Challenge

This repository responsible for creating a REST-API which responds with different salutations for different customers. And the application is Containerized and deployed in a selected container orchestrator (Kubernetes)

TLDR;

1. Assumptions Made

2. Directory Hierarchy

3. Application Architecture and the Solution

4. Build Deploy and Run

4.1 Prerequisites
4.2 Run the API
- 4.2.1 Get the most out of Makefile

5. Test API Endpoint

6. Future Improvements

7. Clean

👨‍💻 TLDR;

You can simply deploy the application on Kubernetes. (Make sure Docker is running)

make deploy

Once above command is executed, it performs the following actions

Install the K3s
Build a docker image
Export the docker image to to K3s image repository
Deploy the helm charts to the K3s Cluster

3 instances of the application will be deployed in 3 different namespaces for each customer

add the following entry to at /etc/hosts - IP of the LoadBalancer Service will be output by the above make deploy command.

10.70.1.173 customer-a.parcellab.com customer-b.parcellab.com customer-c.parcellab.com

Then access each service with;

curl -kv http://customer-a.parcellab.com
curl -kv http://customer-b.parcellab.com
curl -kv http://customer-c.parcellab.com

You can Test the endpoint of each customer application with;

make test

ⓘ For detailed explanations, refer the below sections.

🤔 Assumptions Made

As per the requirements, the application is deployed individually for each customer.

Therefore, the assumptions is to have an Application that is a very generic boilerplate and keep it DRY (Don't Repeat Yourself) - i.e. for each customer, it only requires to adjust a very minimum set of configurations to make it more specific to the customer (to get the specific response they expect). By this, application and it's associated resources will be re-usable.

Therefore this solution is intended to deploy as a single-tenant solution.

📂 Directory Hierarchy

.
├── Makefile
├── README.md
├── app
│   ├── Dockerfile
│   ├── __init__.py
│   ├── main.py
│   ├── requirements.txt
│   └── test_main.py
├── app-chart
   ├── customer-values/A.values.yaml
   ├── customer-values/B.values.yaml
   ├── customer-values/C.values.yaml
   ├── values.yaml
   ├── Chart.yaml
   └── templates
       ├── _helpers.tpl
       ├── deployment.yaml
       ├── ingress.yaml
       └── service.yaml

Makefile - Contains a set of rules (commands to build, run and deploy the application) to be executed.
app/ - Directory containaining the application logic and relavent resources associated with it.
- main.py - Source code for running the python application containing the API End Point
- test_main.py - A simple test suite to run the application written with fastapi test-client
- requirements.txt - A list of all of a project's dependencies
- Dockerfile - Contains instructions to build the Docker image
app-chart/ - This directory encompasses the Helm chart for deploying the REST API on Kubernetes.

Note: In here, we have made the helm chart DRY (Don't Repeat Yourself). By this, it means, we are using the same helm chart for different customers. Values are dynamically assigned for each user. values.yaml has all the common variables while customer-specific values.yaml files conains customer-specific variables.

The differentaition happens at the *Values.yaml level. The more details about this can be read in the 🏠 Application Architecture and the Solution section

🏠 Application Architecture and the Solution

The architecture consists of the following components:

A container-ready, python-based REST API written using FastAPI
A local Kubernetes cluster provisioned with K3S
A Helm Chart for Kubernetes manifests
An automated script (Makefile) for building and deploying applications

According to the 1. Assumptions Made above, I believe the best way to make things easy, conveninent and re-usable is to use a solution where it can be DRY (Don't Repeat Yourself). This means, making the logic more re-usable with the minimalistic efforts without re-inventing the wheel again and again for each customer. According to the assumption, each instance (in this context, it is a Pod) of the application is responsible for its respective response based on the customer. Therefore, the solution is as below:

Solution:

Set an ENV variable containing the customer-related metadata (in this case, it is the Customer Name) and inject it into the application where the application logic will read this ENV variable and return the relavent response based on the value of the ENV variable set. Therefore the solution is to create a blue-print, re-use it with the minimal configuration changes (i.e. changing the ENV variable).

