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api

README.md

Description

This repository contains the JSON API and Task Runner components for Prowler, which facilitate a complete backend that interacts with the Prowler SDK and is used by the Prowler UI.

Components

The Prowler API is composed of the following components:

  • The JSON API, which is an API built with Django Rest Framework.
  • The Celery worker, which is responsible for executing the background tasks that are defined in the JSON API.
  • The PostgreSQL database, which is used to store the data.
  • The Valkey database, which is an in-memory database which is used as a message broker for the Celery workers.

Note about Valkey

Valkey is an open source (BSD) high performance key/value datastore.

Valkey exposes a Redis 7.2 compliant API. Any service that exposes the Redis API can be used with Prowler API.

Modify environment variables

Under the root path of the project, you can find a file called .env. This file shows all the environment variables that the project uses. You should review it and set the values for the variables you want to change.

If you don’t set DJANGO_TOKEN_SIGNING_KEY or DJANGO_TOKEN_VERIFYING_KEY, the API will generate them at ~/.config/prowler-api/ with 0600 and 0644 permissions; back up these files to persist identity across redeploys.

Important note: Every Prowler version (or repository branches and tags) could have different variables set in its .env file. Please use the .env file that corresponds with each version.

Local deployment

Keep in mind if you export the .env file to use it with local deployment that you will have to do it within the context of the virtual environment, not before. Otherwise, variables will not be loaded properly.

To do this, you can run:

set -a
source .env

🚀 Production deployment

Docker deployment

This method requires docker and docker compose.

Clone the repository

# HTTPS
git clone https://github.com/prowler-cloud/api.git

# SSH
git clone git@github.com:prowler-cloud/api.git

Build the base image

docker compose --profile prod build

Run the production service

This command will start the Django production server and the Celery worker and also the Valkey and PostgreSQL databases.

docker compose --profile prod up -d

You can access the server in http://localhost:8080.

NOTE: notice how the port is different. When developing using docker, the port will be 8080 to prevent conflicts.

View the Production Server Logs

To view the logs for any component (e.g., Django, Celery worker), you can use the following command with a wildcard. This command will follow logs for any container that matches the specified pattern:

docker logs -f $(docker ps --format "{{.Names}}" | grep 'api-')

## Local deployment

To use this method, you'll need to set up a Python virtual environment (version ">=3.11,<3.13") and keep dependencies updated. Additionally, ensure that `uv` and `docker compose` are installed.

