Agones controller exposes metrics via OpenCensus. OpenCensus is a single distribution of libraries that collect metrics and distributed traces from your services, we only use it for metrics but it will allow us to support multiple exporters in the future.
We choose to start with Prometheus as this is the most popular with Kubernetes but it is also compatible with Stackdriver. If you need another exporter, check the list of supported exporters. It should be pretty straightforward to register a new one. (GitHub PRs are more than welcome.)
We plan to support multiple exporters in the future via environment variables and helm flags.
If you are running a Prometheus instance you just need to ensure that metrics and kubernetes service discovery are enabled. (helm chart values
agones.metrics.prometheusServiceDiscovery). This will automatically add annotations required by Prometheus to discover Agones metrics and start collecting them. (see example)
apiVersion: monitoring.coreos.com/v1 kind: ServiceMonitor metadata: name: agones labels: app: agones spec: selector: matchLabels: agones.dev/role: controller endpoints: - port: web
Finally include that
ServiceMonitor in your Prometheus instance CRD, this is usually done by adding a label to the
ServiceMonitor above that is matched by the prometheus instance of your choice.
We support the OpenCensus Stackdriver exporter. In order to use it you should enable Stackdriver Monitoring API in Google Cloud Console. Follow the Stackdriver Installation steps to see your metrics on Stackdriver Monitoring website.
|agones_gameservers_count||The number of gameservers per fleet and status||gauge|
|agones_gameserver_allocations_duration_seconds||The distribution of gameserver allocation requests latencies||histogram|
|agones_gameservers_total||The total of gameservers per fleet and status||counter|
|agones_fleets_replicas_count||The number of replicas per fleet (total, desired, ready, allocated)||gauge|
|agones_fleet_autoscalers_able_to_scale||The fleet autoscaler can access the fleet to scale||gauge|
|agones_fleet_autoscalers_buffer_limits||The limits of buffer based fleet autoscalers (min, max)||gauge|
|agones_fleet_autoscalers_buffer_size||The buffer size of fleet autoscalers (count or percentage)||gauge|
|agones_fleet_autoscalers_current_replicas_count||The current replicas count as seen by autoscalers||gauge|
|agones_fleet_autoscalers_desired_replicas_count||The desired replicas count as seen by autoscalers||gauge|
|agones_fleet_autoscalers_limited||The fleet autoscaler is capped (1)||gauge|
|agones_gameservers_node_count||The distribution of gameservers per node||histogram|
|agones_nodes_count||The count of nodes empty and with gameservers||gauge|
|agones_k8s_client_http_request_total||The total of HTTP requests to the Kubernetes API by status code||counter|
|agones_k8s_client_http_request_duration_seconds||The distribution of HTTP requests latencies to the Kubernetes API by status code||histogram|
|agones_k8s_client_cache_list_total||The total number of list operations for client-go caches||counter|
|agones_k8s_client_cache_list_duration_seconds||Duration of a Kubernetes list API call in seconds||histogram|
|agones_k8s_client_cache_list_items||Count of items in a list from the Kubernetes API||histogram|
|agones_k8s_client_cache_watches_total||The total number of watch operations for client-go caches||counter|
|agones_k8s_client_cache_last_resource_version||Last resource version from the Kubernetes API||gauge|
|agones_k8s_client_workqueue_depth||Current depth of the work queue||gauge|
|agones_k8s_client_workqueue_latency_seconds||How long an item stays in the work queue||histogram|
|agones_k8s_client_workqueue_items_total||Total number of items added to the work queue||counter|
|agones_k8s_client_workqueue_work_duration_seconds||How long processing an item from the work queue takes||histogram|
|agones_k8s_client_workqueue_retries_total||Total number of items retried to the work queue||counter|
|agones_k8s_client_workqueue_longest_running_processor||How long the longest running workqueue processor has been running in microseconds||gauge|
|agones_k8s_client_workqueue_unfinished_work_seconds||How long unfinished work has been sitting in the workqueue in seconds||gauge|
Agones Autoscalers allows you to monitor your current autoscalers replicas request as well as fleet replicas allocation and readyness statuses. You can only select one autoscaler at the time using the provided dropdown.
Agones GameServers displays your current game servers workload status (allocations, game servers statuses, fleets replicas) with optional fleet name filtering.
Agones GameServer Allocations displays Agones gameservers allocations rates and counts per fleet.
Agones Allocator Resource displays Agones Allocators CPU, memory usage and also some useful Golang runtime metrics.
Agones Status displays Agones controller health status.
Agones Controller Resource Usage displays Agones Controller CPU and memory usage and also some Golang runtime metrics.
Agones Controller go-client requests displays Agones Controller Kubernetes API consumption.
Agones Controller go-client caches displays Agones Controller Kubernetes Watches/Lists operations used.
Agones Controller go-client workqueues displays Agones Controller workqueue processing time and rates.
Agones Controller API Server requests displays your current API server request rate, errors rate and request latencies with optional CustomResourceDefinition filtering by Types: fleets, gameserversets, gameservers, gameserverallocations.
