Two changes combined:
1. historical-query-slow-connection: Migrate all historical query pages
to read_sql_df_slow with semaphore concurrency control (max 3),
raise DB slow timeout to 300s, gunicorn timeout to 360s, and
unify frontend timeouts to 360s for all historical pages.
2. hold-resource-history-dataset-cache: Convert hold-history and
resource-history from multi-query to single-query + dataset cache
pattern (L1 ProcessLevelCache + L2 Redis parquet/base64, TTL=900s).
Replace old GET endpoints with POST /query + GET /view two-phase
API. Frontend auto-retries on 410 cache_expired.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Reject History:
- Compute dimension pareto (package/type/workflow/workcenter/equipment) from
cached DataFrame instead of re-querying Oracle per dimension change
- Propagate supplementary filters and trend date selection to dimension pareto
- Add staleness tracking to prevent race conditions on rapid dimension switches
- Add WORKFLOWNAME to detail and export outputs
- Fix button hover visibility with CSS specificity
MSD (製程不良追溯分析):
- Separate raw events caching from aggregation computation so changing
loss_reasons uses EventFetcher per-domain cache (fast) and recomputes
aggregation with current filters instead of returning stale cached results
- Exclude loss_reasons from MSD seed cache key since seed resolution does
not use it, avoiding unnecessary Oracle re-queries
- Add suspect context panel, analysis summary, upstream station/spec filters
- Add machine bar click drill-down and filtered attribution charts
Query Tool:
- Support batch container_ids in lot CSV export (history/materials/rejects/holds)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add DEFECTQTY to reject SUM in station_detection, station_detection_by_ids,
and downstream_rejects SQL so KPI/charts include both charge-off and
non-charge-off reject quantities
- Wire forward direction through events-based trace pipeline so downstream
pareto charts and detail table populate correctly
- Remove inappropriate 5-min auto-refresh from query tool page; replace
useAutoRefresh with local createAbortSignal for request cancellation
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Transform /mid-section-defect from TMTT-only backward analysis into a full-line
bidirectional defect traceability center supporting all detection stations.
Key changes:
- Parameterized station detection: any workcenter group as detection station
- Bidirectional tracing: backward (upstream attribution) + forward (downstream reject rates)
- Dual query mode: date range OR LOT/工單/WAFER container-based seed resolution
- Multi-select filters for upstream station, equipment model (RESOURCEFAMILYNAME), and loss reasons
- Progressive 3-stage trace pipeline (seed-resolve → lineage → events) with streaming UI
- Equipment model lookup via resource cache instead of SPECNAME
- Session caching, auto-refresh, searchable MultiSelect with fuzzy matching
- Remove legacy tmtt-defect module (fully superseded)
- Archive openspec change artifacts
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Address 6 code review findings (P0-P3): add Redis distributed lock to prevent
duplicate Oracle pipeline on cold cache, apply rate limiting to 3 high-cost
routes, separate UI filter state from committed query state, add AbortController
for request cancellation, push workcenter group classification into Oracle SQL
CASE WHEN, and add 18 route+service tests. Also add workcenter group selection
to job-query equipment selector and rename button to "查詢".
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
New page for tracing TMTT test station defects back to upstream machines,
stations, and workflows. Three-stage data pipeline (TMTT detection →
SPLITFROMID BFS + COMBINEDASSYLOTS merge expansion → upstream history),
6 KPI cards, 6 Pareto charts, daily trend, paginated LOT detail table.
Summary/detail API separation reduces response from 72 MB to ~16 KB summary
+ ~110 KB/page detail. Loss reasons cached in Redis with 24h TTL (205 types).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
