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docs(cache): remove static dataset invalidation

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Amirhossein Khalili
2026-08-20 18:40:28 +03:30
parent f471eaf3a2
commit 27903ff67e
9 changed files with 62 additions and 102 deletions
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docs/decisions/0007-calendar-aware-analytics-projections.md
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@@ -17,7 +17,7 @@ Add a reporting-date model containing Gregorian and Jalali date attributes, expl
Build idempotent, rebuildable analytics-section queries containing additive components such as session count, attempt count, successful count, amount sum, and fee sum as approved by the metric catalogue. Derive week/month/season totals through the reporting-date model and recompute ratios from their numerators and denominators.
Calculate non-additive metrics such as exact unique payer-token counts directly at their requested grain; never obtain them by summing daily distinct counts. Cache each typed section in Redis under source/cohort and metric-definition revisions.
Calculate non-additive metrics such as exact unique payer-token counts directly at their requested grain; never obtain them by summing daily distinct counts. Cache each typed section in Redis under the immutable completed-import snapshot and metric-definition version.
Keep normalized fact tables unpartitioned initially. Add targeted indexes from approved API access paths and verified query plans. Introduce PostgreSQL partitioning only through a future ADR supported by measured table growth, query latency, vacuum/maintenance behavior, or retention operations.
@@ -26,5 +26,5 @@ Keep normalized fact tables unpartitioned initially. Add targeted indexes from a
- The normalized ERD remains semantically correct while dashboards reuse disposable Redis results.
- Section calculations support approved period groupings without storing calculated facts as PostgreSQL business truth.
- Explicit reporting-period definitions and metric provenance make future AI observations traceable.
- Lazy parallel calculation, Redis revision keys, job deduplication, partial failure, and SSE visibility tests become required behavior.
- Lazy parallel calculation, dataset-versioned Redis keys, job deduplication, partial failure, and SSE visibility tests become required behavior.
- Schema/index/cache details remain subject to data profiling and approved metric/API requirements.