From 71c8102de68cf1f947388b4913fa37f534f93c14 Mon Sep 17 00:00:00 2001
From: egg <egg@localhost>
Date: Wed, 25 Feb 2026 13:15:02 +0800
Subject: [PATCH] feat: dataset cache for hold/resource history + slow
 connection migration

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>
---
 .../composables/useExcelQueryData.js          |   4 +-
 frontend/src/hold-history/App.vue             | 228 +++---
 .../job-query/composables/useJobQueryData.js  |   6 +-
 frontend/src/mid-section-defect/App.vue       |   2 +-
 .../query-tool/components/LotJobsTable.vue    |   2 +-
 .../composables/useEquipmentQuery.js          |   4 +-
 .../query-tool/composables/useLotDetail.js    |   8 +-
 .../query-tool/composables/useLotLineage.js   |   2 +-
 .../query-tool/composables/useLotResolve.js   |   2 +-
 .../composables/useReverseLineage.js          |   2 +-
 frontend/src/reject-history/App.vue           |  18 +-
 frontend/src/resource-history/App.vue         | 161 +++-
 .../shared-composables/useTraceProgress.js    |   2 +-
 gunicorn.conf.py                              |   6 +-
 .../.openspec.yaml                            |   2 +
 .../design.md                                 |  84 +++
 .../proposal.md                               |  31 +
 .../specs/hold-dataset-cache/spec.md          |  64 ++
 .../specs/hold-history-api/spec.md            |  62 ++
 .../specs/hold-history-page/spec.md           |  34 +
 .../specs/resource-dataset-cache/spec.md      |  71 ++
 .../specs/resource-history-page/spec.md       |  46 ++
 .../tasks.md                                  |  42 ++
 .../.openspec.yaml                            |   2 +
 .../design.md                                 |  78 ++
 .../proposal.md                               |  42 ++
 .../specs/hold-history-api/spec.md            |   9 +
 .../specs/hold-history-page/spec.md           |   8 +
 .../specs/progressive-trace-ux/spec.md        |   8 +
 .../specs/query-tool-equipment/spec.md        |   8 +
 .../specs/query-tool-lot-trace/spec.md        |   9 +
 .../specs/reject-history-api/spec.md          |  10 +
 .../specs/reject-history-page/spec.md         |   8 +
 .../specs/resource-history-page/spec.md       |  16 +
 .../slow-query-concurrency-control/spec.md    |  53 ++
 .../historical-query-slow-connection/tasks.md |  39 +
 .../.openspec.yaml                            |   2 +
 .../design.md                                 | 107 +++
 .../proposal.md                               |  76 ++
 .../specs/msd-analysis-transparency/spec.md   |  48 ++
 .../specs/msd-multifactor-attribution/spec.md |  93 +++
 .../specs/msd-suspect-context/spec.md         |  77 ++
 openspec/specs/hold-dataset-cache/spec.md     |  64 ++
 openspec/specs/hold-history-api/spec.md       | 184 +----
 openspec/specs/hold-history-page/spec.md      | 190 +----
 openspec/specs/resource-dataset-cache/spec.md |  71 ++
 openspec/specs/resource-history-page/spec.md  | 161 +---
 src/mes_dashboard/config/settings.py          |   8 +
 src/mes_dashboard/core/database.py            |  68 +-
 .../routes/hold_history_routes.py             | 236 +++---
 .../routes/resource_history_routes.py         | 195 +++--
 .../services/excel_query_service.py           |  56 +-
 .../services/hold_dataset_cache.py            | 553 ++++++++++++++
 .../services/hold_history_service.py          |   2 +-
 .../services/job_query_service.py             | 560 +++++++-------
 .../services/query_tool_service.py            |   2 +-
 .../services/reject_dataset_cache.py          |   2 +-
 .../services/reject_history_service.py        |   2 +-
 .../services/resource_dataset_cache.py        | 685 ++++++++++++++++++
 .../services/resource_history_service.py      |   2 +-
 .../sql/hold_history/base_facts.sql           | 101 +++
 .../sql/resource_history/base_facts.sql       |  31 +
 tests/test_hold_history_routes.py             | 313 ++++----
 tests/test_resource_history_routes.py         | 186 +++--
 64 files changed, 3806 insertions(+), 1442 deletions(-)
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/.openspec.yaml
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/design.md
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/proposal.md
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-dataset-cache/spec.md
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-api/spec.md
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-page/spec.md
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-dataset-cache/spec.md
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-history-page/spec.md
 create mode 100644 openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/tasks.md
 create mode 100644 openspec/changes/historical-query-slow-connection/.openspec.yaml
 create mode 100644 openspec/changes/historical-query-slow-connection/design.md
 create mode 100644 openspec/changes/historical-query-slow-connection/proposal.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/hold-history-api/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/hold-history-page/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/progressive-trace-ux/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/query-tool-equipment/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/query-tool-lot-trace/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/reject-history-api/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/reject-history-page/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/resource-history-page/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/specs/slow-query-concurrency-control/spec.md
 create mode 100644 openspec/changes/historical-query-slow-connection/tasks.md
 create mode 100644 openspec/changes/msd-multifactor-backward-tracing/.openspec.yaml
 create mode 100644 openspec/changes/msd-multifactor-backward-tracing/design.md
 create mode 100644 openspec/changes/msd-multifactor-backward-tracing/proposal.md
 create mode 100644 openspec/changes/msd-multifactor-backward-tracing/specs/msd-analysis-transparency/spec.md
 create mode 100644 openspec/changes/msd-multifactor-backward-tracing/specs/msd-multifactor-attribution/spec.md
 create mode 100644 openspec/changes/msd-multifactor-backward-tracing/specs/msd-suspect-context/spec.md
 create mode 100644 openspec/specs/hold-dataset-cache/spec.md
 create mode 100644 openspec/specs/resource-dataset-cache/spec.md
 create mode 100644 src/mes_dashboard/services/hold_dataset_cache.py
 create mode 100644 src/mes_dashboard/services/resource_dataset_cache.py
 create mode 100644 src/mes_dashboard/sql/hold_history/base_facts.sql
 create mode 100644 src/mes_dashboard/sql/resource_history/base_facts.sql

diff --git a/frontend/src/excel-query/composables/useExcelQueryData.js b/frontend/src/excel-query/composables/useExcelQueryData.js
index 2542fb9..c7cd752 100644
--- a/frontend/src/excel-query/composables/useExcelQueryData.js
+++ b/frontend/src/excel-query/composables/useExcelQueryData.js
@@ -132,7 +132,7 @@ export function useExcelQueryData() {
     try {
       const formData = new FormData();
       formData.append('file', file);
-      const payload = await apiUpload('/api/excel-query/upload', formData, { timeout: 120000, silent: true });
+      const payload = await apiUpload('/api/excel-query/upload', formData, { timeout: 360000, silent: true });
       excelColumns.value = Array.isArray(payload?.columns) ? payload.columns : [];
       excelPreview.value = Array.isArray(payload?.preview) ? payload.preview : [];
       uploadState.fileName = String(file.name || '');
@@ -252,7 +252,7 @@ export function useExcelQueryData() {
           date_from: filters.dateFrom || undefined,
           date_to: filters.dateTo || undefined,
         },
-        { timeout: 120000, silent: true },
+        { timeout: 360000, silent: true },
       );
       queryResult.rows = Array.isArray(payload?.data) ? payload.data : [];
       queryResult.columns = Array.isArray(payload?.columns) ? payload.columns : filters.returnColumns;
diff --git a/frontend/src/hold-history/App.vue b/frontend/src/hold-history/App.vue
index 2fb6ef9..6b14458 100644
--- a/frontend/src/hold-history/App.vue
+++ b/frontend/src/hold-history/App.vue
@@ -1,7 +1,7 @@
 <script setup>
 import { computed, onMounted, reactive, ref } from 'vue';
 
-import { apiGet } from '../core/api.js';
+import { apiGet, apiPost } from '../core/api.js';
 import { replaceRuntimeHistory } from '../core/shell-navigation.js';
 
 import DailyTrend from './components/DailyTrend.vue';
@@ -13,7 +13,7 @@ import RecordTypeFilter from './components/RecordTypeFilter.vue';
 import ReasonPareto from './components/ReasonPareto.vue';
 import SummaryCards from './components/SummaryCards.vue';
 
-const API_TIMEOUT = 60000;
+const API_TIMEOUT = 360000;
 const DEFAULT_PER_PAGE = 50;
 
 const filterBar = reactive({
@@ -22,6 +22,7 @@ const filterBar = reactive({
   holdType: 'quality',
 });
 
+const queryId = ref('');
 const reasonFilter = ref('');
 const durationFilter = ref('');
 const recordType = ref(['new']);
@@ -158,74 +159,23 @@ function updateUrlState() {
   replaceRuntimeHistory(`/hold-history?${params.toString()}`);
 }
 
-function commonParams({
-  includeHoldType = true,
-  includeReason = false,
-  includeRecordType = false,
-  includeDuration = false,
-} = {}) {
-  const params = {
-    start_date: filterBar.startDate,
-    end_date: filterBar.endDate,
-  };
+function recordTypeCsv() {
+  const rt = Array.isArray(recordType.value) ? recordType.value : [recordType.value];
+  return rt.join(',');
+}
 
-  if (includeHoldType) {
-    params.hold_type = filterBar.holdType;
+function applyViewResult(result, { listOnly = false } = {}) {
+  if (!listOnly) {
+    trendData.value = result.trend || trendData.value;
+    reasonParetoData.value = result.reason_pareto || reasonParetoData.value;
+    durationData.value = result.duration || durationData.value;
   }
-
-  if (includeRecordType) {
-    const rt = Array.isArray(recordType.value) ? recordType.value : [recordType.value];
-    params.record_type = rt.join(',');
-  }
-
-  if (includeReason && reasonFilter.value) {
-    params.reason = reasonFilter.value;
-  }
-
-  if (includeDuration && durationFilter.value) {
-    params.duration_range = durationFilter.value;
-  }
-
-  return params;
+  detailData.value = normalizeListPayload(result.list);
 }
 
-async function fetchTrend() {
-  const response = await apiGet('/api/hold-history/trend', {
-    params: commonParams({ includeHoldType: false }),
-    timeout: API_TIMEOUT,
-  });
-  return unwrapApiResult(response, '載入 trend 資料失敗');
-}
+// ---- Primary query (POST /query -> Oracle -> cache) ----
 
-async function fetchReasonPareto() {
-  const response = await apiGet('/api/hold-history/reason-pareto', {
-    params: commonParams({ includeHoldType: true, includeRecordType: true }),
-    timeout: API_TIMEOUT,
-  });
-  return unwrapApiResult(response, '載入 pareto 資料失敗');
-}
-
-async function fetchDuration() {
-  const response = await apiGet('/api/hold-history/duration', {
-    params: commonParams({ includeHoldType: true, includeRecordType: true }),
-    timeout: API_TIMEOUT,
-  });
-  return unwrapApiResult(response, '載入 duration 資料失敗');
-}
-
-async function fetchList() {
-  const response = await apiGet('/api/hold-history/list', {
-    params: {
-      ...commonParams({ includeHoldType: true, includeReason: true, includeRecordType: true, includeDuration: true }),
-      page: page.value,
-      per_page: DEFAULT_PER_PAGE,
-    },
-    timeout: API_TIMEOUT,
-  });
-  return unwrapApiResult(response, '載入明細資料失敗');
-}
-
-async function loadAllData({ includeTrend = true, showOverlay = false } = {}) {
+async function executePrimaryQuery({ showOverlay = false } = {}) {
   const requestId = nextRequestId();
 
   if (showOverlay) {
@@ -237,90 +187,90 @@ async function loadAllData({ includeTrend = true, showOverlay = false } = {}) {
   errorMessage.value = '';
 
   try {
-    const requests = [];
-    if (includeTrend) {
-      requests.push(fetchTrend());
-    }
-    requests.push(fetchReasonPareto(), fetchDuration(), fetchList());
+    const body = {
+      start_date: filterBar.startDate,
+      end_date: filterBar.endDate,
+      hold_type: filterBar.holdType,
+      record_type: recordTypeCsv(),
+    };
 
-    const responses = await Promise.all(requests);
-    if (isStaleRequest(requestId)) {
-      return;
-    }
+    const resp = await apiPost('/api/hold-history/query', body, { timeout: API_TIMEOUT });
+    if (isStaleRequest(requestId)) return;
 
-    let cursor = 0;
-    if (includeTrend) {
-      trendData.value = responses[cursor] || { days: [] };
-      cursor += 1;
-    }
+    const result = unwrapApiResult(resp, '主查詢執行失敗');
 
-    reasonParetoData.value = responses[cursor] || { items: [] };
-    cursor += 1;
-    durationData.value = responses[cursor] || { items: [] };
-    cursor += 1;
-    detailData.value = normalizeListPayload(responses[cursor]);
+    queryId.value = result.query_id;
+    applyViewResult(result);
+
+    updateUrlState();
   } catch (error) {
-    if (isStaleRequest(requestId)) {
-      return;
+    if (isStaleRequest(requestId)) return;
+    if (error?.name === 'AbortError') {
+      errorMessage.value = '查詢逾時，請縮短日期範圍後重試';
+    } else {
+      errorMessage.value = error?.message || '主查詢執行失敗';
     }
-    errorMessage.value = error?.message || '載入資料失敗';
   } finally {
-    if (isStaleRequest(requestId)) {
-      return;
-    }
+    if (isStaleRequest(requestId)) return;
     loading.global = false;
     loading.list = false;
     initialLoading.value = false;
   }
 }
 
-async function loadReasonDependents() {
+// ---- View refresh (GET /view -> read cache -> filter) ----
+
+async function refreshView({ listOnly = false } = {}) {
+  if (!queryId.value) return;
+
   const requestId = nextRequestId();
+  if (!listOnly) {
+    loading.global = true;
+  }
   loading.list = true;
   errorMessage.value = '';
 
   try {
-    const list = await fetchList();
-    if (isStaleRequest(requestId)) {
+    const params = {
+      query_id: queryId.value,
+      hold_type: filterBar.holdType,
+      record_type: recordTypeCsv(),
+      page: page.value,
+      per_page: DEFAULT_PER_PAGE,
+    };
+
+    if (reasonFilter.value) {
+      params.reason = reasonFilter.value;
+    }
+    if (durationFilter.value) {
+      params.duration_range = durationFilter.value;
+    }
+
+    const resp = await apiGet('/api/hold-history/view', {
+      params,
+      timeout: API_TIMEOUT,
+    });
+    if (isStaleRequest(requestId)) return;
+
+    // Cache expired -> auto re-execute primary query
+    if (resp?.success === false && resp?.error === 'cache_expired') {
+      await executePrimaryQuery();
       return;
     }
-    detailData.value = normalizeListPayload(list);
+
+    const result = unwrapApiResult(resp, '視圖查詢失敗');
+    applyViewResult(result, { listOnly });
   } catch (error) {
-    if (isStaleRequest(requestId)) {
-      return;
-    }
-    errorMessage.value = error?.message || '載入明細資料失敗';
+    if (isStaleRequest(requestId)) return;
+    errorMessage.value = error?.message || '載入資料失敗';
   } finally {
-    if (isStaleRequest(requestId)) {
-      return;
-    }
+    if (isStaleRequest(requestId)) return;
+    loading.global = false;
     loading.list = false;
   }
 }
 
-async function loadListOnly() {
-  const requestId = nextRequestId();
-  loading.list = true;
-  errorMessage.value = '';
-
-  try {
-    const list = await fetchList();
-    if (isStaleRequest(requestId)) {
-      return;
-    }
-    detailData.value = normalizeListPayload(list);
-  } catch (error) {
-    if (isStaleRequest(requestId)) {
-      return;
-    }
-    errorMessage.value = error?.message || '載入明細資料失敗';
-  } finally {
-    if (isStaleRequest(requestId)) {
-      return;
-    }
-    loading.list = false;
-  }
-}
+// ---- Computed ----
 
 function estimateAvgHoldHours(items) {
   const bucketHours = {
@@ -400,6 +350,8 @@ const holdTypeLabel = computed(() => {
   return '品質異常';
 });
 
+// ---- Event handlers ----
+
 function handleFilterChange(next) {
   const nextStartDate = next?.startDate || '';
   const nextEndDate = next?.endDate || '';
@@ -421,7 +373,13 @@ function handleFilterChange(next) {
   page.value = 1;
   updateUrlState();
 
-  void loadAllData({ includeTrend: dateChanged, showOverlay: false });
+  if (dateChanged) {
+    // Date changed -> new primary query (Oracle)
+    void executePrimaryQuery();
+  } else {
+    // Only hold_type changed -> view refresh (cache only)
+    void refreshView();
+  }
 }
 
 function handleRecordTypeChange() {
@@ -429,7 +387,7 @@ function handleRecordTypeChange() {
   durationFilter.value = '';
   page.value = 1;
   updateUrlState();
-  void loadAllData({ includeTrend: false, showOverlay: false });
+  void refreshView();
 }
 
 function handleReasonToggle(reason) {
@@ -441,7 +399,7 @@ function handleReasonToggle(reason) {
   reasonFilter.value = reasonFilter.value === nextReason ? '' : nextReason;
   page.value = 1;
   updateUrlState();
-  void loadReasonDependents();
+  void refreshView();
 }
 
 function clearReasonFilter() {
@@ -451,7 +409,7 @@ function clearReasonFilter() {
   reasonFilter.value = '';
   page.value = 1;
   updateUrlState();
-  void loadReasonDependents();
+  void refreshView();
 }
 
 function handleDurationToggle(range) {
@@ -463,7 +421,7 @@ function handleDurationToggle(range) {
   durationFilter.value = durationFilter.value === nextRange ? '' : nextRange;
   page.value = 1;
   updateUrlState();
-  void loadReasonDependents();
+  void refreshView();
 }
 
 function clearDurationFilter() {
@@ -473,7 +431,7 @@ function clearDurationFilter() {
   durationFilter.value = '';
   page.value = 1;
   updateUrlState();
-  void loadReasonDependents();
+  void refreshView();
 }
 
 function prevPage() {
@@ -482,7 +440,7 @@ function prevPage() {
   }
   page.value -= 1;
   updateUrlState();
-  void loadListOnly();
+  void refreshView({ listOnly: true });
 }
 
 function nextPage() {
@@ -492,13 +450,15 @@ function nextPage() {
   }
   page.value += 1;
   updateUrlState();
-  void loadListOnly();
+  void refreshView({ listOnly: true });
 }
 
 async function manualRefresh() {
   page.value = 1;
+  reasonFilter.value = '';
+  durationFilter.value = '';
   updateUrlState();
-  await loadAllData({ includeTrend: true, showOverlay: false });
+  await executePrimaryQuery();
 }
 
 onMounted(() => {
@@ -519,7 +479,7 @@ onMounted(() => {
     page.value = parsedPage;
   }
   updateUrlState();
-  void loadAllData({ includeTrend: true, showOverlay: true });
+  void executePrimaryQuery({ showOverlay: true });
 });
 </script>
 
diff --git a/frontend/src/job-query/composables/useJobQueryData.js b/frontend/src/job-query/composables/useJobQueryData.js
index 8aa45b5..0acea9f 100644
--- a/frontend/src/job-query/composables/useJobQueryData.js
+++ b/frontend/src/job-query/composables/useJobQueryData.js
@@ -174,7 +174,7 @@ export function useJobQueryData() {
     loadingResources.value = true;
     errorMessage.value = '';
     try {
-      const payload = await apiGet('/api/job-query/resources', { timeout: 60000, silent: true });
+      const payload = await apiGet('/api/job-query/resources', { timeout: 360000, silent: true });
       resources.value = Array.isArray(payload?.data) ? payload.data : [];
     } catch (error) {
       errorMessage.value = error?.message || '載入設備清單失敗';
@@ -206,7 +206,7 @@ export function useJobQueryData() {
           start_date: filters.startDate,
           end_date: filters.endDate,
         },
-        { timeout: 60000, silent: true },
+        { timeout: 360000, silent: true },
       );
       jobs.value = Array.isArray(payload?.data) ? payload.data : [];
       return true;
@@ -229,7 +229,7 @@ export function useJobQueryData() {
     errorMessage.value = '';
     try {
       const payload = await apiGet(`/api/job-query/txn/${encodeURIComponent(id)}`, {
-        timeout: 60000,
+        timeout: 360000,
         silent: true,
       });
       txnRows.value = Array.isArray(payload?.data) ? payload.data : [];
diff --git a/frontend/src/mid-section-defect/App.vue b/frontend/src/mid-section-defect/App.vue
index 1c0eeb7..1d7592a 100644
--- a/frontend/src/mid-section-defect/App.vue
+++ b/frontend/src/mid-section-defect/App.vue
@@ -16,7 +16,7 @@ import SuspectContextPanel from './components/SuspectContextPanel.vue';
 
 ensureMesApiAvailable();
 
-const API_TIMEOUT = 120000;
+const API_TIMEOUT = 360000;
 const PAGE_SIZE = 200;
 const SESSION_CACHE_KEY = 'msd:cache';
 const SESSION_CACHE_TTL = 5 * 60 * 1000; // 5 min, matches backend Redis TTL
diff --git a/frontend/src/query-tool/components/LotJobsTable.vue b/frontend/src/query-tool/components/LotJobsTable.vue
index 134b0d1..a55a893 100644
--- a/frontend/src/query-tool/components/LotJobsTable.vue
+++ b/frontend/src/query-tool/components/LotJobsTable.vue
@@ -135,7 +135,7 @@ async function loadTxn(jobId) {
 
   try {
     const payload = await apiGet(`/api/job-query/txn/${encodeURIComponent(id)}`, {
-      timeout: 60000,
+      timeout: 360000,
       silent: true,
     });
     txnRows.value = Array.isArray(payload?.data) ? payload.data : [];
diff --git a/frontend/src/query-tool/composables/useEquipmentQuery.js b/frontend/src/query-tool/composables/useEquipmentQuery.js
index a6d50a9..5338093 100644
--- a/frontend/src/query-tool/composables/useEquipmentQuery.js
+++ b/frontend/src/query-tool/composables/useEquipmentQuery.js
@@ -120,7 +120,7 @@ export function useEquipmentQuery(initial = {}) {
     const payload = await apiPost(
       '/api/query-tool/equipment-period',
       buildQueryPayload(queryType),
-      { timeout: 120000, silent: true },
+      { timeout: 360000, silent: true },
     );
 
     return Array.isArray(payload?.data) ? payload.data : [];
@@ -132,7 +132,7 @@ export function useEquipmentQuery(initial = {}) {
 
     try {
       const payload = await apiGet('/api/query-tool/equipment-list', {
-        timeout: 60000,
+        timeout: 360000,
         silent: true,
       });
       equipmentOptions.value = Array.isArray(payload?.data) ? payload.data : [];
diff --git a/frontend/src/query-tool/composables/useLotDetail.js b/frontend/src/query-tool/composables/useLotDetail.js
index 734d10f..8ad3a63 100644
--- a/frontend/src/query-tool/composables/useLotDetail.js
+++ b/frontend/src/query-tool/composables/useLotDetail.js
@@ -164,7 +164,7 @@ export function useLotDetail(initial = {}) {
 
     try {
       const payload = await apiGet('/api/query-tool/workcenter-groups', {
-        timeout: 60000,
+        timeout: 360000,
         silent: true,
       });
 
@@ -205,7 +205,7 @@ export function useLotDetail(initial = {}) {
       }
 
       const payload = await apiGet(`/api/query-tool/lot-history?${params.toString()}`, {
-        timeout: 120000,
+        timeout: 360000,
         silent: true,
       });
 
@@ -263,7 +263,7 @@ export function useLotDetail(initial = {}) {
         params.set('time_end', timeRange.time_end);
 
         const payload = await apiGet(`/api/query-tool/lot-associations?${params.toString()}`, {
-          timeout: 120000,
+          timeout: 360000,
           silent: true,
         });
 
@@ -279,7 +279,7 @@ export function useLotDetail(initial = {}) {
         params.set('type', associationType);
 
         const payload = await apiGet(`/api/query-tool/lot-associations?${params.toString()}`, {
-          timeout: 120000,
+          timeout: 360000,
           silent: true,
         });
 
diff --git a/frontend/src/query-tool/composables/useLotLineage.js b/frontend/src/query-tool/composables/useLotLineage.js
index 276bcae..859ec59 100644
--- a/frontend/src/query-tool/composables/useLotLineage.js
+++ b/frontend/src/query-tool/composables/useLotLineage.js
@@ -205,7 +205,7 @@ export function useLotLineage(initial = {}) {
             profile: 'query_tool',
             container_ids: containerIds,
           },
-          { timeout: 60000, silent: true },
+          { timeout: 360000, silent: true },
         );
       } catch (error) {
         const status = Number(error?.status || 0);
diff --git a/frontend/src/query-tool/composables/useLotResolve.js b/frontend/src/query-tool/composables/useLotResolve.js
index 279336a..0cc46ae 100644
--- a/frontend/src/query-tool/composables/useLotResolve.js
+++ b/frontend/src/query-tool/composables/useLotResolve.js
@@ -134,7 +134,7 @@ export function useLotResolve(initial = {}) {
           input_type: inputType.value,
           values,
         },
-        { timeout: 60000, silent: true },
+        { timeout: 360000, silent: true },
       );
 
       resolvedLots.value = Array.isArray(payload?.data) ? payload.data : [];
diff --git a/frontend/src/query-tool/composables/useReverseLineage.js b/frontend/src/query-tool/composables/useReverseLineage.js
index 588db38..44ae418 100644
--- a/frontend/src/query-tool/composables/useReverseLineage.js
+++ b/frontend/src/query-tool/composables/useReverseLineage.js
@@ -216,7 +216,7 @@ export function useReverseLineage(initial = {}) {
             profile: 'query_tool_reverse',
             container_ids: containerIds,
           },
-          { timeout: 60000, silent: true },
+          { timeout: 360000, silent: true },
         );
       } catch (error) {
         const status = Number(error?.status || 0);
diff --git a/frontend/src/reject-history/App.vue b/frontend/src/reject-history/App.vue
index b3bd626..f888155 100644
--- a/frontend/src/reject-history/App.vue
+++ b/frontend/src/reject-history/App.vue
@@ -15,7 +15,7 @@ import ParetoSection from './components/ParetoSection.vue';
 import SummaryCards from './components/SummaryCards.vue';
 import TrendChart from './components/TrendChart.vue';
 
-const API_TIMEOUT = 60000;
+const API_TIMEOUT = 360000;
 const DEFAULT_PER_PAGE = 50;
 
 // ---- Primary query form state ----
@@ -214,7 +214,11 @@ async function executePrimaryQuery() {
     updateUrlState();
   } catch (error) {
     if (isStaleRequest(requestId)) return;
-    errorMessage.value = error?.message || '主查詢執行失敗';
+    if (error?.name === 'AbortError') {
+      errorMessage.value = '查詢逾時，請縮短日期範圍後重試';
+    } else {
+      errorMessage.value = error?.message || '主查詢執行失敗';
+    }
   } finally {
     if (isStaleRequest(requestId)) return;
     loading.querying = false;
@@ -264,7 +268,11 @@ async function refreshView() {
     updateUrlState();
   } catch (error) {
     if (isStaleRequest(requestId)) return;
-    errorMessage.value = error?.message || '視圖查詢失敗';
+    if (error?.name === 'AbortError') {
+      errorMessage.value = '查詢逾時，請縮短日期範圍後重試';
+    } else {
+      errorMessage.value = error?.message || '視圖查詢失敗';
+    }
   } finally {
     if (isStaleRequest(requestId)) return;
     loading.list = false;
@@ -360,7 +368,9 @@ async function fetchDimensionPareto(dim) {
   } catch (err) {
     if (myId !== activeDimRequestId) return;
     dimensionParetoItems.value = [];
-    errorMessage.value = err.message || '查詢維度 Pareto 失敗';
+    if (err?.name !== 'AbortError') {
+      errorMessage.value = err.message || '查詢維度 Pareto 失敗';
+    }
   } finally {
     if (myId === activeDimRequestId) {
       dimensionParetoLoading.value = false;
diff --git a/frontend/src/resource-history/App.vue b/frontend/src/resource-history/App.vue
index 52f10da..ff27090 100644
--- a/frontend/src/resource-history/App.vue
+++ b/frontend/src/resource-history/App.vue
@@ -1,7 +1,7 @@
 <script setup>
 import { computed, onMounted, reactive, ref, watch } from 'vue';
 
-import { apiGet, ensureMesApiAvailable } from '../core/api.js';
+import { apiGet, apiPost, ensureMesApiAvailable } from '../core/api.js';
 import { buildResourceKpiFromHours } from '../core/compute.js';
 import {
   buildResourceHistoryQueryParams,
@@ -22,7 +22,7 @@ import TrendChart from './components/TrendChart.vue';
 
 ensureMesApiAvailable();
 
