Udacity’s Ensuring Quality Releases: A Local Setup Guide

Overview

In our previous article, we discussed some of the shortcomings of Udacity's Ensuring Quality Releases project.

This article will introduce the first step to address those shortcomings. Turing Taqueria is an application written to be used at each step of Udacity's Ensure Quality Releases project.

Before diving into the Azure-specific tasks, we must familiarize ourselves with Turing Taqueria locally.

Besides, we can write the tests against the locally running application, which helps us optimize the available lab time, ensuring we can use it for Azure-related activities.

Let's start by running Turing Taqueria on our local machine.

Forking and Cloning the Turing Taqueria Repository

Begin by creating your copy of Turing Taqueria:

Fork the Repository:

• Visit Turing Taqueria on GitHub.
• Click the 'Fork' button to create a fork under your GitHub account.

Clone Your Fork:

• Navigate to your forked repository on GitHub.
• Click the 'Code' button and copy the URL for cloning.
• Clone the repository to your local machine using the following:

$ git clone 
        

Now you have a local copy of the Turing Taqueria project, ready for Dockerization and running it locally.

Creating the Docker Image for Turing Taqueria

To begin, could you clone the Turing Taqueria repository from GitHub? Inside the project, you'll find a Dockerfile designed for setting up the environment. Here's how to create your Docker image:

• Clone the Repository:
  git clone https://github.com/TheTuringTacoTales/TuringTaqueria-FastApi.git
• Navigate to the Project Directory:
  cd TuringTaqueria-FastApi
• Build the Docker Image:
  Run docker build -t turingtaqueria . This command reads the Dockerfile in the current directory, building a Docker image named turingtaqueria.

This Docker image encapsulates all the dependencies and setups required, streamlining the initial run of the application on your local machine.

Running Turing Taqueria Locally with Docker

With your fork of Turing Taqueria cloned, let's run it locally using Docker:

• Install Docker:
  If you don't have Docker installed, download it from the official website. Follow the installation guide for your operating system.
• Navigate to Your Local Repository:
  Open a terminal and navigate to the cloned Turing Taqueria directory.
• Build the Docker Image:
  Next, we'll run the command to create a Docker image.

$ docker build -t turingtaqueria .
        

• Run the Application:
  Next, we'll start the application to run a container using the image we created in the previous step and mapping port 8000 on our host machine to port 80 in the container, which is the port that the application uses.

$ docker run -d -p 8000:80 turingtaqueria
        

• Access the Application:
  Now, we can browse to http://localhost:8000 and see our FastAPI application running.
• Explore API Documentation:
  Access http://localhost:8000/docs to view the Swagger UI, where we can interact with your API endpoints.

This setup allows you to run and interact with Turing Taqueria in a local Docker environment, providing a solid base for your development and testing.

Understanding the UI Endpoints in Turing Taqueria

Turing Taqueria includes user interface (UI) endpoints alongside its REST API. The backend API and the UI are combined in a single project for simplicity. However, it's common to separate these aspects in real-world scenarios, often hosting them on different servers.

We can use the REST endpoints to practice writing Data Validation tests using Postman. The endpoints /login, /restaurant, and /show-cart serve HTML pages rather than JSON responses, providing a minimal viable front end we can use to practice writing Selenium tests.

Navigate to the /login page on your local Turing Taqueria instance and use the credentials:

• Username: admin
• Password: password

These credentials will grant you access to the application's features. Explore the various functionalities, interact with the API endpoints via the UI, and experience how the application handles both backend processes and frontend interactions.

To manipulate the API using the Swagger UI, use the authenticate button in the upper right corner.

Conclusion

In conclusion, Turing Taqueria is a helpful alternative for use with Udacity's Ensuring Quality Releases project. Integrating backend and frontend functionalities in a single FastAPI application minimizes the overhead and allows us to work within the limits of the Azure Lab provided by Udacity.

We gain practical experience deploying and interacting with RESTful APIs and web interfaces by running them locally via Docker. And now, we can use it to write the Postman, Selenium, and JMeter tests locally, allowing us to optimize our lab time.

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