Cassandra Chaos Engineering With LitmusChaos

Written by Shubham Chaudhary | Jul 28, 2020 5:28:31 PM

In this blog, I will talk about chaos engineering on Cassandra with LitmusChaos. Before jumping in, let's do a quick recap on Litmus. Litmus is a framework for practicing Chaos Engineering in cloud-native environments. Litmus provides a chaos-operator, a large set of chaos experiments in its hub, detailed documentation, quick demo, and a friendly community.

What is Cassandra?

Apache Cassandra is a free and open-source, distributed, wide column store, NoSQL database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters with asynchronous masterless replication allowing low latency operations for all clients. Cassandra's support for replicating across multiple datacenters is best-in-class, providing lower latency for your users and the peace of mind of knowing that you can survive regional outages.

Cassandra is using Consistent Hashing, which is a distributed hashing scheme that operates independently of the number of servers or objects in a distributed hash table by assigning them a position on an abstract circle, or hash ring. This allows servers and objects to scale without affecting the overall system. Cassandra uniformly distributes the load over the Cassandra ring and re-distribute the load when the Cassandra Statefulset scales up/down.

With Kubernetes popularity skyrocketing and the adoption of Apache Cassandra growing as a NoSQL database well-suited to matching the high availability and scalability needs of cloud-based applications, it should be no surprise that more developers are looking to run Cassandra databases on Kubernetes. However, many devs are finding that doing so is relatively simple to get going with, but considerably more challenging to execute at a high level. We will discuss the installation part later in this blog.

On the positive side, Kubernetes helpfully offers StatefulSets — workload API objects that can be used to manage stateful applications. StatefulSets provide the requisite components to establish stable and unique network identifiers, stable persistent storage, smooth and ordering deployment and scaling (as well as deletion and termination) and automated rolling updates.

Why do we need chaos engineering on Cassandra?

As Cassandra is using consistent hashing and maintaining the Cassandra ring to distribute the load uniformly. If the number of replicas will scale up/down, the load will be redistributed. But we always have a few questions in our mind:

Does load redistribution always happen with 0 probability of failure in scale-up/down?
How will it behave if the one/multiple replicas of the Cassandra Statefulset are killed?
Is it resilient even if the rate of replica deletion is very high?

In the age of data evolution, data is very important, and everyone is looking for a setup where the probability of downtime is least as possible. Here the chaos engineering comes into play. It will help you find out all the corner cases of failure before they happen, and if you believe in "Prevention is better than cure," then the best real-life example award goes to chaos engineering. It can be used to test the resiliency of the Cassandra stateful set. As of now, we have a Cassandra-pod-delete experiment to test the case where one of the replicas of Cassandra Statefulset is deleted. More use-cases/experiments will be added soon.

Chaos On Cassandra: Pod-Delete Experiment

Now that we know the basics of a Cassandra statefulset on Kubernetes, let us execute a chaos experiment to kill one of the replicas of Cassandra. At the same time the load is distributed on all the replicas over the Cassandra ring & verify whether the load is redistributed. This example intends to introduce the user to the steps involved in carrying out a chaos experiment using Litmus.

Pre-Requisites

A (preferably) multi-node Kubernetes cluster. Ensure you are in the Kubernetes-admin context to setup RBAC for the various components involved.

Chaos Experiment Approach

The following steps are performed automatically upon execution of the Chaos Experiment:

