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getting_started.md

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Getting Started

Prerequisites

Before running the APEx Dispatch API locally, ensure the following prerequisites are met:

  • A working Python environment to install and run the API dependencies.
  • A PostgreSQL database to store job-related information.

Running the API on your local environment

Follow these steps to set up and run the API in your local development environment:

Install dependencies

Use pip to install the required Python packages:

pip install -r requirements.txt

Set up the database

To set up a PostgreSQL database locally, you can use Docker for convenience and persistence.

(Optional) Create a Docker Volume

This step ensures your PostgreSQL data is stored persistently:

docker volume create local-postgres-data

To view the physical location of the volume on your host machine:

docker volume inspect local-postgres-data

Start a PostgreSQL Container

Run the following command to start a PostgreSQL instance linked to your volume:

docker run -d --name postgres -p 5432:5432 \
    -e POSTGRES_USER=testuser \
    -e POSTGRES_PASSWORD=secret \
    -e POSTGRES_DB=testdb \
    -v local-postgres-data:/var/lib/docker/volumes/local-postgres-data \
    postgres:latest

Configure the environment

Create a .env file in the root directory of the project and set the necessary environment variables as described in the Environment Configuration documentation.

Apply Database Migrations

Ensure your database schema is up-to-date by running:

alembic upgrade head

Running the API

To start the API, open a new terminal and execute the following:

uvicorn app.main:app --reload

The API will be available at:

Running Tests

Testing is essential to ensure stability and prevent regression issues from affecting the functionality of the API. This project includes a comprehensive suite of tests to validate its core features and maintain code quality.

Unit Testing

The repository contains an extensive collection of unit tests that cover the main components and functionalities of the codebase. To execute the test suite, run:

pytest

This will automatically discover and run all tests located in the designated test directories.

Linting

To maintain consistent code quality and enforce best practices, the project uses flake8 for linting and mypy for static type checking. You can run these tools manually with the following commands:

flake8 app tests
mypy app

These checks help identify potential issues early, such as syntax errors, unused imports, and type mismatches, contributing to a more robust and maintainable codebase.