The easiest option to install Theodolite is using Helm. To install Theodolite with all its dependencies run:
helm repo add theodolite https://www.theodolite.rocks
helm repo update
helm install theodolite theodolite/theodolite
As usual, the installation via Helm can be configured by passing a values YAML file:
helm install theodolite theodolite/theodolite --values <your-config.yaml>
For this purpose the default values file can serve as a template for your custom configuration.
For Kubernetes clusters with limited resources such as on local developer installations, we provide a minimal values file.
To store the results of benchmark executions in a PersistentVolume,
operator.resultsVolume.persistent.enabled has to be set to
true. This requires that either a statically provisioned PersistentVolume is available or a dynamic provisioner exists (which is the case for many Kubernetes installations). If required, you can select a storage class with
You can also use an existing PersistentVolumeClaim by setting
If persistence is not enabled, all results will be gone upon pod termination.
Per default, Theodolite exposes a Grafana instance as NodePort at port
31199. This can configured by setting
As long as you have sufficient permissions on your cluster, you can integrate additional Kubernetes metrics into Prometheus. This involes enabling some exporters, additional Grafana dashboards and additional permissions. We provide a values file for enabling extended metrics.
See the kube-prometheus-stack for more details on configuring the individual exporters.
Installation of the random scheduler can be enabled via
randomScheduler.enabled. Please note that the random scheduler is neither required in operator mode nor in standalone mode. However, it has to be installed if benchmark executions should use random scheduling.
In cases, where you need to install multiple Theodolite instances, it’s best to use dedicated namespaces. Make sure to expose Grafana on different ports for each installation. Installing additional cluster-level metrics multiple times in the same cluster does not work.
Note that for meaningful results, usually only one benchmark should be executed at a time.
You can test the installation with:
helm test theodolite
The Theodolite Helm chart can easily be removed with:
helm uninstall theodolite
Helm does not remove any CRDs created by this chart. You can remove them manually with:
# CRDs for Theodolite
kubectl delete crd executions.theodolite.rocks
kubectl delete crd benchmarks.theodolite.rocks
# CRDs for Prometheus operator (see https://github.com/prometheus-community/helm-charts/tree/main/charts/kube-prometheus-stack#uninstall-chart)
kubectl delete crd alertmanagerconfigs.monitoring.coreos.com
kubectl delete crd alertmanagers.monitoring.coreos.com
kubectl delete crd podmonitors.monitoring.coreos.com
kubectl delete crd probes.monitoring.coreos.com
kubectl delete crd prometheusagents.monitoring.coreos.com
kubectl delete crd prometheuses.monitoring.coreos.com
kubectl delete crd prometheusrules.monitoring.coreos.com
kubectl delete crd scrapeconfigs.monitoring.coreos.com
kubectl delete crd servicemonitors.monitoring.coreos.com
kubectl delete crd thanosrulers.monitoring.coreos.com
# CRDs for Strimzi
kubectl delete crd kafkabridges.kafka.strimzi.io
kubectl delete crd kafkaconnectors.kafka.strimzi.io
kubectl delete crd kafkaconnects.kafka.strimzi.io
kubectl delete crd kafkamirrormaker2s.kafka.strimzi.io
kubectl delete crd kafkamirrormakers.kafka.strimzi.io
kubectl delete crd kafkanodepools.kafka.strimzi.io
kubectl delete crd kafkarebalances.kafka.strimzi.io
kubectl delete crd kafkas.kafka.strimzi.io
kubectl delete crd kafkatopics.kafka.strimzi.io
kubectl delete crd kafkausers.kafka.strimzi.io
kubectl delete crd strimzipodsets.core.strimzi.io