Collecting Metrics for TCP services

This task shows how to configure Istio to automatically gather telemetry for TCP services in a mesh. At the end of this task, a new metric will be enabled for calls to a TCP service within your mesh.

The Bookinfo sample application is used as the example application throughout this task.

Before you begin

  • Install Istio in your cluster and deploy an application.

  • This task assumes that the Bookinfo sample will be deployed in the default namespace. If you use a different namespace, you will need to update the example configuration and commands.

Collecting new telemetry data

  1. Create a new YAML file to hold configuration for the new metrics that Istio will generate and collect automatically.

    Save the following as tcp_telemetry.yaml:

    # Configuration for a metric measuring bytes sent from a server
    # to a client
    apiVersion: "config.istio.io/v1alpha2"
    kind: metric
    metadata:
      name: mongosentbytes
      namespace: default
    spec:
      value: connection.sent.bytes | 0 # uses a TCP-specific attribute
      dimensions:
        source_service: source.workload.name | "unknown"
        source_version: source.labels["version"] | "unknown"
        destination_version: destination.labels["version"] | "unknown"
      monitoredResourceType: '"UNSPECIFIED"'
    ---
    # Configuration for a metric measuring bytes sent from a client
    # to a server
    apiVersion: "config.istio.io/v1alpha2"
    kind: metric
    metadata:
      name: mongoreceivedbytes
      namespace: default
    spec:
      value: connection.received.bytes | 0 # uses a TCP-specific attribute
      dimensions:
        source_service: source.workload.name | "unknown"
        source_version: source.labels["version"] | "unknown"
        destination_version: destination.labels["version"] | "unknown"
      monitoredResourceType: '"UNSPECIFIED"'
    ---
    # Configuration for a Prometheus handler
    apiVersion: "config.istio.io/v1alpha2"
    kind: prometheus
    metadata:
      name: mongohandler
      namespace: default
    spec:
      metrics:
      - name: mongo_sent_bytes # Prometheus metric name
        instance_name: mongosentbytes.metric.default # Mixer instance name (fully-qualified)
        kind: COUNTER
        label_names:
        - source_service
        - source_version
        - destination_version
      - name: mongo_received_bytes # Prometheus metric name
        instance_name: mongoreceivedbytes.metric.default # Mixer instance name (fully-qualified)
        kind: COUNTER
        label_names:
        - source_service
        - source_version
        - destination_version
    ---
    # Rule to send metric instances to a Prometheus handler
    apiVersion: "config.istio.io/v1alpha2"
    kind: rule
    metadata:
      name: mongoprom
      namespace: default
    spec:
      match: context.protocol == "tcp"
             && destination.service == "mongodb.default.svc.cluster.local"
      actions:
      - handler: mongohandler.prometheus
        instances:
        - mongoreceivedbytes.metric
        - mongosentbytes.metric
  2. Push the new configuration.

    $ kubectl apply -f tcp_telemetry.yaml
    Created config metric/default/mongosentbytes at revision 3852843
    Created config metric/default/mongoreceivedbytes at revision 3852844
    Created config prometheus/default/mongohandler at revision 3852845
    Created config rule/default/mongoprom at revision 3852846
  3. Setup Bookinfo to use MongoDB.

    1. Install v2 of the ratings service.

      If you are using a cluster with automatic sidecar injection enabled, simply deploy the services using kubectl:

      $ kubectl apply -f @samples/bookinfo/platform/kube/bookinfo-ratings-v2.yaml@

      If you are using manual sidecar injection, use the following command instead:

      $ kubectl apply -f <(istioctl kube-inject -f @samples/bookinfo/platform/kube/bookinfo-ratings-v2.yaml@)
      deployment "ratings-v2" configured
    2. Install the mongodb service:

      If you are using a cluster with automatic sidecar injection enabled, simply deploy the services using kubectl:

      $ kubectl apply -f @samples/bookinfo/platform/kube/bookinfo-db.yaml@

      If you are using manual sidecar injection, use the following command instead:

      $ kubectl apply -f <(istioctl kube-inject -f @samples/bookinfo/platform/kube/bookinfo-db.yaml@)
      service "mongodb" configured
      deployment "mongodb-v1" configured
    3. The Bookinfo sample deploys multiple versions of each microservice, so you will start by creating destination rules that define the service subsets corresponding to each version, and the load balancing policy for each subset.

