Using Prometheus and Grafana

This section will describe how to monitor Ray Clusters in Kubernetes using Prometheus & Grafana.

If you do not have any experience with Prometheus and Grafana on Kubernetes, watch this YouTube playlist.

Preparation

Clone the KubeRay repository and checkout the master branch. This tutorial requires several files in the repository.

Step 1: Create a Kubernetes cluster with Kind

kind create cluster

Step 2: Install Kubernetes Prometheus Stack via Helm chart

# Path: kuberay/
./install/prometheus/install.sh --auto-load-dashboard true

# Check the installation
kubectl get all -n prometheus-system

# (part of the output)
# NAME                                                  READY   UP-TO-DATE   AVAILABLE   AGE
# deployment.apps/prometheus-grafana                    1/1     1            1           46s
# deployment.apps/prometheus-kube-prometheus-operator   1/1     1            1           46s
# deployment.apps/prometheus-kube-state-metrics         1/1     1            1           46s

• KubeRay provides an install.sh script to:

  • Install the kube-prometheus-stack v48.2.1 chart and related custom resources, including PodMonitor for Ray Pods and PrometheusRule, in the namespace prometheus-system automatically.

  • Import Ray Dashboard’s Grafana JSON files into Grafana using the --auto-load-dashboard true flag. If the flag isn’t set, the following step also provides instructions for manual import. See Step 12: Import Grafana dashboards manually (optional) for more details.

• We made some modifications to the original values.yaml in kube-prometheus-stack chart to allow embedding Grafana panels in Ray Dashboard. See overrides.yaml for more details.

  grafana:
    grafana.ini:
      security:
        allow_embedding: true
      auth.anonymous:
        enabled: true
        org_role: Viewer
  

Step 3: Install a KubeRay operator

• Follow this document to install the latest stable KubeRay operator via Helm repository.

• Set metrics.serviceMonitor.enabled=true when installing the KubeRay operator with Helm to create a ServiceMonitor that scrapes metrics exposed by the KubeRay operator’s service.

  # Enable the ServiceMonitor and set the label `release: prometheus` to the ServiceMonitor so that Prometheus can discover it
  helm install kuberay-operator kuberay/kuberay-operator --version 1.4.0 \ 
    --set metrics.serviceMonitor.enabled=true \ 
    --set metrics.serviceMonitor.selector.release=prometheus 
  

  You can verify the ServiceMonitor creation with:

  kubectl get servicemonitor
  # NAME               AGE
  # kuberay-operator   11s
  

Step 4: Install a RayCluster

# path: ray-operator/config/samples/
kubectl apply -f ray-cluster.embed-grafana.yaml

# Check there's a Service that specifies port 8080 for the metrics endpoint.
# There may be a slight delay between RayCluster and Service creation.
kubectl get service -l ray.io/cluster=raycluster-embed-grafana

# NAME                                TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)                                                    AGE
# raycluster-embed-grafana-head-svc   ClusterIP   None            <none>        44217/TCP,10001/TCP,44227/TCP,8265/TCP,6379/TCP,8080/TCP   13m

# Wait until all Ray Pods are ready.
kubectl wait pods -l ray.io/cluster=raycluster-embed-grafana --timeout 2m --for condition=Ready

# pod/raycluster-embed-grafana-head-2jk7c condition met
# pod/raycluster-embed-grafana-small-group-worker-8g2vv condition met

# Forward the port of the Prometheus metrics endpoint.
kubectl port-forward service/raycluster-embed-grafana-head-svc metrics

# Check metrics in a new terminal.
curl localhost:8080

# Example output (Prometheus metrics format):
# # HELP ray_spill_manager_request_total Number of {spill, restore} requests.
# # TYPE ray_spill_manager_request_total gauge
# ray_spill_manager_request_total{Component="raylet", NodeAddress="10.244.0.13", SessionName="session_2025-01-02_07-58-21_419367_11", Type="FailedDeletion", Version="2.9.0", container="ray-head", endpoint="metrics", instance="10.244.0.13:8080", job="prometheus-system/ray-head-monitor", namespace="default", pod="raycluster-embed-grafana-head-98fqt", ray_io_cluster="raycluster-embed-grafana"} 0

• KubeRay exposes a Prometheus metrics endpoint in port 8080 via a built-in exporter by default. Hence, we do not need to install any external exporter.

• If you want to configure the metrics endpoint to a different port, see kuberay/#954 for more details.

• Prometheus metrics format:

  • # HELP: Describe the meaning of this metric.

  • # TYPE: See this document for more details.

