Caching is often treated as a separate subsystem: cache stores, invalidation logic, and bespoke keys. In a graph-native system, caching is a property of topology.
Presence as Cache
If a transformation has already been executed for a given input, the result is already present as a node or row. The graph’s structure is the cache. You don’t need to recompute or revalidate. You traverse the existing structure.
Replay by Traversal
Because every transformation emits outputs and links them to inputs, you can replay by walking forward from any point. If you update a function, you can mark downstream nodes as stale and recompute only what’s necessary.
Partial Reuse
Paths in the graph can be reused even if downstream logic changes. This makes optimization natural: keep stable parts, recompute only what changed.
Explicit Lineage
Each output has a lineage of inputs and transformations. This yields:
- Provenance tracking
- Debuggable audit trails
- Time-travel comparisons between runs
Memory Before Effort
Functions can be treated as fallbacks. If the graph already remembers a result, you use it. If it doesn’t, you compute and then extend the memory. Computation becomes growth, not repetition.
Why This Matters
- Performance: You skip redundant work by default.
- Clarity: You can see what has already been done.
- Adaptability: You can invalidate and recompute precisely.
Caching becomes a byproduct of your architecture rather than a separate concern.