Theodolite Execution Framework

This directory contains the Theodolite framework for executing scalability benchmarks in a Kubernetes cluster. As Theodolite aims for executing benchmarks in realistic execution environments, some third-party components are required. After everything is installed and configured, you can move on the execution of benchmarks.

Installation

For executing benchmarks, access to a Kubernetes cluster is required. If you already run other applications inside your cluster, you might want to consider creating a dedicated namespace for your benchmarks.

Installing Dependencies

The following third-party services need to be installed in your cluster. For most of them, the suggested way to install them is via Helm.

Prometheus

We suggest to use the Prometheus Operator and create a dedicated Prometheus instance for these benchmarks.

If Prometheus Operator is not already available on your cluster, a convenient way to install it is via the Prometheus community Helm chart. As you may not need an entire cluster monitoring stack, you can use our Helm configuration to only install the operator:

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm repo update
helm install prometheus-operator prometheus-community/kube-prometheus-stack -f infrastructure/prometheus/helm-values.yaml

After installation, you need to create a Prometheus instance:

kubectl apply -f infrastructure/prometheus/prometheus.yaml

You might also need to apply the ClusterRole, the CusterRoleBinding and the ServiceAccount, depending on your cluster's security policies. If you are not in the default namespace, alter the namespace in Prometheus' ClusterRoleBinding accordingly.

kubectl apply -f infrastructure/prometheus/cluster-role.yaml
kubectl apply -f infrastructure/prometheus/cluster-role-binding.yaml
kubectl apply -f infrastructure/prometheus/service-account.yaml

For the individual benchmarking components to be monitored, ServiceMonitors are used. See the corresponding sections below for how to install them.

Grafana

As with Prometheus, we suggest to create a dedicated Grafana instance. Grafana with our default configuration can be installed with Helm:

helm repo add grafana https://grafana.github.io/helm-charts
helm repo update
helm install grafana grafana/grafana -f infrastructure/grafana/values.yaml

The official Grafana Helm Chart repository provides further documentation including a table of configuration options.

We provide ConfigMaps for a Grafana dashboard and a Grafana data source. Create them as follows:

kubectl apply -f infrastructure/grafana/dashboard-config-map.yaml
kubectl apply -f infrastructure/grafana/prometheus-datasource-config-map.yaml

A Kafka cluster

We suggest to set up a Kafka cluster via Confluent's Helm Charts. Currently, these charts do not expose all metrics, we would like to display. Thus, we provide a patched version of this chart. We also provide a default configuration. If you do not want to deploy 10 Kafka and 3 Zookeeper instances, alter the configuration file accordingly. To install the patched Confluent's Kafka with our configuration:

helm install my-confluent https://github.com/SoerenHenning/cp-helm-charts/releases/download/v6.0.1-1-JMX-FIX/cp-helm-charts-0.6.0.tgz -f infrastructure/kafka/values.yaml

To let Prometheus scrape Kafka metrics, deploy a ServiceMonitor:

kubectl apply -f infrastructure/kafka/service-monitor.yaml

Other Kafka deployments, for example, using Strimzi, should work in a similar way.

*Please note that currently, even if installed differently, the corresponding services must run at my-confluent-cp-kafka:9092, my-confluent-cp-zookeeper:2181 and my-confluent-cp-schema-registry:8081.

A Kafka Client Pod

A permanently running pod used for Kafka configuration is started via:

kubectl apply -f infrastructure/kafka/kafka-client.yaml

A Zookeeper Client Pod

Also a permanently running pod for ZooKeeper access is started via:

kubectl apply -f infrastructure/zookeeper-client.yaml

The Kafka Lag Exporter

Lightbend's Kafka Lag Exporter can be installed via Helm. We also provide a default configuration. To install it:

helm install kafka-lag-exporter https://github.com/lightbend/kafka-lag-exporter/releases/download/v0.6.3/kafka-lag-exporter-0.6.3.tgz -f infrastructure/kafka-lag-exporter/values.yaml

Installing Theodolite

While Theodolite itself has not be installed as it is loaded at runtime (see execution), it requires some resources to be deployed in your cluster. These resources are grouped under RBAC and Volume in the following paragraphs.

Theodolite RBAC

The following step is only required if RBAC is enabled in your cluster. If you are not sure whether this is the case, you want to simply try it without the following step.

If RBAC is enabled in your cluster, you have to allow Theodolite to start and stop pods etc. To do so, deploy the RBAC resources via:

kubectl apply -f infrastructure/kubernetes/rbac/role.yaml
kubectl apply -f infrastructure/kubernetes/rbac/role-binding.yaml
kubectl apply -f infrastructure/kubernetes/rbac/service-account.yaml

Theodolite Volume

In order to persistently store benchmark results, Theodolite needs a volume mounted. We provide pre-configured declarations for different volume types.

hostPath volume

Using a hostPath volume is the easiest option when running Theodolite locally, e.g., with minikube or kind.