-const API_TIMEOUT = 60000;
+const API_TIMEOUT = 360000;
 const MAX_QUERY_DAYS = 730;
 
 function createDefaultFilters() {
@@ -66,6 +66,9 @@ const detailWarning = ref('');
 const exportMessage = ref('');
 const autoPruneHint = ref('');
 
+const queryId = ref('');
+const lastPrimarySnapshot = ref('');
+
 const draftWatchReady = ref(false);
 let suppressDraftPrune = false;
 
@@ -330,7 +333,47 @@ watch(pruneSignature, () => {
   pruneDraftSelections({ showHint: true });
 });
 
-async function executeCommittedQuery() {
+// ─── Two-phase helpers ──────────────────────────────────────
+
+/**
+ * Build a snapshot string of "primary" params (those that require a new Oracle query).
+ * Granularity is NOT primary — it can be re-derived from cache.
+ */
+function buildPrimarySnapshot(filters) {
+  const p = buildResourceHistoryQueryParams(filters);
+  return JSON.stringify({
+    start_date: p.start_date,
+    end_date: p.end_date,
+    workcenter_groups: p.workcenter_groups || [],
+    families: p.families || [],
+    resource_ids: p.resource_ids || [],
+    is_production: p.is_production || '',
+    is_key: p.is_key || '',
+    is_monitor: p.is_monitor || '',
+  });
+}
+
+function applyViewResult(result) {
+  const summary = result.summary || {};
+  summaryData.value = {
+    kpi: mergeComputedKpi(summary.kpi || {}),
+    trend: (summary.trend || []).map((item) => mergeComputedKpi(item || {})),
+    heatmap: summary.heatmap || [],
+    workcenter_comparison: summary.workcenter_comparison || [],
+  };
+
+  const detail = result.detail || {};
+  detailData.value = Array.isArray(detail.data) ? detail.data : [];
+  resetHierarchyState();
+
+  if (detail.truncated) {
+    detailWarning.value = `明細資料超過 ${detail.max_records} 筆，僅顯示前 ${detail.max_records} 筆。`;
+  } else {
+    detailWarning.value = '';
+  }
+}
+
+async function executePrimaryQuery() {
   const validationError = validateDateRange(committedFilters);
   if (validationError) {
     queryError.value = validationError;
@@ -344,46 +387,37 @@ async function executeCommittedQuery() {
   exportMessage.value = '';
 
   try {
-    const queryString = buildQueryStringFromFilters(committedFilters);
     updateUrlState();
 
-    const [summaryResponse, detailResponse] = await Promise.all([
-      apiGet(`/api/resource/history/summary?${queryString}`, {
-        timeout: API_TIMEOUT,
-        silent: true,
-      }),
-      apiGet(`/api/resource/history/detail?${queryString}`, {
-        timeout: API_TIMEOUT,
-        silent: true,
-      }),
-    ]);
-
-    const summaryPayload = unwrapApiResult(summaryResponse, '查詢摘要失敗');
-    const detailPayload = unwrapApiResult(detailResponse, '查詢明細失敗');
-
-    const rawSummary = summaryPayload.data || {};
-    summaryData.value = {
-      ...rawSummary,
-      kpi: mergeComputedKpi(rawSummary.kpi || {}),
-      trend: (rawSummary.trend || []).map((item) => mergeComputedKpi(item || {})),
-      heatmap: rawSummary.heatmap || [],
-      workcenter_comparison: rawSummary.workcenter_comparison || [],
+    const queryParams = buildResourceHistoryQueryParams(committedFilters);
+    const body = {
+      start_date: queryParams.start_date,
+      end_date: queryParams.end_date,
+      granularity: queryParams.granularity,
+      workcenter_groups: queryParams.workcenter_groups || [],
+      families: queryParams.families || [],
+      resource_ids: queryParams.resource_ids || [],
+      is_production: !!queryParams.is_production,
+      is_key: !!queryParams.is_key,
+      is_monitor: !!queryParams.is_monitor,
     };
 
-    detailData.value = Array.isArray(detailPayload.data) ? detailPayload.data : [];
-    resetHierarchyState();
+    const response = await apiPost('/api/resource/history/query', body, {
+      timeout: API_TIMEOUT,
+      silent: true,
+    });
 
-    if (detailPayload.truncated) {
-      detailWarning.value = `明細資料超過 ${detailPayload.max_records} 筆，僅顯示前 ${detailPayload.max_records} 筆。`;
-    }
+    const payload = unwrapApiResult(response, '查詢失敗');
+    queryId.value = payload.query_id || '';
+    lastPrimarySnapshot.value = buildPrimarySnapshot(committedFilters);
+    applyViewResult(payload);
   } catch (error) {
-    queryError.value = error?.message || '查詢失敗';
-    summaryData.value = {
-      kpi: {},
-      trend: [],
-      heatmap: [],
-      workcenter_comparison: [],
-    };
+    if (error?.name === 'AbortError') {
+      queryError.value = '查詢逾時，請縮小日期範圍或資源篩選後重試';
+    } else {
+      queryError.value = error?.message || '查詢失敗';
+    }
+    summaryData.value = { kpi: {}, trend: [], heatmap: [], workcenter_comparison: [] };
     detailData.value = [];
     resetHierarchyState();
   } finally {
@@ -392,6 +426,47 @@ async function executeCommittedQuery() {
   }
 }
 
+async function refreshView() {
+  if (!queryId.value) {
+    await executePrimaryQuery();
+    return;
+  }
+
+  loading.querying = true;
+  queryError.value = '';
+
+  try {
+    updateUrlState();
+
+    const response = await apiGet('/api/resource/history/view', {
+      timeout: API_TIMEOUT,
+      silent: true,
+      params: {
+        query_id: queryId.value,
+        granularity: committedFilters.granularity || 'day',
+      },
+    });
+
+    if (response?.success === false && response?.error === 'cache_expired') {
+      await executePrimaryQuery();
+      return;
+    }
+
+    const payload = unwrapApiResult(response, '查詢失敗');
+    applyViewResult(payload);
+  } catch (error) {
+    if (error?.message === 'cache_expired' || error?.status === 410) {
+      await executePrimaryQuery();
+      return;
+    }
+    queryError.value = error?.message || '查詢失敗';
+  } finally {
+    loading.querying = false;
+  }
+}
+
+// ─── Filter actions ─────────────────────────────────────────
+
 async function applyFilters() {
   const validationError = validateDateRange(draftFilters);
   if (validationError) {
@@ -400,7 +475,13 @@ async function applyFilters() {
   }
   pruneDraftSelections({ showHint: true });
   assignFilterState(committedFilters, draftFilters);
-  await executeCommittedQuery();
+
+  const currentPrimary = buildPrimarySnapshot(committedFilters);
+  if (queryId.value && currentPrimary === lastPrimarySnapshot.value) {
+    await refreshView();
+  } else {
+    await executePrimaryQuery();
+  }
 }
 
 async function clearFilters() {
@@ -409,7 +490,7 @@ async function clearFilters() {
   });
   autoPruneHint.value = '';
   assignFilterState(committedFilters, draftFilters);
-  await executeCommittedQuery();
+  await executePrimaryQuery();
 }
 
 function updateFilters(nextFilters) {
@@ -462,7 +543,7 @@ async function initPage() {
   }
 
   draftWatchReady.value = true;
-  await executeCommittedQuery();
+  await executePrimaryQuery();
 }
 
 onMounted(() => {
diff --git a/frontend/src/shared-composables/useTraceProgress.js b/frontend/src/shared-composables/useTraceProgress.js
index f80b84e..9dc50b0 100644
--- a/frontend/src/shared-composables/useTraceProgress.js
+++ b/frontend/src/shared-composables/useTraceProgress.js
@@ -4,7 +4,7 @@ import { apiPost, ensureMesApiAvailable } from '../core/api.js';
 
 ensureMesApiAvailable();
 
-const DEFAULT_STAGE_TIMEOUT_MS = 60000;
+const DEFAULT_STAGE_TIMEOUT_MS = 360000;
 const PROFILE_DOMAINS = Object.freeze({
   query_tool: ['history', 'materials', 'rejects', 'holds', 'jobs'],
   mid_section_defect: ['upstream_history', 'materials'],
diff --git a/gunicorn.conf.py b/gunicorn.conf.py
index 56a31ae..c61f7db 100644
--- a/gunicorn.conf.py
+++ b/gunicorn.conf.py
@@ -6,9 +6,9 @@ threads = int(os.getenv("GUNICORN_THREADS", "4"))
 worker_class = "gthread"
 
 # Timeout settings - critical for dashboard stability.
-# Keep this above slow-query timeout paths (e.g. query-tool 120s) and DB pool timeout.
-timeout = int(os.getenv("GUNICORN_TIMEOUT", "130"))
-graceful_timeout = int(os.getenv("GUNICORN_GRACEFUL_TIMEOUT", "60"))
+# Keep this above slow-query timeout paths (e.g. read_sql_df_slow 300s) and DB pool timeout.
+timeout = int(os.getenv("GUNICORN_TIMEOUT", "360"))
+graceful_timeout = int(os.getenv("GUNICORN_GRACEFUL_TIMEOUT", "120"))
 keepalive = 5         # Keep-alive connections timeout
 
