How to install and use Gremlin locally with Kubernetes

How to install and use Gremlin locally with Kubernetes

This tutorial explains how to install and use Gremlin locally with Kubernetes. You will run a Chaos Engineering experiment using Gremlin to inject packet loss to a Kubernetes pod running the frontend of a microservices e-commerce store.


Before you begin this tutorial, you'll need the following:

Step 1.0 – Install Docker For Mac

First you will need to install Docker For Mac if you do not yet have it on your local computer, follow the instructions provided by Docker. Next enable Kubernetes, by clicking Enable Kubernetes and Show system containers (advanced). Then click apply:

install k8s

Step 2.0 – Confirm your local Kubernetes cluster has been created

Run the following command:

kubectl cluster-info

You will see the following output:

Kubernetes master is running at https://localhost:6443

KubeDNS is running at https://localhost:6443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy

Step 3.0 – Create a local Gremlin directory

Next create a folder on your Desktop to store files you will need for Kubernetes and Gremlin authentication.

$ cd Desktop
$ mkdir gremlin
$ cd gremlin

Step 4.0 – Setup a Kubernetes Add-On for Networking Features and Policy

Save the Weave Net yaml file to your Gremlin directory:

$ curl -o weave.yaml

Apply the Kubernetes daemonset by running the following command:

$ kubectl apply -f weave.yaml

You will see the following result:

serviceaccount "weave-net" created
clusterrole "weave-net" created
clusterrolebinding "weave-net" created
role "weave-net" created
rolebinding "weave-net" created
daemonset "weave-net" created

It may take a minute or two for DNS to be ready, run the following command to check for DNS to be ready:

kubectl get pods --all-namespaces

The successful result will look like this, every container should be running:

NAMESPACE     NAME                              READY     STATUS    RESTARTS   AGE
kube-system   etcd-kube-01                      1/1       Running   0          5m
kube-system   kube-apiserver-kube-01            1/1       Running   0          6m
kube-system   kube-controller-manager-kube-01   1/1       Running   0          5m
kube-system   kube-dns-6f4fd4bdf-whbhd          3/3       Running   0          6m
kube-system   kube-proxy-2hdhk                  1/1       Running   0          6m
kube-system   kube-proxy-tvhjk                  1/1       Running   0          5m
kube-system   kube-proxy-wspmv                  1/1       Running   0          5m
kube-system   kube-scheduler-kube-01            1/1       Running   0          6m
kube-system   weave-net-9ghn5                   2/2       Running   1          5m
kube-system   weave-net-lh8tq                   2/2       Running   0          5m
kube-system   weave-net-qhr25                   2/2       Running   0

Congratulations, now your Kubernetes cluster running on Ubuntu 16.04 is up and ready for you to deploy a microservices application.

Step 5.0 - Deploying the Weaveworks Microservices Sock Shop

First you will need to download the Weaveworks Microservices Sock Shop demo app to your Gremlin folder, run the following command:

git clone

Create a namespace for your Sock Shop demo app:

kubectl create namespace sock-shop

You will see the following result:

namespace "sock-shop" created

Navigate to the microservices-demo/deploy/kubernetes folder:

cd microservices-demo/deploy/kubernetes

Next apply the demo to your kubernetes cluster:

kubectl apply -f complete-demo.yaml

Check to confirm that all the Sock Shop pods are now running:

kubectl get pods --namespace sock-shop

You will see the following result when all pods are ready, they will have the status of “Running”:

NAMESPACE     NAME                                         READY     STATUS    RESTARTS   AGEdefault       carts-db-784446fdd6-kp7sm                    1/1       Running   0          1mdefault       gremlin-8xbnm                                1/1       Running   0          1mdocker        compose-74649b4db6-xdkwf                     1/1       Running   0          1mdocker        compose-api-6ff6b7fb4f-g29km                 1/1       Running   0          1mkube-system   etcd-docker-for-desktop                      1/1       Running   2          1mkube-system   kube-apiserver-docker-for-desktop            1/1       Running   2          1mkube-system   kube-controller-manager-docker-for-desktop   1/1       Running   2          1mkube-system   kube-dns-86f4d74b45-4n8b6                    3/3       Running   3          1mkube-system   kube-proxy-dsqqf                             1/1       Running   1          1mkube-system   kube-scheduler-docker-for-desktop            1/1       Running   2          1mkube-system   weave-net-wsbk9                              2/2       Running   3          1msock-shop     carts-6cd457d86c-x6vjs                       1/1       Running   0          1msock-shop     carts-db-784446fdd6-dxkvx                    1/1       Running   0          1msock-shop     catalogue-779cd58f9b-hk4vr                   1/1       Running   0          1msock-shop     catalogue-db-6794f65f5d-7bzr4                1/1       Running   0          1msock-shop     front-end-679d7bcb77-m2995                   1/1       Running   0          1msock-shop     orders-755bd9f786-w46z4                      1/1       Running   0          1msock-shop     orders-db-84bb8f48d6-nfzlq                   1/1       Running   0          1msock-shop     payment-674658f686-6br5w                     1/1       Running   0          1msock-shop     queue-master-5f98bbd67-gfhxx                 1/1       Running   0          1msock-shop     rabbitmq-86d44dd846-sqt7f                    1/1       Running   0          1msock-shop     shipping-79786fb956-z8xxg                    1/1       Running   0          1msock-shop     user-6995984547-lk9dg                        1/1       Running   0          1msock-shop     user-db-fc7b47fb9-xsqzw                      1/1       Running   0          1m

