Design proposal: This page preserves exploratory architecture ideas, not verified runtime capabilities. Persistent Neptune integration, Karma CLI/service commands, coordinated changes, and learning systems are not implemented end-to-end in the current source. Read the implementation status and proof criteria first. Consequential AWS execution requires separate human authorization.
Graph Consistency in Karma #
When Karma uses Amazon Neptune as its source of truth, the system must ensure that the graph is kept in a consistent and valid state — even as multiple processes or users interact with it.
This page outlines potential race conditions, failure scenarios, and best practices for maintaining consistency.
Why Consistency Matters #
Karma’s infrastructure graph is used to:
- Understand current system state
- Resolve dependencies between components
- Drive actions such as Terraform deployments or runtime rebinds
If the graph is incomplete or temporarily invalid, those actions may behave incorrectly — or fail altogether.
Common Race Conditions #
1. Concurrent Graph Builds #
Multiple karma graph build processes (e.g. CLI, Lambda, cron) could overwrite each other’s writes if run at the same time.
Solution:
- Use a lock mechanism per environment (DynamoDB, SSM, etc.)
- Queue builds or serialize them by policy
2. Partial Writes or Failures #
If a build process crashes or times out mid-update, Neptune may contain:
- Incomplete graphs
- Nodes without edges
- Edges pointing to nonexistent nodes
Solution:
- Build the graph in-memory first
- Write changes in a single batch
- Tag graphs as
"building" → "ready"or versioned
3. Eventually Consistent Reads #
A future graph implementation must specify and test read-after-write behavior for its chosen storage engine, API, and replica topology.
Solution:
- Verify the chosen database API supports the required consistency semantics; no specific Neptune setting is prescribed here
- Accept eventual consistency for non-critical queries
Recommended Practices #
| Problem | Strategy |
|---|---|
| Simultaneous builds | Use environment-level lock |
| Incomplete writes | In-memory staging + atomic publish |
| Conflicting updates | Queue or version change requests |
| Flaky reads | Test read-after-write behavior and handle stale observations |
| Rollbacks | Use batch delete or rebuild logic |
Future Enhancements #
- Graph change journaling (for replays or rollback)
- Change-set simulation before applying to Neptune
- Node/edge versioning or timestamp tagging
- Separate “working” and “committed” graph layers
Karma’s design keeps the graph as a stable and inspectable surface. Even in a distributed system, you can keep it trustworthy with minimal guarantees.