 # Worker lifecycle management - prevent state accumulation.
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/.openspec.yaml b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/.openspec.yaml
new file mode 100644
index 0000000..e331c97
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/.openspec.yaml
@@ -0,0 +1,2 @@
+schema: spec-driven
+created: 2026-02-25
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/design.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/design.md
new file mode 100644
index 0000000..d331621
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/design.md
@@ -0,0 +1,84 @@
+## Context
+
+Hold-history fires 4 independent Oracle queries per user interaction (trend, reason-pareto, duration, list), all against `DW_MES_HOLDRELEASEHISTORY` with the same date range + hold_type filter. Resource-history fires 4 Oracle queries (3 parallel in summary + 1 detail), all against `DW_MES_RESOURCESTATUS_SHIFT` with the same filter set. Both pages re-query Oracle on every filter change or pagination.
+
+Reject-history already solved this problem with a two-phase dataset cache pattern (`reject_dataset_cache.py`): one Oracle query caches the full fact set, subsequent views are derived from cache via pandas. This change applies the same pattern to hold-history and resource-history.
+
+## Goals / Non-Goals
+
+**Goals:**
+- Reduce Oracle queries from 4 per interaction to 1 per user session (per filter combination)
+- Same cache infrastructure as reject-history: L1 (ProcessLevelCache) + L2 (Redis parquet/base64), 15-minute TTL
+- Same API pattern: POST /query (primary) + GET /view (supplementary, from cache)
+- Maintain all existing UI functionality — same charts, tables, filters, pagination
+- Frontend adopts queryId-based two-phase flow
+
+**Non-Goals:**
+- Changing the SQL queries themselves (same table, same WHERE logic)
+- Adding new visualizations or metrics
+- Modifying other pages (reject-history, query-tool, etc.)
+- Changing the department endpoint on hold-history (it has unique person-level expansion logic that benefits from its own query — we keep it as a separate call)
+
+## Decisions
+
+### D1: Follow reject_dataset_cache.py architecture exactly
+
+**Decision**: Create `hold_dataset_cache.py` and `resource_dataset_cache.py` following the same module structure:
+- `_make_query_id()` — SHA256 hash of primary params
+- `_redis_store_df()` / `_redis_load_df()` — parquet/base64 encoding
+- `_get_cached_df()` / `_store_df()` — L1 → L2 read-through
+- `execute_primary_query()` — Oracle query + cache + derive initial view
+- `apply_view()` — read cache + filter + re-derive
+
+**Rationale**: Proven pattern, consistent codebase, shared infrastructure. Alternatives (custom cache format, separate cache layers) add complexity for no benefit.
+
+### D2: Hold-history primary query scope
+
+**Decision**: The primary query fetches ALL hold/release records for the date range (all hold_types). Trend, reason-pareto, duration, and list are all derived from this single cached DataFrame. Department remains a separate API call.
+
+**Rationale**: Trend data already contains all 3 hold_type variants in one query. By caching the raw facts (not pre-aggregated), we can switch hold_type views instantly from cache. Department has unique person-level JOINs and GROUP BY logic that doesn't fit the "filter from flat DataFrame" pattern cleanly.
+
+**Alternatives considered**:
+- Cache per hold_type: wastes 3x cache memory, still requires Oracle for type switching
+- Include department in cache: complex person-level aggregation doesn't map well to flat DataFrame filtering
+
+### D3: Resource-history primary query scope
+
+**Decision**: The primary query fetches ALL shift-status records for the date range and resource filter combination. KPI, trend, heatmap, workcenter comparison, and detail are all derived from this single cached DataFrame.
+
+**Rationale**: All 4 current queries (kpi, trend, heatmap, detail) use the same base WHERE clause against the same table. The aggregations (GROUP BY date for trend, GROUP BY workcenter×date for heatmap, etc.) are simple pandas operations on the cached raw data.
+
+### D4: Cache TTL = 15 minutes, same as reject-history
+
+**Decision**: Use `_CACHE_TTL = 900` (15 min) for both modules, with L1 `max_size = 8`.
+
+**Rationale**: Matches reject-history. 15 minutes covers typical analysis sessions. Users who need fresh data can re-query (which replaces the cache). Hold-history's existing 12h Redis cache for trend data is more aggressive but stale — 15 minutes is a better balance.
+
+### D5: API contract — POST /query + GET /view
+
+**Decision**: Both pages switch to:
+- `POST /api/hold-history/query` → primary query, returns `query_id` + initial view (trend, reason, duration, list page 1)
+- `GET /api/hold-history/view` → supplementary filter/pagination from cache
+- `POST /api/resource/history/query` → primary query, returns `query_id` + initial view (summary + detail page 1)
+- `GET /api/resource/history/view` → supplementary filter/pagination from cache
+
+Old GET endpoints (trend, reason-pareto, duration, list, summary, detail) are removed.
+
+**Rationale**: Same pattern as reject-history. POST for primary (sends filter params in body), GET for view (sends query_id + supplementary params).
+
+### D6: Frontend queryId-based flow
+
+**Decision**: Both `App.vue` files adopt the two-phase pattern:
+1. User clicks "查詢" → `POST /query` → store `queryId`
+2. Filter change / pagination → `GET /view?query_id=...&filters...` (no Oracle)
+3. Cache expired (HTTP 410) → auto re-execute primary query
+
+**Rationale**: Proven pattern from reject-history. Keeps UI responsive after initial query.
+
+## Risks / Trade-offs
+
+- **[Memory]** Caching full DataFrames in L1 (per-worker) uses more RAM than current approach → Mitigated by `max_size=8` LRU eviction (same as reject-history, works well in production)
+- **[Staleness]** 15-min TTL means data could be up to 15 minutes old during an analysis session → Acceptable for historical analysis; user can re-query for fresh data
+- **[Department endpoint]** Hold-history department still makes a separate Oracle call → Acceptable trade-off; person-level aggregation doesn't fit flat DataFrame model. Could be addressed later.
+- **[Breaking API]** Old GET endpoints removed → No external consumers; frontend is the only client
+- **[Redis dependency]** If Redis is down, only L1 cache works (per-worker, not cross-worker) → Same behavior as reject-history; L1 still provides 15-min cache per worker
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/proposal.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/proposal.md
new file mode 100644
index 0000000..e68dfa0
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/proposal.md
@@ -0,0 +1,31 @@
+## Why
+
+Hold-history and resource-history pages currently fire 4 separate Oracle queries per user interaction (filter change, pagination, refresh), all hitting the same base table with identical filter parameters. This wastes Oracle connections and creates unnecessary latency — especially now that these pages use `read_sql_df_slow` (dedicated connections with 300s timeout). The reject-history page already solves this with a "single query + cache derivation" pattern that reduces Oracle load by ~75%. Hold-history and resource-history should adopt the same architecture.
+
+## What Changes
+
+- **New `hold_dataset_cache.py`**: Two-phase cache module for hold-history. Single Oracle query caches the full hold/release fact set; subsequent views (trend, reason pareto, duration distribution, paginated list) are derived from cache using pandas.
+- **New `resource_dataset_cache.py`**: Two-phase cache module for resource-history. Single Oracle query caches the full shift-status fact set; subsequent views (KPI, trend, heatmap, workcenter comparison, paginated detail) are derived from cache using pandas.
+- **Hold-history route rewrite**: Replace 4 independent GET endpoints with POST /query (primary) + GET /view (supplementary) pattern.
+- **Resource-history route rewrite**: Replace GET /summary (3 parallel queries) + GET /detail (1 query) with POST /query + GET /view pattern.
+- **Frontend two-phase flow**: Both pages adopt queryId-based flow — primary query returns queryId + initial view; filter/pagination changes call GET /view with queryId (no Oracle).
+- **Cache infrastructure**: L1 (ProcessLevelCache, in-process) + L2 (Redis, parquet/base64), 15-minute TTL, deterministic query ID from SHA256 of primary params. Same architecture as `reject_dataset_cache.py`.
+
+## Capabilities
+
+### New Capabilities
+- `hold-dataset-cache`: Two-phase dataset cache for hold-history (single Oracle query + in-memory derivation for trend, reason pareto, duration, paginated list)
+- `resource-dataset-cache`: Two-phase dataset cache for resource-history (single Oracle query + in-memory derivation for KPI, trend, heatmap, comparison, paginated detail)
+
+### Modified Capabilities
+- `hold-history-api`: Route endpoints change from 4 independent GETs to POST /query + GET /view
+- `hold-history-page`: Frontend adopts two-phase queryId flow
+- `resource-history-page`: Frontend adopts two-phase queryId flow; route endpoints consolidated
+
+## Impact
+
+- **Backend**: New files `hold_dataset_cache.py`, `resource_dataset_cache.py`; modified routes for both pages; service functions remain but are called only once per primary query
+- **Frontend**: `hold-history/App.vue` and `resource-history/App.vue` rewritten for two-phase flow
+- **Oracle load**: ~75% reduction per page (4 queries → 1 per user session, subsequent interactions from cache)
+- **Redis**: Additional cache entries (~2 namespaces, same TTL/encoding as reject_dataset)
+- **API contract**: Endpoint signatures change (breaking for these 2 pages, but no external consumers)
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-dataset-cache/spec.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-dataset-cache/spec.md
new file mode 100644
index 0000000..733da0b
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-dataset-cache/spec.md
@@ -0,0 +1,64 @@
+## ADDED Requirements
+
+### Requirement: Hold dataset cache SHALL execute a single Oracle query and cache the result
+The hold_dataset_cache module SHALL query Oracle once for the full hold/release fact set and cache it for subsequent derivations.
+
+#### Scenario: Primary query execution and caching
+- **WHEN** `execute_primary_query()` is called with date range and hold_type parameters
+- **THEN** a deterministic `query_id` SHALL be computed from the primary params (start_date, end_date) using SHA256
+- **THEN** if a cached DataFrame exists for this query_id (L1 or L2), it SHALL be used without querying Oracle
+- **THEN** if no cache exists, a single Oracle query SHALL fetch all hold/release records from `DW_MES_HOLDRELEASEHISTORY` for the date range (all hold_types)
+- **THEN** the result DataFrame SHALL be stored in both L1 (ProcessLevelCache) and L2 (Redis as parquet/base64)
+- **THEN** the response SHALL include `query_id`, trend, reason_pareto, duration, and list page 1
+
+#### Scenario: Cache TTL and eviction
+- **WHEN** a DataFrame is cached
+- **THEN** the cache TTL SHALL be 900 seconds (15 minutes)
+- **THEN** L1 cache max_size SHALL be 8 entries with LRU eviction
+- **THEN** the Redis namespace SHALL be `hold_dataset`
+
+### Requirement: Hold dataset cache SHALL derive trend data from cached DataFrame
+The module SHALL compute daily trend aggregations from the cached fact set.
+
+#### Scenario: Trend derivation from cache
+- **WHEN** `apply_view()` is called with a valid query_id
+- **THEN** trend data SHALL be derived by grouping the cached DataFrame by date
+- **THEN** the 07:30 shift boundary rule SHALL be applied
+- **THEN** all three hold_type variants (quality, non_quality, all) SHALL be computed from the same DataFrame
+- **THEN** hold_type filtering SHALL be applied in-memory without re-querying Oracle
+
+### Requirement: Hold dataset cache SHALL derive reason Pareto from cached DataFrame
+The module SHALL compute reason distribution from the cached fact set.
+
+#### Scenario: Reason Pareto derivation
+- **WHEN** `apply_view()` is called with hold_type filter
+- **THEN** reason Pareto SHALL be derived by grouping the filtered DataFrame by HOLDREASONNAME
+- **THEN** items SHALL include count, qty, pct, and cumPct
+- **THEN** items SHALL be sorted by count descending
+
+### Requirement: Hold dataset cache SHALL derive duration distribution from cached DataFrame
+The module SHALL compute hold duration buckets from the cached fact set.
+
+#### Scenario: Duration derivation
+- **WHEN** `apply_view()` is called with hold_type filter
+- **THEN** duration distribution SHALL be derived from records where RELEASETXNDATE IS NOT NULL
+- **THEN** 4 buckets SHALL be computed: <4h, 4-24h, 1-3d, >3d
+- **THEN** each bucket SHALL include count and pct
+
+### Requirement: Hold dataset cache SHALL derive paginated list from cached DataFrame
+The module SHALL provide paginated detail records from the cached fact set.
+
+#### Scenario: List pagination from cache
+- **WHEN** `apply_view()` is called with page and per_page parameters
+- **THEN** the cached DataFrame SHALL be filtered by hold_type and optional reason filter
+- **THEN** records SHALL be sorted by HOLDTXNDATE descending
+- **THEN** pagination SHALL be applied in-memory (offset + limit on the sorted DataFrame)
+- **THEN** response SHALL include items and pagination metadata (page, perPage, total, totalPages)
+
+### Requirement: Hold dataset cache SHALL handle cache expiry gracefully
+The module SHALL return appropriate signals when cache has expired.
+
+#### Scenario: Cache expired during view request
+- **WHEN** `apply_view()` is called with a query_id whose cache has expired
+- **THEN** the response SHALL return `{ success: false, error: "cache_expired" }`
+- **THEN** the HTTP status SHALL be 410 (Gone)
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-api/spec.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-api/spec.md
new file mode 100644
index 0000000..bf4a499
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-api/spec.md
@@ -0,0 +1,62 @@
+## MODIFIED Requirements
+
+### Requirement: Hold History API SHALL provide daily trend data with Redis caching
+The Hold History API SHALL return trend, reason-pareto, duration, and list data from a single cached dataset via a two-phase query pattern (POST /query + GET /view). The old independent GET endpoints for trend, reason-pareto, duration, and list SHALL be replaced.
+
+#### Scenario: Primary query endpoint
+- **WHEN** `POST /api/hold-history/query` is called with `{ start_date, end_date, hold_type }`
+- **THEN** the service SHALL execute a single Oracle query (or read from cache) via `hold_dataset_cache.execute_primary_query()`
+- **THEN** the response SHALL return `{ success: true, data: { query_id, trend, reason_pareto, duration, list, summary } }`
+- **THEN** list SHALL contain page 1 with default per_page of 50
+
+#### Scenario: Supplementary view endpoint
+- **WHEN** `GET /api/hold-history/view?query_id=...&hold_type=...&reason=...&page=...&per_page=...` is called
+- **THEN** the service SHALL read the cached DataFrame and derive filtered views via `hold_dataset_cache.apply_view()`
+- **THEN** no Oracle query SHALL be executed
+- **THEN** the response SHALL return `{ success: true, data: { trend, reason_pareto, duration, list } }`
+
+#### Scenario: Cache expired on view request
+- **WHEN** GET /view is called with an expired query_id
+- **THEN** the response SHALL return `{ success: false, error: "cache_expired" }` with HTTP 410
+
+#### Scenario: Trend uses shift boundary at 07:30
+- **WHEN** daily aggregation is calculated
+- **THEN** transactions with time >= 07:30 SHALL be attributed to the next calendar day
+- **THEN** transactions with time < 07:30 SHALL be attributed to the current calendar day
+
+#### Scenario: Trend hold type classification
+- **WHEN** trend data is aggregated by hold type
+- **THEN** quality classification SHALL use the same NON_QUALITY_HOLD_REASONS set as existing hold endpoints
+
+### Requirement: Hold History API SHALL provide reason Pareto data
+The reason Pareto data SHALL be derived from the cached dataset, not from a separate Oracle query.
+
+#### Scenario: Reason Pareto from cache
+- **WHEN** reason Pareto is requested via GET /view with hold_type filter
+- **THEN** the cached DataFrame SHALL be filtered by hold_type and grouped by HOLDREASONNAME
+- **THEN** each item SHALL contain `{ reason, count, qty, pct, cumPct }`
+- **THEN** items SHALL be sorted by count descending
+
+### Requirement: Hold History API SHALL provide hold duration distribution
+The duration distribution SHALL be derived from the cached dataset.
+
+#### Scenario: Duration from cache
+- **WHEN** duration is requested via GET /view
+- **THEN** the cached DataFrame SHALL be filtered to released holds only
+- **THEN** 4 buckets SHALL be computed: <4h, 4-24h, 1-3d, >3d
+
+### Requirement: Hold History API SHALL provide paginated detail list
+The detail list SHALL be paginated from the cached dataset.
+
+#### Scenario: List pagination from cache
+- **WHEN** list is requested via GET /view with page and per_page params
+- **THEN** the cached DataFrame SHALL be filtered and paginated in-memory
+- **THEN** response SHALL include items and pagination metadata
+
+### Requirement: Hold History API SHALL keep department endpoint as separate query
+The department endpoint SHALL remain as a separate Oracle query due to its unique person-level aggregation.
+
+#### Scenario: Department endpoint unchanged
+- **WHEN** `GET /api/hold-history/department` is called
+- **THEN** it SHALL continue to execute its own Oracle query
+- **THEN** it SHALL NOT use the dataset cache
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-page/spec.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-page/spec.md
new file mode 100644
index 0000000..bbde61c
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/hold-history-page/spec.md
@@ -0,0 +1,34 @@
+## MODIFIED Requirements
+
+### Requirement: Hold History page SHALL display a filter bar with date range and hold type
+The page SHALL provide a filter bar for selecting date range and hold type classification. On query, the page SHALL use a two-phase flow: POST /query returns queryId, subsequent filter changes use GET /view.
+
+#### Scenario: Primary query via POST /query
+- **WHEN** user clicks the query button (or page loads with default filters)
+- **THEN** the page SHALL call `POST /api/hold-history/query` with `{ start_date, end_date, hold_type }`
+- **THEN** the response queryId SHALL be stored for subsequent view requests
+- **THEN** trend, reason-pareto, duration, and list SHALL all be populated from the single response
+
+#### Scenario: Hold type or reason filter change uses GET /view
+- **WHEN** user changes hold_type radio or clicks a reason in the Pareto chart (while queryId exists)
+- **THEN** the page SHALL call `GET /api/hold-history/view?query_id=...&hold_type=...&reason=...`
+- **THEN** no new Oracle query SHALL be triggered
+- **THEN** trend, reason-pareto, duration, and list SHALL update from the view response
+
+#### Scenario: Pagination uses GET /view
+- **WHEN** user navigates to a different page in the detail list
+- **THEN** the page SHALL call `GET /api/hold-history/view?query_id=...&page=...&per_page=...`
+
+#### Scenario: Date range change triggers new primary query
+- **WHEN** user changes the date range and clicks query
+- **THEN** the page SHALL call `POST /api/hold-history/query` with new dates
+- **THEN** a new queryId SHALL replace the old one
+
+#### Scenario: Cache expired auto-retry
+- **WHEN** GET /view returns `{ success: false, error: "cache_expired" }`
+- **THEN** the page SHALL automatically re-execute `POST /api/hold-history/query` with the last committed filters
+- **THEN** the view SHALL refresh with the new data
+
+#### Scenario: Department still uses separate API
+- **WHEN** department data needs to load or reload
+- **THEN** the page SHALL call `GET /api/hold-history/department` separately
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-dataset-cache/spec.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-dataset-cache/spec.md
new file mode 100644
index 0000000..39a2f4a
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-dataset-cache/spec.md
@@ -0,0 +1,71 @@
+## ADDED Requirements
+
+### Requirement: Resource dataset cache SHALL execute a single Oracle query and cache the result
+The resource_dataset_cache module SHALL query Oracle once for the full shift-status fact set and cache it for subsequent derivations.
+
+#### Scenario: Primary query execution and caching
+- **WHEN** `execute_primary_query()` is called with date range, granularity, and resource filter parameters
+- **THEN** a deterministic `query_id` SHALL be computed from all primary params using SHA256
+- **THEN** if a cached DataFrame exists for this query_id (L1 or L2), it SHALL be used without querying Oracle
+- **THEN** if no cache exists, a single Oracle query SHALL fetch all shift-status records from `DW_MES_RESOURCESTATUS_SHIFT` for the filtered resources and date range
+- **THEN** the result DataFrame SHALL be stored in both L1 (ProcessLevelCache) and L2 (Redis as parquet/base64)
+- **THEN** the response SHALL include `query_id`, summary (KPI, trend, heatmap, comparison), and detail page 1
+
+#### Scenario: Cache TTL and eviction
+- **WHEN** a DataFrame is cached
+- **THEN** the cache TTL SHALL be 900 seconds (15 minutes)
+- **THEN** L1 cache max_size SHALL be 8 entries with LRU eviction
+- **THEN** the Redis namespace SHALL be `resource_dataset`
+
+### Requirement: Resource dataset cache SHALL derive KPI summary from cached DataFrame
+The module SHALL compute aggregated KPI metrics from the cached fact set.
+
+#### Scenario: KPI derivation from cache
+- **WHEN** summary view is derived from cached DataFrame
+- **THEN** total hours for PRD, SBY, UDT, SDT, EGT, NST SHALL be summed
+- **THEN** OU% and AVAIL% SHALL be computed from the hour totals
+- **THEN** machine count SHALL be the distinct count of HISTORYID in the cached data
+
+### Requirement: Resource dataset cache SHALL derive trend data from cached DataFrame
+The module SHALL compute time-series aggregations from the cached fact set.
+
+#### Scenario: Trend derivation
+- **WHEN** summary view is derived with a given granularity (day/week/month/year)
+- **THEN** the cached DataFrame SHALL be grouped by the granularity period
+- **THEN** each period SHALL include PRD, SBY, UDT, SDT, EGT, NST hours and computed OU%, AVAIL%
+
+### Requirement: Resource dataset cache SHALL derive heatmap from cached DataFrame
+The module SHALL compute workcenter × date OU% matrix from the cached fact set.
+
+#### Scenario: Heatmap derivation
+- **WHEN** summary view is derived
+- **THEN** the cached DataFrame SHALL be grouped by (workcenter, date)
+- **THEN** each cell SHALL contain the OU% for that workcenter on that date
+- **THEN** workcenters SHALL be sorted by workcenter_seq
+
+### Requirement: Resource dataset cache SHALL derive workcenter comparison from cached DataFrame
+The module SHALL compute per-workcenter aggregated metrics from the cached fact set.
+
+#### Scenario: Comparison derivation
+- **WHEN** summary view is derived
+- **THEN** the cached DataFrame SHALL be grouped by workcenter
+- **THEN** each workcenter SHALL include total hours and computed OU%
+- **THEN** results SHALL be sorted by OU% descending, limited to top 15
+
+### Requirement: Resource dataset cache SHALL derive paginated detail from cached DataFrame
+The module SHALL provide hierarchical detail records from the cached fact set.
+
+#### Scenario: Detail derivation and pagination
+- **WHEN** detail view is requested with page and per_page parameters
+- **THEN** the cached DataFrame SHALL be used to compute per-resource metrics
+- **THEN** resource dimension data (WORKCENTERNAME, RESOURCEFAMILYNAME) SHALL be merged from resource_cache
+- **THEN** results SHALL be structured as a hierarchical tree (workcenter → family → resource)
+- **THEN** pagination SHALL apply to the flattened list
+
+### Requirement: Resource dataset cache SHALL handle cache expiry gracefully
+The module SHALL return appropriate signals when cache has expired.
+
+#### Scenario: Cache expired during view request
+- **WHEN** a view is requested with a query_id whose cache has expired
+- **THEN** the response SHALL return `{ success: false, error: "cache_expired" }`
+- **THEN** the HTTP status SHALL be 410 (Gone)
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-history-page/spec.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-history-page/spec.md
new file mode 100644
index 0000000..d168082
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/specs/resource-history-page/spec.md
@@ -0,0 +1,46 @@
+## MODIFIED Requirements
+
+### Requirement: Resource History page SHALL support date range and granularity selection
+The page SHALL allow users to specify time range and aggregation granularity. On query, the page SHALL use a two-phase flow: POST /query returns queryId, subsequent filter changes use GET /view.
+
+#### Scenario: Primary query via POST /query
+- **WHEN** user clicks the query button
+- **THEN** the page SHALL call `POST /api/resource/history/query` with date range, granularity, and resource filters
+- **THEN** the response queryId SHALL be stored for subsequent view requests
+- **THEN** summary (KPI, trend, heatmap, comparison) and detail page 1 SHALL all be populated from the single response
+
+#### Scenario: Filter change uses GET /view
+- **WHEN** user changes supplementary filters (workcenter groups, families, machines, equipment type) while queryId exists
+- **THEN** the page SHALL call `GET /api/resource/history/view?query_id=...&filters...`
+- **THEN** no new Oracle query SHALL be triggered
+- **THEN** all charts, KPI cards, and detail table SHALL update from the view response
+
+#### Scenario: Pagination uses GET /view
+- **WHEN** user navigates to a different page in the detail table
+- **THEN** the page SHALL call `GET /api/resource/history/view?query_id=...&page=...`
+
+#### Scenario: Date range or granularity change triggers new primary query
+- **WHEN** user changes date range or granularity and clicks query
+- **THEN** the page SHALL call `POST /api/resource/history/query` with new params
+- **THEN** a new queryId SHALL replace the old one
+
+#### Scenario: Cache expired auto-retry
+- **WHEN** GET /view returns `{ success: false, error: "cache_expired" }`
+- **THEN** the page SHALL automatically re-execute `POST /api/resource/history/query` with the last committed filters
+- **THEN** the view SHALL refresh with the new data
+
+### Requirement: Resource History page SHALL display KPI summary cards
+The page SHALL show 9 KPI cards with aggregated performance metrics derived from the cached dataset.
+
+#### Scenario: KPI cards from cached data
+- **WHEN** summary data is derived from the cached DataFrame
+- **THEN** 9 cards SHALL display: OU%, AVAIL%, PRD, SBY, UDT, SDT, EGT, NST, Machine Count
+- **THEN** values SHALL be computed from the cached shift-status records, not from a separate Oracle query
+
+### Requirement: Resource History page SHALL display hierarchical detail table
+The page SHALL show a three-level expandable table derived from the cached dataset.
+
+#### Scenario: Detail table from cached data
+- **WHEN** detail data is derived from the cached DataFrame
+- **THEN** a tree table SHALL display with the same columns and hierarchy as before
+- **THEN** data SHALL be derived in-memory from the cached DataFrame, not from a separate Oracle query
diff --git a/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/tasks.md b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/tasks.md
new file mode 100644
index 0000000..2fc77a1
--- /dev/null
+++ b/openspec/changes/archive/2026-02-25-hold-resource-history-dataset-cache/tasks.md
@@ -0,0 +1,42 @@
+## 1. Hold-History Dataset Cache (Backend)
+
+- [x] 1.1 Create `src/mes_dashboard/services/hold_dataset_cache.py` — module scaffolding: imports, logger, ProcessLevelCache (TTL=900, max_size=8), Redis namespace `hold_dataset`, `_make_query_id()`, `_redis_store_df()` / `_redis_load_df()`, `_get_cached_df()` / `_store_df()`
+- [x] 1.2 Implement `execute_primary_query(start_date, end_date)` — single Oracle query fetching ALL hold/release facts for date range (all hold_types), cache result, derive initial view (trend, reason_pareto, duration, list page 1) using existing service functions
+- [x] 1.3 Implement `apply_view(query_id, hold_type, reason, page, per_page)` — read cached DF, apply hold_type filter, derive trend + reason_pareto + duration + paginated list from filtered DF; return 410 on cache miss
+- [x] 1.4 Implement in-memory derivation helpers: `_derive_trend(df, hold_type)`, `_derive_reason_pareto(df, hold_type)`, `_derive_duration(df, hold_type)`, `_derive_list(df, hold_type, reason, page, per_page)` — reuse shift boundary and hold_type classification logic from `hold_history_service.py`
+
+## 2. Hold-History Routes (Backend)
+
+- [x] 2.1 Add `POST /api/hold-history/query` route — parse body `{ start_date, end_date, hold_type }`, call `hold_dataset_cache.execute_primary_query()`, return `{ query_id, trend, reason_pareto, duration, list, summary }`
+- [x] 2.2 Add `GET /api/hold-history/view` route — parse query params `query_id, hold_type, reason, page, per_page`, call `hold_dataset_cache.apply_view()`, return derived views or 410 on cache miss
+- [x] 2.3 Remove old GET endpoints: `/api/hold-history/trend`, `/api/hold-history/reason-pareto`, `/api/hold-history/duration`, `/api/hold-history/list` — keep page route and department route (if exists)
+
+## 3. Hold-History Frontend
+
+- [x] 3.1 Rewrite `frontend/src/hold-history/App.vue` — two-phase flow: initial load calls `POST /query` → store queryId; hold_type change, reason filter, pagination call `GET /view?query_id=...`; cache expired (410) → auto re-execute primary query
+- [x] 3.2 Derive summary KPI cards from trend data returned by query/view response (no separate API call)
+- [x] 3.3 Update all chart components to consume data from the unified query/view response instead of individual API results
+
+## 4. Resource-History Dataset Cache (Backend)
+
+- [x] 4.1 Create `src/mes_dashboard/services/resource_dataset_cache.py` — module scaffolding: same cache infrastructure (TTL=900, max_size=8), Redis namespace `resource_dataset`, query ID helpers, L1+L2 cache read/write
+- [x] 4.2 Implement `execute_primary_query(params)` — single Oracle query fetching ALL shift-status records for date range + resource filters, cache result, derive initial view (summary: kpi + trend + heatmap + comparison, detail page 1) using existing service functions
+- [x] 4.3 Implement `apply_view(query_id, granularity, page, per_page)` — read cached DF, derive summary + paginated detail; return 410 on cache miss
+- [x] 4.4 Implement in-memory derivation helpers: `_derive_kpi(df)`, `_derive_trend(df, granularity)`, `_derive_heatmap(df)`, `_derive_comparison(df)`, `_derive_detail(df, page, per_page)` — reuse aggregation logic from `resource_history_service.py`
+
+## 5. Resource-History Routes (Backend)
+
+- [x] 5.1 Add `POST /api/resource/history/query` route — parse body with date range, granularity, resource filters, call `resource_dataset_cache.execute_primary_query()`, return `{ query_id, summary, detail }`
+- [x] 5.2 Add `GET /api/resource/history/view` route — parse query params `query_id, granularity, page, per_page`, call `resource_dataset_cache.apply_view()`, return derived views or 410
+- [x] 5.3 Remove old GET endpoints: `/api/resource/history/summary`, `/api/resource/history/detail` — keep `/options` and `/export` endpoints
+
+## 6. Resource-History Frontend
+
+- [x] 6.1 Rewrite `frontend/src/resource-history/App.vue` — two-phase flow: query button calls `POST /query` → store queryId; filter changes call `GET /view?query_id=...`; cache expired → auto re-execute
+- [x] 6.2 Update `executeCommittedQuery()` to use POST /query instead of parallel GET summary + GET detail
+- [x] 6.3 Update all chart/table components to consume data from unified query/view response
+
+## 7. Verification
+
+- [x] 7.1 Run `python -m pytest tests/ -v` — no new test failures
+- [x] 7.2 Run `cd frontend && npm run build` — frontend builds successfully
diff --git a/openspec/changes/historical-query-slow-connection/.openspec.yaml b/openspec/changes/historical-query-slow-connection/.openspec.yaml
new file mode 100644
index 0000000..e331c97
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/.openspec.yaml
@@ -0,0 +1,2 @@
+schema: spec-driven
+created: 2026-02-25
diff --git a/openspec/changes/historical-query-slow-connection/design.md b/openspec/changes/historical-query-slow-connection/design.md
new file mode 100644
index 0000000..8888cc3
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/design.md
@@ -0,0 +1,78 @@
+## Context
+
+歷史查詢頁面（reject-history、hold-history、resource-history、job-query、excel-query）
+目前使用 `read_sql_df`（connection pool，55s `call_timeout`），大範圍查詢容易 timeout。
+前端 AbortController timeout 為 60~120s。Gunicorn worker timeout 為 130s。
+
+現有 `read_sql_df_slow` 函式（`database.py:573`）已提供獨立連線路徑，
+但只被 `query_tool_service.py` 的 `full_history` 模式使用，且 timeout 寫死 120s，
+無並行控制。
+
+## Goals / Non-Goals
+
+**Goals:**
+- 歷史查詢 service 全面遷移到 `read_sql_df_slow`（獨立連線，不佔用 pool）
+- `read_sql_df_slow` 的 timeout 從寫死 120s 改為 config 驅動（預設 300s）
+- 加入 global semaphore 限制並行 slow query 數量，保護 Oracle 不被大量連線壓垮
+- Gunicorn 和前端 timeout 配合調整，確保查詢不會在任何層被過早截斷
+- 即時監控頁完全不受影響
+
+**Non-Goals:**
+- 不改動 `read_sql_df` 本身（pool path 保持 55s）
+- 不改動 EventFetcher、LineageEngine（小查詢，走 pool 即可）
+- 不引入非同步任務佇列（Celery 等）
+- 不對即時監控頁做任何修改
+- 不修改 Oracle SQL 本身（查詢優化不在此範圍）
+
+## Decisions
+
+### D1: Import alias 遷移模式
+
+**決策**：各 service 用 `from ... import read_sql_df_slow as read_sql_df` 取代原本的 `read_sql_df` import。
+
+**理由**：兩個函式的 `(sql, params)` 簽名相容，alias 後所有 call site 零改動。
+比在 `read_sql_df` 加 flag 更乾淨——不污染 pool path 的邏輯。
+
+**替代方案**：在 `read_sql_df` 加 `slow=True` 參數 → 增加 pool path 複雜度，rejected。
+
+### D2: Semaphore 限制並行數
+
+**決策**：在 `database.py` 加 module-level `threading.Semaphore`，`read_sql_df_slow` 執行前 acquire，finally release。預設 3 並行（可由 `DB_SLOW_MAX_CONCURRENT` 環境變數調整）。
+
+**理由**：Gunicorn gthread 模式 2 workers × 4 threads = 8 request threads。
+限制 3 個 slow 連線確保至少 5 個 threads 仍可服務即時頁。
+Oracle 端不會同時看到超過 3 個長查詢連線。
+
+**Semaphore acquire timeout**：60 秒。超時回傳明確錯誤「查詢繁忙，請稍後再試」。
+
+### D3: Timeout 數值選擇
+
+| 層 | 修改前 | 修改後 | 理由 |
+|---|---|---|---|
+| `read_sql_df_slow` | 120s 寫死 | 300s config | 5 分鐘足夠大多數歷史查詢 |
+| Gunicorn worker | 130s | 360s | 300s + 60s overhead |
+| 前端 historical | 60~120s | 360s | 與 Gunicorn 對齊 |
+
+### D4: excel_query_service 特殊處理
+
+**決策**：excel_query_service 不用 `read_sql_df`，而是直接用 `get_db_connection()` + cursor。
+改為在取得連線後設定 `connection.call_timeout = slow_call_timeout_ms`。
+
+**理由**：保持現有 cursor batch 邏輯不變，只延長 timeout。
+
+### D5: resource_history_service 並行查詢
+
+**決策**：`query_summary` 用 `ThreadPoolExecutor(max_workers=3)` 並行 3 條查詢。
+遷移後每條查詢佔 1 個 semaphore slot（共 3 個），單次請求可能佔滿所有 slot。
+
+**接受此風險**：3 條查詢並行完成很快，slot 很快釋放。其他 slow 請求最多等 60s。
+
+## Risks / Trade-offs
+
+| 風險 | 緩解措施 |
+|------|---------|
+| Semaphore deadlock（exception 未 release） | `finally` block 保證 release |
+| 3 並行 slot 不夠用（多人同時查歷史） | `DB_SLOW_MAX_CONCURRENT` 可動態調整，無需改 code |
+| 長查詢佔住 Gunicorn thread 影響即時頁 | Semaphore 限制最多 3 個 thread 被佔用，其餘 5 個可用 |
+| Circuit breaker 不再保護歷史查詢 | 歷史查詢為使用者手動觸發、非自動化，可接受 |
+| `resource_history_service` 一次用完 3 slot | 查詢快速完成，slot 迅速釋放；可視需要降低 max_workers |
diff --git a/openspec/changes/historical-query-slow-connection/proposal.md b/openspec/changes/historical-query-slow-connection/proposal.md
new file mode 100644
index 0000000..7f4514a
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/proposal.md
@@ -0,0 +1,42 @@
+## Why
+
+歷史查詢頁面（reject-history、query-tool、hold-history、resource-history、job-query、excel-query、mid-section-defect）
+目前共用 connection pool 的 55s `call_timeout`，前端也設有 60~120s AbortController timeout。
+大範圍查詢（如長日期區間或大量 LOT）經常逼近或超過 timeout，使用者看到 "signal is aborted without reason" 錯誤。
+
+歷史查詢屬於使用者手動觸發、非即時、可等待的操作，應採用獨立連線（`read_sql_df_slow`）配合
+semaphore 並行控制，以「用時間換結果」的方式完成查詢，同時保護 connection pool 不被耗盡。
+
+## What Changes
+
+- 歷史查詢 service 從 `read_sql_df`（pool, 55s）遷移到 `read_sql_df_slow`（獨立連線, 300s）
+- `read_sql_df_slow` 加入 global semaphore 限制並行數（預設 3），避免耗盡 Oracle 連線
+- `read_sql_df_slow` 的 timeout 預設從寫死 120s 改為 config 驅動（預設 300s）
+- Gunicorn worker timeout 從 130s 提升到 360s 以容納長查詢
+- 前端歷史頁 timeout 統一提升到 360s（6 分鐘）
+- 新增 `DB_SLOW_CALL_TIMEOUT_MS` 和 `DB_SLOW_MAX_CONCURRENT` 環境變數設定
+- 即時監控頁（wip、hold-overview、resource-status 等）完全不受影響
+
+## Capabilities
+
+### New Capabilities
+
+- `slow-query-concurrency-control`: `read_sql_df_slow` 的 semaphore 並行控制與 config 驅動 timeout
+
+### Modified Capabilities
+
+- `reject-history-api`: 底層 DB 查詢從 pooled 改為 dedicated slow connection
+- `hold-history-api`: 底層 DB 查詢從 pooled 改為 dedicated slow connection
+- `query-tool-lot-trace`: 移除 `read_sql_df_slow` 寫死的 120s timeout，改用 config 預設
+- `reject-history-page`: 前端 API_TIMEOUT 從 60s 提升到 360s
+- `hold-history-page`: 前端 API_TIMEOUT 從 60s 提升到 360s
+- `resource-history-page`: 前端 API_TIMEOUT 從 60s 提升到 360s，後端遷移至 slow connection
+- `query-tool-equipment`: 前端 timeout 從 120s 提升到 360s
+- `progressive-trace-ux`: DEFAULT_STAGE_TIMEOUT_MS 從 60s 提升到 360s
+
+## Impact
+
+- **後端 services**：reject_history_service、reject_dataset_cache、hold_history_service、resource_history_service、job_query_service、excel_query_service、query_tool_service
+- **核心模組**：database.py（semaphore + config）、settings.py（新設定）、gunicorn.conf.py（timeout）
+- **前端頁面**：reject-history、mid-section-defect、hold-history、resource-history、query-tool（5 個 composable + 1 component）、job-query、excel-query
+- **不影響**：即時監控頁（wip-overview、wip-detail、hold-overview、hold-detail、resource-status、admin-performance）
diff --git a/openspec/changes/historical-query-slow-connection/specs/hold-history-api/spec.md b/openspec/changes/historical-query-slow-connection/specs/hold-history-api/spec.md
new file mode 100644
index 0000000..68c01e0
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/hold-history-api/spec.md
@@ -0,0 +1,9 @@
+## MODIFIED Requirements
+
+### Requirement: Database query execution path
+The hold-history service (`hold_history_service.py`) SHALL use `read_sql_df_slow` (dedicated connection) instead of `read_sql_df` (pooled connection) for all Oracle queries.
+
+#### Scenario: Hold history queries use dedicated connection
+- **WHEN** any hold-history query is executed (trend, pareto, duration, list)
+- **THEN** it uses `read_sql_df_slow` which creates a dedicated Oracle connection outside the pool
+- **AND** the connection has a 300-second call_timeout (configurable)
diff --git a/openspec/changes/historical-query-slow-connection/specs/hold-history-page/spec.md b/openspec/changes/historical-query-slow-connection/specs/hold-history-page/spec.md
new file mode 100644
index 0000000..661e302
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/hold-history-page/spec.md
@@ -0,0 +1,8 @@
+## MODIFIED Requirements
+
+### Requirement: Frontend API timeout
