feat(lineage): unified LineageEngine, EventFetcher, and progressive trace API
Introduce a unified Seed→Lineage→Event pipeline replacing per-page Python BFS with Oracle CONNECT BY NOCYCLE queries, add staged /api/trace/* endpoints with rate limiting and L2 Redis caching, and wire progressive frontend loading via useTraceProgress composable. Key changes: - Add LineageEngine (split ancestors / merge sources / full genealogy) with QueryBuilder bind-param safety and batched IN clauses - Add EventFetcher with 6-domain support and L2 Redis cache - Add trace_routes Blueprint (seed-resolve, lineage, events) with profile dispatch, rate limiting, and Redis TTL=300s caching - Refactor query_tool_service to use LineageEngine and QueryBuilder, removing raw string interpolation (SQL injection fix) - Add rate limits and resolve cache to query_tool_routes - Integrate useTraceProgress into mid-section-defect with skeleton placeholders and fade-in transitions - Add lineageCache and on-demand lot lineage to query-tool - Add TraceProgressBar shared component - Remove legacy query-tool.js static script (3k lines) - Fix MatrixTable package column truncation (.slice(0,15) removed) - Archive unified-lineage-engine change, add trace-progressive-ui specs Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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egg
@@ -8,6 +8,7 @@ from unittest.mock import patch
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import pandas as pd
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from mes_dashboard.services.mid_section_defect_service import (
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build_trace_aggregation_from_events,
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query_analysis,
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query_analysis_detail,
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query_all_loss_reasons,
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@@ -126,3 +127,116 @@ def test_query_all_loss_reasons_cache_miss_queries_and_caches_sorted_values(
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{'loss_reasons': ['A_REASON', 'B_REASON']},
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ttl=86400,
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)
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@patch('mes_dashboard.services.mid_section_defect_service.cache_set')
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@patch('mes_dashboard.services.mid_section_defect_service.cache_get', return_value=None)
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@patch('mes_dashboard.services.mid_section_defect_service.release_lock')
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@patch('mes_dashboard.services.mid_section_defect_service.try_acquire_lock', return_value=True)
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@patch('mes_dashboard.services.mid_section_defect_service._fetch_upstream_history')
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@patch('mes_dashboard.services.mid_section_defect_service._resolve_full_genealogy')
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@patch('mes_dashboard.services.mid_section_defect_service._fetch_tmtt_data')
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def test_trace_aggregation_matches_query_analysis_summary(
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mock_fetch_tmtt_data,
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mock_resolve_genealogy,
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mock_fetch_upstream_history,
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_mock_lock,
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_mock_release_lock,
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_mock_cache_get,
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_mock_cache_set,
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):
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tmtt_df = pd.DataFrame([
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{
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'CONTAINERID': 'CID-001',
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'CONTAINERNAME': 'LOT-001',
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'TRACKINQTY': 100,
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'REJECTQTY': 5,
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'LOSSREASONNAME': 'R1',
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'WORKFLOW': 'WF-A',
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'PRODUCTLINENAME': 'PKG-A',
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'PJ_TYPE': 'TYPE-A',
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'TMTT_EQUIPMENTNAME': 'TMTT-01',
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'TRACKINTIMESTAMP': '2025-01-10 10:00:00',
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'FINISHEDRUNCARD': 'FR-001',
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},
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{
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'CONTAINERID': 'CID-002',
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'CONTAINERNAME': 'LOT-002',
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'TRACKINQTY': 120,
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'REJECTQTY': 6,
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'LOSSREASONNAME': 'R2',
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'WORKFLOW': 'WF-B',
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'PRODUCTLINENAME': 'PKG-B',
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'PJ_TYPE': 'TYPE-B',
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'TMTT_EQUIPMENTNAME': 'TMTT-02',
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'TRACKINTIMESTAMP': '2025-01-11 10:00:00',
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'FINISHEDRUNCARD': 'FR-002',
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},
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])
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ancestors = {
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'CID-001': {'CID-101'},
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'CID-002': set(),
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}
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upstream_normalized = {
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'CID-101': [{
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'workcenter_group': '中段',
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'equipment_id': 'EQ-01',
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'equipment_name': 'EQ-01',
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'spec_name': 'SPEC-A',
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'track_in_time': '2025-01-09 08:00:00',
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}],
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'CID-002': [{
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'workcenter_group': '中段',
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'equipment_id': 'EQ-02',
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'equipment_name': 'EQ-02',
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'spec_name': 'SPEC-B',
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'track_in_time': '2025-01-11 08:00:00',
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}],
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}
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upstream_events = {
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'CID-101': [{
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'WORKCENTER_GROUP': '中段',
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'EQUIPMENTID': 'EQ-01',
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'EQUIPMENTNAME': 'EQ-01',
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'SPECNAME': 'SPEC-A',
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'TRACKINTIMESTAMP': '2025-01-09 08:00:00',
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}],
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'CID-002': [{
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'WORKCENTER_GROUP': '中段',
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'EQUIPMENTID': 'EQ-02',
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'EQUIPMENTNAME': 'EQ-02',
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'SPECNAME': 'SPEC-B',
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'TRACKINTIMESTAMP': '2025-01-11 08:00:00',
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}],
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}
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mock_fetch_tmtt_data.return_value = tmtt_df
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mock_resolve_genealogy.return_value = ancestors
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mock_fetch_upstream_history.return_value = upstream_normalized
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summary = query_analysis('2025-01-01', '2025-01-31')
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staged_summary = build_trace_aggregation_from_events(
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'2025-01-01',
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'2025-01-31',
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seed_container_ids=['CID-001', 'CID-002'],
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lineage_ancestors={
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'CID-001': ['CID-101'],
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'CID-002': [],
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},
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upstream_events_by_cid=upstream_events,
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)
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assert staged_summary['available_loss_reasons'] == summary['available_loss_reasons']
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assert staged_summary['genealogy_status'] == summary['genealogy_status']
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assert staged_summary['detail_total_count'] == len(summary['detail'])
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assert staged_summary['kpi']['total_input'] == summary['kpi']['total_input']
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assert staged_summary['kpi']['lot_count'] == summary['kpi']['lot_count']
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assert staged_summary['kpi']['total_defect_qty'] == summary['kpi']['total_defect_qty']
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assert abs(
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staged_summary['kpi']['total_defect_rate'] - summary['kpi']['total_defect_rate']
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) <= 0.01
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assert staged_summary['daily_trend'] == summary['daily_trend']
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assert staged_summary['charts'].keys() == summary['charts'].keys()
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