An external liveness pod will be created, which will ensure the liveness of Cassandra's statefulset during chaos execution. It is continuously running the liveness cycles of cqlsh commands. Liveness cycle stands for a set of cqlsh operations (create keyspace, create the table, insert data in the table, delete the tables, delete the keyspaces). It runs a webserver container as a side cart, which exposes the status of the liveness cycle (cycleInProgress or cycleCompleted). The experiment looks for the status of the webserver service and cleans up the liveness deployment at the end of the cycle. In case of a timeout, the liveness container terminates ungracefully with an exception.
It will ensure that the load is distributed across all the replicas on the Cassandra ring before and after the chaos injection. It ensures that the load is re-distributed across all available replicas after every kill. In case if the load is not distributed on any of the available replicas, this check will be failed.
In its default mode, the experiment derives the random single/multiple replicas of Cassandra statefulset, performs a pod kill (delete), and checks the liveness and load distribution on the Cassandra ring across all replicas, which implies that the Cassandra statefulset remains alive and load redistribution takes place after every kill. If this is true, the experiment verdict is set to “pass,” indicating the current statefulset is tolerant to pod-failures. A terminated statefulset sets the verdict to “fail” and implies that the statefulset is not resilient enough, demanding a closer inspection.

Hypothesis

Upon killing the replica of Cassandra statefulset, the load will be re-distributed over the Cassandra ring.
After the deletion of the replica, the new replica will be created by the replica controller to maintain the desired count of the available replicas.

Preparing the Testbed

Setup the Cassandra Cluster

We are going to discuss two approaches to set up the Cassandra cluster quickly.

This approach will guide you through setting up a Cassandra cluster in AWS EKS with OpenEBS as a storage orchestration tool. Follow the following tutorial to setup the cluster Setup Cassandra Cluster on EKS.
This approach will guide you to set up the Cassandra cluster in Minikube. We can follow the underlying steps to set up the Cassandra cluster.

Step-1: Creating a headless Service for Cassandra

Creating the headless service for Cassandra used for DNS lookups between Cassandra Pods and clients within your cluster:

root@demo:~# cat <<EOF>  cassandra-service.yaml

apiVersion: v1
kind: Service
metadata:
  labels:
    app: cassandra
  name: cassandra
spec:
  clusterIP: None
  ports:
  - port: 9042
  selector:
    app: cassandra
EOF

root@demo:~# kubectl create -f cassandra-service.yaml -n cassandra
 service/cassandra created

Validation of Cassandra service: 

root@demo:~# kubectl get svc cassandra -n cassandra 
The response is:
     NAME        TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)    AGE
     cassandra   ClusterIP   None         <none>        9042/TCP   45s

If you don't see a Service named Cassandra, that means creation failed.

Step-2: Using a StatefulSet to create a Cassandra ring

The StatefulSet manifest included below, creates a Cassandra ring that consists of three Pods.

Note: This example uses the default provisioner for Minikube. Please update the following StatefulSet for the cloud you are working with.

root@demo:~# cat <<EOF> cassandra-sts.yaml 

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: cassandra
  labels:
    app: cassandra
spec:
  serviceName: cassandra
  replicas: 3
  selector:
    matchLabels:
      app: cassandra
  template:
    metadata:
      labels:
        app: cassandra
    spec:
      terminationGracePeriodSeconds: 1800
      containers:
      - name: cassandra
        image: gcr.io/google-samples/cassandra:v13
        imagePullPolicy: Always
        ports:
        - containerPort: 7000
          name: intra-node
        - containerPort: 7001
          name: tls-intra-node
        - containerPort: 7199
          name: jmx
        - containerPort: 9042
          name: cql
        resources:
          limits:
            cpu: "500m"
            memory: 1Gi
          requests:
            cpu: "500m"
            memory: 1Gi
        securityContext:
          capabilities:
            add:
              - IPC_LOCK
        lifecycle:
          preStop:
            exec:
              command: 
              - /bin/sh
              - -c
              - nodetool drain
        env:
          - name: MAX_HEAP_SIZE
            value: 512M
          - name: HEAP_NEWSIZE
            value: 100M
          - name: CASSANDRA_SEEDS
            value: "cassandra-0.cassandra.default.svc.cluster.local"
          - name: CASSANDRA_CLUSTER_NAME
            value: "K8Demo"
          - name: CASSANDRA_DC
            value: "DC1-K8Demo"
          - name: CASSANDRA_RACK
            value: "Rack1-K8Demo"
          - name: POD_IP
            valueFrom:
              fieldRef:
                fieldPath: status.podIP
        readinessProbe:
          exec:
            command:
            - /bin/bash
            - -c
            - /ready-probe.sh
          initialDelaySeconds: 15
          timeoutSeconds: 5
        # These volume mounts are persistent. They are like inline claims,
        # but not exactly because the names need to match exactly one of
        # the stateful pod volumes.
        volumeMounts:
        - name: cassandra-data
          mountPath: /cassandra_data
  # These are converted to volume claims by the controller
  # and mounted at the paths mentioned above.
  # do not use these in production until ssd GCEPersistentDisk or other ssd pd
  volumeClaimTemplates:
  - metadata:
      name: cassandra-data
    spec:
      accessModes: [ "ReadWriteOnce" ]
      storageClassName: fast
      resources:
        requests:
          storage: 1Gi
---
kind: StorageClass
apiVersion: storage.k8s.io/v1
metadata:
  name: fast
provisioner: k8s.io/minikube-hostpath
parameters:
  type: pd-ssd
EOF