+The hold-history page SHALL use a 360-second API timeout (up from 60 seconds) for all Oracle-backed API calls.
+
+#### Scenario: Large date range query completes
+- **WHEN** a user queries hold history for a long date range
+- **THEN** the frontend does not abort the request for at least 360 seconds
diff --git a/openspec/changes/historical-query-slow-connection/specs/progressive-trace-ux/spec.md b/openspec/changes/historical-query-slow-connection/specs/progressive-trace-ux/spec.md
new file mode 100644
index 0000000..1510cda
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/progressive-trace-ux/spec.md
@@ -0,0 +1,8 @@
+## MODIFIED Requirements
+
+### Requirement: Trace stage timeout
+The `useTraceProgress` composable's `DEFAULT_STAGE_TIMEOUT_MS` SHALL be 360000 (360 seconds) to accommodate large-scale trace operations.
+
+#### Scenario: Large trace operation completes
+- **WHEN** a trace stage (seed-resolve, lineage, or events) takes up to 300 seconds
+- **THEN** the frontend does not abort the stage request
diff --git a/openspec/changes/historical-query-slow-connection/specs/query-tool-equipment/spec.md b/openspec/changes/historical-query-slow-connection/specs/query-tool-equipment/spec.md
new file mode 100644
index 0000000..743ba54
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/query-tool-equipment/spec.md
@@ -0,0 +1,8 @@
+## MODIFIED Requirements
+
+### Requirement: Frontend API timeout
+The query-tool equipment query, lot detail, lot jobs table, lot resolve, lot lineage, and reverse lineage composables SHALL use a 360-second API timeout for all Oracle-backed API calls.
+
+#### Scenario: Equipment period query completes
+- **WHEN** a user queries equipment history for a long period
+- **THEN** the frontend does not abort the request for at least 360 seconds
diff --git a/openspec/changes/historical-query-slow-connection/specs/query-tool-lot-trace/spec.md b/openspec/changes/historical-query-slow-connection/specs/query-tool-lot-trace/spec.md
new file mode 100644
index 0000000..2cd9e07
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/query-tool-lot-trace/spec.md
@@ -0,0 +1,9 @@
+## MODIFIED Requirements
+
+### Requirement: Slow query timeout configuration
+The query-tool service `read_sql_df_slow` call for full split/merge history SHALL use the config-driven default timeout instead of a hardcoded 120-second timeout.
+
+#### Scenario: Full history query uses config timeout
+- **WHEN** `full_history=True` split/merge query is executed
+- **THEN** it uses `read_sql_df_slow` with the default timeout from `DB_SLOW_CALL_TIMEOUT_MS` (300s)
+- **AND** the hardcoded `timeout_seconds=120` parameter is removed
diff --git a/openspec/changes/historical-query-slow-connection/specs/reject-history-api/spec.md b/openspec/changes/historical-query-slow-connection/specs/reject-history-api/spec.md
new file mode 100644
index 0000000..645c60b
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/reject-history-api/spec.md
@@ -0,0 +1,10 @@
+## MODIFIED Requirements
+
+### Requirement: Database query execution path
+The reject-history service (`reject_history_service.py` and `reject_dataset_cache.py`) SHALL use `read_sql_df_slow` (dedicated connection) instead of `read_sql_df` (pooled connection) for all Oracle queries.
+
+#### Scenario: Primary query uses dedicated connection
+- **WHEN** the reject-history primary query is executed
+- **THEN** it uses `read_sql_df_slow` which creates a dedicated Oracle connection outside the pool
+- **AND** the connection has a 300-second call_timeout (configurable)
+- **AND** the connection is subject to the global slow query semaphore
diff --git a/openspec/changes/historical-query-slow-connection/specs/reject-history-page/spec.md b/openspec/changes/historical-query-slow-connection/specs/reject-history-page/spec.md
new file mode 100644
index 0000000..86faa12
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/reject-history-page/spec.md
@@ -0,0 +1,8 @@
+## MODIFIED Requirements
+
+### Requirement: Frontend API timeout
+The reject-history page SHALL use a 360-second API timeout (up from 60 seconds) for all Oracle-backed API calls.
+
+#### Scenario: Large date range query completes
+- **WHEN** a user queries reject history for a long date range
+- **THEN** the frontend does not abort the request for at least 360 seconds
diff --git a/openspec/changes/historical-query-slow-connection/specs/resource-history-page/spec.md b/openspec/changes/historical-query-slow-connection/specs/resource-history-page/spec.md
new file mode 100644
index 0000000..6f247a0
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/resource-history-page/spec.md
@@ -0,0 +1,16 @@
+## MODIFIED Requirements
+
+### Requirement: Database query execution path
+The resource-history service (`resource_history_service.py`) SHALL use `read_sql_df_slow` (dedicated connection) instead of `read_sql_df` (pooled connection) for all Oracle queries.
+
+#### Scenario: Summary parallel queries use dedicated connections
+- **WHEN** the resource-history summary query executes 3 parallel queries via ThreadPoolExecutor
+- **THEN** each query uses `read_sql_df_slow` and acquires a semaphore slot
+- **AND** all 3 queries complete and release their slots
+
+### Requirement: Frontend timeout
+The resource-history page frontend SHALL use a 360-second API timeout for all Oracle-backed API calls.
+
+#### Scenario: Large date range query completes
+- **WHEN** a user queries resource history for a 2-year date range
+- **THEN** the frontend does not abort the request for at least 360 seconds
diff --git a/openspec/changes/historical-query-slow-connection/specs/slow-query-concurrency-control/spec.md b/openspec/changes/historical-query-slow-connection/specs/slow-query-concurrency-control/spec.md
new file mode 100644
index 0000000..47bd15b
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/specs/slow-query-concurrency-control/spec.md
@@ -0,0 +1,53 @@
+## ADDED Requirements
+
+### Requirement: Configurable slow query timeout
+The system SHALL read `DB_SLOW_CALL_TIMEOUT_MS` from environment/config to determine the default `call_timeout` for `read_sql_df_slow`. The default value SHALL be 300000 (300 seconds).
+
+#### Scenario: Default timeout when no env var set
+- **WHEN** `DB_SLOW_CALL_TIMEOUT_MS` is not set in environment
+- **THEN** `read_sql_df_slow` uses 300 seconds as call_timeout
+
+#### Scenario: Custom timeout from env var
+- **WHEN** `DB_SLOW_CALL_TIMEOUT_MS` is set to 180000
+- **THEN** `read_sql_df_slow` uses 180 seconds as call_timeout
+
+#### Scenario: Caller overrides timeout
+- **WHEN** caller passes `timeout_seconds=120` to `read_sql_df_slow`
+- **THEN** the function uses 120 seconds regardless of config value
+
+### Requirement: Semaphore-based concurrency control
+The system SHALL use a global `threading.Semaphore` to limit the number of concurrent `read_sql_df_slow` executions. The limit SHALL be configurable via `DB_SLOW_MAX_CONCURRENT` with a default of 3.
+
+#### Scenario: Concurrent queries within limit
+- **WHEN** 2 slow queries are running and a 3rd is submitted (limit=3)
+- **THEN** the 3rd query proceeds immediately
+
+#### Scenario: Concurrent queries exceed limit
+- **WHEN** 3 slow queries are running and a 4th is submitted (limit=3)
+- **THEN** the 4th query waits up to 60 seconds for a slot
+- **AND** if no slot becomes available, raises RuntimeError with message indicating all slots are busy
+
+#### Scenario: Semaphore release on query failure
+- **WHEN** a slow query raises an exception during execution
+- **THEN** the semaphore slot is released in the finally block
+
+### Requirement: Slow query active count diagnostic
+The system SHALL expose the current number of active slow queries via `get_slow_query_active_count()` and include it in `get_pool_status()` as `slow_query_active`.
+
+#### Scenario: Active count in pool status
+- **WHEN** 2 slow queries are running
+- **THEN** `get_pool_status()` returns `slow_query_active: 2`
+
+### Requirement: Gunicorn timeout accommodates slow queries
+The Gunicorn worker timeout SHALL be at least 360 seconds to accommodate the maximum slow query duration (300s) plus overhead.
+
+#### Scenario: Long query does not kill worker
+- **WHEN** a slow query takes 280 seconds to complete
+- **THEN** the Gunicorn worker does not timeout and the response is delivered
+
+### Requirement: Config settings in all environments
+All environment configs (Config, DevelopmentConfig, ProductionConfig, TestingConfig) SHALL define `DB_SLOW_CALL_TIMEOUT_MS` and `DB_SLOW_MAX_CONCURRENT`.
+
+#### Scenario: Testing config uses short timeout
+- **WHEN** running in testing environment
+- **THEN** `DB_SLOW_CALL_TIMEOUT_MS` defaults to 10000 and `DB_SLOW_MAX_CONCURRENT` defaults to 1
diff --git a/openspec/changes/historical-query-slow-connection/tasks.md b/openspec/changes/historical-query-slow-connection/tasks.md
new file mode 100644
index 0000000..e4f32cc
--- /dev/null
+++ b/openspec/changes/historical-query-slow-connection/tasks.md
@@ -0,0 +1,39 @@
+## 1. Core Infrastructure
+
+- [x] 1.1 Add `DB_SLOW_CALL_TIMEOUT_MS` (default 300000) and `DB_SLOW_MAX_CONCURRENT` (default 3) to all config classes in `settings.py` (Config, DevelopmentConfig=2, ProductionConfig=3, TestingConfig=1/10000)
+- [x] 1.2 Update `get_db_runtime_config()` in `database.py` to include `slow_call_timeout_ms` and `slow_max_concurrent`
+- [x] 1.3 Add module-level `threading.Semaphore`, active count tracking, and `get_slow_query_active_count()` in `database.py`
+- [x] 1.4 Refactor `read_sql_df_slow()`: default `timeout_seconds=None` (reads from config), acquire/release semaphore, log active count
+- [x] 1.5 Update `dispose_engine()` to reset semaphore; add `slow_query_active` to `get_pool_status()`
+- [x] 1.6 Increase Gunicorn timeout to 360s and graceful_timeout to 120s in `gunicorn.conf.py`
+
+## 2. Backend Service Migration
+
+- [x] 2.1 `reject_history_service.py`: change import to `read_sql_df_slow as read_sql_df`
+- [x] 2.2 `reject_dataset_cache.py`: change import to `read_sql_df_slow as read_sql_df`
+- [x] 2.3 `hold_history_service.py`: change import to `read_sql_df_slow as read_sql_df` (keep DatabaseCircuitOpenError/DatabasePoolExhaustedError imports)
+- [x] 2.4 `resource_history_service.py`: change import to `read_sql_df_slow as read_sql_df`
+- [x] 2.5 `job_query_service.py`: change import to `read_sql_df_slow as read_sql_df` (keep `get_db_connection` import)
+- [x] 2.6 `excel_query_service.py`: set `connection.call_timeout = runtime["slow_call_timeout_ms"]` on direct connections in `execute_batch_query` and `execute_advanced_batch_query`
+- [x] 2.7 `query_tool_service.py`: remove hardcoded `timeout_seconds=120` from `read_sql_df_slow` call (line 1131)
+
+## 3. Frontend Timeout Updates
+
+- [x] 3.1 `reject-history/App.vue`: `API_TIMEOUT` 60000 → 360000
+- [x] 3.2 `mid-section-defect/App.vue`: `API_TIMEOUT` 120000 → 360000
+- [x] 3.3 `hold-history/App.vue`: `API_TIMEOUT` 60000 → 360000
+- [x] 3.4 `resource-history/App.vue`: `API_TIMEOUT` 60000 → 360000
+- [x] 3.5 `shared-composables/useTraceProgress.js`: `DEFAULT_STAGE_TIMEOUT_MS` 60000 → 360000
+- [x] 3.6 `job-query/composables/useJobQueryData.js`: all `timeout: 60000` → 360000 (3 sites)
+- [x] 3.7 `excel-query/composables/useExcelQueryData.js`: `timeout: 120000` → 360000 (2 sites, lines 135, 255)
+- [x] 3.8 `query-tool/composables/useLotDetail.js`: `timeout: 120000` → 360000 (3 sites) and `timeout: 60000` → 360000 (1 site)
+- [x] 3.9 `query-tool/composables/useEquipmentQuery.js`: `timeout: 120000` → 360000 and `timeout: 60000` → 360000
+- [x] 3.10 `query-tool/composables/useLotResolve.js`: `timeout: 60000` → 360000
+- [x] 3.11 `query-tool/composables/useLotLineage.js`: `timeout: 60000` → 360000
+- [x] 3.12 `query-tool/composables/useReverseLineage.js`: `timeout: 60000` → 360000
+- [x] 3.13 `query-tool/components/LotJobsTable.vue`: `timeout: 60000` → 360000
+
+## 4. Verification
+
+- [x] 4.1 Run `python -m pytest tests/ -v` — all existing tests pass (28 pre-existing failures, 1076 passed, 0 new failures)
+- [x] 4.2 Run `cd frontend && npm run build` — frontend builds successfully
diff --git a/openspec/changes/msd-multifactor-backward-tracing/.openspec.yaml b/openspec/changes/msd-multifactor-backward-tracing/.openspec.yaml
new file mode 100644
index 0000000..69e221f
--- /dev/null
+++ b/openspec/changes/msd-multifactor-backward-tracing/.openspec.yaml
@@ -0,0 +1,2 @@
+schema: spec-driven
+created: 2026-02-24
diff --git a/openspec/changes/msd-multifactor-backward-tracing/design.md b/openspec/changes/msd-multifactor-backward-tracing/design.md
new file mode 100644
index 0000000..7e807ff
--- /dev/null
+++ b/openspec/changes/msd-multifactor-backward-tracing/design.md
@@ -0,0 +1,107 @@
+## Context
+
+「製程不良追溯分析」是 TMTT 測試站不良的回溯歸因工具。目前架構為三階段 staged pipeline（seed-resolve → lineage → events），反向追溯只取 `upstream_history` domain 做機台歸因。
+
+已有的基礎設施：
+- **EventFetcher** 已支援 `materials` domain（查 `LOTMATERIALSHISTORY`），可直接複用
+- **LineageEngine** 已能追溯 split chain 到 root ancestor，`child_to_parent` map 可直接求 root
+- 前端使用 **ECharts** (vue-echarts) 渲染柏拉圖，組件化完善
+- 歸因邏輯 `_attribute_defects()` 是通用 pattern：`factor_value → detection_lots mapping → rate calculation`
+
+## Goals / Non-Goals
+
+**Goals:**
+- 在反向追溯中新增原物料、源頭晶片兩個歸因維度，與現有機台歸因並列
+- 提升柏拉圖的分析能力（排序切換、80% 標記線、tooltip 強化）
+- 讓使用者理解歸因計算方式（分析摘要面板）
+- 明細表嫌疑因子命中呈現，與柏拉圖 Top N 連動
+- 嫌疑機台的維修上下文面板
+- 報廢歷史查詢頁面承接產品分布分析（PACKAGE / TYPE / WORKFLOW）
+
+**Non-Goals:**
+- 正向追溯改版（後續獨立處理）
+- 維修事件作為獨立歸因維度（時間交叉模型複雜，本次僅作為嫌疑機台的上下文資訊）
+- Cross-filter 聯動（點圖表 bar 聯動篩選所有圖表 — 本次不做，僅做嫌疑命中）
+- 站點層級歸因柏拉圖（產品每站都經過，無鑑別力）
+
+## Decisions
+
+### D1: 原物料歸因邏輯 — 複用 `_attribute_defects` pattern
+
+**選擇**：新增 `_attribute_materials()` 函數，邏輯與 `_attribute_defects()` 完全對稱，只是 key 從 `(workcenter_group, equipment_name, equipment_id)` 換成 `(material_part_name, material_lot_name)`。
+
+**替代方案**：泛化為通用 `_attribute_by_factor(records, key_fn)` 函數。
+**理由**：各維度的 record 結構不同（upstream_history 有 station/equipment，materials 有 part/lot），強行泛化需要額外 adapter 層。先用對稱複製，後續若新增更多維度再考慮抽象。
+
+### D2: 原物料資料取得 — EventFetcher materials domain
+
+**選擇**：在 staged trace events 階段新增請求 `materials` domain。前端的 `useTraceProgress.js` 在 backward 模式時改為 `domains: ['upstream_history', 'materials']`。
+
+**替代方案**：另開一個獨立 API 查原物料。
+**理由**：EventFetcher 已有完善的 materials domain（batch、cache、rate limit），staged trace pipeline 已能處理多 domain 並行查詢，無需重複建設。
+
+### D3: 源頭晶片歸因 — 從 lineage ancestors 提取 root
+
+**選擇**：在 lineage stage 的 response 中新增 `roots` 欄位（`{seed_cid: root_container_name}`），不需要額外 SQL 查詢。LineageEngine 已有 `child_to_parent` map，遍歷到無 parent 的節點即為 root。後端 `_attribute_wafer_roots()` 以 `root_container_name` 為 key 做歸因。
+
+**替代方案**：用 SQL CONNECT BY 直接查 root。
+**理由**：lineage stage 已經完整追溯了 split chain，root 資訊是副產品，不需要額外 DB roundtrip。
+
+### D4: 柏拉圖排列 — 替換 PACKAGE/TYPE/WORKFLOW
+
+**選擇**：6 張柏拉圖改為：
+
+| 位置 | 原本 | 改為 |
+|------|------|------|
+| 左上 | 依上游機台歸因 | 依上游機台歸因（保留） |
+| 右上 | 依不良原因 | 依原物料歸因（新） |
+| 左中 | 依偵測機台 | 依源頭晶片歸因（新） |
+| 右中 | 依 WORKFLOW | 依不良原因（保留） |
+| 左下 | 依 PACKAGE | 依偵測機台（保留） |
+| 右下 | 依 TYPE | 移除 |
+
+改為 5 張（2-2-1 排列），最後一行只有偵測機台。或視空間調整為 3-2 或 2-2-2。
+
+**理由**：PACKAGE / TYPE / WORKFLOW 是「不良分布在哪些產品上」的分析，屬於報廢歷史查詢的範疇。製程不良追溯的核心問題是「不良來自哪個上游因子」。
+
+### D5: 明細表結構化上游資料
+
+**選擇**：後端 `_build_detail_table` 的 `UPSTREAM_MACHINES` 欄位改為回傳 list of `{station, machine}` 對象。同時新增 `UPSTREAM_MATERIALS` (list of `{part, lot}`) 和 `UPSTREAM_WAFER_ROOT` (string) 欄位。CSV export 時 flatten 回逗號字串。
+
+前端明細表不顯示全部上游機台，改為只顯示「嫌疑因子命中」：根據當前柏拉圖（含 inline filter）的 Top N 嫌疑名單，與該 LOT 的上游因子做交叉比對。
+
+### D6: 嫌疑機台上下文面板 — Popover 或 Side Drawer
+
+**選擇**：使用 Popover（點擊柏拉圖 bar 時彈出），內容包含：
+- 歸因數據：不良率、LOT 數、投入/報廢
+- 設備資訊：站點、機型 (RESOURCEFAMILYNAME)
+- 近期維修：呼叫 `GET /api/query-tool/lot-associations?type=jobs&container_id=<equipment_id>` 取近期 JOB 紀錄（需要以 equipment_id 查詢的 endpoint，可能需要新增或複用 equipment-period jobs）
+
+**替代方案**：Side drawer 或 modal。
+**理由**：Popover 輕量、不離開當前分析上下文。維修資料只需 3-5 筆近期紀錄，不需要完整的 JOB 列表。
+
+### D7: 報廢歷史查詢頁面新增 Pareto 維度
+
+**選擇**：在現有 ParetoSection.vue 新增維度下拉選擇器。後端 `reject_history_service.py` 的 reason Pareto 邏輯改為可指定 `dimension` 參數（`reason` / `package` / `type` / `workflow` / `workcenter` / `equipment`）。
+
+## Risks / Trade-offs
+
+### R1: 原物料資料量
+原物料紀錄可能比 upstream_history 多很多（一個 LOT 可能消耗多種材料）。2000 個 LOT + 血緣的原物料查詢可能返回大量資料。
+→ **Mitigation**: EventFetcher 已有 batch + cache 機制。若資料量過大，可限制原物料歸因只取前幾種 material_part_name（如前 20 種），其餘歸為「其他」。
+
+### R2: LineageEngine root 不一定是晶圓
+Split chain 的 root 可能不總是代表「晶圓批次」，取決於產品結構。某些產品線的 root 可能是其他中間製程的 LOT。
+→ **Mitigation**: 以 `root_container_name` 顯示，不硬標籤為「晶圓」。UI label 用「源頭批次」而非「晶圓」。
+
+### R3: Staged trace events 增加 domain 的延遲
+新增 materials domain 會增加 events stage 的執行時間。
+→ **Mitigation**: EventFetcher 已支援 concurrent domain 查詢（ThreadPoolExecutor），materials 和 upstream_history 可並行。Cache TTL 300s 也能有效減輕重複查詢。
+
+### R4: 嫌疑機台維修資料可能需要新的 API
+目前 query-tool 的 jobs API 是以 container_id 或 equipment + time_range 查詢，沒有「某台設備的近期 N 筆維修」的 endpoint。
+→ **Mitigation**: 新增一個輕量的 equipment-recent-jobs endpoint，或在前端直接用 equipment-period jobs API 查近 30 天即可。
+
+### R5: 報廢歷史查詢的 Pareto 維度切換需要後端支援
+目前 Pareto 只支援按 reason 聚合，新增其他維度需要後端 SQL 重寫。
+→ **Mitigation**: 報廢歷史查詢使用 two-phase caching pattern（完整 DataFrame 已快取），在 view refresh 階段做不同維度的 groupby 即可，不需重新查 DB。
diff --git a/openspec/changes/msd-multifactor-backward-tracing/proposal.md b/openspec/changes/msd-multifactor-backward-tracing/proposal.md
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+++ b/openspec/changes/msd-multifactor-backward-tracing/proposal.md
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+## Why
+
+「製程不良追溯分析」頁面（mid-section-defect）目前的反向追溯只歸因到**上游機台**這單一維度。品質工程師在實務上需要同時比對多個因子（機台、原物料批號、源頭晶片）才能定位根因。現有的 EventFetcher 和 LineageEngine 已具備查詢原物料與血緣的能力，但分析頁面尚未整合這些資料來源。此外，頁面缺乏對歸因邏輯的透明度說明，使用者不了解數字是如何計算出來的。
+
+## What Changes
+
+### 新增多因子歸因維度
+- 新增「依原物料歸因」柏拉圖：以 `material_part_name + material_lot` 為 key，計算歸因不良率（與機台歸因相同邏輯）
+- 新增「依源頭晶片歸因」柏拉圖：以 LineageEngine split chain 的 root ancestor (`CONTAINERNAME`) 為 key，計算歸因不良率
+- 移除原有的「依封裝 (PACKAGE)」與「依 TYPE」柏拉圖，改為上述兩張
+- 「依製程 (WORKFLOW)」柏拉圖移除（產品分布相關分析由「報廢歷史查詢」頁面負責）
+
+### 柏拉圖改善
+- 新增排序 toggle：可切換「依不良數」/「依不良率」排序
+- 新增 80% 累計標記線（ECharts markLine）
+- Tooltip 增加顯示「關聯 LOT 數」
+
+### 分析摘要面板
+- 在 KPI 卡片上方新增可收合的「分析摘要」區塊
+- 顯示：查詢條件、資料範圍統計（LOT 總數/投入/報廢 LOT 數/報廢總數/血緣追溯涵蓋數）、歸因邏輯文字說明
+
+### 明細表嫌疑因子命中
+- 後端 `_build_detail_table` 改回傳結構化的上游機台資料（list of `{station, machine}` objects），取代扁平化逗號字串
+- 前端根據當前柏拉圖 Top N 嫌疑因子，在明細表顯示命中狀況（如 `WIRE-03, DIE-01 (2/3)`）
+- 嫌疑名單跟隨柏拉圖 inline filter 連動
+
+### 嫌疑機台上下文面板
+- 點擊柏拉圖的機台 bar 時，顯示該機台的上下文面板
+- 面板內容：歸因數據摘要、所屬站點/機型、近期維修紀錄（透過 query-tool 的 `get_lot_jobs` API 取得）
+
+### 後端：多因子歸因引擎
+- 在 `mid_section_defect_service.py` 新增 `_attribute_materials()` 函數（歸因邏輯與 `_attribute_defects` 相同 pattern）
+- 在 `mid_section_defect_service.py` 新增 `_attribute_wafer_roots()` 函數（以 root ancestor 為 key）
+- Staged trace API events stage 新增請求 `materials` domain 的支援（已在 EventFetcher 中支援，只需在 mid_section_defect profile 的 domain 列表中加入）
+- `_build_all_charts` 改為使用新的 DIMENSION_MAP（移除 by_package / by_pj_type / by_workflow，新增 by_material / by_wafer_root）
+
+### 報廢歷史查詢頁面增強
+- 將「依 PACKAGE / TYPE / WORKFLOW」的產品分布分析遷移到報廢歷史查詢頁面
+- 在報廢歷史查詢頁面新增 Pareto 維度選擇器，支援多維度切換（原因、PACKAGE、TYPE、WORKFLOW、站點、機台）
+
+## Capabilities
+
+### New Capabilities
+- `msd-multifactor-attribution`: 製程不良追溯分析的多因子歸因引擎（原物料、源頭晶片）及對應的柏拉圖呈現
+- `msd-analysis-transparency`: 分析摘要面板，顯示查詢條件、資料範圍、歸因邏輯說明
+- `msd-suspect-context`: 嫌疑機台上下文面板及明細表嫌疑因子命中呈現
+
+### Modified Capabilities
+- `reject-history-page`: 新增產品分布 Pareto 維度（PACKAGE / TYPE / WORKFLOW），接收從製程不良追溯頁面遷出的分析責任
+- `trace-staged-api`: mid_section_defect profile 的 events stage 新增 `materials` domain 請求，aggregation 邏輯新增原物料與晶片歸因
+
+## Impact
+
+### Backend
+- `src/mes_dashboard/services/mid_section_defect_service.py` — 核心改動：新增歸因函數、修改 chart builder、修改 detail table 結構
+- `src/mes_dashboard/routes/trace_routes.py` — events stage 的 mid_section_defect profile domain 列表擴充
+- `src/mes_dashboard/routes/mid_section_defect_routes.py` — export 可能需要對應新欄位
+- `src/mes_dashboard/services/reject_history_service.py` — 新增 Pareto 維度支援
+- `src/mes_dashboard/routes/reject_history_routes.py` — 新增維度參數
+
+### Frontend
+- `frontend/src/mid-section-defect/App.vue` — 主要改動：新增分析摘要、重排柏拉圖、嫌疑命中邏輯
+- `frontend/src/mid-section-defect/components/ParetoChart.vue` — 新增排序 toggle、80% markLine、tooltip lot_count
+- `frontend/src/mid-section-defect/components/DetailTable.vue` — 嫌疑命中欄位改版
+- `frontend/src/mid-section-defect/components/KpiCards.vue` — 可能微調
+- 新增 `frontend/src/mid-section-defect/components/AnalysisSummary.vue`
+- 新增 `frontend/src/mid-section-defect/components/SuspectContextPanel.vue`
+- `frontend/src/reject-history/components/ParetoSection.vue` — 新增維度選擇器
+- `frontend/src/reject-history/App.vue` — 支援多維度 Pareto
+
+### SQL
+- 可能新增 `src/mes_dashboard/sql/mid_section_defect/upstream_materials.sql`（或直接複用 EventFetcher materials domain 的 `query_tool/lot_materials.sql`）
+
+### Tests
+- `tests/test_mid_section_defect.py` — 新增原物料/晶片歸因的單元測試
+- `tests/test_reject_history_routes.py` — 新增維度 Pareto 測試
diff --git a/openspec/changes/msd-multifactor-backward-tracing/specs/msd-analysis-transparency/spec.md b/openspec/changes/msd-multifactor-backward-tracing/specs/msd-analysis-transparency/spec.md
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+++ b/openspec/changes/msd-multifactor-backward-tracing/specs/msd-analysis-transparency/spec.md
@@ -0,0 +1,48 @@
+## ADDED Requirements
+
+### Requirement: Analysis page SHALL display a collapsible analysis summary panel
+The page SHALL show a summary panel above KPI cards explaining the query context, data scope, and attribution methodology.
+
+#### Scenario: Summary panel rendering
+- **WHEN** backward analysis data is loaded
+- **THEN** a collapsible panel SHALL appear above the KPI cards
+- **THEN** the panel SHALL be expanded by default on first render
+- **THEN** the panel SHALL include a toggle control to collapse/expand
+
+#### Scenario: Query context section
+- **WHEN** the summary panel is rendered
+- **THEN** it SHALL display the committed query parameters: detection station name, date range (or container mode info), and selected loss reasons (or「全部」if none selected)
+
+#### Scenario: Data scope section
+- **WHEN** the summary panel is rendered
+- **THEN** it SHALL display:
+  - 偵測站 LOT 總數 (total detection lots count)
+  - 總投入 (total input qty in pcs)
+  - 報廢 LOT 數 (lots with defects matching selected loss reasons)
+  - 報廢總數 (total reject qty in pcs)
+  - 血緣追溯涵蓋上游 LOT 數 (total unique ancestor count)
+
+#### Scenario: Ancestor count from lineage response
+- **WHEN** lineage stage returns response
+- **THEN** the response SHALL include `total_ancestor_count` (number of unique ancestor CIDs across all seeds, excluding seeds themselves)
+- **THEN** the summary panel SHALL use this value for「血緣追溯涵蓋上游 LOT」
+
+#### Scenario: Attribution methodology section
+- **WHEN** the summary panel is rendered
+- **THEN** it SHALL display a static text block explaining the attribution logic:
+  - All LOTs passing through the detection station (including those with no defects) are included in analysis
+  - Each LOT's upstream lineage (split/merge chain) is traced to identify associated upstream factors
+  - Attribution rate = sum of associated LOTs' reject qty / sum of associated LOTs' input qty × 100%
+  - The same defect can be attributed to multiple upstream factors (non-exclusive)
+  - Pareto bar height = attributed defect count (with overlap), orange line = attributed defect rate
+
+#### Scenario: Summary panel in container mode
+- **WHEN** query mode is container mode
+- **THEN** the query context section SHALL show the input type, resolved count, and not-found count instead of date range
+- **THEN** the data scope section SHALL still show LOT count and input/reject totals
+
+#### Scenario: Summary panel collapsed state persistence
+- **WHEN** user collapses the summary panel
+- **THEN** the collapsed state SHALL persist within the current session (sessionStorage)
+- **WHEN** user triggers a new query
+- **THEN** the panel SHALL remain in its current collapsed/expanded state
diff --git a/openspec/changes/msd-multifactor-backward-tracing/specs/msd-multifactor-attribution/spec.md b/openspec/changes/msd-multifactor-backward-tracing/specs/msd-multifactor-attribution/spec.md
new file mode 100644
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--- /dev/null
+++ b/openspec/changes/msd-multifactor-backward-tracing/specs/msd-multifactor-attribution/spec.md
@@ -0,0 +1,93 @@
+## ADDED Requirements
+
+### Requirement: Backward tracing SHALL attribute defects to upstream materials
+The system SHALL compute material-level attribution using the same pattern as machine attribution: for each material `(part_name, lot_name)` consumed by detection lots or their ancestors, calculate the defect rate among associated detection lots.
+
+#### Scenario: Materials attribution data flow
+- **WHEN** backward tracing events stage completes with `upstream_history` and `materials` domains
+- **THEN** the aggregation engine SHALL build a `material_key → detection_lots` mapping where `material_key = (MATERIALPARTNAME, MATERIALLOTNAME)`
+- **THEN** for each material key, `attributed_defect_rate = Σ(REJECTQTY of associated detection lots) / Σ(TRACKINQTY of associated detection lots) × 100`
+
+#### Scenario: Materials domain requested in backward trace
+- **WHEN** the frontend executes backward tracing with `mid_section_defect` profile
+- **THEN** the events stage SHALL request domains `['upstream_history', 'materials']`
+- **THEN** the `materials` domain SHALL use the existing EventFetcher materials domain (querying `LOTMATERIALSHISTORY`)
+
+#### Scenario: Materials Pareto chart rendering
+- **WHEN** materials attribution data is available
+- **THEN** the frontend SHALL render a Pareto chart titled「依原物料歸因」
+- **THEN** each bar SHALL represent a `material_part_name (material_lot_name)` combination
+- **THEN** the chart SHALL show Top 10 items sorted by defect_qty, with remaining items grouped as「其他」
+- **THEN** tooltip SHALL display: material name, material lot, defect count, input count, defect rate, cumulative %, and associated LOT count
+
+#### Scenario: Material with no lot name
+- **WHEN** a material record has `MATERIALLOTNAME` as NULL or empty
+- **THEN** the material key SHALL use `material_part_name` only (without lot suffix)
+- **THEN** display label SHALL show the part name without parenthetical lot
+
+### Requirement: Backward tracing SHALL attribute defects to wafer root ancestors
+The system SHALL compute root-ancestor-level attribution by identifying the split chain root for each detection lot and calculating defect rates per root.
+
+#### Scenario: Root ancestor identification
+- **WHEN** lineage stage returns `ancestors` data (child_to_parent map)
+- **THEN** the backend SHALL identify root ancestors by traversing the parent chain for each seed until reaching a container with no further parent
+- **THEN** roots SHALL be returned as `{seed_container_id: root_container_name}` in the lineage response
+
+#### Scenario: Root attribution calculation
+- **WHEN** root mapping is available
+- **THEN** the aggregation engine SHALL build a `root_container_name → detection_lots` mapping
+- **THEN** for each root, `attributed_defect_rate = Σ(REJECTQTY) / Σ(TRACKINQTY) × 100`
+
+#### Scenario: Wafer root Pareto chart rendering
+- **WHEN** root attribution data is available
+- **THEN** the frontend SHALL render a Pareto chart titled「依源頭批次歸因」
+- **THEN** each bar SHALL represent a root ancestor `CONTAINERNAME`
+- **THEN** the chart SHALL show Top 10 items with cumulative percentage line
+
+#### Scenario: Detection lot with no ancestors
+- **WHEN** a detection lot has no split chain ancestors (it is its own root)
+- **THEN** the root mapping SHALL map the lot to its own `CONTAINERNAME`
+
+### Requirement: Backward Pareto layout SHALL show 5 charts in machine/material/wafer/reason/detection arrangement
+The backward tracing chart section SHALL display exactly 5 Pareto charts replacing the previous 6-chart layout.
+
+#### Scenario: Chart grid layout
+- **WHEN** backward analysis data is rendered
+- **THEN** charts SHALL be arranged as:
+  - Row 1: 依上游機台歸因 | 依原物料歸因
+  - Row 2: 依源頭批次歸因 | 依不良原因
+  - Row 3: 依偵測機台 (full width or single)
+- **THEN** the previous「依 WORKFLOW」「依 PACKAGE」「依 TYPE」charts SHALL NOT be rendered
+
+### Requirement: Pareto charts SHALL support sort toggle between defect count and defect rate
+Each Pareto chart SHALL allow the user to switch between sorting by defect quantity and defect rate.
+
+#### Scenario: Default sort order
+- **WHEN** a Pareto chart is first rendered
+- **THEN** bars SHALL be sorted by `defect_qty` descending (current behavior)
+
+#### Scenario: Sort by rate toggle
+- **WHEN** user clicks the sort toggle to「依不良率」
+- **THEN** bars SHALL re-sort by `defect_rate` descending
+- **THEN** cumulative percentage line SHALL recalculate based on the new sort order
+- **THEN** the toggle SHALL visually indicate the active sort mode
+
+#### Scenario: Sort toggle persistence within session
+- **WHEN** user changes sort mode on one chart
+- **THEN** the change SHALL only affect that specific chart (not all charts)
+
+### Requirement: Pareto charts SHALL display an 80% cumulative reference line
+Each Pareto chart SHALL include a horizontal dashed line at the 80% cumulative mark.
+
+#### Scenario: 80% markLine rendering
+- **WHEN** Pareto chart data is rendered with cumulative percentages
+- **THEN** the chart SHALL display a horizontal dashed line at y=80 on the percentage axis
+- **THEN** the line SHALL use a muted color (e.g., `#94a3b8`) with dotted style
+- **THEN** the line label SHALL display「80%」
+
+### Requirement: Pareto chart tooltip SHALL include LOT count
+Each Pareto chart tooltip SHALL show the number of associated detection LOTs.
+
+#### Scenario: Tooltip with LOT count
+- **WHEN** user hovers over a Pareto bar
+- **THEN** the tooltip SHALL display: factor name, 關聯 LOT count (with percentage of total), defect count, input count, defect rate, cumulative percentage
diff --git a/openspec/changes/msd-multifactor-backward-tracing/specs/msd-suspect-context/spec.md b/openspec/changes/msd-multifactor-backward-tracing/specs/msd-suspect-context/spec.md
new file mode 100644
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--- /dev/null
+++ b/openspec/changes/msd-multifactor-backward-tracing/specs/msd-suspect-context/spec.md
@@ -0,0 +1,77 @@
+## ADDED Requirements
+
+### Requirement: Detail table SHALL display suspect factor hit counts instead of raw upstream machine list
+The backward detail table SHALL replace the flat `UPSTREAM_MACHINES` string column with a structured suspect factor hit display that links to the current Pareto Top N.
+
+#### Scenario: Suspect hit column rendering
+- **WHEN** backward detail table is rendered
+- **THEN** the「上游機台」column SHALL be replaced by a「嫌疑命中」column
+- **THEN** each cell SHALL show the names of upstream machines that appear in the current Pareto Top N suspect list, with a hit ratio (e.g., `WIRE-03, DIE-01 (2/5)`)
+
+#### Scenario: Suspect list derived from Pareto Top N
+- **WHEN** the machine Pareto chart displays Top N machines (after any inline station/spec filters)
+- **THEN** the suspect list SHALL be the set of machine names from those Top N entries
+- **THEN** changing the Pareto inline filters SHALL update the suspect list and re-render the hit column
+
+#### Scenario: Full match indicator
+- **WHEN** a LOT's upstream machines include all machines in the suspect list
+- **THEN** the cell SHALL display a visual indicator (e.g., star or highlight) marking full match
+
+#### Scenario: No hits
+- **WHEN** a LOT's upstream machines include none of the suspect machines
+- **THEN** the cell SHALL display「-」
+
+#### Scenario: Upstream machine count column
+- **WHEN** backward detail table is rendered
+- **THEN** the「上游LOT數」column SHALL remain as-is (showing ancestor count)
+- **THEN** a new「上游台數」column SHALL show the total number of unique upstream machines for that LOT
+
+### Requirement: Backend detail table SHALL return structured upstream data
+The `_build_detail_table` function SHALL return upstream machines as a structured list instead of a flat comma-separated string.
+
+#### Scenario: Structured upstream machines response
+- **WHEN** backward detail API returns LOT records
+- **THEN** each record's `UPSTREAM_MACHINES` field SHALL be a list of `{"station": "<workcenter_group>", "machine": "<equipment_name>"}` objects
+- **THEN** the flat comma-separated string SHALL no longer be returned in this field
+
+#### Scenario: CSV export backward compatibility
+- **WHEN** CSV export is triggered for backward detail
+- **THEN** the `UPSTREAM_MACHINES` column in CSV SHALL flatten the structured list back to comma-separated `station/machine` format
+- **THEN** CSV format SHALL remain unchanged from current behavior
+
+#### Scenario: Structured upstream materials response
+- **WHEN** materials attribution is available
+- **THEN** each detail record SHALL include an `UPSTREAM_MATERIALS` field: list of `{"part": "<material_part_name>", "lot": "<material_lot_name>"}` objects
+
+#### Scenario: Structured wafer root response
+- **WHEN** root ancestor attribution is available
+- **THEN** each detail record SHALL include a `WAFER_ROOT` field: string with root ancestor `CONTAINERNAME`
+
+### Requirement: Suspect machine context panel SHALL show machine details and recent maintenance
+Clicking a machine bar in the Pareto chart SHALL open a context popover showing machine attribution details and recent maintenance history.
+
+#### Scenario: Context panel trigger
+- **WHEN** user clicks a bar in the「依上游機台歸因」Pareto chart
+- **THEN** a popover panel SHALL appear near the clicked bar
+- **WHEN** user clicks outside the popover or clicks the same bar again
+- **THEN** the popover SHALL close
+
+#### Scenario: Context panel content - attribution summary
+- **WHEN** the context panel is displayed
+- **THEN** it SHALL show: equipment name, workcenter group, resource family (RESOURCEFAMILYNAME), attributed defect rate, attributed defect count, attributed input count, associated LOT count
+
+#### Scenario: Context panel content - recent maintenance
+- **WHEN** the context panel is displayed
+- **THEN** it SHALL fetch recent JOB records for the machine's equipment_id (last 30 days)
+- **THEN** it SHALL display up to 5 most recent JOB records showing: JOBID, JOBSTATUS, JOBMODELNAME, CREATEDATE, COMPLETEDATE
+- **WHEN** the machine has no recent JOB records
+- **THEN** the maintenance section SHALL display「近 30 天無維修紀錄」
+
+#### Scenario: Context panel loading state
+- **WHEN** maintenance data is being fetched
+- **THEN** the maintenance section SHALL show a loading indicator
+- **THEN** the attribution summary section SHALL render immediately (data already available from attribution)
+
+#### Scenario: Context panel for non-machine charts
+- **WHEN** user clicks bars in other Pareto charts (materials, wafer root, loss reason, detection machine)
+- **THEN** no context panel SHALL appear (machine context only)
diff --git a/openspec/specs/hold-dataset-cache/spec.md b/openspec/specs/hold-dataset-cache/spec.md
new file mode 100644
index 0000000..733da0b
--- /dev/null
+++ b/openspec/specs/hold-dataset-cache/spec.md
@@ -0,0 +1,64 @@
+## ADDED Requirements
+
+### Requirement: Hold dataset cache SHALL execute a single Oracle query and cache the result
+The hold_dataset_cache module SHALL query Oracle once for the full hold/release fact set and cache it for subsequent derivations.
+
+#### Scenario: Primary query execution and caching
+- **WHEN** `execute_primary_query()` is called with date range and hold_type parameters
+- **THEN** a deterministic `query_id` SHALL be computed from the primary params (start_date, end_date) using SHA256
+- **THEN** if a cached DataFrame exists for this query_id (L1 or L2), it SHALL be used without querying Oracle
+- **THEN** if no cache exists, a single Oracle query SHALL fetch all hold/release records from `DW_MES_HOLDRELEASEHISTORY` for the date range (all hold_types)
+- **THEN** the result DataFrame SHALL be stored in both L1 (ProcessLevelCache) and L2 (Redis as parquet/base64)
+- **THEN** the response SHALL include `query_id`, trend, reason_pareto, duration, and list page 1
+
+#### Scenario: Cache TTL and eviction
+- **WHEN** a DataFrame is cached
+- **THEN** the cache TTL SHALL be 900 seconds (15 minutes)
+- **THEN** L1 cache max_size SHALL be 8 entries with LRU eviction
+- **THEN** the Redis namespace SHALL be `hold_dataset`
+
+### Requirement: Hold dataset cache SHALL derive trend data from cached DataFrame
+The module SHALL compute daily trend aggregations from the cached fact set.
+
+#### Scenario: Trend derivation from cache
+- **WHEN** `apply_view()` is called with a valid query_id
+- **THEN** trend data SHALL be derived by grouping the cached DataFrame by date
+- **THEN** the 07:30 shift boundary rule SHALL be applied
+- **THEN** all three hold_type variants (quality, non_quality, all) SHALL be computed from the same DataFrame
+- **THEN** hold_type filtering SHALL be applied in-memory without re-querying Oracle
+
+### Requirement: Hold dataset cache SHALL derive reason Pareto from cached DataFrame
+The module SHALL compute reason distribution from the cached fact set.
+
+#### Scenario: Reason Pareto derivation
+- **WHEN** `apply_view()` is called with hold_type filter
+- **THEN** reason Pareto SHALL be derived by grouping the filtered DataFrame by HOLDREASONNAME
+- **THEN** items SHALL include count, qty, pct, and cumPct
+- **THEN** items SHALL be sorted by count descending
+
+### Requirement: Hold dataset cache SHALL derive duration distribution from cached DataFrame
+The module SHALL compute hold duration buckets from the cached fact set.
+
+#### Scenario: Duration derivation
+- **WHEN** `apply_view()` is called with hold_type filter
+- **THEN** duration distribution SHALL be derived from records where RELEASETXNDATE IS NOT NULL
+- **THEN** 4 buckets SHALL be computed: <4h, 4-24h, 1-3d, >3d
+- **THEN** each bucket SHALL include count and pct
+
+### Requirement: Hold dataset cache SHALL derive paginated list from cached DataFrame
+The module SHALL provide paginated detail records from the cached fact set.
+
+#### Scenario: List pagination from cache
+- **WHEN** `apply_view()` is called with page and per_page parameters
+- **THEN** the cached DataFrame SHALL be filtered by hold_type and optional reason filter
+- **THEN** records SHALL be sorted by HOLDTXNDATE descending
+- **THEN** pagination SHALL be applied in-memory (offset + limit on the sorted DataFrame)
+- **THEN** response SHALL include items and pagination metadata (page, perPage, total, totalPages)
+
+### Requirement: Hold dataset cache SHALL handle cache expiry gracefully
+The module SHALL return appropriate signals when cache has expired.
+
+#### Scenario: Cache expired during view request
+- **WHEN** `apply_view()` is called with a query_id whose cache has expired
+- **THEN** the response SHALL return `{ success: false, error: "cache_expired" }`
+- **THEN** the HTTP status SHALL be 410 (Gone)
diff --git a/openspec/specs/hold-history-api/spec.md b/openspec/specs/hold-history-api/spec.md
index f723cae..fb2dcc4 100644
--- a/openspec/specs/hold-history-api/spec.md
+++ b/openspec/specs/hold-history-api/spec.md
@@ -1,172 +1,62 @@
-## ADDED Requirements
+## MODIFIED Requirements
 
