Example of FastAPI integration supported by almighty Pydantic 2.0 with SQLAlchemy ORM and PostgreSQL16 connected via fastest Database Client Library for python/asyncio asyncpg.

Beside of using latest and greatest version of SQLAlchemy with it robustness, powerfulness and speed of asyncpg there is FastAPI (modern, fast (high-performance), web framework for building APIs with Python 3.8+ based on standard Python type hints.) already reviewed on thoughtworks and noted in Python Developers Survey 2021 Results as the fifth official annual Python Developers Survey, conducted as a collaborative effort between the Python Software Foundation and JetBrains.

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To build , run and test and more ... use magic of make help to play with this project.

1. make docker-build
2. make docker-up
3. make docker-apply-db-migrations
4. make docker-feed-db

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It took me a while to find nice data set. Hope works of Shakespeare as example will be able to cover first part with read only declarative base configuration and all type of funny selects :) Data set is coming form https://github.com/catherinedevlin/opensourceshakespeare Next models were generated with https://github.com/agronholm/sqlacodegen

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To deliver better user(developer) experience when watching logs with tons of information from few emitters (which are really needy on development stage) project is using rich library. Event with rich superpowers reading logs is not easy. Found rich really nice - but it took time to learn how to integrate it as logger object properly and keep it as singleton.

To address below needs:

• it is hard to find what I am looking for even with glasses on.
• don’t want to hire ELK to be able to use logs.
• want to move fast enough with debugging.

Below steps were done to integrate rich into project.

1. Configure emitters with config.ini
2. Eliminate duplicates i.e. sqlalchemy echo by separate handlers
3. Keep logger as singleton pattern to avoid multiple instances
4. add uvicorn parameter --log-config config.ini

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Setup user authentication with JWT and Redis as token storage.

pyenv install 3.12 && pyenv local 3.12

poetry install

Hope you enjoy it.

Power of Polars Library in data manipulation and analysis. It uses the polars library to read the Excel data into a DataFrame by passing the bytes to the pl.read_excel() function - https://docs.pola.rs/py-polars/html/reference/api/polars.read_excel.html In pl.read_excel() “calamine” engine can be used for reading all major types of Excel Workbook (.xlsx, .xlsb, .xls) and is dramatically faster than the other options, using the fastexcel module to bind calamine.

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Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!

• Open Source Shakespeare Dataset
• SQL Code Generator
• Passlib - password hashing library for Python
• Polars - fast DataFrame library for Rust and Python

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• [long time ago...] it was a long time ago in galaxy far far away...
• [JUN 4 2022] alembic migrations added to project
• [JUN 6 2022] initial dataset for shakespeare models
• [OCT 3 2022] poetry added to project
• [NOV 12 2022] ruff implemented to project as linting tool
• [FEB 14 2023] bump project to Python 3.11
• [APR 10 2023] implement logging with rich
• [APR 28 2023] Rainbow logs with rich 🌈
• [JUL 7 2023] migrate to pydantic 2.0 ⏩
• [JUL 25 2023] add user authentication with JWT and Redis as token storage 🔒 🔑
• [SEP 2 2023] add passlib and bcrypt for password hashing 🔒 🔑
• [OCT 21 2023] refactor shakespeare models to use sqlalchemy 2.0 ⏩
• [FEB 1 2024] bump project to Python 3.12 ⏩
• [MAR 15 2024] add polars and calamine to project 😻

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