Advantage of this approach is;

The deployment team can deploy the same application (blue-print) many times, without changing the underlying application logic at all. And also this solution avoid ammending any query parameters to the URI and avoid any unncessary conditional checkings at the application logic to differentiate the customer.

To automate this process, a Helm chart is created with different Values.yaml files. Each Values.yaml file responsible for each customer-related metadata and importantly carrying the ENV variable value specific ONLY to that customer.

Since we have 3 different customers, each customer-specific values are stored as below.

customer-values/A.values.yaml - Customer A realated deployment variables
customer-values/B.values.yaml - Customer B realated deployment variables
customer-values/C.values.yaml - Customer C realated deployment variables

// customer-values/A.values.yaml
...
labels:
  customer: A
...

// customer-values/B.values.yaml
...
labels:
  customer: B
...

// customer-values/C.values.yaml
...
labels:
  customer: C   
...

Inside the app-chart/templates/deployment.yaml, a env variable is set with the key and value as below:

. . .
    spec:
          env:
          - name: CUSTOMER_NAME
            value: {{ .Values.labels.customer }}
. . .

The {{ .Values.labels.customer }} will be initialized by "A", "B" or "C" for each customer respectively.

And the application logic is written to grasp the value of CUSTOMER_NAME and return the appropriate response.

    CUSTOMER_NAME = os.environ

    . . .          

    greeting_messages = {
        'A': 'Hi!',
        'B': 'Dear Sir or Madam!',
        'C': 'Moin!'
    }

    greeting = greeting_messages.get(CUSTOMER_NAME)

    if greeting:    
        return {'response' : greeting}

Here, a hash table is used to improve the application performance and time-complexity. (The response message can also be passed as an environment variable, which is much easier. But since I didn't have much time to change the application logic and the kubernetes manifests, I left the initial code base as it is. This can also be considered for 🔮 Future Improvements )

🚀 Build Deploy and Run

4.1 Prerequisites

A Kubernetes Cluster - In this example a simple K3S cluster is used (and the commands hereby are based on the K3S tool)

Installation:

make install-k3s

Helm
Docker
Python 3.x or later - If planning to run locally
You are using Linux :)

4.2 Run the API

4.2.1 Get the most out of `Makefile`

In this example, for automating the build and deployment process of this application, a Makefile is introduced. It is nothing but a file with set of tasks defined to be executed. Consider it as a simple pipeline to automate the manual tasks associated with building, running and deploying the application both locally and in kubernetes.

1. Run Locally

make run

This will run the REST API locally for the Customer A.

If you want to run it for Customer B or C, set a value for the CUSTOMER_NAME before running make run - (eg:- export CUSTOMER_NAME=B)

2. Build Image & Run

Since the application is a container-ready application, a Dockerfile is used to build the image. This is the most hassle-free solution to share your application and run it anywhere where the Docker is installed.

Build the Docker image

make build-app

Run the Docker container

make run-app

Access the applications

curl -kv http://0.0.0.0:8080
curl -kv http://0.0.0.0:8081
curl -kv http://0.0.0.0:8082

2. Deploy in Kubernetes

Before deploying the application in Kubernetes, make sure you have installed helm and k3s (and it is up and running) as mentioned in the Prerequsites section above.

And also since we are using local repository for storing the Docker image of our application, we need to import that image to the K3S's (This is useful for anyone who cannot get k3s server to work with the --docker flag). Image will be automatically exported from the local repository to the K3S repository with the make deploy command

Deploy the application for customer "A", "B", and "C"

make deploy

Check the deployed resources

k3s kubectl get po,svc,ing -n customer-a
k3s kubectl get po,svc,ing -n customer-b
k3s kubectl get po,svc,ing -n customer-c

By default K3S uses Traefik as the ingress-controller.

There are many other tools out there to satisfy the need of an ingress-controller, but to make things super clean, clear and easier, we are using the Traefik that comes by-default with K3S.