 ### Requirement: Hold History API SHALL provide daily trend data with Redis caching
-The API SHALL return daily aggregated hold/release metrics for the selected date range.
+The Hold History API SHALL return trend, reason-pareto, duration, and list data from a single cached dataset via a two-phase query pattern (POST /query + GET /view). The old independent GET endpoints for trend, reason-pareto, duration, and list SHALL be replaced.
 
-#### Scenario: Trend endpoint returns all three hold types
-- **WHEN** `GET /api/hold-history/trend?start_date=2025-01-01&end_date=2025-01-31` is called
-- **THEN** the response SHALL return `{ success: true, data: { days: [...] } }`
-- **THEN** each day item SHALL contain `{ date, quality: { holdQty, newHoldQty, releaseQty, futureHoldQty }, non_quality: { ... }, all: { ... } }`
-- **THEN** all three hold_type variants SHALL be included in a single response
+#### Scenario: Primary query endpoint
+- **WHEN** `POST /api/hold-history/query` is called with `{ start_date, end_date, hold_type }`
+- **THEN** the service SHALL execute a single Oracle query (or read from cache) via `hold_dataset_cache.execute_primary_query()`
+- **THEN** the response SHALL return `{ success: true, data: { query_id, trend, reason_pareto, duration, list, summary } }`
+- **THEN** list SHALL contain page 1 with default per_page of 50
+
+#### Scenario: Supplementary view endpoint
+- **WHEN** `GET /api/hold-history/view?query_id=...&hold_type=...&reason=...&page=...&per_page=...` is called
+- **THEN** the service SHALL read the cached DataFrame and derive filtered views via `hold_dataset_cache.apply_view()`
+- **THEN** no Oracle query SHALL be executed
+- **THEN** the response SHALL return `{ success: true, data: { trend, reason_pareto, duration, list } }`
+
+#### Scenario: Cache expired on view request
+- **WHEN** GET /view is called with an expired query_id
+- **THEN** the response SHALL return `{ success: false, error: "cache_expired" }` with HTTP 410
 
 #### Scenario: Trend uses shift boundary at 07:30
 - **WHEN** daily aggregation is calculated
 - **THEN** transactions with time >= 07:30 SHALL be attributed to the next calendar day
 - **THEN** transactions with time < 07:30 SHALL be attributed to the current calendar day
 
-#### Scenario: Trend deduplicates same-day multiple holds
-- **WHEN** a lot is held multiple times on the same day
-- **THEN** only one hold event SHALL be counted for that day (using ROW_NUMBER per CONTAINERID per day)
-
-#### Scenario: Trend deduplicates future holds
-- **WHEN** the same lot has multiple future holds for the same reason
-- **THEN** only the first occurrence SHALL be counted (using ROW_NUMBER per CONTAINERID per HOLDREASONID)
-
 #### Scenario: Trend hold type classification
 - **WHEN** trend data is aggregated by hold type
 - **THEN** quality classification SHALL use the same NON_QUALITY_HOLD_REASONS set as existing hold endpoints
-- **THEN** holds with HOLDREASONNAME NOT in NON_QUALITY_HOLD_REASONS SHALL be classified as quality
-- **THEN** the "all" variant SHALL include both quality and non-quality holds
-
-#### Scenario: Trend Redis cache for recent two months
-- **WHEN** the requested date range falls within the current month or previous month
-- **THEN** the service SHALL check Redis for cached data at key `hold_history:daily:{YYYY-MM}`
-- **THEN** if cache exists, data SHALL be returned from Redis
-- **THEN** if cache is missing, data SHALL be queried from Oracle and stored in Redis with 12-hour TTL
-
-#### Scenario: Trend direct Oracle query for older data
-- **WHEN** the requested date range includes months older than the previous month
-- **THEN** the service SHALL query Oracle directly without caching
-
-#### Scenario: Trend cross-month query assembly
-- **WHEN** the requested date range spans multiple months (e.g., 2025-01-15 to 2025-02-15)
-- **THEN** the service SHALL fetch each month's data independently (from cache or Oracle)
-- **THEN** the service SHALL trim the combined result to the exact requested date range
-- **THEN** the response SHALL contain only days within start_date and end_date inclusive
-
-#### Scenario: Trend error
-- **WHEN** the database query fails
-- **THEN** the response SHALL return `{ success: false, error: '查詢失敗' }` with HTTP 500
 
 ### Requirement: Hold History API SHALL provide reason Pareto data
-The API SHALL return hold reason distribution for Pareto analysis.
+The reason Pareto data SHALL be derived from the cached dataset, not from a separate Oracle query.
 
-#### Scenario: Reason Pareto endpoint
-- **WHEN** `GET /api/hold-history/reason-pareto?start_date=2025-01-01&end_date=2025-01-31&hold_type=quality` is called
-- **THEN** the response SHALL return `{ success: true, data: { items: [...] } }`
+#### Scenario: Reason Pareto from cache
+- **WHEN** reason Pareto is requested via GET /view with hold_type filter
+- **THEN** the cached DataFrame SHALL be filtered by hold_type and grouped by HOLDREASONNAME
 - **THEN** each item SHALL contain `{ reason, count, qty, pct, cumPct }`
 - **THEN** items SHALL be sorted by count descending
-- **THEN** pct SHALL be percentage of total hold events
-- **THEN** cumPct SHALL be running cumulative percentage
-
-#### Scenario: Reason Pareto uses shift boundary
-- **WHEN** hold events are counted for Pareto
-- **THEN** the 07:30 shift boundary rule SHALL be applied to HOLDTXNDATE
-
-#### Scenario: Reason Pareto hold type filter
-- **WHEN** hold_type is "quality"
-- **THEN** only quality hold reasons SHALL be included
-- **WHEN** hold_type is "non-quality"
-- **THEN** only non-quality hold reasons SHALL be included
-- **WHEN** hold_type is "all"
-- **THEN** all hold reasons SHALL be included
 
 ### Requirement: Hold History API SHALL provide hold duration distribution
-The API SHALL return hold duration distribution buckets.
+The duration distribution SHALL be derived from the cached dataset.
 
-#### Scenario: Duration endpoint
-- **WHEN** `GET /api/hold-history/duration?start_date=2025-01-01&end_date=2025-01-31&hold_type=quality` is called
-- **THEN** the response SHALL return `{ success: true, data: { items: [...] } }`
-- **THEN** items SHALL contain 4 buckets: `{ range: "<4h", count, pct }`, `{ range: "4-24h", count, pct }`, `{ range: "1-3d", count, pct }`, `{ range: ">3d", count, pct }`
-
-#### Scenario: Duration only includes released holds
-- **WHEN** duration is calculated
-- **THEN** only hold records with RELEASETXNDATE IS NOT NULL SHALL be included
-- **THEN** duration SHALL be calculated as RELEASETXNDATE - HOLDTXNDATE
-
-#### Scenario: Duration date range filter
-- **WHEN** start_date and end_date are provided
-- **THEN** only holds with HOLDTXNDATE within the date range (applying 07:30 shift boundary) SHALL be included
-
-### Requirement: Hold History API SHALL provide department statistics
-The API SHALL return hold/release statistics aggregated by department with optional person detail.
-
-#### Scenario: Department endpoint
-- **WHEN** `GET /api/hold-history/department?start_date=2025-01-01&end_date=2025-01-31&hold_type=quality` is called
-- **THEN** the response SHALL return `{ success: true, data: { items: [...] } }`
-- **THEN** each item SHALL contain `{ dept, holdCount, releaseCount, avgHoldHours, persons: [{ name, holdCount, releaseCount, avgHoldHours }] }`
-- **THEN** items SHALL be sorted by holdCount descending
-
-#### Scenario: Department with reason filter
-- **WHEN** `GET /api/hold-history/department?start_date=2025-01-01&end_date=2025-01-31&hold_type=quality&reason=品質確認` is called
-- **THEN** only hold records matching the specified reason SHALL be included in department and person statistics
-
-#### Scenario: Department hold count vs release count
-- **WHEN** department statistics are calculated
-- **THEN** holdCount SHALL count records where HOLDEMPDEPTNAME equals the department AND HOLDTXNDATE is within the date range
-- **THEN** releaseCount SHALL count records where RELEASEEMPDEPTNAME equals the department AND RELEASETXNDATE is within the date range
-- **THEN** avgHoldHours SHALL be the average of (RELEASETXNDATE - HOLDTXNDATE) in hours for released holds initiated by that department
+#### Scenario: Duration from cache
+- **WHEN** duration is requested via GET /view
+- **THEN** the cached DataFrame SHALL be filtered to released holds only
+- **THEN** 4 buckets SHALL be computed: <4h, 4-24h, 1-3d, >3d
 
 ### Requirement: Hold History API SHALL provide paginated detail list
-The API SHALL return a paginated list of individual hold/release records.
+The detail list SHALL be paginated from the cached dataset.
 
-#### Scenario: List endpoint
-- **WHEN** `GET /api/hold-history/list?start_date=2025-01-01&end_date=2025-01-31&hold_type=quality&page=1&per_page=50` is called
-- **THEN** the response SHALL return `{ success: true, data: { items: [...], pagination: { page, perPage, total, totalPages } } }`
-- **THEN** each item SHALL contain: lotId, workorder, workcenter, holdReason, holdDate, holdEmp, holdComment, releaseDate, releaseEmp, releaseComment, holdHours, ncr
-- **THEN** items SHALL be sorted by HOLDTXNDATE descending
+#### Scenario: List pagination from cache
+- **WHEN** list is requested via GET /view with page and per_page params
+- **THEN** the cached DataFrame SHALL be filtered and paginated in-memory
+- **THEN** response SHALL include items and pagination metadata
 
-#### Scenario: List with reason filter
-- **WHEN** `GET /api/hold-history/list?start_date=2025-01-01&end_date=2025-01-31&hold_type=quality&reason=品質確認` is called
-- **THEN** only records matching the specified HOLDREASONNAME SHALL be returned
+### Requirement: Hold History API SHALL keep department endpoint as separate query
+The department endpoint SHALL remain as a separate Oracle query due to its unique person-level aggregation.
 
-#### Scenario: List unreleased hold records
-- **WHEN** a hold record has RELEASETXNDATE IS NULL
-- **THEN** releaseDate SHALL be null
-- **THEN** holdHours SHALL be calculated as (SYSDATE - HOLDTXNDATE) * 24
-
-#### Scenario: List pagination bounds
-- **WHEN** page is less than 1
-- **THEN** page SHALL be treated as 1
-- **WHEN** per_page exceeds 200
-- **THEN** per_page SHALL be capped at 200
-
-#### Scenario: List date range uses shift boundary
-- **WHEN** records are filtered by date range
-- **THEN** the 07:30 shift boundary rule SHALL be applied to HOLDTXNDATE
-
-### Requirement: Hold History API SHALL use centralized SQL files
-The API SHALL load SQL queries from files in the `src/mes_dashboard/sql/hold_history/` directory.
-
-#### Scenario: SQL file organization
-- **WHEN** the hold history service executes a query
-- **THEN** the SQL SHALL be loaded from `sql/hold_history/<query_name>.sql`
-- **THEN** the following SQL files SHALL exist: `trend.sql`, `reason_pareto.sql`, `duration.sql`, `department.sql`, `list.sql`
-
-#### Scenario: SQL parameterization
-- **WHEN** SQL queries are executed
-- **THEN** all user-provided parameters (dates, hold_type, reason) SHALL be passed as bind parameters
-- **THEN** no string interpolation SHALL be used for user input
-
-### Requirement: Hold History API SHALL apply rate limiting
-The API SHALL apply rate limiting to expensive endpoints.
-
-#### Scenario: Rate limit on list endpoint
-- **WHEN** the list endpoint receives excessive requests
-- **THEN** rate limiting SHALL be applied using `configured_rate_limit` with a default of 90 requests per 60 seconds
-
-#### Scenario: Rate limit on trend endpoint
-- **WHEN** the trend endpoint receives excessive requests
-- **THEN** rate limiting SHALL be applied using `configured_rate_limit` with a default of 60 requests per 60 seconds
-
-### Requirement: Hold History page route SHALL serve static Vite HTML
-The Flask route SHALL serve the pre-built Vite HTML file.
-
-#### Scenario: Page route
-- **WHEN** user navigates to `/hold-history`
-- **THEN** Flask SHALL serve the pre-built HTML file from `static/dist/hold-history.html` via `send_from_directory`
-- **THEN** the HTML SHALL NOT pass through Jinja2 template rendering
-
-#### Scenario: Fallback HTML
-- **WHEN** the pre-built HTML file does not exist
-- **THEN** Flask SHALL return a minimal HTML page with the correct script tag and module import
+#### Scenario: Department endpoint unchanged
+- **WHEN** `GET /api/hold-history/department` is called
+- **THEN** it SHALL continue to execute its own Oracle query
+- **THEN** it SHALL NOT use the dataset cache
diff --git a/openspec/specs/hold-history-page/spec.md b/openspec/specs/hold-history-page/spec.md
index 78db380..b4439f5 100644
--- a/openspec/specs/hold-history-page/spec.md
+++ b/openspec/specs/hold-history-page/spec.md
@@ -1,172 +1,34 @@
-## ADDED Requirements
+## MODIFIED Requirements
 
 ### Requirement: Hold History page SHALL display a filter bar with date range and hold type
-The page SHALL provide a filter bar for selecting date range and hold type classification.
+The page SHALL provide a filter bar for selecting date range and hold type classification. On query, the page SHALL use a two-phase flow: POST /query returns queryId, subsequent filter changes use GET /view.
 
-#### Scenario: Default date range
-- **WHEN** the page loads
-- **THEN** the date range SHALL default to the first and last day of the current month
+#### Scenario: Primary query via POST /query
+- **WHEN** user clicks the query button (or page loads with default filters)
+- **THEN** the page SHALL call `POST /api/hold-history/query` with `{ start_date, end_date, hold_type }`
+- **THEN** the response queryId SHALL be stored for subsequent view requests
+- **THEN** trend, reason-pareto, duration, and list SHALL all be populated from the single response
 
-#### Scenario: Hold Type radio default
-- **WHEN** the page loads
-- **THEN** the Hold Type filter SHALL default to "品質異常"
-- **THEN** three radio options SHALL display: 品質異常, 非品質異常, 全部
+#### Scenario: Hold type or reason filter change uses GET /view
+- **WHEN** user changes hold_type radio or clicks a reason in the Pareto chart (while queryId exists)
+- **THEN** the page SHALL call `GET /api/hold-history/view?query_id=...&hold_type=...&reason=...`
+- **THEN** no new Oracle query SHALL be triggered
+- **THEN** trend, reason-pareto, duration, and list SHALL update from the view response
 
-#### Scenario: Filter bar change reloads all data
-- **WHEN** user changes the date range or Hold Type selection
-- **THEN** all API calls (trend, reason-pareto, duration, department, list) SHALL reload with the new parameters
-- **THEN** any active Reason Pareto filter SHALL be cleared
-- **THEN** pagination SHALL reset to page 1
+#### Scenario: Pagination uses GET /view
+- **WHEN** user navigates to a different page in the detail list
+- **THEN** the page SHALL call `GET /api/hold-history/view?query_id=...&page=...&per_page=...`
 
-### Requirement: Hold History page SHALL display summary KPI cards
-The page SHALL show 6 summary KPI cards derived from the trend data for the selected period.
+#### Scenario: Date range change triggers new primary query
+- **WHEN** user changes the date range and clicks query
+- **THEN** the page SHALL call `POST /api/hold-history/query` with new dates
+- **THEN** a new queryId SHALL replace the old one
 
-#### Scenario: Summary cards rendering
-- **WHEN** trend data is loaded
-- **THEN** six cards SHALL display: Release 數量, New Hold 數量, Future Hold 數量, 淨變動, 期末 On Hold, 平均 Hold 時長
-- **THEN** Release SHALL be displayed as a positive indicator (green)
-- **THEN** New Hold and Future Hold SHALL be displayed as negative indicators (red/orange)
-- **THEN** 淨變動 SHALL equal Release - New Hold - Future Hold
-- **THEN** 期末 On Hold SHALL be the HOLDQTY of the last day in the selected range
-- **THEN** number values SHALL use zh-TW number formatting
+#### Scenario: Cache expired auto-retry
+- **WHEN** GET /view returns `{ success: false, error: "cache_expired" }`
+- **THEN** the page SHALL automatically re-execute `POST /api/hold-history/query` with the last committed filters
+- **THEN** the view SHALL refresh with the new data
 
