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.

Prerequisites

• 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
• Developer Preview gcloud Commands
• gcloud Alpha Commands
• gcloud app Python Extensions
• kubectl

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

The 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 locust-master and locust-worker controllers.

Deploy Controllers and Services

Before deploying the locust-master and 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

Build Locust Docker Image

To build and publish Locust contoller 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:

$ docker build -t USERNAME/locust-tasks .
$ docker tag USERNAME/locust-tasks gcr.io/PROJECT-ID/locust-tasks
$ gcloud preview docker --project PROJECT-ID push gcr.io/PROJECT-ID/locust-tasks

Note: you are not required to use the Google Container Registry. If you'd like to publish your images to the Docker Hub please refer to the steps in Working with Docker Hub.

Once the Docker image has been built and uploaded to the registry you will need to edit the deployments with your new image location. Specifically, the spec.template.spec.containers.image field in each deployment controls which Docker image to use.

If you uploaded your Docker image to the Google Container Registry:

image: gcr.io/PROJECT-ID/locust-tasks:latest

If you uploaded your Docker image to the Docker Hub:

image: USERNAME/locust-tasks:latest

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.

Note: This command defaults to creating a three node Kubernetes cluster (not counting the master) using the n1-standard-1 machine type. Refer to the gcloud alpha container clusters create documentation information on specifying a different cluster configuration.

$ gcloud container clusters create [CLUSTER-NAME]

After a few minutes, you'll have a working Kubernetes cluster with three nodes (not counting the Kubernetes master). Next, configure your system to use the kubectl command:

$ gcloud container clusters get-credentials [CLUSTER-NAME]

Deploy locust-master

Now that kubectl is setup, deploy the locust-master-deployment:

$ kubectl create -f kubernetes-config/locust-master-deployment.yaml

To confirm that the deployment and Pod are created, run the following:

$ kubectl get deployments
$ kubectl get pods -l name=locust,role=master

Next, deploy the locust-master-service:

$ kubectl create -f kubernetes-config/locust-master-service.yaml

This step will expose the Pod with an internal DNS name (locust-master) and ports 8089, 5557, and 5558. As part of this step, the type: LoadBalancer directive in locust-master-service.yaml will tell Google Container Engine to create a Google Compute Engine forwarding-rule from a publicly avaialble IP address to the locust-master Pod. To see the the service IP address ('LoadBalancer'), issue the below command:

$ kubectl get services locust-master

Deploy locust-worker

Now deploy locust-worker-deployment:

$ kubectl create -f kubernetes-config/locust-worker-deployment.yaml

The locust-worker-deployment 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 deployment scale command.

$ kubectl scale --replicas=20 deployment locust-worker

To confirm that the Pods have launched and are ready, get the list of locust-worker Pods:

$ 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 container clusters create documentation for more information.

Execute Tests

To execute the Locust tests, navigate to the IP address of your locust-master-service LoadBalancer (see above) and port 8089 and enter the number of clients to spawn and the client hatch rate then start the simulation.

Deployment Cleanup

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.

License

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 docker-image/licenses directory.