Dashboard screenshots :
NoteYou can import our dashboards by copying the json content from each config map into your own instance of Grafana (+ > Create > Import > Or paste json) or follow the installation guide.
When operating a live multiplayer game you will need to observe performances, resource usage and availability to learn more about your system. This guide will explain how you can setup Prometheus and Grafana into your own Kubernetes cluster to monitor your Agones workload.
Prometheus is an open source monitoring solution, we will use it to store Agones controller metrics and query back the data.
Let’s install Prometheus using the helm stable repository.
helm upgrade --install --wait prom stable/prometheus --namespace metrics \ --set server.global.scrape_interval=30s \ --set server.persistentVolume.enabled=true \ --set server.persistentVolume.size=64Gi \ -f ./build/prometheus.yaml
NoteYou can also run our Makefile target
make minikube-setup-prometheusfor Kind and Minikube .
For resiliency it is recommended to run Prometheus on a dedicated node which is separate from nodes where Game Servers
are scheduled. If you use the above command, with our
to set up Prometheus, it will schedule Prometheus pods on nodes
agones.dev/agones-metrics=true:NoExecute and labeled with
agones.dev/agones-metrics=true if available.
As an example, to set up dedicated node pool for Prometheus on GKE, run the following command before installing Prometheus. Alternatively you can taint and label nodes manually.
gcloud container node-pools create agones-metrics --cluster=... --zone=... \ --node-taints agones.dev/agones-metrics=true:NoExecute \ --node-labels agones.dev/agones-metrics=true \ --num-nodes=1
By default we will disable the push gateway (we don’t need it for Agones) and other exporters.
This will install a Prometheus Server in your current cluster with Persistent Volume Claim (Deactivated for Minikube and Kind) for storing and querying time series, it will automatically start collecting metrics from Agones Controller.
Finally to access Prometheus metrics, rules and alerts explorer use
kubectl port-forward deployments/prom-prometheus-server 9090 -n metrics
NoteAgain you can use our Makefile
make prometheus-portforward. (For Kind and Minikube use their specific targets
Now you can access the prometheus dashboard http://localhost:9090.
On the landing page you can start exploring metrics by creating queries. You can also verify what targets Prometheus currently monitors (Header Status > Targets), you should see Agones controller pod in the
NoteMetrics will be first registered when you will start using Agones.
Now let’s install some Grafana dashboards.
Grafana is a open source time series analytics platform which supports Prometheus data source. We can also install easily import pre-built dashboards.
kubectl apply -f ../build/grafana/
Now we can install grafana chart from stable repository. (Replace
<your-admin-password> with the admin password of your choice)
helm install --wait --name grafana stable/grafana --namespace metrics \ --set adminPassword=<your-admin-password> -f ../build/grafana.yaml
This will install Grafana with our prepopulated dashboards and prometheus datasource previously installed
NoteYou can also use our Makefile targets (
Finally to access dashboards run
kubectl port-forward deployments/grafana 3000 -n metrics
NoteYou can also use our
In order to use Stackdriver monitoring you should enable Stackdriver Monitoring API on Google Cloud Console. You need to grant all the necessary permissions to the users (see Access Control Guide). Stackdriver exporter uses a strategy called Application Default Credentials (ADC) to find your application’s credentials. Details could be found here Setting Up Authentication for Server to Server Production Applications.
Note that Stackdriver monitoring is enabled by default on GKE clusters, however you can follow this guide if it was disabled on your GKE cluster.
Default metrics exporter is Prometheus. If you are using the Helm installation, you can install or upgrade Agones to use Stackdriver, using the following chart parameters:
helm upgrade --install --wait --set agones.metrics.stackdriverEnabled=true --set agones.metrics.prometheusEnabled=false --set agones.metrics.prometheusServiceDiscovery=false my-release-name agones/agones --namespace=agones-system
With this configuration only Stackdriver exporter would be used instead of Prometheus exporter.
Create a Fleet or a Gameserver in order to check that connection with stackdriver API is configured properly and so that you will be able to see the metrics data.
Visit Stackdriver monitoring website, select your project, or choose
Create a new Workspace and select GCP project where your cluster resides. In Stackdriver metrics explorer you should be able to find new metrics with prefix
agones/ after a couple of minutes. Choose the metrics you are interested in and add to a single or separate graphs. Select
Kubernetes Container resource type for each of them. You can create multiple graphs, save them into your dashboard and use various aggregation parameters and reducers for each graph.
Example of the dashboard appearance is provided below:
Currently there exists only manual way of configuring Stackdriver Dashboard. So it is up to you to set an Alignment Period (minimal is 1 minute), GroupBy, Filter parameters and other graph settings.
If you can’t see Agones metrics you should have a look at the controller logs for connection errors. Also ensure that your cluster has the necessary credentials to interact with Stackdriver Monitoring. You can configure
stackdriverProjectID manually, if the automatic discovery is not working.
Permissions problem example from controller logs:
Failed to export to Stackdriver: rpc error: code = PermissionDenied desc = Permission monitoring.metricDescriptors.create denied (or the resource may not exist).
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