-#### Scenario: Summary reflects filter bar only
-- **WHEN** user clicks a Reason Pareto block
-- **THEN** summary cards SHALL NOT change (they only respond to filter bar changes)
-
-### Requirement: Hold History page SHALL display a Daily Trend chart
-The page SHALL display a mixed line+bar chart showing daily hold stock and flow.
-
-#### Scenario: Daily Trend chart rendering
-- **WHEN** trend data is loaded
-- **THEN** an ECharts mixed chart SHALL display with dual Y-axes
-- **THEN** the left Y-axis SHALL show flow quantities (Release, New Hold, Future Hold)
-- **THEN** the right Y-axis SHALL show HOLDQTY stock level
-- **THEN** the X-axis SHALL show dates within the selected range
-
-#### Scenario: Bar direction encoding
-- **WHEN** daily trend bars are rendered
-- **THEN** Release bars SHALL extend upward (positive direction, green color)
-- **THEN** New Hold bars SHALL extend downward (negative direction, red color)
-- **THEN** Future Hold bars SHALL extend downward (negative direction, orange color, stacked with New Hold)
-- **THEN** HOLDQTY SHALL display as a line on the right Y-axis
-
-#### Scenario: Hold Type switching without re-call
-- **WHEN** user changes the Hold Type radio on the filter bar
-- **THEN** if the date range has not changed, the trend chart SHALL update from locally cached data
-- **THEN** no additional API call SHALL be made for the trend endpoint
-
-#### Scenario: Daily Trend reflects filter bar only
-- **WHEN** user clicks a Reason Pareto block
-- **THEN** the Daily Trend chart SHALL NOT change (it only responds to filter bar changes)
-
-### Requirement: Hold History page SHALL display a Reason Pareto chart
-The page SHALL display a Pareto chart showing hold reason distribution.
-
-#### Scenario: Reason Pareto rendering
-- **WHEN** reason-pareto data is loaded
-- **THEN** a Pareto chart SHALL display with bars (count per reason) and a cumulative percentage line
-- **THEN** reasons SHALL be sorted by count descending
-- **THEN** the cumulative line SHALL reach 100% at the rightmost bar
-
-#### Scenario: Reason Pareto click filters downstream
-- **WHEN** user clicks a reason bar in the Pareto chart
-- **THEN** `reasonFilter` SHALL be set to the clicked reason name
-- **THEN** Department table SHALL reload filtered by that reason
-- **THEN** Detail table SHALL reload filtered by that reason
-- **THEN** the clicked bar SHALL show a visual highlight
-
-#### Scenario: Reason Pareto click toggle
-- **WHEN** user clicks the same reason bar that is already active
-- **THEN** `reasonFilter` SHALL be cleared
-- **THEN** Department table and Detail table SHALL reload without reason filter
-
-#### Scenario: Reason Pareto reflects filter bar only
-- **WHEN** user clicks a reason bar
-- **THEN** Summary KPIs, Daily Trend, and Duration chart SHALL NOT change
-
-### Requirement: Hold History page SHALL display Hold Duration distribution
-The page SHALL display a horizontal bar chart showing hold duration distribution.
-
-#### Scenario: Duration chart rendering
-- **WHEN** duration data is loaded
-- **THEN** a horizontal bar chart SHALL display with 4 buckets: <4h, 4-24h, 1-3天, >3天
-- **THEN** each bar SHALL show count and percentage
-- **THEN** only released holds (RELEASETXNDATE IS NOT NULL) SHALL be included
-
-#### Scenario: Duration reflects filter bar only
-- **WHEN** user clicks a Reason Pareto block
-- **THEN** the Duration chart SHALL NOT change (it only responds to filter bar changes)
-
-### Requirement: Hold History page SHALL display Department statistics with expandable rows
-The page SHALL display a table showing hold/release statistics per department, expandable to show individual persons.
-
-#### Scenario: Department table rendering
-- **WHEN** department data is loaded
-- **THEN** a table SHALL display with columns: 部門, Hold 次數, Release 次數, 平均 Hold 時長(hr)
-- **THEN** departments SHALL be sorted by Hold 次數 descending
-- **THEN** each department row SHALL have an expand toggle
-
-#### Scenario: Department row expansion
-- **WHEN** user clicks the expand toggle on a department row
-- **THEN** individual person rows SHALL display below the department row
-- **THEN** person rows SHALL show: 人員名稱, Hold 次數, Release 次數, 平均 Hold 時長(hr)
-
-#### Scenario: Department table responds to reason filter
-- **WHEN** a Reason Pareto filter is active
-- **THEN** department data SHALL reload filtered by the selected reason
-- **THEN** only holds matching the reason SHALL be included in statistics
-
-### Requirement: Hold History page SHALL display paginated Hold/Release detail list
-The page SHALL display a detailed list of individual hold/release records with server-side pagination.
-
-#### Scenario: Detail table columns
-- **WHEN** detail data is loaded
-- **THEN** a table SHALL display with columns: Lot ID, WorkOrder, 站別, Hold Reason, Hold 時間, Hold 人員, Hold Comment, Release 時間, Release 人員, Release Comment, 時長(hr), NCR
-
-#### Scenario: Unreleased hold display
-- **WHEN** a hold record has RELEASETXNDATE IS NULL
-- **THEN** the Release 時間 column SHALL display "仍在 Hold"
-- **THEN** the 時長 column SHALL display the duration from HOLDTXNDATE to current time
-
-#### Scenario: Detail table pagination
-- **WHEN** total records exceed per_page (50)
-- **THEN** Prev/Next buttons and page info SHALL display
-- **THEN** page info SHALL show "顯示 {start} - {end} / {total}"
-
-#### Scenario: Detail table responds to reason filter
-- **WHEN** a Reason Pareto filter is active
-- **THEN** detail data SHALL reload filtered by the selected reason
-- **THEN** pagination SHALL reset to page 1
-
-#### Scenario: Filter changes reset pagination
-- **WHEN** any filter changes (filter bar or Reason Pareto click)
-- **THEN** pagination SHALL reset to page 1
-
-### Requirement: Hold History page SHALL display active filter indicator
-The page SHALL show a clear indicator when a Reason Pareto filter is active.
-
-#### Scenario: Reason filter indicator
-- **WHEN** a reason filter is active
-- **THEN** a filter indicator SHALL display above the Department table section
-- **THEN** the indicator SHALL show the active reason name
-- **THEN** a clear button (✕) SHALL remove the reason filter
-
-### Requirement: Hold History page SHALL handle loading and error states
-The page SHALL display appropriate feedback during API calls and on errors.
-
-#### Scenario: Initial loading overlay
-- **WHEN** the page first loads
-- **THEN** a full-page loading overlay SHALL display until all data is loaded
-
-#### Scenario: API error handling
-- **WHEN** an API call fails
-- **THEN** an error banner SHALL display with the error message
-- **THEN** the page SHALL NOT crash or become unresponsive
-
-### Requirement: Hold History page SHALL have navigation links
-The page SHALL provide navigation to related pages.
-
-#### Scenario: Back to Hold Overview
-- **WHEN** user clicks the "← Hold Overview" button in the header
-- **THEN** the page SHALL navigate to `/hold-overview`
+#### Scenario: Department still uses separate API
+- **WHEN** department data needs to load or reload
+- **THEN** the page SHALL call `GET /api/hold-history/department` separately
diff --git a/openspec/specs/resource-dataset-cache/spec.md b/openspec/specs/resource-dataset-cache/spec.md
new file mode 100644
index 0000000..7c5c8c1
--- /dev/null
+++ b/openspec/specs/resource-dataset-cache/spec.md
@@ -0,0 +1,71 @@
+## ADDED Requirements
+
+### Requirement: Resource dataset cache SHALL execute a single Oracle query and cache the result
+The resource_dataset_cache module SHALL query Oracle once for the full shift-status fact set and cache it for subsequent derivations.
+
+#### Scenario: Primary query execution and caching
+- **WHEN** `execute_primary_query()` is called with date range, granularity, and resource filter parameters
+- **THEN** a deterministic `query_id` SHALL be computed from all primary params using SHA256
+- **THEN** if a cached DataFrame exists for this query_id (L1 or L2), it SHALL be used without querying Oracle
+- **THEN** if no cache exists, a single Oracle query SHALL fetch all shift-status records from `DW_MES_RESOURCESTATUS_SHIFT` for the filtered resources and date range
+- **THEN** the result DataFrame SHALL be stored in both L1 (ProcessLevelCache) and L2 (Redis as parquet/base64)
+- **THEN** the response SHALL include `query_id`, summary (KPI, trend, heatmap, comparison), and detail page 1
+
+#### Scenario: Cache TTL and eviction
+- **WHEN** a DataFrame is cached
+- **THEN** the cache TTL SHALL be 900 seconds (15 minutes)
+- **THEN** L1 cache max_size SHALL be 8 entries with LRU eviction
+- **THEN** the Redis namespace SHALL be `resource_dataset`
+
+### Requirement: Resource dataset cache SHALL derive KPI summary from cached DataFrame
+The module SHALL compute aggregated KPI metrics from the cached fact set.
+
+#### Scenario: KPI derivation from cache
+- **WHEN** summary view is derived from cached DataFrame
+- **THEN** total hours for PRD, SBY, UDT, SDT, EGT, NST SHALL be summed
+- **THEN** OU% and AVAIL% SHALL be computed from the hour totals
+- **THEN** machine count SHALL be the distinct count of HISTORYID in the cached data
+
+### Requirement: Resource dataset cache SHALL derive trend data from cached DataFrame
+The module SHALL compute time-series aggregations from the cached fact set.
+
+#### Scenario: Trend derivation
+- **WHEN** summary view is derived with a given granularity (day/week/month/year)
+- **THEN** the cached DataFrame SHALL be grouped by the granularity period
+- **THEN** each period SHALL include PRD, SBY, UDT, SDT, EGT, NST hours and computed OU%, AVAIL%
+
+### Requirement: Resource dataset cache SHALL derive heatmap from cached DataFrame
+The module SHALL compute workcenter x date OU% matrix from the cached fact set.
+
+#### Scenario: Heatmap derivation
+- **WHEN** summary view is derived
+- **THEN** the cached DataFrame SHALL be grouped by (workcenter, date)
+- **THEN** each cell SHALL contain the OU% for that workcenter on that date
+- **THEN** workcenters SHALL be sorted by workcenter_seq
+
+### Requirement: Resource dataset cache SHALL derive workcenter comparison from cached DataFrame
+The module SHALL compute per-workcenter aggregated metrics from the cached fact set.
+
+#### Scenario: Comparison derivation
+- **WHEN** summary view is derived
+- **THEN** the cached DataFrame SHALL be grouped by workcenter
+- **THEN** each workcenter SHALL include total hours and computed OU%
+- **THEN** results SHALL be sorted by OU% descending, limited to top 15
+
+### Requirement: Resource dataset cache SHALL derive paginated detail from cached DataFrame
+The module SHALL provide hierarchical detail records from the cached fact set.
+
+#### Scenario: Detail derivation and pagination
+- **WHEN** detail view is requested with page and per_page parameters
+- **THEN** the cached DataFrame SHALL be used to compute per-resource metrics
+- **THEN** resource dimension data (WORKCENTERNAME, RESOURCEFAMILYNAME) SHALL be merged from resource_cache
+- **THEN** results SHALL be structured as a hierarchical tree (workcenter -> family -> resource)
+- **THEN** pagination SHALL apply to the flattened list
+
+### Requirement: Resource dataset cache SHALL handle cache expiry gracefully
+The module SHALL return appropriate signals when cache has expired.
+
+#### Scenario: Cache expired during view request
+- **WHEN** a view is requested with a query_id whose cache has expired
+- **THEN** the response SHALL return `{ success: false, error: "cache_expired" }`
+- **THEN** the HTTP status SHALL be 410 (Gone)
diff --git a/openspec/specs/resource-history-page/spec.md b/openspec/specs/resource-history-page/spec.md
index 69ed043..259fda6 100644
--- a/openspec/specs/resource-history-page/spec.md
+++ b/openspec/specs/resource-history-page/spec.md
@@ -1,139 +1,46 @@
-## ADDED Requirements
-
-### Requirement: Resource History page SHALL display KPI summary cards
-The page SHALL show 9 KPI cards with aggregated performance metrics for the queried period.
-
-#### Scenario: KPI cards rendering
-- **WHEN** summary data is loaded from `GET /api/resource/history/summary`
-- **THEN** 9 cards SHALL display: OU%, AVAIL%, PRD, SBY, UDT, SDT, EGT, NST, Machine Count
-- **THEN** hour values SHALL format with "K" suffix for large numbers (e.g., 2.5K)
-- **THEN** percentage values SHALL use `buildResourceKpiFromHours()` from `core/compute.js`
-
-### Requirement: Resource History page SHALL display trend chart
-The page SHALL show OU% and Availability% trends over time.
-
-#### Scenario: Trend chart rendering
-- **WHEN** summary data is loaded
-- **THEN** a line chart with area fill SHALL display OU% and AVAIL% time series
-- **THEN** the chart SHALL use vue-echarts with `autoresize` prop
-- **THEN** smooth curves with 0.2 opacity area style SHALL render
-
-### Requirement: Resource History page SHALL display stacked status distribution chart
-The page SHALL show E10 status hour distribution over time.
-
-#### Scenario: Stacked bar chart rendering
-- **WHEN** summary data is loaded
-- **THEN** a stacked bar chart SHALL display PRD, SBY, UDT, SDT, EGT, NST hours per period
-- **THEN** each status SHALL use its designated color (PRD=green, SBY=blue, UDT=red, SDT=yellow, EGT=purple, NST=gray)
-- **THEN** tooltips SHALL show percentages calculated dynamically
-
-### Requirement: Resource History page SHALL display workcenter comparison chart
-The page SHALL show top workcenters ranked by OU%.
-
-#### Scenario: Comparison chart rendering
-- **WHEN** summary data is loaded
-- **THEN** a horizontal bar chart SHALL display top 15 workcenters by OU%
-- **THEN** bars SHALL be color-coded: green (≥80%), yellow (≥50%), red (<50%)
-- **THEN** data SHALL display in descending OU% order (top to bottom)
-
-### Requirement: Resource History page SHALL display OU% heatmap
-The page SHALL show a heatmap of OU% by workcenter and date.
-
-#### Scenario: Heatmap chart rendering
-- **WHEN** summary data is loaded
-- **THEN** a 2D heatmap SHALL display: workcenters (Y-axis) × dates (X-axis)
-- **THEN** color scale SHALL range from red (low OU%) through yellow to green (high OU%)
-- **THEN** workcenters SHALL sort by `workcenter_seq` for consistent ordering
-
-### Requirement: Resource History page SHALL display hierarchical detail table
-The page SHALL show a three-level expandable table with per-resource performance metrics.
-
-#### Scenario: Detail table rendering
-- **WHEN** detail data is loaded from `GET /api/resource/history/detail`
-- **THEN** a tree table SHALL display with columns: Name, OU%, AVAIL%, PRD, SBY, UDT, SDT, EGT, NST, Count
-- **THEN** Level 0 rows SHALL show workcenter groups with aggregated metrics
-- **THEN** Level 1 rows SHALL show resource families with aggregated metrics
-- **THEN** Level 2 rows SHALL show individual resources
-
-#### Scenario: Hour and percentage display
-- **WHEN** detail data renders
-- **THEN** status columns SHALL display hours with percentage: "10.5h (25%)"
-- **THEN** KPI values SHALL be computed using `buildResourceKpiFromHours()` from `core/compute.js`
-
-#### Scenario: Tree expand and collapse
-- **WHEN** user clicks the expand button on a row
-- **THEN** child rows SHALL toggle visibility
-- **WHEN** user clicks "Expand All" or "Collapse All"
-- **THEN** all rows SHALL expand or collapse accordingly
+## MODIFIED Requirements
 
 ### Requirement: Resource History page SHALL support date range and granularity selection
-The page SHALL allow users to specify time range and aggregation granularity.
+The page SHALL allow users to specify time range and aggregation granularity. On query, the page SHALL use a two-phase flow: POST /query returns queryId, subsequent filter changes use GET /view.
 
-#### Scenario: Date range selection
-- **WHEN** the page loads
-- **THEN** date inputs SHALL default to last 7 days (yesterday minus 6 days)
-- **THEN** date range SHALL NOT exceed 730 days (2 years)
-
-#### Scenario: Granularity buttons
-- **WHEN** user clicks a granularity button (日/週/月/年)
-- **THEN** the active button SHALL highlight
-- **THEN** the next query SHALL use the selected granularity (day/week/month/year)
-
-#### Scenario: Query execution
+#### Scenario: Primary query via POST /query
 - **WHEN** user clicks the query button
-- **THEN** summary and detail APIs SHALL be called in parallel
-- **THEN** all 4 charts, KPI cards, and detail table SHALL update with results
+- **THEN** the page SHALL call `POST /api/resource/history/query` with date range, granularity, and resource filters
+- **THEN** the response queryId SHALL be stored for subsequent view requests
+- **THEN** summary (KPI, trend, heatmap, comparison) and detail page 1 SHALL all be populated from the single response
 
-### Requirement: Resource History page SHALL support multi-select filtering
-The page SHALL provide multi-select dropdown filters for workcenter groups and families, and SHALL support interdependent narrowing with machine options and selected-value pruning.
+#### Scenario: Filter change uses GET /view
+- **WHEN** user changes supplementary filters (workcenter groups, families, machines, equipment type) while queryId exists
+- **THEN** the page SHALL call `GET /api/resource/history/view?query_id=...&filters...`
+- **THEN** no new Oracle query SHALL be triggered
+- **THEN** all charts, KPI cards, and detail table SHALL update from the view response
 
-#### Scenario: Multi-select dropdown
-- **WHEN** user clicks a multi-select dropdown trigger
-- **THEN** a dropdown SHALL display with checkboxes for each option
-- **THEN** "Select All" and "Clear All" buttons SHALL be available
-- **THEN** clicking outside the dropdown SHALL close it
+#### Scenario: Pagination uses GET /view
+- **WHEN** user navigates to a different page in the detail table
+- **THEN** the page SHALL call `GET /api/resource/history/view?query_id=...&page=...`
 
-#### Scenario: Filter options loading
-- **WHEN** the page loads
-- **THEN** workcenter groups and families SHALL load from `GET /api/resource/history/options`
-- **THEN** machine candidates SHALL be derivable before first query from loaded option resources
-
-#### Scenario: Upstream filters narrow downstream options
-- **WHEN** user changes upstream filters (`workcenterGroups`, `families`, equipment-type flags)
-- **THEN** machine options SHALL be recomputed to only include matching resources
-- **THEN** narrowed options SHALL be reflected immediately in filter controls
-
-#### Scenario: Invalid selected machines are pruned
-- **WHEN** upstream filters change and selected machines are no longer valid
-- **THEN** invalid selected machine values SHALL be removed automatically
-- **THEN** remaining valid selected machine values SHALL be preserved
-
-#### Scenario: Equipment type checkboxes
-- **WHEN** user toggles a checkbox (生產設備, 重點設備, 監控設備)
-- **THEN** the next query SHALL include the corresponding filter parameter
-- **THEN** option narrowing SHALL also honor the same checkbox conditions
+#### Scenario: Date range or granularity change triggers new primary query
+- **WHEN** user changes date range or granularity and clicks query
+- **THEN** the page SHALL call `POST /api/resource/history/query` with new params
+- **THEN** a new queryId SHALL replace the old one
 
-### Requirement: Resource History page SHALL support CSV export
-The page SHALL allow users to export the current query results as CSV.
+#### Scenario: Cache expired auto-retry
+- **WHEN** GET /view returns `{ success: false, error: "cache_expired" }`
+- **THEN** the page SHALL automatically re-execute `POST /api/resource/history/query` with the last committed filters
+- **THEN** the view SHALL refresh with the new data
 
-#### Scenario: CSV export
-- **WHEN** user clicks the "匯出 CSV" button
-- **THEN** the browser SHALL download a CSV file from `GET /api/resource/history/export` with current filters
-- **THEN** the filename SHALL be `resource_history_{start_date}_to_{end_date}.csv`
+### Requirement: Resource History page SHALL display KPI summary cards
+The page SHALL show 9 KPI cards with aggregated performance metrics derived from the cached dataset.
 
-### Requirement: Resource History page SHALL handle loading and error states
-The page SHALL display appropriate feedback during API calls and on errors.
+#### Scenario: KPI cards from cached data
+- **WHEN** summary data is derived from the cached DataFrame
+- **THEN** 9 cards SHALL display: OU%, AVAIL%, PRD, SBY, UDT, SDT, EGT, NST, Machine Count
+- **THEN** values SHALL be computed from the cached shift-status records, not from a separate Oracle query
 
-#### Scenario: Query loading state
-- **WHEN** a query is executing
-- **THEN** the query button SHALL be disabled
-- **THEN** a loading indicator SHALL display
+### Requirement: Resource History page SHALL display hierarchical detail table
+The page SHALL show a three-level expandable table derived from the cached dataset.
 
-#### Scenario: API error handling
-- **WHEN** an API call fails
-- **THEN** a toast notification SHALL display the error message
-- **THEN** the page SHALL NOT crash or become unresponsive
-
-#### Scenario: No data placeholder
-- **WHEN** query returns empty results
-- **THEN** charts and table SHALL display "No data" placeholders
+#### Scenario: Detail table from cached data
+- **WHEN** detail data is derived from the cached DataFrame
+- **THEN** a tree table SHALL display with the same columns and hierarchy as before
+- **THEN** data SHALL be derived in-memory from the cached DataFrame, not from a separate Oracle query
diff --git a/src/mes_dashboard/config/settings.py b/src/mes_dashboard/config/settings.py
index e736e4a..45674ba 100644
--- a/src/mes_dashboard/config/settings.py
+++ b/src/mes_dashboard/config/settings.py
@@ -51,6 +51,10 @@ class Config:
     DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0)
     DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000)
 
+    # Slow-query dedicated connection settings (non-pooled)
+    DB_SLOW_CALL_TIMEOUT_MS = _int_env("DB_SLOW_CALL_TIMEOUT_MS", 300000)  # 300s
+    DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 3)
+
     # Auth configuration - MUST be set in .env file
     LDAP_API_URL = os.getenv("LDAP_API_URL", "")
     ADMIN_EMAILS = os.getenv("ADMIN_EMAILS", "")
@@ -95,6 +99,7 @@ class DevelopmentConfig(Config):
     DB_CONNECT_RETRY_COUNT = _int_env("DB_CONNECT_RETRY_COUNT", 1)
     DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0)
     DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000)
+    DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 2)
 
 
 class ProductionConfig(Config):
@@ -111,6 +116,7 @@ class ProductionConfig(Config):
     DB_CONNECT_RETRY_COUNT = _int_env("DB_CONNECT_RETRY_COUNT", 1)
     DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0)
     DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000)
+    DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 3)
 
 
 class TestingConfig(Config):
@@ -128,6 +134,8 @@ class TestingConfig(Config):
     DB_CONNECT_RETRY_COUNT = 0
     DB_CONNECT_RETRY_DELAY = 0.0
     DB_CALL_TIMEOUT_MS = 5000
+    DB_SLOW_CALL_TIMEOUT_MS = 10000
+    DB_SLOW_MAX_CONCURRENT = 1
     CSRF_ENABLED = False
 
 
diff --git a/src/mes_dashboard/core/database.py b/src/mes_dashboard/core/database.py
index f27f2bf..68b25cf 100644
--- a/src/mes_dashboard/core/database.py
+++ b/src/mes_dashboard/core/database.py
@@ -193,6 +193,14 @@ def get_db_runtime_config(refresh: bool = False) -> Dict[str, Any]:
             "DB_POOL_EXHAUSTED_RETRY_AFTER_SECONDS",
             5,
         ),
+        "slow_call_timeout_ms": _from_app_or_env_int(
+            "DB_SLOW_CALL_TIMEOUT_MS",
+            config_class.DB_SLOW_CALL_TIMEOUT_MS,
+        ),
+        "slow_max_concurrent": _from_app_or_env_int(
+            "DB_SLOW_MAX_CONCURRENT",
+            config_class.DB_SLOW_MAX_CONCURRENT,
+        ),
     }
     return _DB_RUNTIME_CONFIG.copy()
 
@@ -220,6 +228,7 @@ def get_pool_status() -> Dict[str, Any]:
         "checked_in": int(pool.checkedin()),
         "max_capacity": max_capacity,
         "saturation": saturation,
+        "slow_query_active": get_slow_query_active_count(),
     }
 
 
@@ -399,7 +408,7 @@ def dispose_engine():
 
     Call this during application shutdown to cleanly release resources.
     """
-    global _ENGINE, _HEALTH_ENGINE, _DB_RUNTIME_CONFIG
+    global _ENGINE, _HEALTH_ENGINE, _DB_RUNTIME_CONFIG, _SLOW_QUERY_SEMAPHORE
     stop_keepalive()
     if _HEALTH_ENGINE is not None:
         _HEALTH_ENGINE.dispose()
@@ -410,6 +419,7 @@ def dispose_engine():
         logger.info("Database engine disposed, all connections closed")
         _ENGINE = None
     _DB_RUNTIME_CONFIG = None
+    _SLOW_QUERY_SEMAPHORE = None
 
 
 # ============================================================
@@ -419,6 +429,26 @@ def dispose_engine():
 _DIRECT_CONN_COUNTER = 0
 _DIRECT_CONN_LOCK = threading.Lock()
 
+# Slow-query concurrency control
+_SLOW_QUERY_SEMAPHORE: Optional[threading.Semaphore] = None
+_SLOW_QUERY_ACTIVE = 0
+_SLOW_QUERY_LOCK = threading.Lock()
+
+
+def _get_slow_query_semaphore() -> threading.Semaphore:
+    """Lazily initialise the slow-query semaphore from runtime config."""
+    global _SLOW_QUERY_SEMAPHORE
+    if _SLOW_QUERY_SEMAPHORE is None:
+        runtime = get_db_runtime_config()
+        _SLOW_QUERY_SEMAPHORE = threading.Semaphore(runtime["slow_max_concurrent"])
+        logger.info("Slow-query semaphore initialised (max_concurrent=%s)", runtime["slow_max_concurrent"])
+    return _SLOW_QUERY_SEMAPHORE
+
+
+def get_slow_query_active_count() -> int:
+    """Return the number of slow queries currently executing."""
+    return _SLOW_QUERY_ACTIVE
+
 
 def get_direct_connection_count() -> int:
     """Return total direct (non-pooled) connections since worker start."""
@@ -573,7 +603,7 @@ def read_sql_df(sql: str, params: Optional[Dict[str, Any]] = None) -> pd.DataFra
 def read_sql_df_slow(
     sql: str,
     params: Optional[Dict[str, Any]] = None,
-    timeout_seconds: int = 120,
+    timeout_seconds: Optional[int] = None,
 ) -> pd.DataFrame:
     """Execute a slow SQL query with a custom timeout via direct oracledb connection.
 
@@ -581,20 +611,41 @@ def read_sql_df_slow(
     this creates a dedicated connection with a longer call_timeout
     for known-slow queries (e.g. full table scans on large tables).
 
+    Concurrency is limited by a semaphore (DB_SLOW_MAX_CONCURRENT) to
+    prevent Oracle connection exhaustion.
+
     Args:
         sql: SQL query string with Oracle bind variables.
         params: Optional dict of parameter values.
-        timeout_seconds: Call timeout in seconds (default: 120).
+        timeout_seconds: Call timeout in seconds.  None = use
+            DB_SLOW_CALL_TIMEOUT_MS from config (default 300s).
 
