The whole point of this project is to showcase the concepts of SWE and DevOps that I have learned so far. This project features CI/CD workflows and automations, unittesting, and containerization. Git & Github were used as the VCS, and a Docker Hub image is published for container deployment. I dub this as "FizzBuzz-as-a-Service".
This program aims to create a FastAPI-based microservice that solves the classic FizzBuzz programming problem. The FizzBuzz problem is a common coding challenge often used in interviews to evaluate a candidate's basic programming skills. The task is to write a program that prints numbers from 1 to n; however, for multiples of 3, it should print "Fizz" instead of the number, for multiples of 5, it should print "Buzz," and for numbers that are multiples of both 3 and 5, it should print "FizzBuzz". The FizzBuzz Microservice API provides a simple HTTP-based API to solve the FizzBuzz problem. It allows users to make requests to the microservice and receive the FizzBuzz sequence as a response. This microservice can be easily integrated into other applications or used for testing and learning purposes.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Before running the FizzBuzz Microservice API, make sure you have the following prerequisites installed:
- Python 3.9 or above
- Poetry dependency manager
- Just command runner
To get started with this project, clone the repository to your local machine and install the required dependencies.
git clone https://github.com/jgfranco17/fizzbuzz-api.git
cd fizzbuzz-api
poetry install
poetry shellTo run the API server in dev mode, simply execute either of the following commands:
# Default Python execution
python app.py --port <PORT>
# Or use the Justfile command
just run-debugTo run the microservice in a container, the package comes with both a Dockerfile and a Compose YAML configuration. Run either of the following to get the API launched in a container; by default, the API will be set to listen on port 5050 for the Compose.
# Plain Docker build
docker build -t fizzbuzz-api .
docker run -p 5050:5050 fizzbuzz-api
# Docker-Compose build
docker compose upThe Docker image is also available on Docker Hub
docker pull jgfranco17/fizzbuzz-api:latestOnce the microservice is launched, the API is now reachable. To get a FizzBuzz sequence, simply send a GET request to the server, with the number as a parameter.
curl http://localhost:<PORT>/fizzbuzz?number=<number>In order to run diagnostics and unittests, first install the testing dependencies. This will allow you to utilize the full capacity of the test modules we have built. To run the full test suite, run the Justfile command as follows:
just pytestYou can run these tests using the PyTest CLI tool. To run all tests in the directory containing the test
files, navigate to the directory and enter pytest in the command line; for added verbosity, add the -vv flag after. To run a specific test
file, enter pytest <filename>.
# Run all tests in the testing module with verbose detail
pytest -vv
# Or, run a specific test file
cd ./tests
pytest -v <filename>.pyThis will run the specified test module and generates a detailed result report in the terminal.
Behave is a Python-based BDD framework that allows you to write tests in a natural language style using Gherkin syntax. Gherkin uses a simple, human-readable format that is easy for both technical and non-technical stakeholders to understand. Behave translates these plain-text scenarios into executable code, allowing teams to collaborate on defining and implementing software features.
To run the full Behave suite, run the Justfile command as follows:
just behaveThis will run the specified test module and generates a detailed result report in the terminal.
Behavior-Driven Development (BDD) is a software development methodology that focuses on collaboration among developers, QA, and non-technical stakeholders. BDD aims to enhance communication and understanding by using natural language descriptions of software behaviors and features. BDD allows teams to define the expected behavior of the software before implementation; clear specifications help developers focus on delivering features that meet business requirements. Non-technical team members can also easily understand and contribute to the specification process.
For example, below is a demonstration of a simple test case for pinging the /healthz endpoint.
Scenario: Access health-check endpoint
Given I start the API
When I send a request to "/healthz"
Then the response is returned with status code 200Using Gherkin allows us to run simple test cases without diving too deep into the technicals. As long as the test-writer is familiarized with the basic test steps that can be used, there is no need to use more complex testing frameworks for routine tests. Feel free to write your own Gherkin steps for this project!
Load testing is a type of performance testing that evaluates how a system behaves under a specific load. The primary goal is to ensure the system can handle expected user traffic and identify potential bottlenecks or performance issues before they affect end users. During load testing, the system is subjected to increasing numbers of simultaneous users or transactions to determine its capacity, stability, and scalability.
For this project, we use Locust, an open-source load testing tool that is highly flexible and scalable, making it a popular choice for load testing web applications, APIs, and other services.