glm-5.1
13d55b981e
Explores the diff-based approach (TypeBox Value.Diff → graphology mutation mapping) as an alternative to rebuild-on-change. Key findings: - The diff must happen at the graph level, not the source level, because TaskInput.dependsOn doesn't directly map to edge mutations - graphology's import(merge=true) handles merges but not deletions - The real win is reactivity (fine-grained event notifications), not performance - For <200 node graphs, rebuild is always sub-millisecond - A hybrid approach (diff for attribute-only changes, rebuild for structural changes) is possible but adds significant complexity Decision: defer to v2. ADR-002 (rebuild) stands. The exploration is preserved for future reference.
1.7 KiB
ADR-002: Rebuild graph on change, not incremental updates
Status: Accepted
Context
When task data changes (file edits, DB updates), the in-memory graph needs to reflect the new state. Two approaches: incremental updates (add/remove individual nodes/edges) or full rebuild from source data.
Decision
Rebuild. For our graph sizes (10–200 nodes), graph.import() from a serialized blob is sub-millisecond. Both consumers (alkhub builds from DB query results; OpenCode plugin rebuilds from directory on file change) are well-served by rebuild.
Consequences
Positive
- No change-detection layer needed — no tracking ID renames, dependency removals, edge reconciliation
- Simpler codebase — no diff algorithm, no incremental update logic
- Always consistent — rebuild guarantees the graph matches the source data exactly
Negative
- Technically wasteful for small changes (rebuilding entire graph when one task changed)
- Not suitable for very large graphs or extremely frequent updates
Mitigation
If a future use case requires incremental updates, add it as an optimization then. The API surface (construction methods) supports both patterns — incremental construction exists via addTask/addDependency.
An incremental update architecture has been explored in incremental-update-exploration.md. The key finding is that the win is reactivity (fine-grained event notifications), not performance. For <200 node graphs, rebuild is always sub-millisecond. If a consumer needs reactive updates, they can use graphology's event system directly via graph.raw and implement change detection at the consumer layer, without the library taking on the complexity of diff-based updates.