SO Analytics API

This project is an API for indexing messages in Elasticsearch and saving the message data to a JSON file. It provides an endpoint /messages for creating events.

Prerequisites

• Python 3.9
• Elasticsearch

Installation

1. Clone the repository:

   git clone https://github.com/notapixelstudio/analytics

2. Install the dependencies:

   pip install -r requirements.txt

Configuration

Before running the application, make sure to configure the Elasticsearch connection and other environment variables. The following environment variables can be set:

• ELASTIC_HOSTNAME: Elasticsearch hostname (default: https://localhost:9200)
• ELASTIC_USERNAME: Elasticsearch username (default: elastic)
• ELASTIC_PASSWORD: Elasticsearch password (default: changeme)
• TOKEN: Secret token for authentication (default: secret_token)

Usage

Run the following command to start the API server:

uvicorn main:app --reload

The API will be available at http://localhost:8000.

Endpoint: POST /messages

This endpoint allows you to create an event by indexing a message in Elasticsearch and saving the message data to a JSON file.

Request

• Header:

  • Authorization: Basic authentication token (e.g., "Basic base64(username:password)")

• Body:

  • id (string): Event ID
  • version (string): Event version
  • event_name (string): Name of the event

Response

• Status: 200 OK
• Body: JSON object with a message indicating whether the message has been indexed successfully or if an error occurred.

Example

curl -X POST -H "Authorization: Basic base64(username:password)" -H "Content-Type: application/json" \
     -d '{"id": "godot_1", "version": "1.0", "event_name": "example_event"}' \
     http://localhost:8000/messages

Logging

The application logs information using the logger named api_logger. By default, log messages are displayed on the console.

License

This project is licensed under the MIT License.

Development

The following make targets are available for development:

• install-dev: Install development dependencies.
• setup-dev-env: Set up the development environment, including pre-commit hooks and commit message validation.
• clean: Remove temporary files and directories generated during development.
• bump, bump-minor, bump-major, bump-patch: Bump the project version, generate changelog, create a git tag, and commit the changes.
• docker-build: Build a Docker image for the project.
• local-run: Run the project locally using uvicorn with environment variables from the .env file.

To execute a make target, run the following command:

make <target>

For example, to install development dependencies, run make install-dev.

Notes

• The application uses the pydantic library for data validation and modeling.
• IP address validation and anonymization functions are provided.
• The Elasticsearch index used is "starship_olympics_analytics".
• The application saves the event data to a JSON file in the "data" directory with the name <event_id>.json.

Feel free to modify and adapt this code to suit your specific needs.