Tracing Microservices in AI Workflows with Jaeger on Kubernetes

Question

Pulumi · Accepted Answer

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:

```python
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.