Chaos Engineering with Memcache and Kubernetes

Chaos Engineering with Memcache and Kubernetes

Introduction

Gremlin is a simple, safe and secure service for performing Chaos Engineering experiments through a SaaS-based platform. Memcache is general-purpose distributed memory caching system. Datadog is a monitoring service for cloud-scale applications, providing monitoring of servers, databases, tools, and services, through a SaaS-based data analytics platform. Datadog provides an integration to monitor Memcache.

Chaos Engineering Hypothesis

For the purposes of this tutorial we will run Chaos Engineering experiments for Memcache on Kubernetes. We will use Gremlin to run Chaos Engineering experiments on our cluster where run an IO attack to increase the number of reads. This will give us confidence in the reliability and resiliency of our memcached cluster. Additional experiments that are recommended to run include shutting down Memcache instances and pods and insuring this does not take down your database/storage layer.

Prerequisites

To complete this tutorial you will need the following:

  • 4 cloud infrastructure hosts running Ubuntu 16.04 with 4GM RAM and private networking enabled
  • A Gremlin account (sign up here)
  • A Datadog account (sign up here)

You will need to install the following on each of your 4 cloud infrastructure hosts. This will enable you to run your Chaos Engineering experiments.

  • Memcache
  • Kubernetes
  • Helm
  • Docker
  • Gremlin
  • Datadog

Overview

This tutorial will walk you through the required steps to run the Memcache IO Chaos Engineering experiment.

  • Step 1 - Creating a Kubernetes cluster with 3 nodes
  • Step 2 - Installing Memcache
  • Step 3 - Installing Helm
  • Step 4 - Installing Gremlin
  • Step 5 - Installing Datadog
  • Step 6 - Performing Chaos Engineering experiments on Memcache
  • Step 7 - Installing mcrouter
  • Step 8 - Performing Chaos Engineering experiments on mcrouter

Step 1 - Creating a Kubernetes cluster with 3 nodes

We will start with creating three Ubuntu 16.04 servers. This will give you four servers to configure. Create 4 hosts and call them kube-01, kube-02, kube-03 and kube-04. You need to be running hosts with a minimum of 4GB RAM.

Set your hostnames for your servers as follows:

  • Server 1 - Hostname: k8-01
  • Server 2 - Hostname: k8-02
  • Server 3 - Hostname: k8-03
  • Server 4 - Hostname: k8-04

Kubernetes will need to assign specialized roles to each server. We will setup one server to act as the master:

  • k8-01 - role: master
  • k8-02 - role: node
  • k8-03 - role: node
  • k8-04 - role: node

Set up each server in the cluster to run Kubernetes

On each of the three Ubuntu 16.04 servers run the following commands as root:

bash
1apt-get update && apt-get install -y apt-transport-httpscurl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key add -cat <<EOF >/etc/apt/sources.list.d/kubernetes.listdeb http://apt.kubernetes.io/ kubernetes-xenial mainEOFapt-get updateapt-get install -y kubelet kubeadm kubectl docker.io

Setup the Kubernetes Master

On the kube-01 node run the following command:

bash
1kubeadm init

To start using your cluster, you need to run the following as a regular user:

bash
1mkdir -p $HOME/.kubesudo cp -i /etc/kubernetes/admin.conf $HOME/.kube/configsudo chown $(id -u):$(id -g) $HOME/.kube/config

Your Kubernetes master has initialized successfully!

Join your nodes to your Kubernetes cluster

You can now join any number of machines by running the kubeadm join command on each node as root. This command will be created for you as displayed in your terminal for you to copy and run. An example of what this looks like is below:

bash
1kubeadm join --token 702ff6.bc7aacff7aacab17 174.138.15.158:6443 --discovery-token-ca-cert-hash sha256:68bc22d2c631800fd358a6d7e3998e598deb2980ee613b3c2f1da8978960c8ab

When you join your kube-02 and kube-01 nodes you will see the following on the node:

1This node has joined the cluster:* Certificate signing request was sent to master and a response was received.* The Kubelet was informed of the new secure connection details.

To check that all nodes are now joined to the master run the following command on the Kubernetes master kube-01:

bash
1kubectl get nodes

The successful result will look like this:

1NAME STATUS ROLES AGE VERSIONk8-01 NotReady master 111s v1.12.2k8-03 NotReady <none> 14s v1.12.2k8-04 NotReady <none> 12s v1.12.2

Setup a Kubernetes Add-On For Networking Features And Policy

Kubernetes Add-Ons are pods and services that implement cluster features. Pods extend the functionality of Kubernetes. You can install addons for a range of cluster features including Networking and Visualization.

We are going to install the Weave Net Add-On on the kube-01 master which provides networking and network policy. It will continue working on both sides of a network partition and does not require an external database.

