1. Enhancing Observability in AI Applications with Jaeger on Kubernetes


    To enhance observability in AI applications using Jaeger on Kubernetes, you'll want to install and run Jaeger within your Kubernetes cluster. Jaeger is an open-source end-to-end distributed tracing system that helps you monitor and troubleshoot complex microservices environments, like those commonly found in AI applications.

    The following Pulumi program demonstrates how to deploy Jaeger to a Kubernetes cluster using the Pulumi Kubernetes SDK. Understanding of Python and basic knowledge of Kubernetes objects are prerequisites to follow this demonstration.

    The program creates a namespace for Jaeger, deploys all necessary Jaeger components via Kubernetes deployments and services, and sets up an agent daemon set to collect spans from your AI application pods.

    Before running this Pulumi program, make sure you have:

    1. Installed Pulumi: https://www.pulumi.com/docs/get-started/install/
    2. Set up the Pulumi CLI with your chosen cloud provider, and Kubernetes cluster.
    3. Installed the Pulumi Kubernetes SDK with pip install pulumi_kubernetes.

    Now, let's walk through the Pulumi program:

    import pulumi import pulumi_kubernetes as k8s # Create a Kubernetes Namespace for Jaeger jaeger_namespace = k8s.core.v1.Namespace("jaeger-namespace", metadata=k8s.meta.v1.ObjectMetaArgs( name="jaeger" ) ) # Deploy Jaeger components to the Kubernetes cluster. # This example includes only a few components for simplicity, # but a full-fledged production deployment may require additional components # and configurations. # Create a Jaeger agent DaemonSet. jaeger_agent_daemonset = k8s.apps.v1.DaemonSet("jaeger-agent-daemonset", metadata=k8s.meta.v1.ObjectMetaArgs( name="jaeger-agent", namespace=jaeger_namespace.metadata["name"] ), spec=k8s.apps.v1.DaemonSetSpecArgs( selector=k8s.meta.v1.LabelSelectorArgs( match_labels={"app": "jaeger", "component": "agent"} ), template=k8s.core.v1.PodTemplateSpecArgs( metadata=k8s.meta.v1.ObjectMetaArgs( labels={"app": "jaeger", "component": "agent"} ), spec=k8s.core.v1.PodSpecArgs( containers=[ k8s.core.v1.ContainerArgs( name="agent", image="jaegertracing/jaeger-agent:1.21", args=[ "--reporter.grpc.host-port=jaeger-collector.jaeger.svc:14250" ], ports=[ k8s.core.v1.ContainerPortArgs(container_port=5775, protocol="UDP"), k8s.core.v1.ContainerPortArgs(container_port=6831, protocol="UDP"), k8s.core.v1.ContainerPortArgs(container_port=6832, protocol="UDP"), k8s.core.v1.ContainerPortArgs(container_port=5778, protocol="TCP") ] ) ] ) ) ) ) # Create a Jaeger Collector Deployment. jaeger_collector_deployment = k8s.apps.v1.Deployment("jaeger-collector-deployment", metadata=k8s.meta.v1.ObjectMetaArgs( name="jaeger-collector", namespace=jaeger_namespace.metadata["name"] ), spec=k8s.apps.v1.DeploymentSpecArgs( selector=k8s.meta.v1.LabelSelectorArgs( match_labels={"app": "jaeger", "component": "collector"} ), template=k8s.core.v1.PodTemplateSpecArgs( metadata=k8s.meta.v1.ObjectMetaArgs( labels={"app": "jaeger", "component": "collector"} ), spec=k8s.core.v1.PodSpecArgs( containers=[ k8s.core.v1.ContainerArgs( name="collector", image="jaegertracing/jaeger-collector:1.21", ports=[ k8s.core.v1.ContainerPortArgs(container_port=14250, protocol="TCP"), k8s.core.v1.ContainerPortArgs(container_port=14268, protocol="TCP"), k8s.core.v1.ContainerPortArgs(container_port=9411, protocol="TCP") ], env=[ k8s.core.v1.EnvVarArgs( name="SPAN_STORAGE_TYPE", value="memory" ), # Additional environment variables can be set here. ] ) ] ) ) ) ) # Create a Jaeger UI Service. jaeger_ui_service = k8s.core.v1.Service("jaeger-ui-service", metadata=k8s.meta.v1.ObjectMetaArgs( name="jaeger-ui", namespace=jaeger_namespace.metadata["name"] ), spec=k8s.core.v1.ServiceSpecArgs( selector={"app": "jaeger", "component": "collector"}, type="NodePort", # For demo purposes, exposing the service with a NodePort. ports=[k8s.core.v1.ServicePortArgs(port=16686, target_port=16686)] ) ) # Export Jaeger UI service NodePort to access the Jaeger UI. jaeger_ui_nodeport = pulumi.export('jaeger_ui_nodeport', jaeger_ui_service.spec.apply( lambda spec: spec.ports[0].node_port if spec.ports else None)) # The program does not include persistent storage configuration and assumes that your # Kubernetes cluster has access to the necessary Jaeger Docker images. # For production use, you would configure persistent storage for the Collector # and potentially other components like the Query service or Agents.

    This program creates the necessary Kubernetes objects to get Jaeger running in your cluster. Here's a summary of each part of the code:

    • jaeger_namespace: A namespace where all Jaeger-related Kubernetes resources will live.
    • jaeger_agent_daemonset: A DaemonSet that ensures a Jaeger agent is running on each node, collecting traces from the pods.
    • jaeger_collector_deployment: The Jaeger collector receives traces from Jaeger agents and runs them through a processing pipeline.
    • jaeger_ui_service: A service that exposes the Jaeger UI to the outside world.
    • jaeger_ui_nodeport: Exports the NodePort through which you can access the Jaeger UI.

    The Jaeger configuration presented here is very basic and for illustrative purposes. Depending on your needs, you may want to adjust storage, add security settings, and scale the collector and query services. Ensure you consult the Jaeger documentation to fine-tune your deployment according to your specific requirements.

    Remember, this is only a starting point for running Jaeger on Kubernetes for observability enhancement in AI applications. Each application's requirements may warrant additional settings and tweaks.