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Ultimate starterkit for JMeter in Kubernetes. WIth live test reporting, JMeter monitoring, kubernetes monitoring and mock as a service

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You can follow the full tutorial here : https://romain-billon.medium.com/ultimate-jmeter-kubernetes-starter-kit-7eb1a823649b

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JMeter k8s startekit

This is a template repository from which you can start load testing faster when injecting load from a kubernetes cluster.

You will find inside it the necessary to organize and run your performance scenario. There is also a node monitoring tool which will monitor all your injection nodes. As well an embeded live monitoring with InfluxDB and Grafana

Thanks to Kubernauts for the inspiration !

Features

jmeter-starterkit

Feature Supported Comment
Flexibility at run time Yes With .env file (threads, duration, host)
Distributed testing Yes Virtually unlimited with auto-scaling
JMeter Plugin support Yes Modules are installed at run time by scanning the JMX needs
JMeter Module support Yes JMeter include controller are supported if path is just the name of the file in the Include Controler
JMeter CSV support Yes CSV files are splitted prior to launch the test and unique pieces copied to each pods, in the JMeter scenario, just put the name of the file in the path field
Node auto-scaling Yes By requesting ressources at deployment time, the cluster will scale automatically if needed
Reporting Yes The JMeter report is generated at the end of the test inside the master pod if the -r flag is used in the start_test.sh
Live monitoring Yes An InfluxDB instance and a Grafana are available in the stack
Report persistance Yes A persistence volume is used to store the reports and results
Injector nodes monitoring Yes Even if autoscaling, a Daemon Set will deploy a telegraf instance and persist the monitoring data to InfluxDB. A board is available in Grafana to show the Telegraf monitoring
Multi thread group support Not really You can add multi thread groups, but if you want to use JMeter properties (like threads etc..) you need to add them in the .env and update the start_test.sh to update the "user_param" variable to add the desired variables
Mocking service Yes A deployment of Wiremock is done inside the cluster, the mappings are done inside the wiremock configmap. Also an horizontal pod autoscaler have been added
JVM Monitoring Yes JMeter and Wiremock are both Java application. They have been packaged with Jolokia and Telegraf and are monitored
Pre built Grafana Dashboards Yes 4 Grafana dashboards are shipped with the starter kit. Node monitoring, Kubernetes ressources monitoring, JVM monitoring and JMeter result dashboard.
Ressource friendly Yes JMeter is deployed as batch job inside the cluster. Thus at the end of the execution, pods are deleted and ressources freed

Getting started

Prerequisites :

  • A kubernetes cluster (of course)
  • kubectl installed and a usable context to work with
  • (Optionnal) A JMeter scenario (the default one attack Google.com)

1. Preparing the repository

You need to put your JMeter project inside the scenario folder, inside a folder named after the JMX (without the extension). Put your CSV file inside the dataset folder, child of scenario Put your JMeter modules (include controlers) inside the module folder, child of scenario

datasetand moduleare in scenario and not below inside the <project> folder because, in some cases, you can have multiple JMeter projects that are sharing the JMeter modules (that's the goal of using modules after all).

Below a visual representation of the file structure

+-- scenario
|   +-- dataset
|   +-- module
|   +-- my-scenario
|       +-- my-scenario.jmx
|       +-- .env

2. Deploying the Stack

kubectl create -R -f k8s/

This will deploy all the needed applications :

  • JMeter master and slaves
  • Telegraf operator to automatically monitor the specified applications
  • Telegraf as a DaemonSet on all the nodes
  • InfluxDB to store the date (with a 5GB volume in a PVC)
  • Grafana with a LB services and 4 included dashboard
  • Wiremock

3. Starting the test

./start_test.sh -j my-scenario.jmx -n default -c -m -i 20 -r

Usage :

   -j <filename.jmx>
   -n <namespace>
   -c flag to split and copy csv if you use csv in your test
   -m flag to copy fragmented jmx present in scenario/project/module if you use include controller and external test fragment
   -i <injectorNumber> to scale slaves pods to the desired number of JMeter injectors
   -r flag to enable report generation at the end of the test

The script will :

  • Delete and create again the JMeter jobs.
  • Scale the JMeter slave deployment to the desired number of injectors
  • Wait to all the slaves pods to be available. Here, available means that the filesystem is reacheable (liveness probe that cat a file inside the fs)
  • If needed will split the CSV locally then copy them inside the slave pods
  • If needed will upload the JMeter modules inside the slave pods
  • Send the JMX file to each slave pods
  • Generate and send a shell script to the slaves pods to download the necessary plugins and launch the JMeter server.
  • Send the JMX to the controller
  • Generate a shell script and send it to the controller to wait for all pods to have their JMeter slave port listening (TCP 1099) and launch the performance test.

4. Gethering results from the master pod

After the test have been executed, the master pod job is in completed state and then, is deleted by the cleaner cronjob.

To be able to get your result, a jmeter master pod must be in running state (because the pod is mounting the persistantVolume with the reports inside).

The master pod default behaviour is to wait indefinetly

You can run

# If a master pod is not available, create one
kubectl apply -f k8s/jmeter/jmeter-master.yaml
# Wait for the pod is Running, then
kubectl cp -n <namespace> <master-pod-id>:/report/<result> ${PWD}/<local-result-name>
# To copy the content of the report from the pod to your local

You can do this for the generated report and the JTL for example.

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Ultimate starterkit for JMeter in Kubernetes. WIth live test reporting, JMeter monitoring, kubernetes monitoring and mock as a service

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