Next you will deploy a pod network to the cluster. The options are listed at: https://kubernetes.io/docs/concepts/cluster-administration/addons/

Installing the Weave Net Add-On

Get the Weave Net yaml:

bash
1curl -o weave.yaml https://cloud.weave.works/k8s/v1.8/net.yaml

Inspect the yaml contents:

bash
1cat weave.yaml

On the kube-01 Kubernetes master node run the following commands:

bash
1kubectl apply -f weave.yaml

The result will look like this:

1serviceaccount/weave-net createdclusterrole.rbac.authorization.k8s.io/weave-net createdclusterrolebinding.rbac.authorization.k8s.io/weave-net createdrole.rbac.authorization.k8s.io/weave-net createdrolebinding.rbac.authorization.k8s.io/weave-net createddaemonset.extensions/weave-net created

It may take a minute or two for DNS to be ready. Continue to check for DNS to be ready before moving on by running the following command:

bash
1kubectl get pods --all-namespaces

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

1NAMESPACE NAME READY STATUS RESTARTS AGEkube-system coredns-576cbf47c7-gm6kt 1/1 Running 0 3m20skube-system coredns-576cbf47c7-h5v5k 1/1 Running 0 3m20skube-system etcd-k8-01 1/1 Running 0 2m14skube-system kube-apiserver-k8-01 1/1 Running 0 2m14skube-system kube-controller-manager-k8-01 1/1 Running 0 2m18skube-system kube-proxy-7m87q 1/1 Running 0 111skube-system kube-proxy-mk9h9 1/1 Running 0 113skube-system kube-proxy-wkxxm 1/1 Running 0 3m20skube-system kube-scheduler-k8-01 1/1 Running 0 2m35skube-system weave-net-lvp6x 2/2 Running 0 34skube-system weave-net-pjxk2 2/2 Running 0 34skube-system weave-net-qrrvl 2/2 Running 0 34s

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

Step 2 - Deploying Memcache

First download the helm binary on your Kubernetes master, kube-01:

bash
1wget https://kubernetes-helm.storage.googleapis.com/helm-v2.6.0-linux-amd64.tar.gz

Create a helm directory and unzip the helm binary to your local system:

bash
1mkdir helm-v2.6.0tar zxfv helm-v2.6.0-linux-amd64.tar.gz -C helm-v2.6.0

Add the helm binary’s directory to your PATH environment variable:

bash
1export PATH="$(echo ~)/helm-v2.6.0/linux-amd64:$PATH"

Create a service account with the cluster admin role for Tiller, the Helm server:

bash
1kubectl create serviceaccount --namespace kube-system tiller
2kubectl create clusterrolebinding tiller --clusterrole=cluster-admin --serviceaccount=kube-system:tiller

Initialize Tiller in your cluster, and update information of available charts:

bash
1helm init --service-account tiller
2helm repo update

You will need to wait until the tiller deploy pod is ready before proceeding. Use the following command to check for when the tiller deploy pod is ready:

bash
1kubectl -n kube-system get pods

You will see the following output:

1NAME READY STATUS RESTARTS AGEcoredns-576cbf47c7-gm6kt 1/1 Running 0 14mcoredns-576cbf47c7-h5v5k 1/1 Running 0 14metcd-k8-01 1/1 Running 0 13mkube-apiserver-k8-01 1/1 Running 0 13mkube-controller-manager-k8-01 1/1 Running 0 13mkube-proxy-7m87q 1/1 Running 0 12mkube-proxy-mk9h9 1/1 Running 0 12mkube-proxy-wkxxm 1/1 Running 0 14mkube-scheduler-k8-01 1/1 Running 0 13mtiller-deploy-9cfccbbcf-6f8j9 1/1 Running 0 93sweave-net-lvp6x 2/2 Running 0 11mweave-net-pjxk2 2/2 Running 0 11mweave-net-qrrvl 2/2 Running 0 11m

Check the logs for the tiller pod, run the following command replacing _tiller-deploy-9cfccbbcf-kflph _with your pod name:

bash
1kubectl logs --namespace kube-system tiller-deploy-9cfccbbcf-kflph

You will see the following output:

1[main] 2018/11/20 20:00:41 Starting Tiller v2.6.0 (tls=false)[main] 2018/11/20 20:00:41 GRPC listening on :44134[main] 2018/11/20 20:00:41 Probes listening on :44135[main] 2018/11/20 20:00:41 Storage driver is ConfigMap

Install a new Memcached Helm chart release with three replicas, one for each node:

bash
1helm install stable/memcached --name mycache --set replicaCount=3

You will see the folllowing output:

1NAME READY STATUS RESTARTS AGEmycache-memcached-0 1/1 Running 0 89smycache-memcached-1 1/1 Running 0 61smycache-memcached-2 0/1 Pending 0 48s

Execute the following command to see the running pods:

bash
1kubectl get pods

You should see the following:

1NAME READY STATUS RESTARTS AGEmycache-memcached-0 1/1 Running 0 3m54smycache-memcached-1 1/1 Running 0 3m26smycache-memcached-2 0/1 Pending 0 3m13s

