dataflow-graph-architecture

To design a dataflow graph for real-time audiovisual processing, define typed node and port schemas, establish connection rules, and implement a scheduler plan that can run in pure Python with optional C++ acceleration. This structure supports scalable and maintainable AV pipelines.

A node and port schema in a dataflow graph architecture defines typed, data-driven execution endpoints for AV pipelines. It specifies the exact data types flowing between connected nodes, ensuring execution semantics and connection rules are strictly enforced during real-time processing.

To migrate a linear pipeline to a real-time dataflow graph, evaluate topology and dependencies to translate them into typed node and port schemas. This migration includes proposing concrete execution semantics and assessing parallel processing trade-offs for real-time AV scheduling.

Yes, you can implement a dataflow graph scheduler in pure Python for AV pipelines. The architecture requires a Python-based graph design approach with clear port typing and connection rules, which can subsequently be augmented with optional C++ acceleration for performance.

Hexagonal architecture limitations for real-time AV processing include struggling with complex temporal buffers and parallel execution scheduling. Migrating to a dataflow graph provides explicit topology management and typed port connections better suited for continuous data-driven execution.