_in_one = kubernetes.yaml.ConfigGroup( "jaeger-all-in-one", yaml=jaeger_all_in_one_yaml, opts=pulumi.ResourceOptions(depends_on=[jaeger_namespace])) # Export the Jaeger Query Service URL pulumi.export("jaeger_query_url", pulumi.Output.concat( "http://jaeger-query.jaeger.svc.cluster.local:", "16686"))

    In this program:

    • Kubernetes Namespace: We create a Kubernetes Namespace named jaeger. This is a logical separation for resources within the Kubernetes cluster, which is particularly useful when different teams or projects share the cluster.
    • Jaeger Deployment: We define a multi-line YAML string representing a Kubernetes Deployment resource. This deployment configures the Jaeger all-in-one container with the necessary ports.
    • Jaeger YAML Config Group: We use Pulumi’s ConfigGroup class from the pulumi_kubernetes.yaml module to create the resources defined in the jaeger_all_in_one_yaml string. The depends_on option ensures that the Namespace is created before the Jaeger deployment.
    • Exported URL: We export the URL for accessing the Jaeger Query Service. This is the internal URL within the cluster. To access the Jaeger UI externally, you would expose the service using a LoadBalancer, NodePort, or an Ingress (not covered in this example).

    To use this program, make sure you have Pulumi and kubectl configured to communicate with your Kubernetes cluster. Adding persistent storage, separating components for a production setup, and configuring your microservices to report traces to Jaeger are steps that you would need to take beyond this introductory program.