![]() Flask Beginner Level Difficulty Part 1: Hello World Part 2: URL Path Parameters & Type Hints Part 3: Query Parameters Part 4: Pydantic Schemas & Data Validation Part 5: Basic Error Handling Part 6: Jinja Templates Part 6b: Basic FastAPI App Deployment on Linode Intermediate Level Difficulty Part 7: Setting up a Database with SQLAlchemy and its ORM Part 8: Production app structure and API versioning Part 9: Creating High Performance Asynchronous Logic via async def and await Part 10: Authentication via JWT Part 11: Dependency Injection and FastAPI Depends Part 12: Setting Up A React Frontend Part 13: Using Docker, Uvicorn and Gunicorn to Deploy Our App to Heroku Part 14: Using Docker and Uvicorn to Deploy Our App to IaaS (Coming soon) Part 15: Exploring the Open Source Starlette Toolbox - GraphQL (Coming soon) Part 16: Alternative Backend/Python Framework Comparisons (i.e. Project github repo directory for this part of the tutorial Tutorial Series Contents Optional Preamble: FastAPI vs. The series is designed to be followed in order, but if you already know FastAPI you can jump to the relevant part. Each post gradually adds more complex functionality, showcasing the capabilities of FastAPI, ending with a realistic, production-ready API. Tutorial where we will build a cooking recipe API. + Information to highlight.Welcome to the Ultimate FastAPI tutorial series. Once the mandatory files are structured, the deployment is done in Railway dashboard. Procfile configuration in the Procfile file, and version of python to be used is specified in runtime.txt. It contains the FastAPI instantiated class object -> app.įor deployment in Railway, the explicit requirements were established in requirements.txt. Although we only create functions for reading data, in the future it can be used for creating, updating and deleting data. In this file we create reusable functions that interact with Database. crud.py: Create, Read, Update and Delete.schemas.py: Pydantic models (schemas in this project) is to perform data validation, conversion, and documentation classes and instances.Here, models is the way SQLAlchemy represents tables. models.py: SQLAlchemy models are built so they can interact with the Database tables.database.py: Here we create the link between SQL Database and Python libraries/frameworks.3, 4, 5 -> Create connection with Python libraries (SQLAlchemy, Pydantic) | Model & Schema development in SQLAlchemy | main file structuringįor these procedural steps FastAPI SQL Relational Database documentation was extremely helpful. There, primary keys and relationships among tables were made. Once the datasets have been exported, the Database is created in SQLite for which DB Browser interface was used. jsons were exported as csv and meet the following load requirements: FIELDS TERMINATED BY ',' ENCLOSED BY '"' ESCAPED BY '' LINES TERMINATED BY '\r\n' IGNORE 1 ROWS 2. ![]() In this first process, data exploration was carried out, null data, repeated data and data that do not constitute the easilyĪccessible data types were searched for. ![]() The dataset consists of all information on the Formula 1 from 1950 till the latest 2021 season. The main dataset is a version of Kaggle Formula 1 World Championship dataset containing the following tables:įormula One is the highest class of international racing for open-wheel single-seater formula racing cars sanctioned by the Fédération Internationale de l'Automobile (FIA).
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