Imagine a function that processes transactions. In a traditional design, it branches internally: if the transaction is a refund, call one handler; if it’s a purchase, call another. That branching hides inside code, which means reasoning about flow requires reading logic. In graph-native computation, branching is not inside the function. It is the graph.
The Core Shift
A function emits what it sees. It doesn’t decide where the output should go. The graph routes outputs to multiple consumers based on explicit conditions. The function remains linear and deterministic. The system carries the branching logic.
This shift matters because it unifies flow. Instead of dozens of internal conditionals, you get a visible topology of edges and filters. You can traverse it, visualize it, and change it without touching the function.
Example: Fan-Out by Type
You have a function that emits:
- `{ type: "refund", amount: 10, ... }`
- `{ type: "purchase", amount: 50, ... }`
The output table is a single stream. Downstream, you define graph routes:
- `refunds_input` receives rows where `type == "refund"`
- `purchases_input` receives rows where `type == "purchase"`
Now each downstream function is pure and focused. Routing is a declarative filter, not a code path.
Why This Improves Clarity
1) Determinism: Each function has one path. Outputs are not conditional inside the function.
2) Auditing: You can ask, “Why did this record route here?” The answer is in the graph edge condition, not hidden in code.
3) Refactoring Without Risk: You can add a new consumer by adding a new edge. Existing functions remain untouched.
4) Testing Simplicity: You test the function once. You test routing separately. The test matrix collapses.
Branching as a First-Class Structure
A graph is naturally branching. When multiple edges leave a node, you have parallel paths. This is not a special case or a conditional. It’s a fundamental structure. That structure is now the program.
Co-location Without Coupling
You can keep related functions in the same file or package without mixing their logic. Each function still has its own input and output table. They are neighbors, not entangled.
Practical Implications
- Routing becomes versionable and queryable.
- Adding or removing branches is a graph change, not a code change.
- The system becomes legible at the architectural level.
When you move branching into the graph, you stop teaching functions to be aware of the system. They become pure transformers. The system becomes the routing mind.