This repository focuses on implementing dynamic optimization of Apache Kafka configurations using process mining techniques. The project aims to enhance Kafka's performance by dynamically analyzing logs and adjusting configurations. Reinforcement learning integration is planned for future iterations.

Dockerized environment for running Kafka and Zookeeper.

Process mining for identifying performance bottlenecks.

Scripts to interact with Kafka and log producer-consumer data.

Planned integration of reinforcement learning for automated configuration optimization.

The setup is designed to create a managed Kafka environment using Docker. The implementation includes:

Environment Setup:

Logging and Monitoring:

Dynamic Configuration:

Docker and Docker Compose installed on your system.

Python 3.8+ with pip.

Clone the repository:

Set up the Kafka environment:

Install Python dependencies:

Run the implementation script:

The future plan involves integrating reinforcement learning to dynamically adjust Kafka configurations in real time. The RL algorithm will aim to:

Continuously monitor performance metrics.

Learn optimal configurations for producer and consumer settings.

Automate adjustments to improve throughput and reduce latency.

Modify Kafka configurations in docker-compose.yml for your testing needs.

Analyze the log files generated during Kafka operations to gain performance insights.

Prepare for reinforcement learning integration by customizing the monitoring scripts.

Contributions are welcome! Fork the repository, make your changes, and submit a pull request.

This project is licensed under the MIT License.

For questions or feedback, please reach out to Saim Sheikh.