Discovering Memcached service endpoints

First, run the following command to retrieve the endpoints’ IP addresses:

bash
1kubectl get endpoints mycache-memcached

The output should be similar to the following:

1NAME ENDPOINTS AGEmycache-memcached 10.40.0.1:11211,10.46.0.4:11211 4m10s

Test the deployment by opening a telnet session with one of the running Memcached servers on port 11211:

bash
1kubectl run -it --rm alpine --image=alpine:3.6 --restart=Never telnet mycache-memcached-0.mycache-memcached.default.svc.cluster.local 11211

At the telnet prompt, run these commands using the Memcached ASCII protocol:

bash
1set mykey 0 0 5helloget mykeyquit

The resulting output is shown here in bold:

1If you don't see a command prompt, try pressing enter.set mykey 0 0 5helloSTOREDget mykeyVALUE mykey 0 5helloENDquitConnection closed by foreign host

Implementing the service discovery logic

Next we will implement service discovery logic with Python. Run the following command to create a python pod in your Kubernetes cluster:

bash
1kubectl run -it --rm python --image=python:3.6-alpine --restart=Never sh

Install the pymemcache library:

bash
1pip install pymemcache

You will see the following output

1Collecting pymemcache Downloading https://files.pythonhosted.org/packages/91/14/f4fb51de1a27b12df6af42e6ff794a13409bdca6c8880e562f7486e78b5b/pymemcache-2.0.0-py2.py3-none-any.whlCollecting six (from pymemcache) Downloading https://files.pythonhosted.org/packages/67/4b/141a581104b1f6397bfa78ac9d43d8ad29a7ca43ea90a2d863fe3056e86a/six-1.11.0-py2.py3-none-any.whlInstalling collected packages: six, pymemcacheSuccessfully installed pymemcache-2.0.0 six-1.11.0

Start a Python interactive console by running the following command:

bash
1python

In the Python console, run these commands:

bash
1import socketfrom pymemcache.client.hash import HashClient_, _, ips = socket.gethostbyname_ex('mycache-memcached.default.svc.cluster.local')servers = [(ip, 11211) for ip in ips]client = HashClient(servers, use_pooling=True)client.set('mykey', 'hello')client.get('mykey')

You will see the following output:

1b'hello'

Exit the Python console:

bash
1exit()

Exit the pod’s shell session by pressing Control+D. You will see the following:

1/ # pod "python" deleted

Step 4 – Installing Gremlin for Chaos Engineering experiments

After you have created your Gremlin account (sign up here) you will need to find your Gremlin Daemon credentials. Login to the Gremlin App using your Company name and sign-on credentials. These 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 certificate.zip contains both a public-key certificate and a matching private key.

Unzip the certificate.zip 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:

bash
1kubectl 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:

bash
1export GREMLIN_TEAM_ID="YOUR_TEAM_ID"

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.

bash
1export GREMLIN_CLUSTER_ID="Your cluster id"

Now add the Gremlin Helm repo, and install Gremlin:

bash
1helm repo add gremlin https://helm.gremlin.com
2helm install gremlin/gremlin \
3 --namespace gremlin \
4 --name gremlin \
5 --set gremlin.teamID=$GREMLIN_TEAM_ID \
6 --set gremlin.clusterID=$GREMLIN_CLUSTER_ID

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

Step 5 – 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

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

bash
1kubectl apply -f datadog-agent.yaml

Next install the Memcached Datadog Integration by clicking Install Integration:

Datadog integration memcache

You will see that following notification in your event stream:

memcache integration installed

You can read more about setting up Memcache monitoring in Datadog.

Step 5 - Chaos Engineering experiments for Memcache with Gremlin

We will use the Gremlin Web App to create an IO attack on the memcache pods. The purpose of this experiment will be to ensure that we are able to identify an increase in IO for our memcache cluster. We will also use this attack to understand how the pod and server handles an increase in IO.

First click Attacks in the left navigation bar and then New Attack. Then click the Kubernetes tab to view all the available Kubernetes objects that you can run Chaos Engineering experiments on.

Gremlin Kubernetes integration

Scroll down and expand the StatefulSets section, and select memcached.

Select memcached

Next, select the Resource Gremlin and then choose IO. Scroll down and click the Unleash Gremlin button.

Gremlin IO

You can now monitor your IO attack using Datadog.

IO Attack Datadog

Step 9 – Additional Chaos Engineering experiments to run on Memcache

There are many Chaos Engineering experiments you could possibly run on your Memcache infrastructure:

  • Shutdown Gremlin - will shutting down a memcache node cause unexpected issues?
  • Latency & Packet Loss Gremlins - will they impact the ability to use the Memcache API endpoints?
  • Disk Gremlin - will filling up the disk crash the host? We encourage you to run these Chaos Engineering experiments and share your findings! To get access to Gremlin, sign up here.

Conclusion

This tutorial has explored how to install Memcache and Gremlin with Kubernetes for your Chaos Engineering experiments. We then ran a CPU Chaos Engineering experiment on the Memcache using the Gremlin CPU attack.

Related

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