This small project is a distributed pipeline intended to be used as a testbed for applications of Hidden Markov Models(HMM) in data analytics. Let's suppose you have a bunch of particles, walking randomly in steps on a two-dimensional box i.e. a bounded plane. Particles send their coordinates on the plane to an app whenever they move one step further. Unfortunately, the app receives these coordinates through a noisy channel and therefore its reads are not accurate. The goal is to estimate the actual coordinates of particles through the noisy data using a mathematical framework called Hidden Markov Model.

The implementation includes a couple of particle components sending their coordinates to a topic on a Kafka cluster. The app component consumes these events (noisy coordinates) and persists them on a Cassandra cluster. An estimator component uses the data to learn the transition and emission matrices of an HMM to be used to decode the future noisy coordinates and obtain the actual placement of particles. A webapp component is also introduced to visualize both the noisy and estimated locations of each particle.

All components are managed over Kubernetes to facilitate the deployment process. Individual images are accessible here.

To be completed...

The project is meant to run over a Kubernetes cluster. We will use Minikube for the sake of simplicity. The instructions for installing minikube can be found here.

After installing minikube, start it with your preferred driver. If you already have docker on your host machine, you can use docker as the driver:

minikube start --driver=docker --memory=4096

The next step is to install Strimzi, an open source tool to manage Kafka on Kubernetes. Strimzi takes advantage of operator pattern in k8s. Create a namespace on your k8s cluster using the following command:

kubectl create namespace kafka

We have named our new namespace kafka. Now, we apply Strimzi install files on k8s using the following command:

kubectl create -f 'https://strimzi.io/install/latest?namespace=kafka' -n kafka

More details can be found here. Note that the kafka namespace we have created and the namespace query parameter inside the installation url of Strimzi must match. And then create a kafka cluster using:

kubectl apply -f .\kafka.yaml -n kafka

Check for the status of the cluster you are creating with one of the below commands:

kubectl get kafka -n kafka -w
kubectl get pods -n kafka -w
``

When the output of the first commands indicates `READY` is `TRUE`, proceed with making a kafka topic:
```bash
kubectl create -f ./kafka-topic.yaml -n kafka

Now let's deploy our components. Create another namespace named dev:

kubectl create namespace dev

Create a config map in that namespace to hold the information that is needed to connect components:

kubectl apply -f .\dev-config.yaml -n dev

Deploy the particle and app components on k8s:

kubectl apply -f .\particles.yaml -n dev
kubectl apply -f .\app.yaml -n dev

Now let's create a cluster of Cassandra nodes as our data persistence medium using k8s statefulset:

kubectl apply -f .\cassandra.yml

Note that we maintain the Cassandra service within the default namespace of k8s.

To be completed...

To be completed...