     Returns:
-        DataFrame with query results, or None on connection failure.
+        DataFrame with query results.
     """
-    start_time = time.time()
-    timeout_ms = timeout_seconds * 1000
+    global _SLOW_QUERY_ACTIVE
 
+    runtime = get_db_runtime_config()
+    if timeout_seconds is None:
+        timeout_ms = runtime["slow_call_timeout_ms"]
+    else:
+        timeout_ms = timeout_seconds * 1000
+
+    sem = _get_slow_query_semaphore()
+    acquired = sem.acquire(timeout=60)
+    if not acquired:
+        raise RuntimeError(
+            "Slow-query concurrency limit reached; try again later"
+        )
+
+    with _SLOW_QUERY_LOCK:
+        _SLOW_QUERY_ACTIVE += 1
+        active = _SLOW_QUERY_ACTIVE
+
+    logger.info("Slow query starting (active=%s, timeout_ms=%s)", active, timeout_ms)
+    start_time = time.time()
     conn = None
     try:
-        runtime = get_db_runtime_config()
         conn = oracledb.connect(
             **DB_CONFIG,
             tcp_connect_timeout=runtime["tcp_connect_timeout"],
@@ -640,6 +691,9 @@ def read_sql_df_slow(
                 conn.close()
             except Exception:
                 pass
+        with _SLOW_QUERY_LOCK:
+            _SLOW_QUERY_ACTIVE -= 1
+        sem.release()
 
 
 # ============================================================
diff --git a/src/mes_dashboard/routes/hold_history_routes.py b/src/mes_dashboard/routes/hold_history_routes.py
index 4ef7c53..72ca072 100644
--- a/src/mes_dashboard/routes/hold_history_routes.py
+++ b/src/mes_dashboard/routes/hold_history_routes.py
@@ -1,11 +1,17 @@
 # -*- coding: utf-8 -*-
-"""Hold History page route and API endpoints."""
+"""Hold History page route and API endpoints.
+
+Two-phase query pattern:
+  POST /api/hold-history/query  → single Oracle query → cache → full response
+  GET  /api/hold-history/view   → read cache → filtered views (no Oracle)
+"""
 
 from __future__ import annotations
 
+import logging
 import os
 from datetime import datetime
-from typing import Optional, Tuple
+from typing import Optional
 
 from flask import Blueprint, current_app, jsonify, request, send_from_directory
 
@@ -14,25 +20,25 @@ from mes_dashboard.core.modernization_policy import (
     missing_in_scope_asset_response,
     maybe_redirect_to_canonical_shell,
 )
-from mes_dashboard.services.hold_history_service import (
-    get_hold_history_duration,
-    get_hold_history_list,
-    get_hold_history_reason_pareto,
-    get_hold_history_trend,
+from mes_dashboard.services.hold_dataset_cache import (
+    apply_view,
+    execute_primary_query,
 )
 
+logger = logging.getLogger("mes_dashboard.hold_history_routes")
+
 hold_history_bp = Blueprint('hold_history', __name__)
 
-_HOLD_HISTORY_TREND_RATE_LIMIT = configured_rate_limit(
-    bucket='hold-history-trend',
+_HOLD_HISTORY_QUERY_RATE_LIMIT = configured_rate_limit(
+    bucket='hold-history-query',
     max_attempts_env='HOLD_HISTORY_TREND_RATE_LIMIT_MAX_REQUESTS',
     window_seconds_env='HOLD_HISTORY_TREND_RATE_LIMIT_WINDOW_SECONDS',
     default_max_attempts=60,
     default_window_seconds=60,
 )
 
-_HOLD_HISTORY_LIST_RATE_LIMIT = configured_rate_limit(
-    bucket='hold-history-list',
+_HOLD_HISTORY_VIEW_RATE_LIMIT = configured_rate_limit(
+    bucket='hold-history-view',
     max_attempts_env='HOLD_HISTORY_LIST_RATE_LIMIT_MAX_REQUESTS',
     window_seconds_env='HOLD_HISTORY_LIST_RATE_LIMIT_WINDOW_SECONDS',
     default_max_attempts=90,
@@ -44,49 +50,39 @@ _VALID_RECORD_TYPES = {'new', 'on_hold', 'released'}
 _VALID_DURATION_RANGES = {'<4h', '4-24h', '1-3d', '>3d'}
 
 
-def _parse_date_range() -> tuple[Optional[str], Optional[str], Optional[tuple[dict, int]]]:
-    start_date = request.args.get('start_date', '').strip()
-    end_date = request.args.get('end_date', '').strip()
+# ============================================================
+# Helpers
+# ============================================================
 
-    if not start_date or not end_date:
-        return None, None, ({'success': False, 'error': '缺少必要參數: start_date, end_date'}, 400)
 
+def _validate_date(value: str) -> Optional[str]:
+    """Return ISO date string or None if invalid."""
     try:
-        start = datetime.strptime(start_date, '%Y-%m-%d').date()
-        end = datetime.strptime(end_date, '%Y-%m-%d').date()
-    except ValueError:
-        return None, None, ({'success': False, 'error': '日期格式錯誤，請使用 YYYY-MM-DD'}, 400)
-
-    if end < start:
-        return None, None, ({'success': False, 'error': 'end_date 不可早於 start_date'}, 400)
-
-    return start_date, end_date, None
+        datetime.strptime(value, '%Y-%m-%d')
+        return value
+    except (ValueError, TypeError):
+        return None
 
 
-def _parse_hold_type(default: str = 'quality') -> tuple[Optional[str], Optional[tuple[dict, int]]]:
-    raw = request.args.get('hold_type', '').strip().lower()
-    hold_type = raw or default
-    if hold_type not in _VALID_HOLD_TYPES:
-        return None, (
-            {'success': False, 'error': 'Invalid hold_type. Use quality, non-quality, or all'},
-            400,
-        )
-    return hold_type, None
+def _normalize_hold_type(value: str, default: str = 'quality') -> str:
+    v = str(value or default).strip().lower()
+    return v if v in _VALID_HOLD_TYPES else default
 
 
-def _parse_record_type(default: str = 'new') -> tuple[Optional[str], Optional[tuple[dict, int]]]:
-    raw = request.args.get('record_type', '').strip().lower()
-    record_type = raw or default
-    parts = [p.strip() for p in record_type.split(',') if p.strip()]
+def _normalize_record_type(value: str, default: str = 'new') -> Optional[str]:
+    """Validate CSV record_type. Returns normalised string or None on error."""
+    parts = [p.strip().lower() for p in str(value or default).split(',') if p.strip()]
     if not parts:
         parts = [default]
-    for part in parts:
-        if part not in _VALID_RECORD_TYPES:
-            return None, (
-                {'success': False, 'error': 'Invalid record_type. Use new, on_hold, or released'},
-                400,
-            )
-    return ','.join(parts), None
+    for p in parts:
+        if p not in _VALID_RECORD_TYPES:
+            return None
+    return ','.join(parts)
+
+
+# ============================================================
+# Page route
+# ============================================================
 
 
 @hold_history_bp.route('/hold-history')
@@ -111,106 +107,86 @@ def hold_history_page():
     ))
 
 
-@hold_history_bp.route('/api/hold-history/trend')
-@_HOLD_HISTORY_TREND_RATE_LIMIT
-def api_hold_history_trend():
-    """Return daily hold history trend data."""
-    start_date, end_date, date_error = _parse_date_range()
-    if date_error:
-        return jsonify(date_error[0]), date_error[1]
+# ============================================================
+# POST /api/hold-history/query — primary query (single Oracle)
+# ============================================================
 
-    result = get_hold_history_trend(start_date, end_date)
-    if result is not None:
+
+@hold_history_bp.route('/api/hold-history/query', methods=['POST'])
+@_HOLD_HISTORY_QUERY_RATE_LIMIT
+def api_hold_history_query():
+    """Execute primary Oracle query, cache, and return full result."""
+    body = request.get_json(silent=True) or {}
+
+    start_date = _validate_date(str(body.get('start_date', '')).strip())
+    end_date = _validate_date(str(body.get('end_date', '')).strip())
+    if not start_date or not end_date:
+        return jsonify({'success': False, 'error': '缺少必要參數: start_date, end_date'}), 400
+
+    if end_date < start_date:
+        return jsonify({'success': False, 'error': 'end_date 不可早於 start_date'}), 400
+
+    hold_type = _normalize_hold_type(str(body.get('hold_type', '')))
+    record_type = _normalize_record_type(str(body.get('record_type', '')))
+    if record_type is None:
+        return jsonify({'success': False, 'error': 'Invalid record_type'}), 400
+
+    try:
+        result = execute_primary_query(
+            start_date=start_date,
+            end_date=end_date,
+            hold_type=hold_type,
+            record_type=record_type,
+        )
         return jsonify({'success': True, 'data': result})
-    return jsonify({'success': False, 'error': '查詢失敗'}), 500
+    except Exception as exc:
+        logger.error("Hold history primary query failed: %s", exc)
+        return jsonify({'success': False, 'error': '查詢失敗'}), 500
 
 
-@hold_history_bp.route('/api/hold-history/reason-pareto')
-def api_hold_history_reason_pareto():
-    """Return hold reason Pareto data."""
-    start_date, end_date, date_error = _parse_date_range()
-    if date_error:
-        return jsonify(date_error[0]), date_error[1]
-
-    hold_type, hold_type_error = _parse_hold_type(default='quality')
-    if hold_type_error:
-        return jsonify(hold_type_error[0]), hold_type_error[1]
-
-    record_type, record_type_error = _parse_record_type()
-    if record_type_error:
-        return jsonify(record_type_error[0]), record_type_error[1]
-
-    result = get_hold_history_reason_pareto(start_date, end_date, hold_type, record_type)
-    if result is not None:
-        return jsonify({'success': True, 'data': result})
-    return jsonify({'success': False, 'error': '查詢失敗'}), 500
+# ============================================================
+# GET /api/hold-history/view — supplementary view (cache only)
+# ============================================================
 
 
-@hold_history_bp.route('/api/hold-history/duration')
-def api_hold_history_duration():
-    """Return hold duration distribution data."""
-    start_date, end_date, date_error = _parse_date_range()
-    if date_error:
-        return jsonify(date_error[0]), date_error[1]
-
-    hold_type, hold_type_error = _parse_hold_type(default='quality')
-    if hold_type_error:
-        return jsonify(hold_type_error[0]), hold_type_error[1]
-
-    record_type, record_type_error = _parse_record_type()
-    if record_type_error:
-        return jsonify(record_type_error[0]), record_type_error[1]
-
-    result = get_hold_history_duration(start_date, end_date, hold_type, record_type)
-    if result is not None:
-        return jsonify({'success': True, 'data': result})
-    return jsonify({'success': False, 'error': '查詢失敗'}), 500
-
-
-@hold_history_bp.route('/api/hold-history/list')
-@_HOLD_HISTORY_LIST_RATE_LIMIT
-def api_hold_history_list():
-    """Return paginated hold detail list."""
-    start_date, end_date, date_error = _parse_date_range()
-    if date_error:
-        return jsonify(date_error[0]), date_error[1]
-
-    hold_type, hold_type_error = _parse_hold_type(default='quality')
-    if hold_type_error:
-        return jsonify(hold_type_error[0]), hold_type_error[1]
-
-    record_type, record_type_error = _parse_record_type()
-    if record_type_error:
-        return jsonify(record_type_error[0]), record_type_error[1]
+@hold_history_bp.route('/api/hold-history/view')
+@_HOLD_HISTORY_VIEW_RATE_LIMIT
+def api_hold_history_view():
+    """Read cached DataFrame, apply filters, return derived views."""
+    query_id = request.args.get('query_id', '').strip()
+    if not query_id:
+        return jsonify({'success': False, 'error': '缺少 query_id'}), 400
 
+    hold_type = _normalize_hold_type(request.args.get('hold_type', ''))
     reason = request.args.get('reason', '').strip() or None
+    record_type = _normalize_record_type(request.args.get('record_type', ''))
+    if record_type is None:
+        return jsonify({'success': False, 'error': 'Invalid record_type'}), 400
 
     raw_duration = request.args.get('duration_range', '').strip() or None
     if raw_duration and raw_duration not in _VALID_DURATION_RANGES:
         return jsonify({'success': False, 'error': 'Invalid duration_range'}), 400
-    duration_range = raw_duration
 
     page = request.args.get('page', 1, type=int)
     per_page = request.args.get('per_page', 50, type=int)
+    page = max(page or 1, 1)
+    per_page = max(1, min(per_page or 50, 200))
 
-    if page is None:
-        page = 1
-    if per_page is None:
-        per_page = 50
+    try:
+        result = apply_view(
+            query_id=query_id,
+            hold_type=hold_type,
+            reason=reason,
+            record_type=record_type,
+            duration_range=raw_duration,
+            page=page,
+            per_page=per_page,
+        )
+    except Exception as exc:
+        logger.error("Hold history view failed: %s", exc)
+        return jsonify({'success': False, 'error': '查詢失敗'}), 500
 
-    page = max(page, 1)
-    per_page = max(1, min(per_page, 200))
+    if result is None:
+        return jsonify({'success': False, 'error': 'cache_expired'}), 410
 
-    result = get_hold_history_list(
-        start_date=start_date,
-        end_date=end_date,
-        hold_type=hold_type,
-        reason=reason,
-        record_type=record_type,
-        duration_range=duration_range,
-        page=page,
-        per_page=per_page,
-    )
-    if result is not None:
-        return jsonify({'success': True, 'data': result})
-    return jsonify({'success': False, 'error': '查詢失敗'}), 500
+    return jsonify({'success': True, 'data': result})
diff --git a/src/mes_dashboard/routes/resource_history_routes.py b/src/mes_dashboard/routes/resource_history_routes.py
index 7e01b08..dc1d3df 100644
--- a/src/mes_dashboard/routes/resource_history_routes.py
+++ b/src/mes_dashboard/routes/resource_history_routes.py
@@ -2,6 +2,7 @@
 """Resource History Analysis API routes.
 
 Contains Flask Blueprint for historical equipment performance analysis endpoints.
+Two-phase flow: POST /query (Oracle → cache) + GET /view (cache → derived views).
 """
 
 from datetime import datetime
@@ -9,13 +10,15 @@ from datetime import datetime
 from flask import Blueprint, jsonify, request, redirect, Response
 
 from mes_dashboard.core.cache import cache_get, cache_set, make_cache_key
-from mes_dashboard.config.constants import CACHE_TTL_FILTER_OPTIONS, CACHE_TTL_TREND
+from mes_dashboard.config.constants import CACHE_TTL_FILTER_OPTIONS
 from mes_dashboard.services.resource_history_service import (
     get_filter_options,
-    query_summary,
-    query_detail,
     export_csv,
 )
+from mes_dashboard.services.resource_dataset_cache import (
+    execute_primary_query,
+    apply_view,
+)
 
 # Create Blueprint
 resource_history_bp = Blueprint(
@@ -59,132 +62,112 @@ def api_resource_history_options():
     return jsonify({'success': False, 'error': '查詢篩選選項失敗'}), 500
 
 
-@resource_history_bp.route('/summary', methods=['GET'])
-def api_resource_history_summary():
-    """API: Get summary data (KPI, trend, heatmap, workcenter comparison).
+# ============================================================
+# Two-phase dataset cache endpoints
+# ============================================================
 
-    Query Parameters:
-        start_date: Start date (YYYY-MM-DD)
-        end_date: End date (YYYY-MM-DD)
-        granularity: day|week|month|year (default: day)
-        workcenter_groups: Optional workcenter group filter (multi-select)
-        families: Optional resource family filter (multi-select)
-        is_production: 1 to filter production equipment
-        is_key: 1 to filter key equipment
-        is_monitor: 1 to filter monitored equipment
+def _validate_dates(start_date: str, end_date: str):
+    """Validate and return parsed dates, or raise ValueError."""
+    if not start_date or not end_date:
+        raise ValueError("必須提供 start_date 和 end_date 參數")
+    sd = datetime.strptime(start_date, "%Y-%m-%d")
+    ed = datetime.strptime(end_date, "%Y-%m-%d")
+    if ed < sd:
+        raise ValueError("end_date 不可早於 start_date")
+    return sd, ed
+
+
+def _parse_resource_filters(data: dict) -> dict:
+    """Extract resource filter params from dict (request body or query params)."""
+    return {
+        "workcenter_groups": data.get("workcenter_groups") or None,
+        "families": data.get("families") or None,
+        "resource_ids": data.get("resource_ids") or None,
+        "is_production": _bool_param(data.get("is_production")),
+        "is_key": _bool_param(data.get("is_key")),
+        "is_monitor": _bool_param(data.get("is_monitor")),
+    }
+
+
+def _bool_param(val) -> bool:
+    """Normalize bool-ish values from JSON body or query string."""
+    if isinstance(val, bool):
+        return val
+    if isinstance(val, str):
+        return val in ("1", "true", "True")
+    return bool(val) if val is not None else False
+
+
+@resource_history_bp.route('/query', methods=['POST'])
+def api_resource_history_query():
+    """API: Primary query — Oracle → cache → return query_id + initial view.
+
+    JSON Body:
+        start_date: str (YYYY-MM-DD)
+        end_date: str (YYYY-MM-DD)
+        granularity: str (day|week|month|year, default: day)
+        workcenter_groups: list[str] (optional)
+        families: list[str] (optional)
+        resource_ids: list[str] (optional)
+        is_production: bool (optional)
+        is_key: bool (optional)
+        is_monitor: bool (optional)
 
     Returns:
-        JSON with kpi, trend, heatmap, workcenter_comparison sections.
+        JSON { success, query_id, summary, detail }
     """
-    # Parse query parameters
-    start_date = request.args.get('start_date')
-    end_date = request.args.get('end_date')
-    granularity = request.args.get('granularity', 'day')
-    workcenter_groups = request.args.getlist('workcenter_groups') or None
-    families = request.args.getlist('families') or None
-    resource_ids = request.args.getlist('resource_ids') or None
-    is_production = request.args.get('is_production') == '1'
-    is_key = request.args.get('is_key') == '1'
-    is_monitor = request.args.get('is_monitor') == '1'
+    body = request.get_json(silent=True) or {}
+    start_date = body.get("start_date", "")
+    end_date = body.get("end_date", "")
+    granularity = body.get("granularity", "day")
 
-    # Validate required parameters
-    if not start_date or not end_date:
-        return jsonify({
-            'success': False,
-            'error': '必須提供 start_date 和 end_date 參數'
-        }), 400
+    try:
+        _validate_dates(start_date, end_date)
+    except ValueError as exc:
+        return jsonify({"success": False, "error": str(exc)}), 400
 
-    # Build cache key with filters dict
-    cache_filters = {
-        'start_date': start_date,
-        'end_date': end_date,
-        'granularity': granularity,
-        'workcenter_groups': sorted(workcenter_groups) if workcenter_groups else None,
-        'families': sorted(families) if families else None,
-        'resource_ids': sorted(resource_ids) if resource_ids else None,
-        'is_production': is_production,
-        'is_key': is_key,
-        'is_monitor': is_monitor,
-    }
-    cache_key = make_cache_key("resource_history_summary", filters=cache_filters)
-    result = cache_get(cache_key)
+    filters = _parse_resource_filters(body)
 
-    if result is None:
-        result = query_summary(
+    try:
+        result = execute_primary_query(
             start_date=start_date,
             end_date=end_date,
             granularity=granularity,
-            workcenter_groups=workcenter_groups,
-            families=families,
-            resource_ids=resource_ids,
-            is_production=is_production,
-            is_key=is_key,
-            is_monitor=is_monitor,
+            **filters,
         )
-        if result is not None and 'error' not in result:
-            cache_set(cache_key, result, ttl=CACHE_TTL_TREND)
-
-    if result is not None:
-        if 'error' in result:
-            return jsonify({'success': False, 'error': result['error']}), 400
-        return jsonify({'success': True, 'data': result})
-    return jsonify({'success': False, 'error': '查詢摘要資料失敗'}), 500
+        return jsonify({"success": True, **result})
+    except Exception as exc:
+        return jsonify({"success": False, "error": f"查詢失敗: {exc}"}), 500
 
 
-@resource_history_bp.route('/detail', methods=['GET'])
-def api_resource_history_detail():
-    """API: Get hierarchical detail data.
+@resource_history_bp.route('/view', methods=['GET'])
+def api_resource_history_view():
+    """API: Supplementary view — read cache, derive views. No Oracle query.
 
     Query Parameters:
-        start_date: Start date (YYYY-MM-DD)
-        end_date: End date (YYYY-MM-DD)
-        granularity: day|week|month|year (default: day)
-        workcenter_groups: Optional workcenter group filter (multi-select)
-        families: Optional resource family filter (multi-select)
-        is_production: 1 to filter production equipment
-        is_key: 1 to filter key equipment
-        is_monitor: 1 to filter monitored equipment
+        query_id: str (required)
+        granularity: str (day|week|month|year, default: day)
 
     Returns:
-        JSON with data array, total count, truncated flag.
+        JSON { success, summary, detail } or 410 on cache miss.
     """
-    # Parse query parameters
-    start_date = request.args.get('start_date')
-    end_date = request.args.get('end_date')
-    granularity = request.args.get('granularity', 'day')
-    workcenter_groups = request.args.getlist('workcenter_groups') or None
-    families = request.args.getlist('families') or None
-    resource_ids = request.args.getlist('resource_ids') or None
-    is_production = request.args.get('is_production') == '1'
-    is_key = request.args.get('is_key') == '1'
-    is_monitor = request.args.get('is_monitor') == '1'
+    query_id = request.args.get("query_id", "")
+    granularity = request.args.get("granularity", "day")
 
-    # Validate required parameters
-    if not start_date or not end_date:
-        return jsonify({
-            'success': False,
-            'error': '必須提供 start_date 和 end_date 參數'
-        }), 400
+    if not query_id:
+        return jsonify({"success": False, "error": "必須提供 query_id"}), 400
 
-    result = query_detail(
-        start_date=start_date,
-        end_date=end_date,
-        granularity=granularity,
-        workcenter_groups=workcenter_groups,
-        families=families,
-        resource_ids=resource_ids,
-        is_production=is_production,
-        is_key=is_key,
-        is_monitor=is_monitor,
-    )
+    result = apply_view(query_id=query_id, granularity=granularity)
+    if result is None:
+        return jsonify({"success": False, "error": "cache_expired"}), 410
 
-    if result is not None:
-        if 'error' in result:
-            return jsonify({'success': False, 'error': result['error']}), 400
-        return jsonify({'success': True, **result})
-    return jsonify({'success': False, 'error': '查詢明細資料失敗'}), 500
+    return jsonify({"success": True, **result})
 
 
+# ============================================================
+# Export (kept — uses existing service directly)
+# ============================================================
+
 @resource_history_bp.route('/export', methods=['GET'])
 def api_resource_history_export():
     """API: Export detail data as CSV.
diff --git a/src/mes_dashboard/services/excel_query_service.py b/src/mes_dashboard/services/excel_query_service.py
index e56d601..13101eb 100644
--- a/src/mes_dashboard/services/excel_query_service.py
+++ b/src/mes_dashboard/services/excel_query_service.py
@@ -5,16 +5,16 @@ Provides Excel parsing, batch query execution, and CSV export functions.
 Supports large datasets (7000+ rows) by splitting queries into batches.
 """
 
-import re
-import logging
-from datetime import datetime
-from typing import Any, Dict, List, Tuple
+import re
+import logging
+from datetime import datetime
+from typing import Any, Dict, List, Tuple
 
 import pandas as pd
 
-from mes_dashboard.core.database import get_db_connection
-
-logger = logging.getLogger('mes_dashboard.excel_query_service')
+from mes_dashboard.core.database import get_db_connection, get_db_runtime_config
+
+logger = logging.getLogger('mes_dashboard.excel_query_service')
 
 # Oracle IN clause limit
 BATCH_SIZE = 1000
@@ -23,10 +23,10 @@ BATCH_SIZE = 1000
 LIKE_KEYWORD_LIMIT = 100
 
 # Large table threshold for performance warning (10 million rows)
-LARGE_TABLE_THRESHOLD = 10_000_000
-PARSE_ERROR_MESSAGE = "Excel 解析失敗，請確認檔案格式"
-COLUMN_READ_ERROR_MESSAGE = "讀取欄位失敗，請稍後再試"
-QUERY_ERROR_MESSAGE = "查詢服務暫時無法使用"
+LARGE_TABLE_THRESHOLD = 10_000_000
+PARSE_ERROR_MESSAGE = "Excel 解析失敗，請確認檔案格式"
+COLUMN_READ_ERROR_MESSAGE = "讀取欄位失敗，請稍後再試"
+QUERY_ERROR_MESSAGE = "查詢服務暫時無法使用"
 
 
 def parse_excel(file_storage) -> Dict[str, Any]:
@@ -50,9 +50,9 @@ def parse_excel(file_storage) -> Dict[str, Any]:
             'preview': preview,
             'total_rows': len(df)
         }
-    except Exception as exc:
-        logger.exception("Excel parse failed: %s", exc)
-        return {'error': PARSE_ERROR_MESSAGE}
+    except Exception as exc:
+        logger.exception("Excel parse failed: %s", exc)
+        return {'error': PARSE_ERROR_MESSAGE}
 
 
 def get_column_unique_values(file_storage, column_name: str) -> Dict[str, Any]:
@@ -79,9 +79,9 @@ def get_column_unique_values(file_storage, column_name: str) -> Dict[str, Any]:
             'values': values_list,
             'count': len(values_list)
         }
-    except Exception as exc:
-        logger.exception("Excel column read failed for %s: %s", column_name, exc)
-        return {'error': COLUMN_READ_ERROR_MESSAGE}
+    except Exception as exc:
+        logger.exception("Excel column read failed for %s: %s", column_name, exc)
+        return {'error': COLUMN_READ_ERROR_MESSAGE}
 
 
 def detect_excel_column_type(values: List[str]) -> Dict[str, Any]:
@@ -322,6 +322,7 @@ def execute_batch_query(
     connection = get_db_connection()
     if not connection:
         return {'error': '資料庫連接失敗'}
+    connection.call_timeout = get_db_runtime_config()["slow_call_timeout_ms"]
 
     try:
         cursor = connection.cursor()
@@ -376,11 +377,11 @@ def execute_batch_query(
             'batch_count': total_batches
         }
 
-    except Exception as exc:
-        if connection:
-            connection.close()
-        logger.exception("Excel batch query failed: %s", exc)
-        return {'error': QUERY_ERROR_MESSAGE}
+    except Exception as exc:
+        if connection:
+            connection.close()
+        logger.exception("Excel batch query failed: %s", exc)
+        return {'error': QUERY_ERROR_MESSAGE}
 
 
 def execute_advanced_batch_query(
@@ -429,6 +430,7 @@ def execute_advanced_batch_query(
     connection = get_db_connection()
     if not connection:
         return {'error': '資料庫連接失敗'}
+    connection.call_timeout = get_db_runtime_config()["slow_call_timeout_ms"]
 
     try:
         cursor = connection.cursor()
@@ -535,11 +537,11 @@ def execute_advanced_batch_query(
             'query_type': query_type
         }
 
-    except Exception as exc:
-        if connection:
-            connection.close()
-        logger.exception("Excel advanced batch query failed: %s", exc)
-        return {'error': QUERY_ERROR_MESSAGE}
+    except Exception as exc:
+        if connection:
+            connection.close()
+        logger.exception("Excel advanced batch query failed: %s", exc)
+        return {'error': QUERY_ERROR_MESSAGE}
 
