Notes

Standards and Constraints in Project Design and Implementation
The design and implementation of our project followed strict standards and guidelines to ensure robustness, maintainability, and performance. Below we discuss the key standards adhered to and the constraints we considered during the design and implementation of our project.

Standards
Coding Standards: Throughout the implementation phase, we followed best coding practices and standards specific to each technology used. In Python, for instance, we adhered to PEP 8, the Python community's standard for code style. All Python code was thoroughly reviewed to ensure it complied with this standard.

Data Exchange Standards: Apache Kafka, the messaging system used in our project, supports multiple data serialization and deserialization formats. We chose to use JSON, a popular and lightweight data-interchange format that is easy to understand and write. JSON is also language-independent, making it a universal standard for data exchange.

Networking Standards: Cilium Tetragon was used for monitoring TCP events and processes. The TCP/IP model is the foundation of internet communications, and our system fully complies with this standard. All our network communications adhered to the protocols in this model.

Data Storage Standards: MongoDB, our chosen database, follows the BSON format for data storage, an extended version of JSON that supports additional data types. This ensured consistency in data storage and retrieval across the application.

Visualization Standards: Grafana, the visualization tool we used, supports a wide range of data sources and provides numerous options for data representation. We followed best practices for data visualization to ensure our dashboards were clear, meaningful, and accessible.

Containerization and Orchestration Standards: We used Kubernetes, the industry standard for container orchestration, to manage our services. We followed the 12-Factor App principles and ensured all our services were stateless, self-contained, and scalable.

Constraints
Performance Constraints: Monitoring TCP events and visualizing them in real time presents a significant performance challenge. Ensuring that the application could handle high volumes of data in real time constrained some design decisions.

Storage Constraints: Storing the TCP event data and other relevant data in MongoDB presents storage constraints, particularly as the data volume increases. MongoDB performance, such as read and write speed, was a significant consideration during the design process.

Network Constraints: The network traffic generated by monitoring TCP events could be substantial. This necessitated careful network design and efficient use of bandwidth.

Computational Resources: Depending on the scale, the application could require significant computational resources to operate effectively. This includes CPU for processing, memory for data handling, and disk space for storage, affecting both cost and infrastructure requirements.

In conclusion, the design and implementation of this project were guided by key standards and mindful of the various constraints. By adhering to these, we've created a robust, efficient, and maintainable system to monitor TCP events, monitor processes, and provide a visual representation of events, responding accordingly to maintain system performance and integrity.