      $ kubectl apply -f @samples/bookinfo/networking/destination-rule-all.yaml@

      If you enabled mutual TLS, please run the following instead

      $ kubectl apply -f @samples/bookinfo/networking/destination-rule-all-mtls.yaml@

      You can display the destination rules with the following command:

      $ kubectl get destinationrules -o yaml

      Since the subset references in virtual services rely on the destination rules, wait a few seconds for destination rules to propagate before adding virtual services that refer to these subsets.

    4. Create ratings and reviews virtual services:

      $ kubectl apply -f @samples/bookinfo/networking/virtual-service-ratings-db.yaml@
      Created config virtual-service/default/reviews at revision 3003
      Created config virtual-service/default/ratings at revision 3004
  4. Send traffic to the sample application.

    For the Bookinfo sample, visit http://$GATEWAY_URL/productpage in your web browser or issue the following command:

    $ curl http://$GATEWAY_URL/productpage
  5. Verify that the new metric values are being generated and collected.

    In a Kubernetes environment, setup port-forwarding for Prometheus by executing the following command:

    $ kubectl -n istio-system port-forward $(kubectl -n istio-system get pod -l app=prometheus -o jsonpath='{.items[0].metadata.name}') 9090:9090 &

    View values for the new metric via the Prometheus UI.

    The provided link opens the Prometheus UI and executes a query for values of the istio_mongo_received_bytes metric. The table displayed in the Console tab includes entries similar to:

    istio_mongo_received_bytes{destination_version="v1",instance="172.17.0.18:42422",job="istio-mesh",source_service="ratings-v2",source_version="v2"} 2317

    Istio also collects protocol-specific statistics for MongoDB. For example, the value of total OP_QUERY messages sent from the ratings service is collected in the following metric: envoy_mongo_outbound_27017__mongodb_default_svc_cluster_local_collection_ratings_query_total (click here to execute the query).

Understanding TCP telemetry collection

In this task, you added Istio configuration that instructed Mixer to automatically generate and report a new metric for all traffic to a TCP service within the mesh.

Similar to the Collecting Metrics and Logs Task, the new configuration consisted of instances, a handler, and a rule. Please see that Task for a complete description of the components of metric collection.

Metrics collection for TCP services differs only in the limited set of attributes that are available for use in instances.

TCP attributes

Several TCP-specific attributes enable TCP policy and control within Istio. These attributes are generated by server-side Envoy proxies. They are forwarded to Mixer at connection establishment, and forwarded periodically when connection is alive (periodical report), and forwarded at connection close (final report). The default interval for periodical report is 10 seconds, and it should be at least 1 second. Additionally, context attributes provide the ability to distinguish between http and tcp protocols within policies.

Attribute Generation Flow for TCP Services in an Istio Mesh.
TCP Attribute Flow

Cleanup

  • Remove the new telemetry configuration:

    $ kubectl delete -f tcp_telemetry.yaml
  • Remove the port-forward process:

    $ killall kubectl
  • If you are not planning to explore any follow-on tasks, refer to the Bookinfo cleanup instructions to shutdown the application.

See also

This task shows you how to configure Istio to collect metrics and logs.

This task shows you how to query for Istio Metrics using Prometheus.

Demonstrates how to obtain uniform metrics, logs, traces across different services using Istio Mixer and Istio sidecar.

Describes a simple scenario based on Istio's Bookinfo example.

Improving availability and reducing latency.

Provides an overview of Mixer's plug-in architecture.