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

# 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 ServiceMonitor, PodMonitor and PrometheusRule, in the namespace prometheus-system automatically.
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.

Step 4: Install a RayCluster#

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

# Check ${RAYCLUSTER_HEAD_POD}
kubectl get pod -l ray.io/node-type=head

# Example output:
# NAME                            READY   STATUS    RESTARTS   AGE
# raycluster-kuberay-head-btwc2   1/1     Running   0          63s

# Wait until all Ray Pods are running and forward the port of the Prometheus metrics endpoint in a new terminal.
kubectl port-forward ${RAYCLUSTER_HEAD_POD} 8080:8080
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",Type="Restored",Version="2.0.0"} 0.0

# Ensure that the port (8080) for the metrics endpoint is also defined in the head's Kubernetes service.
kubectl get service

# NAME                          TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)                                         AGE
# raycluster-kuberay-head-svc   ClusterIP   10.96.201.142   <none>        6379/TCP,8265/TCP,8080/TCP,8000/TCP,10001/TCP   106m

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 ServiceMonitor#

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: ray-head-monitor
  namespace: prometheus-system
  labels:
    # `release: $HELM_RELEASE`: Prometheus can only detect ServiceMonitor with this label.
    release: prometheus
spec:
  jobLabel: ray-head
  # Only select Kubernetes Services in the "default" namespace.
  namespaceSelector:
    matchNames:
      - default
  # Only select Kubernetes Services with "matchLabels".
  selector:
    matchLabels:
      ray.io/node-type: head
  # A list of endpoints allowed as part of this ServiceMonitor.
  endpoints:
    - port: metrics
  targetLabels:
  - ray.io/cluster

The YAML example above is serviceMonitor.yaml, and it is created by install.sh. Hence, no need to create anything here.
See ServiceMonitor official document for more details about the configurations.
release: $HELM_RELEASE: Prometheus can only detect ServiceMonitor with this label.

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
# serviceMonitorSelector:
#   matchLabels:
#     release: prometheus
# podMonitorSelector:
#   matchLabels:
#     release: prometheus
# ruleSelector:
#   matchLabels:
#     release: prometheus

namespaceSelector and seletor are used to select exporter’s Kubernetes service. Because Ray uses a built-in exporter, the ServiceMonitor selects Ray’s head service which exposes the metrics endpoint (i.e. port 8080 here).

kubectl get service -n default -l ray.io/node-type=head
# NAME                          TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)                                         AGE
# raycluster-kuberay-head-svc   ClusterIP   10.96.201.142   <none>        6379/TCP,8265/TCP,8080/TCP,8000/TCP,10001/TCP   153m

targetLabels: We added spec.targetLabels[0].ray.io/cluster because we want to include the name of the RayCluster in the metrics that will be generated by this ServiceMonitor. The ray.io/cluster label is part of the Ray head node service and it will be transformed into a ray_io_cluster metric label. That is, any metric that will be imported, will also contain the following label ray_io_cluster=<ray-cluster-name>. This may seem optional but it becomes mandatory if you deploy multiple RayClusters.

Step 6: Collect Worker Node metrics with PodMonitors#

KubeRay operator does not create a Kubernetes service for the Ray worker Pods, therefore we cannot use a Prometheus ServiceMonitor to scrape the metrics from the worker Pods. To collect worker metrics, we can use Prometheus PodMonitors CRD instead.

Note: We could create a Kubernetes service with selectors a common label subset from our worker pods, however, this is not ideal because our workers are independent from each other, that is, they are not a collection of replicas spawned by replicaset controller. Due to that, we should 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
    ray.io/cluster: raycluster-kuberay # $RAY_CLUSTER_NAME: "kubectl get rayclusters.ray.io"
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

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

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

ray.io/cluster: $RAY_CLUSTER_NAME: We also define metadata.labels by manually adding ray.io/cluster: <ray-cluster-name> and then instructing the PodMonitors resource to add that label in the scraped metrics via spec.podTargetLabels[0].ray.io/cluster.

Step 7: Collect custom metrics with Recording Rules#

Recording Rules allow us to precompute frequently needed or computationally expensive PromQL expressions and save their result as custom metrics. Note this is different from Custom Application-level Metrics which aim 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 PrometheusRule official document for more details about the 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 8: Define Alert Conditions with Alerting Rules#

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 9: Access Prometheus Web UI#

# Forward the port of Prometheus Web UI in the Prometheus server Pod.
kubectl port-forward prometheus-prometheus-kube-prometheus-prometheus-0 -n prometheus-system 9090:9090

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)

Prometheus Web UI

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 10: Access Grafana#

# Forward the port of Grafana
kubectl port-forward deployment/prometheus-grafana -n prometheus-system 3000:3000
# 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.
After logging in to Grafana successfully, we can import Ray Dashboard into Grafana via dashboard_default.json.
- 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 2.9.0, you can use the sample config files in GitHub repository. The file names have a pattern of xxx_grafana_dashboard.json.
  - Case 2: Otherwise, you should import the JSON files from /tmp/ray/session_latest/metrics/grafana/dashboards/ in the head Pod. You can use kubectl cp to copy the files from the head Pod to your local machine.
- Click “Import”.
TODO: Note that importing the dashboard manually is not ideal. We should find a way to import the dashboard automatically.

Grafana Ray Dashboard

Step 11: Embed Grafana panels in Ray Dashboard#

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

Ray Dashboard with Grafana panels