Try FastAPI Test Driven Development

install python 3.x on windows from python.org and add it to the path. verify the installation using the command prompt and python --version to confirm python 3.12.1.

Learn to install Python on a Mac using Python.org or Homebrew, complete the installation wizard, and verify with python3 --version to confirm Python 3.12.1 is installed.

Install Visual Studio Code on Windows, set up a Python project, and customize settings such as zoom, terminal font, and auto save. Learn to manage profiles and sync settings.

Install and configure Visual Studio Code on Mac, including Apple Silicon vs Intel options, adjust zoom and autosave, set terminal font size, then create a project folder and run hello.py.

Master Python virtual environments to create isolated projects with separate dependencies and versions, enabling reproducible setups via requirements.txt for Django projects.

Learn to create a Python virtual environment on Windows, activate it in VS Code, install packages with pip, and resolve script execution policy during setup.

Learn to create and activate a macOS virtual environment using python3 -m venv, explore the venv directories, and activate in terminal to manage project dependencies.

Emphasizes careful planning, thorough analysis, and design before coding, balancing structure and flexibility to save time, reduce errors, and foster mastery of core concepts.

Access the centralized docs folder to view functional specs and installation guides; use commands for Python, SQLAlchemy, Docker, Pytest, and Uvicorn, with markdown docs readable in Visual Studio Code.

Set up a virtual environment, install FastAPI and uvicorn, create a main.py with a FastAPI app and routes like / and /hello, and run with uvicorn.

Learn to manage Python project dependencies with pip, use pip freeze to capture installed packages, and recreate virtual environments with a requirements.txt file.

Learn to set up Python linting and formatting in VS Code with Ruff and the Python extension, enabling format on save and 88-character line guidelines for cleaner, collaborative code.

Use Docker containers to host a Postgres database for development and testing, with a uvicorn server outside Docker.

Install docker desktop, explore containers and images, run the Adminer container, verify installation with docker --version, and note optional VS Code extension for management.

Learn to dockerize Postgres with docker compose by defining a dev db service, mapping host ports to container ports, and configuring environment variables for an inventory database.

Install and use Adminer to manage a Postgres database via a lightweight web interface. Access the inventory database at 127.0.0.1:8080 and log in as Postgres to explore the public schema.

Learn how environment variables decouple configuration from code, store sensitive info securely, reference os-level variables in your Python apps with python-dotenv, and align with the 12-factor app principles.

Install python-dotenv, create a .env file for key=value environment variables, update requirements, and set up an app folder with an initialization file that loads env vars on startup using load_dotenv.

Learn how to load environment variables from a dot env file into a docker compose setup, securely handling Postgres username and password and avoiding exposing credentials in version control.

Build a docker-based postgres setup script that runs on startup to create the inventory database and install the uuid extension, using a mounted scripts directory via docker-entrypoint-initdb.d.

Learn how SQLAlchemy works as a Python SQL toolkit and ORM to simplify relational database interactions in FastAPI projects, translating Python models to SQL and back.

Configure a FastAPI app to connect to a Docker Postgres database using SQLAlchemy engine, declarative base, and a get_db session, via environment variables.

Install Alembic, initialize migrations, and configure a version-controlled setup with SQLAlchemy to track, apply, and rollback database schema changes.

Set up a test database workflow by building a Docker container with Postgres, migrate the tables with Olympic migrations, seed fixtures, run tests, and tear down the container.

Install and configure Pytest and Olympic integration, enable test discovery by naming test files as test_*.py and test functions starting with test_, and set up a conftest.py for pretest initialization.

Create a Docker test database programmatically using a Python Docker client, manage container lifecycle with pytest fixtures, and ensure readiness and cleanup for reliable tests.

Set up a postgres test database with a startup script that creates the database, installs extensions, and wires a scripts directory into the docker entrypoint via volumes.

Learn to run and connect docker containers for a FastAPI project by placing them on a shared bridge network, enabling Adminer access to dev and test databases.

Configure Alembic for a multi-database setup by creating a separate testdb config and URL, building a test engine and connection, and upgrading migrations to migrate tables into the test database.

Learn to stop the test database container after tests and consider removing it later, using fixtures to pass the container back into functions and deferring removal for future features.

Learn the structural testing phase for database design in a test-driven workflow: write tests first, then build and verify tables, columns, indexes, constraints, and relationships to ensure schema correctness.

Explore the project database ERD, covering primary keys, not null constraints, autoincrement integers, and per-table requirements, with downloadable PDFs from Lucid App and tutorials for each table.

Model the category table with not null fields, autoincrement primary key, self-referential foreign key for hierarchy, a unique slug, defaults, and a nullable parent field.

Explore structural testing for databases by validating schemas, tables, columns, constraints, indexes, and relationships, and plan per-table tests from table-level validation to database server validation.