Traefik creates LoadBalancer type Service in the kube-system namepsace And since we are using different Ingress rules for each customer with custom-domains that doesn't exist, we have to map the custom-domains to Traefik's LoadBalancer Service if we need to access our deployments. To access;

Get the EXTERNAL-IP of the Traefik's LoadBalancer type Service

k3s kubectl get svc -n kube-system | grep traefik

traefik          LoadBalancer   10.43.11.175    10.70.1.173   80:32537/TCP,443:30694/TCP   5h8m

Edit the /etc/hosts

sudo vim /etc/hosts

Add the following entry. 10.70.1.173 is the EXTERNAL-IP and DNS names are specific to customers and defined in the Ingress rules (eg: "- host: customer-a.parcellab.com")

10.70.1.173 customer-a.parcellab.com customer-b.parcellab.com customer-c.parcellab.com

Once the entries are added, you can access the each customer application instance as below:

Access the Customer "A" application

curl -kv http://customer-a.parcellab.com

*   Trying 127.0.0.1:80...
* Connected to customer-b.parcellab.com (127.0.0.1) port 80 (#0)
...
< HTTP/1.1 200 OK
...
* Connection #0 to host customer-b.parcellab.com left intact

{"response":"Hi!"}

Access the Customer "B" application

curl -kv http://customer-b.parcellab.com

*   Trying 127.0.0.1:80...
* Connected to customer-b.parcellab.com (127.0.0.1) port 80 (#0)
...
< HTTP/1.1 200 OK
...
* Connection #0 to host customer-b.parcellab.com left intact

{"response":"Dear Sir or Madam!"}

Access the Customer "C" application

curl -kv http://customer-c.parcellab.com

*   Trying 127.0.0.1:80...
* Connected to customer-b.parcellab.com (127.0.0.1) port 80 (#0)
...
< HTTP/1.1 200 OK
...
* Connection #0 to host customer-b.parcellab.com left intact

{"response":"Moin!"}

As mentioned above, the response varies as per the customer

📑 Test API Endpoint

Having an automated testing solution to test the API endpoins helps engineers to fix any bugs faster, reduce testing cost amidst the deployment lifecycle, quicker relases and more.

To test this API endpoint (/), a small test case is provided. For this, framework's in-built FastApi-Test-Client is used.

Exeucte the test case

make test

Above command create 3 different instances (Docker containers, not Pods) of the application for each customer and test the endpoints of each customer. This can be Considered as pre-deployment testing.

Since we are adhering to the Sigle-Tenant approach, we are limited to only test the endpoint for 2 positive (status_code, response) and 1 negative case (invalid customer) Unlike in a multi-tenancy mode, we could write variety of test cases for different scenarios for different users

Note: You can also execute into the docker container and test the api endpoint like below:

// execute into the container
docker exec -it hello-app-A bash

// test
pytest

🔮 Future Improvements

There are several areas that could be improved in the future:

Extend the application to Maximize robustness with fast startup and graceful shutdown
Enhance the unit and integration test cases
Consider using a managed Kubernetes service such as EKS, AKS or GKE for ease of maintenance and scaling
Implement continuous integration and delivery pipelines for all applications using CI/CD tools such GitGub actions, Gitlab CI/CD or Jenkins
Explore the use of GitOps for managing the infrastructure and applications
Add monitoring and alerting for the entire architecture

🧹 Clean

To clean the resources you created, run;

make clean

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Repository files navigation

Challenge

👨‍💻 TLDR;

🤔 Assumptions Made

📂 Directory Hierarchy

🏠 Application Architecture and the Solution

Solution:

🚀 Build Deploy and Run

4.1 Prerequisites

4.2 Run the API

4.2.1 Get the most out of `Makefile`

📑 Test API Endpoint

🔮 Future Improvements

🧹 Clean

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Name		Name	Last commit message	Last commit date
Latest commit History 90 Commits
.github/workflows		.github/workflows
app-chart		app-chart
app		app
.gitignore		.gitignore
Makefile		Makefile
README.md		README.md
architecture_diagram.png		architecture_diagram.png

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Challenge

👨‍💻 TLDR;

🤔 Assumptions Made

📂 Directory Hierarchy

🏠 Application Architecture and the Solution

Solution:

🚀 Build Deploy and Run

4.1 Prerequisites

4.2 Run the API

4.2.1 Get the most out of Makefile

📑 Test API Endpoint

🔮 Future Improvements

🧹 Clean

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4.2.1 Get the most out of `Makefile`

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