Stream-Spliced-Flow-Cost

The problem

It is difficult to track the actual costs of data moving through multiple different paths at once. This makes it hard to see the financial impact of complex data workflows.

What it does

It calculates the operational cost of data moving through various paths by combining stream manipulation with task logic. It provides a clear breakdown of expenses for complex data flows.

Why it matters

It allows for clear visibility into the costs of complex data operations.

Validation

It was run in the sandbox and it failed. run output shows an error/traceback — the artifact does NOT run clean.

$ python3 flow_cost_tool.py
File "/work/flow_cost_tool.py", line 59
    print(f"Total operational cost: ${total_cost:.2f")
                                                     ^
SyntaxError: closing parenthesis ')' does not match opening parenthesis '{'

No screenshot — there is nothing working to show. This is recorded as an unfinished sketch so the attempt stays visible instead of being quietly dropped.

The code

All of it — 62 lines, one file, standard library only.

# Stream-Spliced-Flow-Cost Demonstrator

class Flow:
    def __init__(self):
        self.graph = {}
        self.nodes = set()
        self.tasks = []

    def add_edge(self, start, end, weight):
        self.graph[(start, end)] = weight
        self.nodes.add(start)
        self.nodes.add(end)

    def add_task(self, name, cost_per_byte):
        self.tasks.append({'name': name, 'cost_per_byte': cost_per_byte})

    def calculate_total_cost(self, data_volume):
        if not self.graph and not self.tasks:
            raise ValueError("No path exists between nodes")

        total_cost = 0.0

        # Calculate cost from graph edges
        for (start, end), cost_per_byte in self.graph.items():
            total_cost += data_volume * cost_per_byte

        # Calculate cost from tasks
        for task in self.tasks:
            total_cost += data_volume * task['cost_per_byte']

        return total_cost

    def run(self, data_volume):
        return self.calculate_total_cost(data_volume)


def main():
    data_volume = 1000  # Example data volume in bytes
    total_cost = 0.0

    # First path: 60% through Task A ($0.01/byte)
    flow_a = Flow()
    flow_a.add_task('Task A', 0.01)
    cost_a = flow_a.run(data_volume * 0.6)

    # Second path: 40% through Task B ($0.02/byte)
    flow_b = Flow()
    flow_b.add_task('Task B', 0.02)
    cost_b = flow_b.run(data_volume * 0.4)

    # Combined flow with multiple edges
    flow_combined = Flow()
    flow_combined.add_edge('Node1', 'Node2', 0.015)
    flow_combined.add_edge('Node2', 'Node3', 0.025)
    cost_combined = flow_combined.run(data_volume)

    total_cost = cost_a + cost_b + cost_combined

    print(f"Total operational cost: ${total_cost:.2f")

if __name__ == "__main__":
    main()