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 (Preview)
• 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 preview app deploy command.

$ gcloud preview app deploy sample-webapp/app.yaml --project=PROJECT-ID --set-default

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
  key: TARGET_HOST
  value: http://PROJECT-ID.appspot.com

Update Controller Docker Image (Optional)

The locust-master and 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:

$ 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 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:

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 Container 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 alpha 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:

$ kubectl config use-context gke_PROJECT-ID_ZONE_CLUSTER-NAME

Note: the output from the previous gcloud cluster create command will contain the specific kubectl config command to execute for your platform/project.

Deploy locust-master

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

$ 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 locust-master-service:

$ kubectl create -f 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 view the newly created forwarding-rule, execute the following:

$ gcloud compute forwarding-rules list 

Deploy locust-worker

Now deploy locust-worker-controller:

$ kubectl create -f locust-worker-controller.yaml

The 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 scale command.

$ kubectl scale --replicas=20 replicationcontrollers 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 alpha container clusters create documentation for more information.

Setup Firewall Rules

The final step in deploying these controllers and services is to allow traffic from your publicly accessible forwarding-rule IP address to the appropriate Container Engine instances.

The only traffic we need to allow externally is to the Locust web interface, running on the locust-master Pod at port 8089. First, get the target tags for the nodes in your Kubernetes cluster using the output from kubectl get nodes:

$ kubectl get nodes
NAME                        LABELS                                             STATUS
gke-ws-0e365264-node-4pdw   kubernetes.io/hostname=gke-ws-0e365264-node-4pdw   Ready
gke-ws-0e365264-node-jdcz   kubernetes.io/hostname=gke-ws-0e365264-node-jdcz   Ready
gke-ws-0e365264-node-kp3d   kubernetes.io/hostname=gke-ws-0e365264-node-kp3d   Ready

The target tag is the node name prefix up to -node and is formatted as gke-CLUSTER-NAME-[...]-node. For example, if your node name is gke-mycluster-12345678-node-abcd, the target tag would be gke-mycluster-12345678-node.

Now to create the firewall rule, execute the following:

$ gcloud compute firewall-rules create FIREWALL-RULE-NAME --allow=tcp:8089 --target-tags gke-CLUSTER-NAME-[...]-node

Execute Tests

To execute the Locust tests, navigate to the IP address of your forwarding-rule (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 alpha container clusters delete CLUSTER-NAME

Next, delete the forwarding rule that forwards traffic into the cluster.

$ gcloud compute forwarding-rules delete FORWARDING-RULE-NAME

Finally, delete the firewall rule that allows incoming traffic to the cluster.

$ gcloud compute firewall-rules delete FIREWALL-RULE-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.