![]() POST: Sends data to the server for processing. When a client submits a GET request, it is requesting data from the server. The following are the most often used API development methods: HTTP (Hypertext Transfer Protocol) methods specify the action to be taken on a resource. How to Containerize the Application with Docker.Automated API Endpoint Testing with PyTest.How to Parse and Insert Course Data into MongoDB.This will greatly simplify deployment, maintenance, and installation. Once we've tested everything, we'll containerize the application using Docker. This tutorial focuses on building a scalable, efficient, and user-friendly API. List detailed information about certain chaptersĪdditionally, for each course, we will aggregate all reviews, providing visitors with relevant information regarding course popularity and quality.The interesting aspect is creating several endpoints with FastAPI. Once the data has been loaded, our API code may connect to this database to allow for simple data retrieval. This data will be stored in the MongoDB instance. We'll begin with a script that reads the course information from courses.json. Lastly, this answer data must be returned in a standard format (JSON). The course information must then be retrieved from MongoDB depending on the request. The goal is to create a system that processes course-related queries. It covers fundamental project concepts as well as Python, MongoDB, and NoSQL databases.īy the end, this useful backend system will manage chapter details, course information, and user ratings, serving as the basis for a complex and rewarding project. This walkthrough shows how to set up a development environment, build a FastAPI backend, integrate MongoDB, define API endpoints, add chapter rating functionality, and compute aggregate course ratings. We will implement functionality to record and retrieve chapter ratings.Ĭourse Aggregated Rating: The system will calculate and display the aggregated rating for each course based on the ratings of its chapters. MongoDB's flexible schema allows us to store data in JSON-like documents, making it suitable for this project.Ĭourse Information: Users will be able to view various course details, such as course name, description, instructor, etc.Ĭhapter Details: The system will provide information about the chapters in a course, including chapter names, descriptions, and any other relevant data.Ĭhapter Rating: Users will have the ability to rate individual chapters. MongoDB Database: A NoSQL database to store course information. FastAPI is chosen for its ease of use, performance, and intuitive design. What We'll BuildįastAPI Backend: It will serve as the interface for handling API requests and responses. Familiarity with MongoDB, Docker, and PyTest is not required since I will be highlighting everything you need to know for the scope of this project. The project is designed for Python developers with basic programming knowledge and some NoSQL knowledge. The system will allow users to access course details, view chapters, rate individual chapters, and aggregate ratings. In this walkthrough project, we'll create a Python backend system using FastAPI, a fast web framework, and a MongoDB database for course information storage and retrieval. The best part is that you will not only be writing APIs but also testing and containerizing the app. APIs are essential in modern software development as they are an application's backend architecture.Īfter reading this quick start guide, you will be able to develop a course administration API using FastAPI and MongoDB. Don't worry if you're new to API programming – we'll start at the beginning.Īn API (Application Programming Interface) connects several software programs allowing them to converse and exchange information. See the MongoDB documentation for more information.Welcome to the world of FastAPI, a sleek and high-performance web framework for constructing Python APIs. Therefore, $sortB圜ount simply counts the numbers from the document provided to it, which included just one dog. This time only one dog is in the document passed to $sortB圜ount, because the first pipeline stage removed dogs over a certain weight. Here’s another example, but with added filtering criteria. Suppose we have a collection called pets with the following documents: ![]() The documents are sorted by count in descending order. a count field containing the number of documents belonging to that grouping.an _id field containing the distinct grouping value, and.In MongoDB the $sortB圜ount aggregation pipeline stage groups incoming documents based on the value of a specified expression, then computes the count of documents in each distinct group.Įach group is output in its own document, which consists of two fields:
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