root@demo:~# kubectl create -f cassandra-sts.yaml -n cassandra
statefulset.apps/cassandra created
storageclass.storage.k8s.io/fast created

Validating the Cassandra StatefulSet
root@demo:~# kubectl get statefulset cassandra -n cassandra

The response should be similar to:
NAME        DESIRED   CURRENT   AGE
cassandra   3         0         13s

root@demo:~# kubectl get pods -l="app=cassandra" -n cassandra

It can take several minutes for all three Pods to deploy. Once they are deployed, the same command returns output similar to:

NAME          READY     STATUS    RESTARTS   AGE
cassandra-0   1/1       Running   0          10m
cassandra-1   1/1       Running   0          9m
cassandra-2   1/1       Running   0          8m

Step-3: Run the Cassandra nodetool inside the first Pod, to display the status of the ring.

kubectl exec -it cassandra-0 -- nodetool status

The response should look something like:

Datacenter: DC1-K8Demo
======================
Status=Up/Down
|/ State=Normal/Leaving/Joining/Moving
--  Address     Load       Tokens       Owns (effective)  Host ID                               Rack
UN  172.17.0.5  83.57 KiB  32           74.0%             e2dd09e6-d9d3-477e-96c5-45094c08db0f  Rack1-K8Demo
UN  172.17.0.4  101.04 KiB  32           58.8%             f89d6835-3a42-4419-92b3-0e62cae1479c  Rack1-K8Demo
UN  172.17.0.6  84.74 KiB  32           67.1%             a6a1e8c2-3dc5-4417-b1a0-26507af2aaad  Rack1-K8Demo

Setup the Litmus Infrastructure

Step-1: Install Litmus Chaos CRDs, Operator & RBAC

root@demo:~# kubectl apply -f https://litmuschaos.github.io/litmus/litmus-operator-v1.6.1.yaml
namespace/litmus created
serviceaccount/litmus created
clusterrole.rbac.authorization.k8s.io/litmus created
clusterrolebinding.rbac.authorization.k8s.io/litmus created
deployment.apps/chaos-operator-ce created customresourcedefinition.apiextensions.k8s.io/chaosengines.litmuschaos.io created
customresourcedefinition.apiextensions.k8s.io/chaosexperiments.litmuschaos.io created
customresourcedefinition.apiextensions.k8s.io/chaosresults.litmuschaos.io created

kubectl apply -f https://litmuschaos.github.io/litmus/litmus-admin-rbac.yaml

Step-2: Create the Cassandra-Pod-Delete ChaosExperiment CR

root@demo:~# kubectl apply -f https://hub.litmuschaos.io/api/chaos/1.6.0?file=charts/cassandra/cassandra-pod-delete/experiment.yaml -n litmus
chaosexperiment.litmuschaos.io/cassandra-pod-delete created

Step-3: Annotate the Kafka statefulset for Chaos

root@demo:~# kubectl annotate sts/cassandra litmuschaos.io/chaos="true" -n cassandra 
statefulset.apps/cassandra annotated