Practice test-driven development by writing tests first to define the category table, then implement and migrate it, using pytest, SQLAlchemy inspector, and a dockerized testing database.

Validate the category table schema by checking columns and data types with a db inspector and pytest to ensure data integrity and compatibility.

Verify nullable and not null constraints for the category table using a test-driven approach with SQLAlchemy, inspecting column metadata to ensure proper data integrity.

Implement check constraints named name length check and slug length check on the category table to prevent empty name or slug values at the database level and in the application.

Enforce database level default values for category table columns such as is active and level, using server defaults and the default option, and validate them with tests.

Verify and enforce string column lengths in the category table by testing name and slug lengths, and explain why 100 and 120 characters support data validation and storage optimization.

enforce unique constraints in the category table by making the slug unique and ensuring name and level are unique together, illustrating database constraints in a hierarchical category structure.

Leverage pytest marks to tag tests with metadata like model and structure for targeted runs of groups such as smoke, regression, and integration, and automate marker assignment from test names.

Analyze the product table requirements and plan tests for unique name and slug. Explain not null category_id, foreign keys, and one-to-many category to product plus one-to-one product to category relationships.

Develop seven test cases for the product table using test driven development. Validate table migration, column presence, nullable fields, and constraints such as unique and check rules.

Implement and test a product table with SQLAlchemy, featuring UUID primary key, name, slug, description, created_at, updated_at with server defaults, stock status enum, and unique constraints on name and slug.

The lecture explains using a product line table to store product variations linked to a product, with fields like SKU, stock, active, and order number for front-end defaults.

Develop tests for the product line table by validating existence, columns, data types, defaults, and constraints, including a numeric price with five digits and two decimals, and foreign key checks.

Implement the product line table with price, sku, stock quantity, active flag, order, weight, and timestamps, plus a foreign key to product and a unique sku.

Develop tests for the product image table. Link product line to images; enforce non-empty alt text, url, image field; ensure unique product line id and order.

Develop a product image table test case by creating a new test file, defining fields (id, alt text, url, order, product line id) and enforcing constraints and length checks.

Implement the product image table in the Try FastAPI test driven development course, defining id, alternative_text, and url with length checks, plus a foreign key to product_line and related constraints.

Analyze and implement the seasonal events table with fields id, name, start date, and end date; adapt product table tests to validate foreign keys for category id and seasonal event.

Demonstrates test driven development for a seasonal events table, detailing start and end date time columns, a name string up to 100 characters, and a unique name constraint.

Define the seasonal event table with id, start date, end date, and a 100-character name, enforce a unique name, and migrate while updating foreign keys.

Explore the attribute table with fields id, name, and description, using normalization to avoid duplicate attributes. Learn to create tests and manually define and name unique constraints.

Adapt seasonal event tests to build attribute table test cases, validating id, name, and nullable description fields, and enforcing length 100 and unique attribute name constraints in a test-driven setup.

Implement the attributes table based on the generated tests, defining id, name (string 100) and nullable description, enforce a unique name and length checks, and update migrations and tests accordingly.

Analyze the product type table and its many-to-many link to products, compare it with the category table, and define attributes, name length, and level-based unique constraints.

Develop tests for the product type table by mirroring the category table, verifying creation in the database and validating fields like id, name, level, and parent_id.

Implement the product type table by modeling it after category table, including id, name, level, and a parent referencing the product type table, enforcing constraints and unique keys via tests.

Explore the attribute value table, linking attribute names to unique values like color red or blue, and enforce a per-attribute unique constraint to prevent duplicates and link to product lines.

Build attribute value table tests by cloning product type tests, configure foreign keys, enforce non-null fields, and apply check and unique constraints, then implement the corresponding models.

Implement the attribute value table in the models with a foreign key to the attribute, a name length check, and a unique constraint on attribute id.

Explore building many-to-many relationship link tables, including product line attribute value and product type links, with an id and two foreign keys, enabling custom fields for relationships.

Develop test cases for the product line attribute value table, validating fields, two foreign keys, and the unique constraint while refining naming and migration checks.

Implement the product line attribute value table by defining foreign keys to the attribute value and product line, adding a unique constraint, and debugging migrations to pass tests.

Design a product and product type link table with two foreign keys and a primary key to connect the product and product type tables, enforcing a unique product_id and product_type_id.

Develop and test the product and product type table, renaming and wiring primary keys, foreign keys, and unique constraints, while validating nullable fields and table relationships.

Implement the product product type table by creating a link table with product id and product type id, defining constraints, and running migrations and tests.

Explore Python's logging module and how to implement and configure logging in a FastAPI app, including log levels, handlers, and writing logs to console and files for development and production.

Define the category api endpoints and data contracts in a test-driven approach. Build get all categories, get by slug, post, put, and delete operations with schemas for serialization and deserialization.

Learn how unit testing isolates the smallest testable parts of code, uses mocking and stubbing, and employs monkey patching to test endpoints and improve reliability before integration testing.

