Docker and DevOps

PostgreSQL + Age

1. Start the Database To spin up the database using the age profile, run:

docker compose --profile age up

2. Inspect the database contents by going to http://localhost:8080/?pgsql=age&username=postgres&db=postgres

• password is postgres

3. Stop and Clean Up Because this database setup can leave orphaned containers behind, use the following command to stop services and clean them up:

docker compose down --remove-orphans

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Memgraph + Memlab

1. Start the Database To spin up the database using the mem profile, run:

docker compose --profile mem up

2. Inspect the database contents by going to http://localhost:3000 First time: Manual connect --> New connection --> Memgraph instance --> Fill field "Host" with memgraph --> Connect

After first time: Click Connect now

3. Stop and Clean Up Because this database setup can leave orphaned containers behind, use the following command to stop services and clean them up:

docker compose down --remove-orphans

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Memgraph + postgres at the same time

1. Start the Database To spin up the database using the all profile, run:

docker compose --profile all up

2. Refer to previous section step 2 for UI access Both adminer and memlab are up, so you can use either or both at the same time.

3. Stop and Clean Up Because this database setup can leave orphaned containers behind, use the following command to stop services and clean them up:

docker compose down --remove-orphans

Pre-commit Hooks

1. Installation (One-time setup) Ensure your virtual environment is active, then install and configure the pre-commit hooks:

python -m pip install pre-commit
pre-commit install

2. Committing Changes Standard git commit commands will automatically trigger the hooks. To manually bypass the hooks (e.g., to skip pylint or tests temporarily), use the --no-verify flag:

git commit --no-verify -m "Your commit message"

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Mutation Testing with Mutmut

Note: mutmut requires a passing test suite before you can begin.

1. Run Mutation Tests Running mutmut run against a large codebase is extremely slow. It is highly recommended to target specific modules instead. For example:

mutmut run paths_to_mutate=pyzx/graph/graph_neo4j.py

2. Browse Results Once the tests are complete, you can view the killed and survived mutants, along with additional commands, by running:

mutmut browse

3. Export Statistics To generate a simple JSON file in the mutants folder for an easy overview of your results, use:

mutmut export-cicd-stats