Run the Chaos Experiment

Step-1: Construct the ChaosEngine for the Cassandra Pod Delete experiment:

root@demo:~# cat <<EOF> cassanddra-chaos.yaml

apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
 name: cassandra-chaos
 namespace: litmus
spec:
  appinfo:
    appns: ‘'cassandra’
    applabel: 'app=cassandra'
    appkind: 'statefulset'
  annotationCheck: 'true'
  engineState: 'active'
  chaosServiceAccount: litmus-admin
  monitoring: false
  jobCleanUpPolicy: 'delete'
  experiments:
    - name: cassandra-pod-delete
      spec:
        components:
          Env:
   - name: TOTAL_CHAOS_DURATION
              value: '15'

            - name: CHAOS_INTERVAL
              value: '15'

            - name: FORCE
              value: 'false'

           - name: CASSANDRA_SVC_NAME
              value: 'cassandra'

            - name: KEYSPACE_REPLICATION_FACTOR
              value: '3'

            - name: CASSANDRA_PORT
              value: '9042'

             - name: CASSANDRA_LIVENESS_CHECK
              value: ''

EOF

Step-2: Apply the ChaosEngine to launch the experiment

root@demo:~# kubectl apply -f cassandra-chaos.yaml -n litmus
chaosengine.litmuschaos.io/cassandra-chaos created

Step-3: Observe experiment execution

Watch the pods on the app namespace (cassandra) to view the chaos actions in progress.

watch -n 1 kubectl get pods -n cassandra

Look out for the following events.

As part of the experiment execution, the experiment job launches a liveness pod (cassandra-liveness) that runs few cqlsh queries (create keyspaces, create tables, data insertion, and cleanup of table & keyspaces) running as separate containers of the same pod.
The liveness pod is failed if it is unable to run cqlsh commands(Cassandra is unavailable). View the cassandra-liveness pod logs during the pod-delete to verify the Uninterrupted availability of Cassandra statefulset.

kubectl logs -f cassandra-liveness -n litmus

Step-4: Verify Result Of the Chaos Experiment

View the verdict (spec.experimentStatus.verdict)of the cassandra-pod-delete chaos experiment to check whether the Cassandra cluster is resilient to the pod-delete.

root@demo:~# kubectl describe chaos result cassandra-chaos-cassandra-pod-delete -n litmus

Name:         cassandra-chaos-cassandra-pod-delete
Namespace:    litmus
Labels:       chaosUID=22d5ba06-1fe8-4b01-bdbb-6246e1cdb2c9
              type=ChaosResult
Annotations:  kubectl.kubernetes.io/last-applied-configuration:
                {"apiVersion":"litmuschaos.io/v1alpha1","kind":"ChaosResult","metadata":{"annotations":{},"labels":{"chaosUID":"22d5ba06-1fe8-4b01-bdbb-62...
API Version:  litmuschaos.io/v1alpha1
Kind:         ChaosResult
Metadata:
  Creation Timestamp:  2020-07-16T12:28:00Z
  Generation:          10
  Resource Version:    20451
  Self Link:           /apis/litmuschaos.io/v1alpha1/namespaces/litmus/chaosresults/cassandra-chaos-cassandra-pod-delete
  UID:                 32ea5093-47ee-41d8-bc34-e513edb46660
Spec:
  Engine:      cassandra-chaos
  Experiment:  cassandra-pod-delete
Status:
  Experimentstatus:
    Fail Step:  N/A
    Phase:      Completed
    Verdict:    Pass
Events:         <none>

Conclusion

The Cassandra chaos experiments are a good way to determine a potential breach of SLAs in terms of data consistency, performance & timeouts due to unexpected replica-kill. It will boost the developers' confidence for those use-cases for which their setup returns a positive/passed result. In the future, even if that chaos happens naturally(finger crossed), then the developers don’t have to worry. They are already trained with chaos management skills as they are chaos engineers/warriors after the adoption of chaos engineering practices.
Do try this experiment & let me know your findings!

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