• Three required environment variables are defined in ray-cluster.embed-grafana.yaml. See Configuring and Managing Ray Dashboard for more details about these environment variables.

  env:
    - name: RAY_GRAFANA_IFRAME_HOST
      value: http://127.0.0.1:3000
    - name: RAY_GRAFANA_HOST
      value: http://prometheus-grafana.prometheus-system.svc:80
    - name: RAY_PROMETHEUS_HOST
      value: http://prometheus-kube-prometheus-prometheus.prometheus-system.svc:9090
  

  • Note that we do not deploy Grafana in the head Pod, so we need to set both RAY_GRAFANA_IFRAME_HOST and RAY_GRAFANA_HOST. RAY_GRAFANA_HOST is used by the head Pod to send health-check requests to Grafana in the backend. RAY_GRAFANA_IFRAME_HOST is used by your browser to fetch the Grafana panels from the Grafana server rather than from the head Pod. Because we forward the port of Grafana to 127.0.0.1:3000 in this example, we set RAY_GRAFANA_IFRAME_HOST to http://127.0.0.1:3000.

  • http:// is required.

Step 5: Collect Head Node metrics with a PodMonitor

RayService creates two Kubernetes services for the head Pod; one managed by the RayService and the other by the underlying RayCluster. Therefore, it’s recommended to use a PodMonitor to monitor the metrics for head Pods to avoid misconfigurations that could result in double counting the same metrics when using a ServiceMonitor.

apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  labels:
    # `release: $HELM_RELEASE`: Prometheus can only detect PodMonitor with this label.
    release: prometheus
  name: ray-head-monitor
  namespace: prometheus-system
spec:
  jobLabel: ray-head
  # Only select Kubernetes Pods in the "default" namespace.
  namespaceSelector:
    matchNames:
      - default
  # Only select Kubernetes Pods with "matchLabels".
  selector:
    matchLabels:
      ray.io/node-type: head
  # A list of endpoints allowed as part of this PodMonitor.
  podMetricsEndpoints:
    - port: metrics
      relabelings:
        - action: replace
          sourceLabels:
            - __meta_kubernetes_pod_label_ray_io_cluster
          targetLabel: ray_io_cluster
    - port: as-metrics # autoscaler metrics
      relabelings:
        - action: replace
          sourceLabels:
            - __meta_kubernetes_pod_label_ray_io_cluster
          targetLabel: ray_io_cluster
    - port: dash-metrics # dashboard metrics
      relabelings:
        - action: replace
          sourceLabels:
            - __meta_kubernetes_pod_label_ray_io_cluster
          targetLabel: ray_io_cluster

• The install.sh script creates the above YAML example, podMonitor.yaml so you don’t need to create anything.

• See the official PodMonitor doc for more details about configurations.

• release: $HELM_RELEASE: Prometheus can only detect PodMonitor with this label. See here for more details.

helm ls -n prometheus-system
# ($HELM_RELEASE is "prometheus".)
# NAME            NAMESPACE               REVISION        UPDATED                                 STATUS          CHART                           APP VERSION
# prometheus      prometheus-system       1               2023-02-06 06:27:05.530950815 +0000 UTC deployed        kube-prometheus-stack-44.3.1    v0.62.0

kubectl get prometheuses.monitoring.coreos.com -n prometheus-system -oyaml
# podMonitorSelector:
#   matchLabels:
#     release: prometheus
# ruleSelector:
#   matchLabels:
#     release: prometheus

• Prometheus uses namespaceSelector and selector to select Kubernetes Pods.

  kubectl get pod -n default -l ray.io/node-type=head
  # NAME                                  READY   STATUS    RESTARTS   AGE
  # raycluster-embed-grafana-head-khfs4   1/1     Running   0          4m38s
  

• relabelings: This configuration renames the label __meta_kubernetes_pod_label_ray_io_cluster to ray_io_cluster in the scraped metrics. It ensures that each metric includes the name of the RayCluster to which the Pod belongs. This configuration is especially useful for distinguishing metrics when deploying multiple RayClusters. For example, a metric with the ray_io_cluster label might look like this:

  ray_node_cpu_count{SessionName="session_2025-01-02_07-58-21_419367_11", container="ray-head", endpoint="metrics", instance="10.244.0.13:8080", ip="10.244.0.13", job="raycluster-embed-grafana-head-svc", namespace="default", pod="raycluster-embed-grafana-head-98fqt", ray_io_cluster="raycluster-embed-grafana", service="raycluster-embed-grafana-head-svc"}
  

  In this example, raycluster-embed-grafana is the name of the RayCluster.

Step 6: Collect Worker Node metrics with PodMonitors

Similar to the head Pod, this tutorial also uses a PodMonitor to collect metrics from worker Pods. The reason for using separate PodMonitors for head Pods and worker Pods is that the head Pod exposes multiple metric endpoints, whereas a worker Pod exposes only one.