Visit http://localhost:30001/ to see the Sock Shop running:

sock shop

Step 6.0 – Set up your Gremlin credentials

After you have created your Gremlin account (sign up here) you will need to get your Gremlin Daemon credentials. Login to the Gremlin App using your Company name and sign-on credentials. These details were emailed to you when you signed up to start using Gremlin. Navigate to Company Teams Settings and click on your Team. Click the blue Download button to get your Team Certificate. The downloaded contains both a public-key certificate and a matching private key.

Unzip the and save it to your gremlin folder on your desktop. Rename your certificate and key files to gremlin.cert and gremlin.key.

gremlin directory

Next create your secret as follows:

kubectl create secret generic gremlin-team-cert --from-file=./gremlin.cert --from-file=./gremlin.key

Installation with Helm

The simplest way to install the Gremlin client on your Kubernetes cluster is to use Helm. If you do not already have Helm installed, go here to get started. Once Helm is installed and configured, the next steps are to add the Gremlin repo and install the client.

To run the Helm install, you will need your Gremlin Team ID. It can be found in the Gremlin app on the Team Settings page, where you downloaded your certs earlier. Click on the name of your team in the list. The ID you’re looking for is found under Configuration as Team ID.

Export your Team ID as an environment variable:


(Replace YOUR_TEAM_ID with the Team ID you obtained from the Gremlin UI.)

Next, export your cluster ID, which is just a friendly name for your Kubernetes cluster. It can be whatever you want.

export GREMLIN_CLUSTER_ID="Your cluster id"

Now add the Gremlin Helm repo, and install Gremlin:

helm repo add gremlin
helm install gremlin/gremlin \
\--namespace gremlin \
\--name gremlin \
\--set gremlin.teamID=$GREMLIN_TEAM_ID \
\--set gremlin.clusterID=$GREMLIN_CLUSTER_ID

For more information on the Gremlin Helm chart, including more configuration options, check out the chart on Github.

Step 7.0 - Installing the Datadog agent using a Kubernetes Daemonset

To install Datadog in a Kubernetes pod you can use the Datadog Kubernetes easy one-step install. It will take a few minutes for Datadog to spin up the Datadog container, collect metrics on your existing containers and display them in the Datadog App.

datadog api key

You will simple copy the Kubernetes DaemonSet, save it as datadog-agent.yaml and then run the following command:

kubectl apply -f datadog-agent.yaml

To confirm that the Datadog agent pod is now up, run the following command:

kubectl get pods -n default

You will see the following output:

NAME                        READY     STATUS    RESTARTS   AGE
datadog-agent-4kbq8         1/1       Running   0          1m
gremlin-tj6wl               1/1       Running   0          17m

Step 8.0 - Performing a Packet Loss Attack using Gremlin

Now you are ready to start performing your Chaos Engineering experiments. The first experiment we will run will be a packet loss attack on the front-end Kubernetes deployment for the Sock Shop.

In the Gremlin UI, click on Attacks in the left navigation bar and then New Attack. Then click on Kubernetes on the right. You can select the cluster you’d like to attack, and the sock-shop namespace to filter the objects available to attack.

Gremlin Kubernetes

Next, click on Deployments to expand the list of Kubernetes deployments that are available, and click on front-end.

Click on Front End

Scroll down and click Choose a Gremlin. Select Network, and Packet loss.

Choose a Gremlin

In the next section below we can customize the attack settings. Scroll down to the bottom of the list and input 60 for the percentage of packet loss to apply in the attack. Then click the green Unleash Gremlin button.

Now refresh the Sock Shop at http://localhost:30001/ and see the impact to the UI. You will notice that none of the items in the store will load.

sock shop no items

This type of Chaos Engineering experiment enables you to see how your application handles packet loss. It also enables you to view the experience of your customer.


You have now successfully run a Chaos Engineering experiment using Gremlin which injected packet loss to a Kubernetes pod running the frontend of a microservices e-commerce store. Join the Chaos Engineering Slack Community to discuss how Chaos Engineering can be practiced on Kubernetes.


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