Plan and implement unit tests for a FastAPI category endpoint, validating request data, enforcing name and slug constraints, and saving new categories to the database.

Define data schemas with Pydantic to validate and serialize data between Python objects and JSON, enabling FastAPI endpoints to deserialize client requests and serialize responses.

Master fastapi test-driven development by building and validating pydantic schemas with unit tests, focusing on category input and output and data validation.

Unit test a FastAPI endpoint to insert a new category, validating input and unique constraints, while using fixtures, factories, and mocking to simulate database interactions.

Organize API endpoints with FastAPI routers by creating a category router, registering it in main with a prefix and tags to keep the code structured and documented.

Learn to create a post endpoint in fastapi for adding new categories, validate input with category create, save to the database via a session, and return a 201 created response.

Explore get, post, put, and delete methods in RESTful API design, and learn how different http methods interact with endpoints to differentiate requests and manage resources.

Develop unit tests for category insertion constraints using pytest parametrize to simulate existing category or slug conflicts, mock database checks, and verify 400 errors and details.

Define an insertion endpoint for categories that enforces uniqueness by slug or by name and level. Use the check existing category utility with SQLAlchemy to prevent duplicates.

Learn how FastAPI handles exceptions during database insertion, distinguishing 400 and 500 errors, and how to implement custom exception handling, logging, and unit tests for category endpoints.

Learn to implement robust exception handling in a category endpoint using try-except blocks, HTTP exceptions, and database rollback, while integrating logging for debugging.

Plan and implement tests for the category endpoint to retrieve all categories, covering successful data retrieval, database errors, and empty responses to ensure correct behavior.

Develop unit tests for the get all categories endpoint, mocking the database to simulate data, empty results, and internal server errors, and validate response status and data against the schema.

Create an endpoint to fetch all categories from the database, returning a list via the category return schema, with try-except error handling, logging, and tests.

Plan and test a FastAPI get endpoint for a single category by slug, validating database queries, success and error responses, and a 200 status.

Develop unit tests for a FastAPI endpoint that retrieves a single category by slug, covering 200 success, 404 not found, and 500 internal server error scenarios.

Implement a get category by slug endpoint that retrieves a single record from the database using a url parameter, handling 404 for not found and 500 for other errors.

Update a single category by id via a put endpoint in a test-driven FastAPI workflow. Plan tests for successful update, not found, and server error.

Develop unit tests for updating a single category via a put endpoint, using a test client and mocks to simulate the database and verify success and 404 and 500 responses.

Develop an endpoint to update a single category by id using put, returning a 201 status with a category model, updating the database and refreshing the result.

Plan and implement a delete single category endpoint using the category id, returning the deleted category's id and name, and build tests for success, not found, and error scenarios.

Develop and validate a unit test for deleting a single record via an endpoint, including status handling. Mock data and status options like 200 and 404 ensure correct behavior.

Implement a delete single category endpoint in fastapi using a database session, with 404 and 500 error handling, a delete response schema, and test-driven development to validate behavior.

Configure multiple loggers to isolate logs by app components, define a dedicated file handler, and use a fully qualified logger name to route logs for scalability, security, and compliance.

Refactor unit tests for FastAPI test-driven development by reducing duplication with fixtures for common mocks, and consider abstraction trade-offs before moving to integration tests.

Explore integration testing by combining units and the database to verify that API endpoints process requests, validate responses, and handle errors, covering component, system, and end-to-end levels.

Explore the integration testing plan to verify end-to-end interactions among the unit, endpoint, and database, ensuring data integrity and correct API behavior when deleting a category and handling 404 errors.

Review and adjust the testing environment to support unit and integration tests using a Docker container and a test database with overrides and per-test isolation.

Learn how to set up an integration test environment for FastAPI with pytest, including fixtures, db session overrides, and container management.

Build an integration test for the create category endpoint with a test database using SQLAlchemy, applying the arrange-act-assert pattern, and verify the response and database state.

Develop integration tests to verify that creating a duplicate category by name and level returns a 400 error, using a real database session and a post to the category endpoint.

Develop an integration test that inserts five categories into the database, calls the category endpoint, and asserts a 200 response with the correct list of inserted categories.

Develop an update category integration test in fastapi, preparing initial data, sending a put request with updated values, and asserting the database reflects the changes.

Demonstrate integration test for deleting a category: create and save a category via a factory, delete with api/category/{id}, verify the response includes id and name and the record is removed.

Apply a test driven development approach with integration-first tests for futures endpoints, then add unit tests only for complex cases, using repeatable patterns from the category table.

Learn how test coverage tools assess which parts of your code are exercised by tests, reveal missing tests, and generate HTML reports to improve your FastAPI project's test suite.

Learn to manually test FastAPI endpoints using the built-in docs (Swagger UI), create and delete categories, and verify 201 responses with IDs and slug queries.