Workout API

An unnecessarily complicated API layer and set of database migrations for my workout tracker application. Can be accessed at this link: https://workout-api-k8e3.onrender.com/v1

Tech Overview

• Built with Python, FastAPI, and SQLAlchemy/Alembic. Tested with Pytest.
• API hosted with Render.
• Database hosted with Neon.
• Secrets managed in Infisical.

Running the code

This project is built with Poetry and intended to be run that way. To run it locally in development you'll need to

• Install Poetry
• Install the psql CLI
  • On Mac this can be done via Homebrew: brew install libpq and then add the installed bin folder to your path

Then run the following code:

# Install the project and dependencies
poetry install
# Start a new shell within the poetry environment
poetry shell
# At this point, you may want to set some dummy environment variables for development
. ./local-env.sh
# Start the server in dev mode
make devserve

Database Management

There are really just two commands that matter for managing the staging and prod databases. Note that the first one uses the Python environment, so you should run poetry shell before kicking these off. Both rely on the $DATABASE_URL environment variable.

# Run all new migrations.
alembic upgrade head
# Drop and replace all views with current definitions.
./db/run_views.sh

To run them easily against the staging/prod environments, I just use infisical run:

infisical run --env prod -- alembic upgrade head
infisical run --env prod -- ./db/run_views.sh

These two commands are enough to bring a database up to schema parity. They ignore seeds though, which are mentioned below.

Seeds

I have a few "seed" data records in this repo because in early development I needed some data. You can run them with:

./db/run_seeds.sh

It's possible to run them in staging/prod too, but that should only be done once (the records have hardcoded IDs so new ones will collide) and really isn't even necessary.

infisical run --env prod -- ./db/run_seeds.sh

Database Schema

The original model is here: link.