Pre-compute 6-dimension metric cubes from cached LOT-level DataFrames so interactive Pareto requests read compact snapshots instead of re-scanning detail rows on every filter change. Includes single-flight build guard, TTL/size guardrails, cross-filter exclude-self evaluation, safe legacy fallback, response metadata exposure, telemetry counters, and a 3-stage rollout plan (telemetry-only → build-enabled → read-through). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1.5 KiB
1.5 KiB
MODIFIED Requirements
Requirement: Cache Telemetry MUST be Queryable for Operations
The system MUST provide cache telemetry suitable for operations diagnostics, including materialized Pareto cache behavior for reject-history workloads.
Scenario: Telemetry inspection
- WHEN operators request deep health status
- THEN cache-related metrics/state SHALL be present and interpretable for troubleshooting
Scenario: Materialized Pareto telemetry visibility
- WHEN materialized Pareto cache is enabled
- THEN telemetry SHALL expose at least hit count/rate, miss count/rate, build count, build failure count, and fallback count
- THEN telemetry SHALL expose latest snapshot freshness indicators and aggregate payload size indicators
ADDED Requirements
Requirement: Pareto materialization fallback reasons SHALL be operationally classifiable
Telemetry MUST classify fallback outcomes with stable reason codes so repeated degradations can be monitored and alerted.
Scenario: Snapshot miss fallback reason
- WHEN request falls back because no snapshot exists
- THEN telemetry SHALL record a stable reason code for snapshot miss
Scenario: Snapshot stale fallback reason
- WHEN request falls back because snapshot fails freshness/version checks
- THEN telemetry SHALL record a stable reason code for stale/incompatible snapshot
Scenario: Build failure fallback reason
- WHEN request falls back because materialization build failed
- THEN telemetry SHALL record a stable reason code for build failure