asyncnsq

Your task is to develop a real-time data processing system using Python that leverages the 'asyncnsq' library to interact with the NSQ (A Realtime Distributed Messaging Platform). This system will serve as a bridge between different components of a microservices architecture, facilitating efficient communication and data flow.

The system should include the following functionalities:
1. **Producer**: A module that sends messages to one or more NSQ topics. These messages could simulate user activity logs, sensor data, or any other form of real-time information.
2. **Consumer**: One or more modules that consume messages from these topics, process them asynchronously, and then either discard them or forward the processed data to another topic or external service.
3. **Message Processing Logic**: Implement a simple but effective logic within the consumer(s) to demonstrate the power of asynchronous message handling. For instance, you might log the received messages, perform some computation, or even store the data in a database.
4. **Error Handling & Retries**: Ensure that your system gracefully handles errors during message consumption and implements a mechanism for retrying failed message processing.
5. **Scalability Considerations**: Design your solution with scalability in mind. Demonstrate how adding more consumers can improve throughput without affecting performance.

To achieve these goals, utilize the 'asyncnsq' library to its fullest extent. Focus on the core features such as connecting to NSQ, publishing messages, consuming messages, and managing connections asynchronously. Additionally, explore advanced options provided by the library to enhance reliability and efficiency.

This project not only tests your ability to work with asynchronous programming in Python but also challenges you to design a robust messaging system capable of handling high volumes of data in real-time.