This page explains how to generate realistic, consistent mock data for local development or testing using our Django backend.

We use a custom Django management command:

python manage.py create_mock_data

It will populate all major database tables with randomly generated data using the Faker library.

• ✅ username and email are always unique (via Faker.unique)
• ✅ (user_id, achievement_id) and (user_id, challenge_id) pairs are deduplicated to respect DB constraints
• ✅ Timezone-aware datetimes for all DateTimeFields
• ✅ No assumptions about prior database state — safe to rerun after a flush

To change dataset size or balance:

1. Open api/management/commands/create_mock_data.py
2. Edit the parameters in the generate_mock_data() function call inside the handle() method.

Example:

generate_mock_data(
    num_users=100,
    num_posts=1000,
    num_max_comments_per_post=7,
    num_tips=10,
    num_max_wastes_per_user=50,
    num_achievements=10,
    num_challenges=200,
    max_challenge_target_amount=50,
    num_reports=50,
)

From the host:

docker exec -it backend-web-1 bash
python manage.py migrate
python manage.py create_mock_data

• If you re-run this without flushing the DB, you may hit uniqueness errors. Either drop/reset the DB or ensure uniqueness manually.
• You can adapt the script to generate edge cases, simulate real user behavior, or run partial generation.