Distributed Load Testing Using Kubernetes
This tutorial demonstrates how to conduct distributed load testing using Kubernetes and includes a sample web application, Docker image, and Kubernetes controllers/services. For more background refer to the Distributed Load Testing Using Kubernetes solution paper.
- Google Cloud Platform account
- Install and setup Google Cloud SDK
Note: when installing the Google Cloud SDK you will need to enable the following additional components:
App Engine Command Line Interface
App Engine SDK for Python and PHP
Compute Engine Command Line Interface
gcloud app Python Extensions
Before continuing, you can also set your preferred zone and project:
$ gcloud config set compute/zone ZONE $ gcloud config set project PROJECT-ID
Deploy Web Application
sample-webapp folder contains a simple Google App Engine Python application as the "system under test". To deploy the application to your project use the
gcloud app deploy command.
$ gcloud app deploy sample-webapp/app.yaml --project=PROJECT-ID
Note: you will need the URL of the deployed sample web application when deploying the
Deploy Controllers and Services
Before deploying the
locust-worker controllers, update each to point to the location of your deployed sample web application. Set the
TARGET_HOST environment variable found in the
spec.template.spec.containers.env field to your sample web application URL.
- name: TARGET_HOST value: http://PROJECT-ID.appspot.com
Update Controller Docker Image (Optional)
locust-worker controllers are set to use the pre-built
locust-tasks Docker image, which has been uploaded to the Google Container Registry and is available at
gcr.io/cloud-solutions-images/locust-tasks. If you are interested in making changes and publishing a new Docker image, refer to the following steps.
First, install Docker on your platform. Once Docker is installed and you've made changes to the
Dockerfile, you can build, tag, and upload the image using the following steps:
$ gcloud container builds submit --tag gcr.io/PROJECT-ID/locust-tasks:latest .
Once the Docker image has been rebuilt and uploaded to the registry you will need to edit the controllers with your new image location. Specifically, the
spec.template.spec.containers.image field in each controller controls which Docker image to use.
If you uploaded your Docker image to the Google Container Registry:
If you uploaded your Docker image to the Docker Hub:
Note: the image location includes the
latest tag so that the image is pulled down every time a new Pod is launched. To use a Kubernetes-cached copy of the image, remove
:latest from the image location.
Deploy Kubernetes Cluster
First create the Google Kubernetes Engine cluster using the
gcloud command as shown below.
$ gcloud container clusters create CLUSTER-NAME
kubectl is setup, deploy the
$ kubectl create -f locust-master-controller.yaml
To confirm that the Replication Controller and Pod are created, run the following:
$ kubectl get rc $ kubectl get pods -l name=locust,role=master
Next, deploy the
$ kubectl create -f locust-master-service.yaml
This step will expose the Pod with an internal DNS name (
locust-master) and ports
5558. As part of this step, the
type: LoadBalancer directive in
locust-master-service.yaml will tell Google Kubernetes Engine to create a Google Compute Engine forwarding-rule from a publicly avaialble IP address to the
locust-master Pod. To view the newly created forwarding-rule, execute the following:
$ kubectl get svc locust-master
$ kubectl create -f locust-worker-controller.yaml
locust-worker-controller is set to deploy 10
locust-worker Pods, to confirm they were deployed run the following:
$ kubectl get pods -l name=locust,role=worker
To scale the number of
locust-worker Pods, issue a replication controller
$ kubectl scale --replicas=20 replicationcontrollers locust-worker
To confirm that the Pods have launched and are ready, get the list of
$ kubectl get pods -l name=locust,role=worker
Note: depending on the desired number of
locust-worker Pods, the Kubernetes cluster may need to be launched with more than 3 compute engine nodes and may also need a machine type more powerful than n1-standard-1. Refer to the
gcloud alpha container clusters create documentation for more information.
To execute the Locust tests, navigate to the IP address of your service (see above) and port
8089 and enter the number of clients to spawn and the client hatch rate then start the simulation.
To teardown the workload simulation cluster, use the following steps. First, delete the Kubernetes cluster:
$ gcloud container clusters delete CLUSTER-NAME
To delete the sample web application, visit the Google Cloud Console.
This code is Apache 2.0 licensed and more information can be found in
LICENSE. For information on licenses for third party software and libraries, refer to the