Just modify infrastructure/kubernetes/volumeSingle.yaml by setting path to the directory on your host machine where all benchmark results should be stored and run:

kubectl apply -f infrastructure/kubernetes/volumeSingle.yaml

local volume

A local volume is a simple option to use when having access (e.g. via SSH) to one of your cluster nodes.

You first need to create a directory on a selected node where all benchmark results should be stored. Next, modify infrastructure/kubernetes/volumeCluster.yaml by setting <node-name> to your selected node (this node will most likely also execute the job). Further, you have to set path to the directory on the node you just created. To deploy you volume run:

kubectl apply -f infrastructure/kubernetes/volumeCluster.yaml

Other volumes

To use volumes provided by public cloud providers or network-based file systems, you can use the definitions in infrastructure/kubernetes/ as a starting point. See the offical volumes documentation for additional information.

Execution

The preferred way to run scalability benchmarks with Theodolite is to deploy Theodolite Kubernetes Jobs in your cluster. For running Theodolite locally on your machine see the description below.

theodolite.yaml provides a template for your own Theodolite job. To run your own job, create a copy, give it a name (metadata.name) and adjust configuration parameters as desired. For a description of available configuration options see the Configuration section below. Note, that you might uncomment the serviceAccountName line if RBAC is enabled on your cluster (see installation of Theodolite RBAC).

To start the execution of a benchmark run (with <your-theodolite-yaml> being your job definition):

kubectl apply -f <your-theodolite-yaml>

This will create a pod with a name such as your-job-name-xxxxxx. You can verifiy this via kubectl get pods. With kubectl logs -f <your-job-name-xxxxxx>, you can follow the benchmark execution logs.

Once your job is completed (you can verify via `kubectl get jobs), its results are stored inside your configured Kubernetes volume.

Make sure to always run only one Theodolite job at a time.

Configuration

Domain Restriction

For dimension value, we have a domain of the amounts of instances. As a consequence, for each dimension value the maximum number of lag experiments is equal to the size of the domain. How the domain is determined is defined by the following domain restriction strategies.

• no-domain-restriction: For each dimension value, the domain of instances is equal to the set of all amounts of instances.
• restrict-domain: For each dimension value, the domain is computed as follows:
  • If the dimension value is the smallest dimension value the domain of the amounts of instances is equal to the set of all amounts of instances.
  • If the dimension value is not the smallest dimension value and N is the amount of minimal amount of instances that was suitable for the last smaller dimension value the domain for this dimension value contains all amounts of instances greater than, or equal to N.

Benchmarking Search Strategies

There are the following benchmarking strategies:

• check-all: For each dimension value, execute one lag experiment for all amounts of instances within the current domain.
• linear-search: A heuristic which works as follows: For each dimension value, execute one lag experiment for all number of instances within the current domain. The execution order is from the lowest number of instances to the highest amount of instances and the execution for each dimension value is stopped, when a suitable amount of instances is found or if all lag experiments for the dimension value were not successful.
• binary-search: A heuristic which works as follows: For each dimension value, execute one lag experiment for all number of instances within the current domain. The execution order is in a binary-search-like manner. The execution is stopped, when a suitable amount of instances is found or if all lag experiments for the dimension value were not successful.

Observation

The installed Grafana instance provides a dashboard to observe the benchmark execution. Unless configured otherwise, this dashboard can be accessed via http://<cluster-ip>:31199 or via http://localhost:31199 if proxied with kubectl port-forward svc/grafana 8080:service. Default credentials are user admin with password admin.

Local Execution (e.g. for Development)

As an alternative to executing Theodolite as a Kubernetes Job, it is also possible to run it from your local system, for example, for development purposes. In addition to the generel installation instructions, the following adjustments are neccessary.

Installation

For local execution a Python 3.7 installation is required. We suggest to use a virtual environment placed in the .venv directory (in the Theodolite root directory). A set of requirements is needed. You can install them with the following command (make sure to be in your virtual environment if you use one):

pip install -r requirements.txt

Kubernetes volumes and service accounts, roles, and role bindings for Theodolite are not required in this case.

Local Execution

The theodolite.py is the entrypoint for all benchmark executions. Is has to be called as follows:

python theodolite.py --uc <uc> --loads <load> [<load> ...] --instances <instances> [<instances> ...]

This command is the minimal command for execution. Further configurations options are described above or available via python theodolite.py -h.