Epic G: Performance and Operability at Scale
Summary
Harden migration performance and operational resilience for larger datasets, repeated runs, and production-like load conditions.
Why This Epic
Current behavior is tuned for smaller or early-stage flows. Scaling to larger reconciliation datasets and non-empty databases requires deliberate performance and reliability engineering.
Goals
- Keep migration review and writes performant at larger volumes.
- Improve reliability for long-running and dependency-heavy operations.
- Define measurable operational targets and alerting.
- Validate behavior with scale and repeat-run tests.
Non-Goals
- Broad non-migration platform performance work.
- Replacing existing infrastructure stack in this phase.
User Stories
Story G1: Add pagination/index strategy for large reconciliation datasets
As a user, I want large result sets to load predictably so review remains usable.
Story G2: Add background processing and progress for long-running writes
As an operator, I want long writes to run safely with progress visibility.
Story G3: Add timeout/retry/circuit behavior for external dependencies
As a maintainer, I want resilient dependency handling so transient failures do not corrupt run state.
Story G4: Add SLOs and alerts for migration duration and failure rate
As an SRE, I want objective targets and alerts so regressions are caught quickly.
Story G5: Add load tests for non-empty DB and repeated-run scenarios
As a QA engineer, I want load coverage for realistic data states so scale risks are known before release.
Definition of Done
- Large dataset review and writes meet defined performance targets.
- Long-running operations expose progress and safe completion states.
- SLOs and alert thresholds are documented and wired.
- Load tests cover non-empty DB and repeat-run behavior.
Epic G: Performance and Operability at Scale
Summary
Harden migration performance and operational resilience for larger datasets, repeated runs, and production-like load conditions.
Why This Epic
Current behavior is tuned for smaller or early-stage flows. Scaling to larger reconciliation datasets and non-empty databases requires deliberate performance and reliability engineering.
Goals
Non-Goals
User Stories
Story G1: Add pagination/index strategy for large reconciliation datasets
As a user, I want large result sets to load predictably so review remains usable.
Story G2: Add background processing and progress for long-running writes
As an operator, I want long writes to run safely with progress visibility.
Story G3: Add timeout/retry/circuit behavior for external dependencies
As a maintainer, I want resilient dependency handling so transient failures do not corrupt run state.
Story G4: Add SLOs and alerts for migration duration and failure rate
As an SRE, I want objective targets and alerts so regressions are caught quickly.
Story G5: Add load tests for non-empty DB and repeated-run scenarios
As a QA engineer, I want load coverage for realistic data states so scale risks are known before release.
Definition of Done