Note: You could create a Kubernetes service with selectors a common label subset from our worker pods, however, this configuration is not ideal because the workers are independent from each other, that is, they aren’t a collection of replicas spawned by replicaset controller. Due to this behavior, avoid using a Kubernetes service for grouping them together.

apiVersion: monitoring.coreos.com/v1
kind: PodMonitor
metadata:
  name: ray-workers-monitor
  namespace: prometheus-system
  labels:
    # `release: $HELM_RELEASE`: Prometheus can only detect PodMonitor with this label.
    release: prometheus
spec:
  jobLabel: ray-workers
  # Only select Kubernetes Pods in the "default" namespace.
  namespaceSelector:
    matchNames:
      - default
  # Only select Kubernetes Pods with "matchLabels".
  selector:
    matchLabels:
      ray.io/node-type: worker
  # A list of endpoints allowed as part of this PodMonitor.
  podMetricsEndpoints:
  - port: metrics
    relabelings:
    - sourceLabels: [__meta_kubernetes_pod_label_ray_io_cluster]
      targetLabel: ray_io_cluster

• PodMonitor in namespaceSelector and selector are used to select Kubernetes Pods.

  kubectl get pod -n default -l ray.io/node-type=worker
  # NAME                                          READY   STATUS    RESTARTS   AGE
  # raycluster-kuberay-worker-workergroup-5stpm   1/1     Running   0          3h16m
  

Step 7: Scrape KubeRay metrics with ServiceMonitor

• See the official ServiceMonitor doc for more details about configurations.

• KubeRay operator provides metrics for RayCluster, RayService, and RayJob. See KubeRay metrics references for more details.

• Prometheus uses namespaceSelector and selector to select Kubernetes Service.

kubectl get service -n default -l app.kubernetes.io/name=kuberay-operator 
NAME               TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
kuberay-operator   ClusterIP   10.96.205.229   <none>        8080/TCP   53m

Step 8: Collect custom metrics with recording rules

Recording Rules allow KubeRay to precompute frequently needed or computationally expensive PromQL expressions and save their result as custom metrics. Note that this behavior is different from Custom application-level metrics, which are for the visibility of Ray applications.

apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: ray-cluster-gcs-rules
  namespace: prometheus-system
  labels:
    # `release: $HELM_RELEASE`: Prometheus can only detect Recording Rules with this label.
    release: prometheus
spec:
  groups:
- #  Rules within a group are run periodically with the same evaluation interval(30s in this example).
    name: ray-cluster-main-staging-gcs.rules
    # How often rules in the group are evaluated.
    interval: 30s
    rules:
    - # The name of the custom metric.
      # Also see best practices for naming metrics created by recording rules:
      # https://prometheus.io/docs/practices/rules/#recording-rules
      record: ray_gcs_availability_30d
      # PromQL expression.
      expr: |
      (
        100 * (
          sum(rate(ray_gcs_update_resource_usage_time_bucket{container="ray-head", le="20.0"}[30d]))
          /
          sum(rate(ray_gcs_update_resource_usage_time_count{container="ray-head"}[30d]))
        )
      )

• The PromQL expression above is: $\(\frac{ number\ of\ update\ resource\ usage\ RPCs\ that\ have\ RTT\ smaller\ then\ 20ms\ in\ last\ 30\ days\ }{total\ number\ of\ update\ resource\ usage\ RPCs\ in\ last\ 30\ days\ } \times 100 \)$

• The recording rule above is one of rules defined in prometheusRules.yaml, and it is created by install.sh. Hence, no need to create anything here.

• See the official PrometheusRule document for more details about configurations.

• release: $HELM_RELEASE: Prometheus can only detect PrometheusRule with this label. See here for more details.

• PrometheusRule can be reloaded at runtime. Use kubectl apply {modified prometheusRules.yaml} to reconfigure the rules if needed.

Step 9: Define Alert Conditions with alerting rules (optional)

Alerting rules allow us to define alert conditions based on PromQL expressions and to send notifications about firing alerts to Alertmanager which adds summarization, notification rate limiting, silencing and alert dependencies on top of the simple alert definitions.

apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: ray-cluster-gcs-rules
  namespace: prometheus-system
  labels:
    # `release: $HELM_RELEASE`: Prometheus can only detect Alerting Rules with this label.
    release: prometheus
spec:
  groups:
  - name: ray-cluster-main-staging-gcs.rules
    # How often rules in the group are evaluated.
    interval: 30s
    rules:
    - alert: MissingMetricRayGlobalControlStore
      # A set of informational labels. Annotations can be used to store longer additional information compared to rules.0.labels.
      annotations:
        description: Ray GCS is not emitting any metrics for Resource Update requests
        summary: Ray GCS is not emitting metrics anymore
      # PromQL expression.
      expr: |
                      (
                       absent(ray_gcs_update_resource_usage_time_bucket) == 1
                      )
      # Time that Prometheus will wait and check if the alert continues to be active during each evaluation before firing the alert.
      # firing alerts may be due to false positives or noise if the setting value is too small.
      # On the other hand, if the value is too big, the alerts may not be handled in time.
      for: 5m
      # A set of additional labels to be attached to the alert.
      # It is possible to overwrite the labels in metadata.labels, so make sure one of the labels match the label in ruleSelector.matchLabels.
      labels:
        severity: critical

• The PromQL expression above checks if there is no time series exist for ray_gcs_update_resource_usage_time_bucket metric. See absent() for more detail.

• The alerting rule above is one of rules defined in prometheusRules.yaml, and it is created by install.sh. Hence, no need to create anything here.

• Alerting rules are configured in the same way as recording rules.

Step 10: Access Prometheus Web UI

# Forward the port of Prometheus Web UI in the Prometheus server Pod.
kubectl port-forward -n prometheus-system service/prometheus-kube-prometheus-prometheus http-web

• Go to ${YOUR_IP}:9090/targets (e.g. 127.0.0.1:9090/targets). You should be able to see:

  • podMonitor/prometheus-system/ray-workers-monitor/0 (1/1 up)

  • serviceMonitor/prometheus-system/ray-head-monitor/0 (1/1 up)

• Go to ${YOUR_IP}:9090/graph. You should be able to query:

  • System Metrics

  • Application Level Metrics

  • Custom Metrics defined in Recording Rules (e.g. ray_gcs_availability_30d)

• Go to ${YOUR_IP}:9090/alerts. You should be able to see:

  • Alerting Rules (e.g. MissingMetricRayGlobalControlStore).

Step 11: Access Grafana

# Forward the Grafana port
kubectl port-forward -n prometheus-system service/prometheus-grafana 3000:http-web
# Note: You need to update `RAY_GRAFANA_IFRAME_HOST` if you expose Grafana to a different port.

# Check ${YOUR_IP}:3000/login for the Grafana login page (e.g. 127.0.0.1:3000/login).
# The default username is "admin" and the password is "prom-operator".

Note: kubectl port-forward is not recommended for production use. Refer to this Grafana document for exposing Grafana behind a reverse proxy.

• The default password is defined by grafana.adminPassword in the values.yaml of the kube-prometheus-stack chart.

Step 12: Import Grafana dashboards manually (optional)

If --auto-load-dashboard true is set when running install.sh, you can skip this step.

• Import Grafana dashboards manually

  • Click “Dashboards” icon in the left panel.

  • Click “New”.

  • Click “Import”.

  • Click “Upload JSON file”.

  • Choose a JSON file.

    • Case 1: If you are using Ray 2.41.0, you can use the sample config files in GitHub repository. The file names have a pattern of xxx_grafana_dashboard.json.

    • Case 2: Otherwise, import the JSON files from the head Pod’s /tmp/ray/session_latest/metrics/grafana/dashboards/ directory. You can use kubectl cp to copy the files from the head Pod to your local machine. kubectl cp $(kubectl get pods --selector ray.io/node-type=head,ray.io/cluster=raycluster-embed-grafana -o jsonpath={..metadata.name}):/tmp/ray/session_latest/metrics/grafana/dashboards/ /tmp/

  • Click “Import”.

Step 13: View metrics from different RayCluster CRs

Once the Ray Dashboard is imported into Grafana, you can filter metrics by using the Cluster variable. Ray Dashboard automatically applies this variable by default when you use the provided PodMonitor configuration. You don’t need any additional setup for this labeling.

If you have multiple RayCluster custom resources, the Cluster variable allows you to filter metrics specific to a particular cluster. This feature ensures that you can easily monitor or debug individual RayCluster instances without being overwhelmed by the data from all clusters.

For example, in the following figures, one selects the metrics from the RayCluster raycluster-embed-grafana, and the other selects metrics from the RayCluster raycluster-embed-grafana-2.

Step 14: View the KubeRay operator dashboard

After importing the KubeRay operator dashboard into Grafana, you can monitor metrics from the KubeRay operator. The dashboard includes a dropdown menu that lets you filter and view controller runtime metrics for specific Ray custom resources CRs: RayCluster, RayJob, and RayService.

The KubeRay operator dashboard should look like this:

Step 15: Embed Grafana panels in the Ray dashboard (optional)

kubectl port-forward service/raycluster-embed-grafana-head-svc dashboard
# Visit http://127.0.0.1:8265/#/metrics in your browser.