### Clone the repository

```console
# HTTPS
git clone https://github.com/prowler-cloud/api.git

# SSH
git clone git@github.com:prowler-cloud/api.git

Install all dependencies with uv

uv sync

Start the PostgreSQL Database and Valkey

The PostgreSQL database (version 16.3) and Valkey (version 7) are required for the development environment. To make development easier, we have provided a docker-compose file that will start these components for you.

Note: Make sure to use the specified versions, as there are features in our setup that may not be compatible with older versions of PostgreSQL and Valkey.

docker compose up postgres valkey -d

Deploy Django and the Celery worker

Run migrations

For migrations, you need to force the admin database router. Assuming you have the correct environment variables and Python virtual environment, run:

cd src/backend
python manage.py migrate --database admin

Run the Celery worker

cd src/backend
python -m celery -A config.celery worker -l info -E

Run the Django server with Gunicorn

cd src/backend
gunicorn -c config/guniconf.py config.wsgi:application

By default, the Gunicorn server will try to use as many workers as your machine can handle. You can manually change that in the src/backend/config/guniconf.py file.

🧪 Development guide

Local deployment

To use this method, you'll need to set up a Python virtual environment (version ">=3.11,<3.13") and keep dependencies updated. Additionally, ensure that uv and docker compose are installed.

Clone the repository

# HTTPS
git clone https://github.com/prowler-cloud/api.git

# SSH
git clone git@github.com:prowler-cloud/api.git

Start the PostgreSQL Database and Valkey

The PostgreSQL database (version 16.3) and Valkey (version 7) are required for the development environment. To make development easier, we have provided a docker-compose file that will start these components for you.

Note: Make sure to use the specified versions, as there are features in our setup that may not be compatible with older versions of PostgreSQL and Valkey.

docker compose up postgres valkey -d

Install the Python dependencies

You must have uv installed

uv sync

Apply migrations

For migrations, you need to force the admin database router. Assuming you have the correct environment variables and Python virtual environment, run:

cd src/backend
python manage.py migrate --database admin

Run the Django development server

cd src/backend
python manage.py runserver

You can access the server in http://localhost:8000. All changes in the code will be automatically reloaded in the server.

Run the Celery worker

python -m celery -A config.celery worker -l info -E

The Celery worker does not detect and reload changes in the code, so you need to restart it manually when you make changes.

Makefile-Assisted Local Deployment

This method is an additional local development workflow. It does not replace the manual local deployment or the Docker deployment described in this guide.

PostgreSQL, Valkey, and Neo4j run with Docker Compose, while Django and the Celery worker run natively through uv. Additionally, this workflow creates a tmux session with panes for the API, worker, and PostgreSQL logs.

Before using this method, ensure docker compose, tmux, and uv are installed.

This workflow is designed for macOS and should also work on Linux when Docker, tmux, and uv are available. Windows requires script changes before it can be supported.

From the repository root, run:

make dev

The API will be available at:

http://localhost:8080/api/v1

Use these commands to manage the local stack:

make dev-setup   # Bootstrap dependencies, migrations, and fixtures
make dev-attach  # Attach to the tmux session
make dev-launch  # Start the stack on fixed ports and attach
make dev-stop    # Stop the tmux session and containers
make dev-clean   # Remove stopped development containers
make dev-wipe    # Stop everything and delete local development data
make dev-status  # Show development container status

This workflow does not start the UI. Start it separately from the ui/ directory when needed.

Docker deployment

This method requires docker and docker compose.

Clone the repository

# HTTPS
git clone https://github.com/prowler-cloud/api.git

# SSH
git clone git@github.com:prowler-cloud/api.git

Build the base image

docker compose --profile dev build

Run the development service

This command will start the Django development server and the Celery worker and also the Valkey and PostgreSQL databases.

docker compose --profile dev up -d

You can access the server in http://localhost:8080. All changes in the code will be automatically reloaded in the server.

NOTE: notice how the port is different. When developing using docker, the port will be 8080 to prevent conflicts.

View the development server logs

To view the logs for any component (e.g., Django, Celery worker), you can use the following command with a wildcard. This command will follow logs for any container that matches the specified pattern:

docker logs -f $(docker ps --format "{{.Names}}" | grep 'api-')

Applying migrations

For migrations, you need to force the admin database router. Assuming you have the correct environment variables and Python virtual environment, run:

cd src/backend
uv run python manage.py migrate --database admin

Apply fixtures

Fixtures are used to populate the database with initial development data.

cd src/backend
uv run python manage.py loaddata api/fixtures/0_dev_users.json --database admin

The default credentials are dev@prowler.com:Thisisapassword123@ or dev2@prowler.com:Thisisapassword123@

Run tests

Note that the tests will fail if you use the same .env file as the development environment.

For best results, run in a new shell with no environment variables set.

cd src/backend
uv run pytest

Custom commands

Django provides a way to create custom commands that can be run from the command line.

These commands can be found in: prowler/api/src/backend/api/management/commands

To run a custom command, you need to be in the prowler/api/src/backend directory and run:

uv run python manage.py <command_name>

Generate dummy data

python manage.py findings --tenant
<TENANT_ID> --findings <NUM_FINDINGS> --re
sources <NUM_RESOURCES> --batch <TRANSACTION_BATCH_SIZE> --alias <ALIAS>

This command creates, for a given tenant, a provider, scan and a set of findings and resources related altogether.

Scan progress and state are updated in real time.

  • 0-33%: Create resources.
  • 33-66%: Create findings.
  • 66%: Create resource-finding mapping.

The last step is required to access the findings details, since the UI needs that to print all the information.

Example

~/backend $ uv run python manage.py findings --tenant
fffb1893-3fc7-4623-a5d9-fae47da1c528 --findings 25000 --re
sources 1000 --batch 5000 --alias test-script

Starting data population
	Tenant: fffb1893-3fc7-4623-a5d9-fae47da1c528
	Alias: test-script
	Resources: 1000
	Findings: 25000
	Batch size: 5000


Creating resources...
100%|███████████████████████| 1/1 [00:00<00:00,  7.72it/s]
Resources created successfully.


Creating findings...
100%|███████████████████████| 5/5 [00:05<00:00,  1.09s/it]
Findings created successfully.


Creating resource-finding mappings...
100%|███████████████████████| 5/5 [00:02<00:00,  1.81it/s]
Resource-finding mappings created successfully.


Successfully populated test data.