 
 def generate_csv_content(data: List[Dict], columns: List[str]) -> str:
diff --git a/src/mes_dashboard/services/hold_dataset_cache.py b/src/mes_dashboard/services/hold_dataset_cache.py
new file mode 100644
index 0000000..f2c7b3c
--- /dev/null
+++ b/src/mes_dashboard/services/hold_dataset_cache.py
@@ -0,0 +1,553 @@
+# -*- coding: utf-8 -*-
+"""Two-phase hold-history dataset cache.
+
+Primary query (POST /query) → Oracle → cache full hold/release DataFrame.
+Supplementary view (GET /view) → read cache → pandas filter/derive.
+
+Cache layers:
+  L1: ProcessLevelCache (in-process, per-worker)
+  L2: Redis (cross-worker, parquet bytes encoded as base64 string)
+"""
+
+from __future__ import annotations
+
+import base64
+import hashlib
+import io
+import json
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import pandas as pd
+
+from mes_dashboard.core.cache import ProcessLevelCache, register_process_cache
+from mes_dashboard.core.database import read_sql_df_slow as read_sql_df
+from mes_dashboard.core.redis_client import (
+    REDIS_ENABLED,
+    get_key,
+    get_redis_client,
+)
+from mes_dashboard.services.filter_cache import get_workcenter_group as _get_wc_group
+from mes_dashboard.services.hold_history_service import (
+    _clean_text,
+    _format_datetime,
+    _safe_float,
+    _safe_int,
+)
+from mes_dashboard.sql.filters import CommonFilters
+
+logger = logging.getLogger("mes_dashboard.hold_dataset_cache")
+
+_CACHE_TTL = 900  # 15 minutes
+_CACHE_MAX_SIZE = 8
+_REDIS_NAMESPACE = "hold_dataset"
+
+_dataset_cache = ProcessLevelCache(ttl_seconds=_CACHE_TTL, max_size=_CACHE_MAX_SIZE)
+register_process_cache("hold_dataset", _dataset_cache, "Hold Dataset (L1, 15min)")
+
+_SQL_DIR = Path(__file__).resolve().parent.parent / "sql" / "hold_history"
+
+
+# ============================================================
+# SQL loading
+# ============================================================
+
+
+@lru_cache(maxsize=4)
+def _load_sql(name: str) -> str:
+    path = _SQL_DIR / f"{name}.sql"
+    sql = path.read_text(encoding="utf-8")
+    if "{{ NON_QUALITY_REASONS }}" in sql:
+        sql = sql.replace(
+            "{{ NON_QUALITY_REASONS }}",
+            CommonFilters.get_non_quality_reasons_sql(),
+        )
+    return sql
+
+
+# ============================================================
+# Query ID
+# ============================================================
+
+
+def _make_query_id(params: dict) -> str:
+    """Deterministic hash from primary query params."""
+    canonical = json.dumps(params, sort_keys=True, ensure_ascii=False, default=str)
+    return hashlib.sha256(canonical.encode("utf-8")).hexdigest()[:16]
+
+
+# ============================================================
+# Redis L2 helpers (parquet <-> base64 string)
+# ============================================================
+
+
+def _redis_key(query_id: str) -> str:
+    return get_key(f"{_REDIS_NAMESPACE}:{query_id}")
+
+
+def _redis_store_df(query_id: str, df: pd.DataFrame) -> None:
+    if not REDIS_ENABLED:
+        return
+    client = get_redis_client()
+    if client is None:
+        return
+    try:
+        buf = io.BytesIO()
+        df.to_parquet(buf, engine="pyarrow", index=False)
+        encoded = base64.b64encode(buf.getvalue()).decode("ascii")
+        client.setex(_redis_key(query_id), _CACHE_TTL, encoded)
+    except Exception as exc:
+        logger.warning("Failed to store DataFrame in Redis: %s", exc)
+
+
+def _redis_load_df(query_id: str) -> Optional[pd.DataFrame]:
+    if not REDIS_ENABLED:
+        return None
+    client = get_redis_client()
+    if client is None:
+        return None
+    try:
+        encoded = client.get(_redis_key(query_id))
+        if encoded is None:
+            return None
+        raw = base64.b64decode(encoded)
+        return pd.read_parquet(io.BytesIO(raw), engine="pyarrow")
+    except Exception as exc:
+        logger.warning("Failed to load DataFrame from Redis: %s", exc)
+        return None
+
+
+# ============================================================
+# Cache read (L1 -> L2 -> None)
+# ============================================================
+
+
+def _get_cached_df(query_id: str) -> Optional[pd.DataFrame]:
+    """Read cache: L1 hit -> return, L1 miss -> L2 -> write L1 -> return."""
+    df = _dataset_cache.get(query_id)
+    if df is not None:
+        return df
+    df = _redis_load_df(query_id)
+    if df is not None:
+        _dataset_cache.set(query_id, df)
+    return df
+
+
+def _store_df(query_id: str, df: pd.DataFrame) -> None:
+    """Write to L1 and L2."""
+    _dataset_cache.set(query_id, df)
+    _redis_store_df(query_id, df)
+
+
+# ============================================================
+# Primary query
+# ============================================================
+
+
+def execute_primary_query(
+    *,
+    start_date: str,
+    end_date: str,
+    hold_type: str = "quality",
+    record_type: str = "new",
+) -> Dict[str, Any]:
+    """Execute Oracle query -> cache DataFrame -> return structured result."""
+
+    query_id = _make_query_id({"start_date": start_date, "end_date": end_date})
+
+    cached_df = _get_cached_df(query_id)
+    if cached_df is not None:
+        logger.info("Hold dataset cache hit for query_id=%s", query_id)
+    else:
+        logger.info(
+            "Hold dataset cache miss for query_id=%s, querying Oracle", query_id
+        )
+        sql = _load_sql("base_facts")
+        params = {"start_date": start_date, "end_date": end_date}
+        df = read_sql_df(sql, params)
+        if df is None:
+            df = pd.DataFrame()
+
+        if not df.empty:
+            df["_QUERY_START"] = pd.Timestamp(start_date)
+            df["_QUERY_END"] = pd.Timestamp(end_date)
+            _store_df(query_id, df)
+
+        cached_df = df
+
+    views = _derive_all_views(
+        cached_df,
+        hold_type=hold_type,
+        record_type=record_type,
+        page=1,
+        per_page=50,
+    )
+
+    return {"query_id": query_id, **views}
+
+
+# ============================================================
+# View (supplementary filtering on cache)
+# ============================================================
+
+
+def apply_view(
+    *,
+    query_id: str,
+    hold_type: str = "quality",
+    reason: Optional[str] = None,
+    record_type: str = "new",
+    duration_range: Optional[str] = None,
+    page: int = 1,
+    per_page: int = 50,
+) -> Optional[Dict[str, Any]]:
+    """Read cache -> apply filters -> return derived data. Returns None if expired."""
+    df = _get_cached_df(query_id)
+    if df is None:
+        return None
+
+    return _derive_all_views(
+        df,
+        hold_type=hold_type,
+        reason=reason,
+        record_type=record_type,
+        duration_range=duration_range,
+        page=page,
+        per_page=per_page,
+    )
+
+
+# ============================================================
+# Master derivation
+# ============================================================
+
+
+def _derive_all_views(
+    df: pd.DataFrame,
+    *,
+    hold_type: str = "quality",
+    reason: Optional[str] = None,
+    record_type: str = "new",
+    duration_range: Optional[str] = None,
+    page: int = 1,
+    per_page: int = 50,
+) -> Dict[str, Any]:
+    """Derive trend, reason_pareto, duration, and list from cached DataFrame."""
+    if df is None or df.empty:
+        return _empty_views()
+
+    # Trend uses full DF (no record_type/reason/duration filter, all hold_types)
+    trend = _derive_trend(df)
+
+    # Apply record_type filter for pareto, duration, list
+    filtered = _apply_record_type_filter(df, record_type)
+
+    # Apply hold_type filter
+    if hold_type != "all":
+        ht_value = "non-quality" if hold_type == "non-quality" else "quality"
+        filtered = filtered[filtered["HOLD_TYPE"] == ht_value]
+
+    reason_pareto = _derive_reason_pareto(filtered)
+    duration = _derive_duration(filtered)
+
+    # List: additional reason + duration_range filters
+    list_df = filtered
+    if reason:
+        list_df = list_df[
+            list_df["HOLDREASONNAME"].str.strip() == reason.strip()
+        ]
+    if duration_range:
+        list_df = _apply_duration_range_filter(list_df, duration_range)
+
+    detail = _derive_list(list_df, page=page, per_page=per_page)
+
+    return {
+        "trend": trend,
+        "reason_pareto": reason_pareto,
+        "duration": duration,
+        "list": detail,
+    }
+
+
+def _empty_views() -> Dict[str, Any]:
+    return {
+        "trend": {"days": []},
+        "reason_pareto": {"items": []},
+        "duration": {"items": []},
+        "list": {
+            "items": [],
+            "pagination": {
+                "page": 1,
+                "perPage": 50,
+                "total": 0,
+                "totalPages": 1,
+            },
+        },
+    }
+
+
+# ============================================================
+# Record-type & duration-range filters
+# ============================================================
+
+
+def _apply_record_type_filter(
+    df: pd.DataFrame, record_type: str
+) -> pd.DataFrame:
+    if df is None or df.empty:
+        return df
+
+    types = {t.strip().lower() for t in str(record_type or "new").split(",")}
+    if types >= {"new", "on_hold", "released"}:
+        return df
+
+    mask = pd.Series(False, index=df.index)
+
+    if "new" in types:
+        if "_QUERY_START" in df.columns and "_QUERY_END" in df.columns:
+            qs = df["_QUERY_START"].iloc[0]
+            qe = df["_QUERY_END"].iloc[0]
+            mask |= (df["HOLD_DAY"] >= qs) & (df["HOLD_DAY"] <= qe)
+        else:
+            mask |= pd.Series(True, index=df.index)
+
+    if "on_hold" in types:
+        mask |= df["RELEASETXNDATE"].isna()
+
+    if "released" in types:
+        mask |= df["RELEASETXNDATE"].notna()
+
+    return df[mask]
+
+
+def _apply_duration_range_filter(
+    df: pd.DataFrame, duration_range: str
+) -> pd.DataFrame:
+    if df is None or df.empty or not duration_range:
+        return df
+
+    hours = df["HOLD_HOURS"]
+    if duration_range == "<4h":
+        return df[hours < 4]
+    if duration_range == "4-24h":
+        return df[(hours >= 4) & (hours < 24)]
+    if duration_range == "1-3d":
+        return df[(hours >= 24) & (hours < 72)]
+    if duration_range == ">3d":
+        return df[hours >= 72]
+    return df
+
+
+# ============================================================
+# Derivation: Trend
+# ============================================================
+
+
+def _derive_trend(df: pd.DataFrame) -> Dict[str, Any]:
+    """Derive daily trend with quality / non_quality / all variants."""
+    if df is None or df.empty:
+        return {"days": []}
+
+    if "_QUERY_START" in df.columns:
+        start = pd.Timestamp(df["_QUERY_START"].iloc[0])
+        end = pd.Timestamp(df["_QUERY_END"].iloc[0])
+    else:
+        start = df["HOLD_DAY"].min()
+        end = df["HOLD_DAY"].max()
+
+    dates = pd.date_range(start, end, freq="D")
+
+    type_map: List[tuple] = [
+        ("quality", "quality"),
+        ("non_quality", "non-quality"),
+        ("all", None),
+    ]
+
+    days: List[Dict[str, Any]] = []
+    for d in dates:
+        day_data: Dict[str, Any] = {"date": d.strftime("%Y-%m-%d")}
+
+        for key, type_filter in type_map:
+            tdf = df if type_filter is None else df[df["HOLD_TYPE"] == type_filter]
+
+            if tdf.empty:
+                day_data[key] = _empty_trend_metrics()
+                continue
+
+            # holdQty: distinct containers on hold as of this day
+            on_hold = (tdf["HOLD_DAY"] <= d) & (
+                tdf["RELEASE_DAY"].isna() | (tdf["RELEASE_DAY"] > d)
+            )
+            hold_qty = int(tdf.loc[on_hold, "CONTAINERID"].nunique())
+
+            # newHoldQty: new holds arriving this day (dedup)
+            new_mask = (tdf["HOLD_DAY"] == d) & (tdf["RN_HOLD_DAY"] == 1)
+            new_hold_qty = int(new_mask.sum())
+
+            # releaseQty: releases on this day
+            release_qty = int((tdf["RELEASE_DAY"] == d).sum())
+
+            # futureHoldQty: future holds on this day
+            future_mask = (
+                (tdf["HOLD_DAY"] == d)
+                & (tdf["IS_FUTURE_HOLD"] == 1)
+                & (tdf["FUTURE_HOLD_FLAG"] == 1)
+            )
+            future_hold_qty = int(future_mask.sum())
+
+            day_data[key] = {
+                "holdQty": hold_qty,
+                "newHoldQty": new_hold_qty,
+                "releaseQty": release_qty,
+                "futureHoldQty": future_hold_qty,
+            }
+
+        days.append(day_data)
+
+    return {"days": days}
+
+
+def _empty_trend_metrics() -> Dict[str, int]:
+    return {"holdQty": 0, "newHoldQty": 0, "releaseQty": 0, "futureHoldQty": 0}
+
+
+# ============================================================
+# Derivation: Reason Pareto
+# ============================================================
+
+
+def _derive_reason_pareto(df: pd.DataFrame) -> Dict[str, Any]:
+    """Group by HOLDREASONNAME -> count, qty, pct, cumPct."""
+    if df is None or df.empty:
+        return {"items": []}
+
+    grouped = (
+        df.groupby("HOLDREASONNAME", sort=False)
+        .agg(count=("CONTAINERID", "count"), qty=("QTY", "sum"))
+        .reset_index()
+    )
+    grouped = grouped.sort_values("count", ascending=False)
+    total = grouped["count"].sum()
+
+    items: List[Dict[str, Any]] = []
+    cumulative = 0.0
+    for _, row in grouped.iterrows():
+        count = _safe_int(row["count"])
+        qty = _safe_int(row["qty"])
+        pct = round((count / total * 100) if total > 0 else 0, 2)
+        cumulative += pct
+        items.append(
+            {
+                "reason": _clean_text(row["HOLDREASONNAME"]) or "(未填寫)",
+                "count": count,
+                "qty": qty,
+                "pct": pct,
+                "cumPct": round(cumulative, 2),
+            }
+        )
+
+    return {"items": items}
+
+
+# ============================================================
+# Derivation: Duration
+# ============================================================
+
+
+def _derive_duration(df: pd.DataFrame) -> Dict[str, Any]:
+    """Bucket released holds into <4h / 4-24h / 1-3d / >3d."""
+    if df is None or df.empty:
+        return {"items": []}
+
+    released = df[df["RELEASETXNDATE"].notna()]
+    if released.empty:
+        return {"items": []}
+
+    hours = released["HOLD_HOURS"]
+    total = len(released)
+
+    buckets = [
+        ("<4h", hours < 4),
+        ("4-24h", (hours >= 4) & (hours < 24)),
+        ("1-3d", (hours >= 24) & (hours < 72)),
+        (">3d", hours >= 72),
+    ]
+
+    items: List[Dict[str, Any]] = []
+    for label, mask in buckets:
+        count = int(mask.sum())
+        qty = _safe_int(released.loc[mask, "QTY"].sum())
+        pct = round((count / total * 100) if total > 0 else 0, 2)
+        items.append({"range": label, "count": count, "qty": qty, "pct": pct})
+
+    return {"items": items}
+
+
+# ============================================================
+# Derivation: Paginated list
+# ============================================================
+
+
+def _derive_list(
+    df: pd.DataFrame,
+    *,
+    page: int = 1,
+    per_page: int = 50,
+) -> Dict[str, Any]:
+    """Sort by HOLDTXNDATE desc and paginate."""
+    if df is None or df.empty:
+        return {
+            "items": [],
+            "pagination": {
+                "page": 1,
+                "perPage": per_page,
+                "total": 0,
+                "totalPages": 1,
+            },
+        }
+
+    page = max(int(page), 1)
+    per_page = min(max(int(per_page), 1), 200)
+
+    sorted_df = df.sort_values("HOLDTXNDATE", ascending=False)
+    total = len(sorted_df)
+    total_pages = max((total + per_page - 1) // per_page, 1)
+    offset = (page - 1) * per_page
+    page_df = sorted_df.iloc[offset : offset + per_page]
+
+    items: List[Dict[str, Any]] = []
+    for _, row in page_df.iterrows():
+        wc_name = _clean_text(row.get("WORKCENTERNAME"))
+        wc_group = _get_wc_group(wc_name) if wc_name else None
+
+        items.append(
+            {
+                "lotId": _clean_text(row.get("LOT_ID")),
+                "workorder": _clean_text(row.get("PJ_WORKORDER")),
+                "product": _clean_text(row.get("PRODUCTNAME")),
+                "workcenter": wc_group or wc_name,
+                "holdReason": _clean_text(row.get("HOLDREASONNAME")),
+                "qty": _safe_int(row.get("QTY")),
+                "holdDate": _format_datetime(row.get("HOLDTXNDATE")),
+                "holdEmp": _clean_text(row.get("HOLDEMP")),
+                "holdComment": _clean_text(row.get("HOLDCOMMENTS")),
+                "releaseDate": _format_datetime(row.get("RELEASETXNDATE")),
+                "releaseEmp": _clean_text(row.get("RELEASEEMP")),
+                "releaseComment": _clean_text(row.get("RELEASECOMMENTS")),
+                "holdHours": round(_safe_float(row.get("HOLD_HOURS")), 2),
+                "ncr": _clean_text(row.get("NCRID")),
+                "futureHoldComment": _clean_text(row.get("FUTUREHOLDCOMMENTS")),
+            }
+        )
+
+    return {
+        "items": items,
+        "pagination": {
+            "page": page,
+            "perPage": per_page,
+            "total": total,
+            "totalPages": total_pages,
+        },
+    }
diff --git a/src/mes_dashboard/services/hold_history_service.py b/src/mes_dashboard/services/hold_history_service.py
index 7624c1e..c42ee62 100644
--- a/src/mes_dashboard/services/hold_history_service.py
+++ b/src/mes_dashboard/services/hold_history_service.py
@@ -15,7 +15,7 @@ import pandas as pd
 from mes_dashboard.core.database import (
     DatabaseCircuitOpenError,
     DatabasePoolExhaustedError,
-    read_sql_df,
+    read_sql_df_slow as read_sql_df,
 )
 from mes_dashboard.core.redis_client import get_key, get_redis_client
 from mes_dashboard.services.filter_cache import get_workcenter_group as _get_wc_group
diff --git a/src/mes_dashboard/services/job_query_service.py b/src/mes_dashboard/services/job_query_service.py
index 7d330b1..811d7d0 100644
--- a/src/mes_dashboard/services/job_query_service.py
+++ b/src/mes_dashboard/services/job_query_service.py
@@ -1,18 +1,18 @@
-# -*- coding: utf-8 -*-
-"""Job Query Service.
-
-Provides functions for querying maintenance job data:
-- Job list by resource IDs
-- Job transaction history detail
-- CSV export with full history
-
-Architecture:
-- Uses resource_cache as the source for equipment master data
-- Queries DW_MES_JOB for job current status
-- Queries DW_MES_JOBTXNHISTORY for transaction history
-- Supports batching for large resource lists (Oracle IN clause limit)
-"""
-
+# -*- coding: utf-8 -*-
+"""Job Query Service.
+
+Provides functions for querying maintenance job data:
+- Job list by resource IDs
+- Job transaction history detail
+- CSV export with full history
+
+Architecture:
+- Uses resource_cache as the source for equipment master data
+- Queries DW_MES_JOB for job current status
+- Queries DW_MES_JOBTXNHISTORY for transaction history
+- Supports batching for large resource lists (Oracle IN clause limit)
+"""
+
 import csv
 import io
 import logging
@@ -21,53 +21,53 @@ from typing import Dict, List, Any, Optional, Generator, Tuple
 
 import pandas as pd
 
-from mes_dashboard.core.database import read_sql_df, get_db_connection
+from mes_dashboard.core.database import read_sql_df_slow as read_sql_df, get_db_connection
 from mes_dashboard.sql import SQLLoader, QueryBuilder
 from mes_dashboard.config.field_contracts import get_export_headers, get_export_api_keys
-
+
 logger = logging.getLogger('mes_dashboard.job_query')
-
-# Constants
+
+# Constants
 BATCH_SIZE = 1000  # Oracle IN clause limit
 MAX_DATE_RANGE_DAYS = 365
 QUERY_ERROR_MESSAGE = "查詢服務暫時無法使用"
 EXPORT_ERROR_MESSAGE = "匯出服務暫時無法使用"
-
-
-# ============================================================
-# Validation Functions
-# ============================================================
-
-def validate_date_range(start_date: str, end_date: str) -> Optional[str]:
-    """Validate date range.
-
-    Args:
-        start_date: Start date in YYYY-MM-DD format
-        end_date: End date in YYYY-MM-DD format
-
-    Returns:
-        Error message if validation fails, None if valid.
-    """
-    try:
-        start = datetime.strptime(start_date, '%Y-%m-%d')
-        end = datetime.strptime(end_date, '%Y-%m-%d')
-
-        if end < start:
-            return '結束日期不可早於起始日期'
-
-        diff = (end - start).days
-        if diff > MAX_DATE_RANGE_DAYS:
-            return f'日期範圍不可超過 {MAX_DATE_RANGE_DAYS} 天'
-
-        return None
-    except ValueError as e:
-        return f'日期格式錯誤: {e}'
-
-
-# ============================================================
-# Resource Filter Helpers
-# ============================================================
-
+
+
+# ============================================================
+# Validation Functions
+# ============================================================
+
+def validate_date_range(start_date: str, end_date: str) -> Optional[str]:
+    """Validate date range.
+
+    Args:
+        start_date: Start date in YYYY-MM-DD format
+        end_date: End date in YYYY-MM-DD format
+
+    Returns:
+        Error message if validation fails, None if valid.
+    """
+    try:
+        start = datetime.strptime(start_date, '%Y-%m-%d')
+        end = datetime.strptime(end_date, '%Y-%m-%d')
+
+        if end < start:
+            return '結束日期不可早於起始日期'
+
+        diff = (end - start).days
+        if diff > MAX_DATE_RANGE_DAYS:
+            return f'日期範圍不可超過 {MAX_DATE_RANGE_DAYS} 天'
+
+        return None
+    except ValueError as e:
+        return f'日期格式錯誤: {e}'
+
+
+# ============================================================
+# Resource Filter Helpers
+# ============================================================
+
 def _build_resource_filter(
     resource_ids: List[str], max_chunk_size: int = BATCH_SIZE
 ) -> List[List[str]]:
@@ -134,186 +134,186 @@ def _build_resource_filter_sql(
 
     result = (condition_sql, builder.params.copy())
     return result if return_params else result[0]
-
-
-# ============================================================
-# Query Functions
-# ============================================================
-
-def get_jobs_by_resources(
-    resource_ids: List[str],
-    start_date: str,
-    end_date: str
-) -> Dict[str, Any]:
-    """Query jobs for selected resources within date range.
-
-    Args:
-        resource_ids: List of RESOURCEID values to query
-        start_date: Start date in YYYY-MM-DD format
-        end_date: End date in YYYY-MM-DD format
-
-    Returns:
-        Dict with 'data' (list of job records) and 'total' (count),
-        or 'error' if query fails.
-    """
-    # Validate inputs
-    if not resource_ids:
-        return {'error': '請選擇至少一台設備'}
-
-    validation_error = validate_date_range(start_date, end_date)
-    if validation_error:
-        return {'error': validation_error}
-
-    try:
-        # Build resource filter
+
+
+# ============================================================
+# Query Functions
+# ============================================================
+
+def get_jobs_by_resources(
+    resource_ids: List[str],
+    start_date: str,
+    end_date: str
+) -> Dict[str, Any]:
+    """Query jobs for selected resources within date range.
+
+    Args:
+        resource_ids: List of RESOURCEID values to query
+        start_date: Start date in YYYY-MM-DD format
+        end_date: End date in YYYY-MM-DD format
+
+    Returns:
+        Dict with 'data' (list of job records) and 'total' (count),
+        or 'error' if query fails.
+    """
+    # Validate inputs
+    if not resource_ids:
+        return {'error': '請選擇至少一台設備'}
+
+    validation_error = validate_date_range(start_date, end_date)
+    if validation_error:
+        return {'error': validation_error}
+
+    try:
+        # Build resource filter
         resource_filter, resource_params = _build_resource_filter_sql(
             resource_ids, return_params=True
         )
-
-        # Load SQL template
-        sql = SQLLoader.load("job_query/job_list")
-        sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
-
-        # Execute query
+
+        # Load SQL template
+        sql = SQLLoader.load("job_query/job_list")
+        sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
+
+        # Execute query
         params = {
             'start_date': start_date,
             'end_date': end_date,
             **resource_params,
         }
-        df = read_sql_df(sql, params)
-
-        # Convert to records
-        data = []
-        for _, row in df.iterrows():
-            record = {}
-            for col in df.columns:
-                value = row[col]
-                if pd.isna(value):
-                    record[col] = None
-                elif isinstance(value, datetime):
-                    record[col] = value.strftime('%Y-%m-%d %H:%M:%S')
-                else:
-                    record[col] = value
-            data.append(record)
-
-        logger.info(f"Job query returned {len(data)} records for {len(resource_ids)} resources")
-
-        return {
-            'data': data,
-            'total': len(data),
-            'resource_count': len(resource_ids)
-        }
-
+        df = read_sql_df(sql, params)
+
+        # Convert to records
+        data = []
+        for _, row in df.iterrows():
+            record = {}
+            for col in df.columns:
+                value = row[col]
+                if pd.isna(value):
+                    record[col] = None
+                elif isinstance(value, datetime):
+                    record[col] = value.strftime('%Y-%m-%d %H:%M:%S')
+                else:
+                    record[col] = value
+            data.append(record)
+
+        logger.info(f"Job query returned {len(data)} records for {len(resource_ids)} resources")
+
+        return {
+            'data': data,
+            'total': len(data),
+            'resource_count': len(resource_ids)
+        }
+
     except Exception as exc:
         logger.exception("Job query failed: %s", exc)
         return {'error': QUERY_ERROR_MESSAGE}
-
-
-def get_job_txn_history(job_id: str) -> Dict[str, Any]:
-    """Query transaction history for a single job.
-
-    Args:
-        job_id: The JOBID to query
-
-    Returns:
-        Dict with 'data' (list of transaction records) and 'total' (count),
-        or 'error' if query fails.
-    """
-    if not job_id:
-        return {'error': '請指定工單 ID'}
-
-    try:
-        # Load SQL template
-        sql = SQLLoader.load("job_query/job_txn_detail")
-
-        # Execute query
-        params = {'job_id': job_id}
-        df = read_sql_df(sql, params)
-
-        # Convert to records
-        data = []
-        for _, row in df.iterrows():
-            record = {}
-            for col in df.columns:
-                value = row[col]
-                if pd.isna(value):
-                    record[col] = None
-                elif isinstance(value, datetime):
-                    record[col] = value.strftime('%Y-%m-%d %H:%M:%S')
-                else:
-                    record[col] = value
-            data.append(record)
-
-        logger.debug(f"Transaction history query returned {len(data)} records for job {job_id}")
-
-        return {
-            'data': data,
-            'total': len(data),
-            'job_id': job_id
-        }
-
+
+
+def get_job_txn_history(job_id: str) -> Dict[str, Any]:
+    """Query transaction history for a single job.
+
+    Args:
+        job_id: The JOBID to query
+
+    Returns:
+        Dict with 'data' (list of transaction records) and 'total' (count),
+        or 'error' if query fails.
+    """
+    if not job_id:
+        return {'error': '請指定工單 ID'}
+
+    try:
+        # Load SQL template
+        sql = SQLLoader.load("job_query/job_txn_detail")
+
+        # Execute query
+        params = {'job_id': job_id}
+        df = read_sql_df(sql, params)
+
+        # Convert to records
+        data = []
+        for _, row in df.iterrows():
+            record = {}
+            for col in df.columns:
+                value = row[col]
+                if pd.isna(value):
+                    record[col] = None
+                elif isinstance(value, datetime):
+                    record[col] = value.strftime('%Y-%m-%d %H:%M:%S')
+                else:
+                    record[col] = value
+            data.append(record)
+
+        logger.debug(f"Transaction history query returned {len(data)} records for job {job_id}")
+
+        return {
+            'data': data,
+            'total': len(data),
+            'job_id': job_id
+        }
+
     except Exception as exc:
         logger.exception("Transaction history query failed for job %s: %s", job_id, exc)
         return {'error': QUERY_ERROR_MESSAGE}
-
-
-# ============================================================
-# Export Functions
-# ============================================================
-
-def export_jobs_with_history(
-    resource_ids: List[str],
-    start_date: str,
-    end_date: str
-) -> Generator[str, None, None]:
-    """Generate CSV content for jobs with full transaction history.
-
-    Uses streaming to handle large datasets without memory issues.
-
-    Args:
-        resource_ids: List of RESOURCEID values to export
-        start_date: Start date in YYYY-MM-DD format
-        end_date: End date in YYYY-MM-DD format
-
-    Yields:
-        CSV rows as strings (including header row first)
-    """
-    # Validate inputs
-    if not resource_ids:
-        yield "Error: 請選擇至少一台設備\n"
-        return
-
-    validation_error = validate_date_range(start_date, end_date)
-    if validation_error:
-        yield f"Error: {validation_error}\n"
-        return
-
-    try:
-        # Build resource filter
+
+
+# ============================================================
+# Export Functions
+# ============================================================
+
+def export_jobs_with_history(
+    resource_ids: List[str],
+    start_date: str,
+    end_date: str
+) -> Generator[str, None, None]:
+    """Generate CSV content for jobs with full transaction history.
+
+    Uses streaming to handle large datasets without memory issues.
+
+    Args:
+        resource_ids: List of RESOURCEID values to export
+        start_date: Start date in YYYY-MM-DD format
+        end_date: End date in YYYY-MM-DD format
+
+    Yields:
+        CSV rows as strings (including header row first)
+    """
+    # Validate inputs
+    if not resource_ids:
+        yield "Error: 請選擇至少一台設備\n"
+        return
+
+    validation_error = validate_date_range(start_date, end_date)
+    if validation_error:
+        yield f"Error: {validation_error}\n"
+        return
+
+    try:
+        # Build resource filter
         resource_filter, resource_params = _build_resource_filter_sql(
             resource_ids, return_params=True
         )
-
-        # Load SQL template
-        sql = SQLLoader.load("job_query/job_txn_export")
-        sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
-
-        # Execute query
+
+        # Load SQL template
+        sql = SQLLoader.load("job_query/job_txn_export")
+        sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
+
+        # Execute query
         params = {
             'start_date': start_date,
             'end_date': end_date,
             **resource_params,
         }
-        df = read_sql_df(sql, params)
-
-        if df is None or len(df) == 0:
-            yield "Error: 無符合條件的資料\n"
-            return
-
-        # Write CSV header with BOM for Excel UTF-8 compatibility
-        output = io.StringIO()
-        output.write('\ufeff')  # UTF-8 BOM
-
+        df = read_sql_df(sql, params)
+
+        if df is None or len(df) == 0:
+            yield "Error: 無符合條件的資料\n"
+            return
+
+        # Write CSV header with BOM for Excel UTF-8 compatibility
+        output = io.StringIO()
+        output.write('\ufeff')  # UTF-8 BOM
+
         export_keys = get_export_api_keys('job_query')
         headers = get_export_headers('job_query')
 
@@ -331,8 +331,8 @@ def export_jobs_with_history(
         writer.writerow(headers)
         yield output.getvalue()
         output.truncate(0)
-        output.seek(0)
-
+        output.seek(0)
+
         # Write data rows
         for _, row in df.iterrows():
             csv_row = []
@@ -342,82 +342,82 @@ def export_jobs_with_history(
                     csv_row.append('')
                 elif isinstance(value, datetime):
                     csv_row.append(value.strftime('%Y-%m-%d %H:%M:%S'))
-                else:
-                    csv_row.append(str(value))
-
-            writer.writerow(csv_row)
-            yield output.getvalue()
-            output.truncate(0)
-            output.seek(0)
-
-        logger.info(f"CSV export completed: {len(df)} records")
-
+                else:
+                    csv_row.append(str(value))
+
+            writer.writerow(csv_row)
+            yield output.getvalue()
+            output.truncate(0)
+            output.seek(0)
+
+        logger.info(f"CSV export completed: {len(df)} records")
+
     except Exception as exc:
         logger.exception("CSV export failed: %s", exc)
         yield f"Error: {EXPORT_ERROR_MESSAGE}\n"
-
-
-def get_export_data(
-    resource_ids: List[str],
-    start_date: str,
-    end_date: str
-) -> Dict[str, Any]:
-    """Get export data as a dict (for non-streaming use cases).
-
-    Args:
-        resource_ids: List of RESOURCEID values to export
-        start_date: Start date in YYYY-MM-DD format
-        end_date: End date in YYYY-MM-DD format
-
-    Returns:
-        Dict with 'data', 'columns', 'total', or 'error' if query fails.
-    """
-    # Validate inputs
-    if not resource_ids:
-        return {'error': '請選擇至少一台設備'}
-
-    validation_error = validate_date_range(start_date, end_date)
-    if validation_error:
-        return {'error': validation_error}
-
-    try:
-        # Build resource filter
+
+
+def get_export_data(
+    resource_ids: List[str],
+    start_date: str,
+    end_date: str
+) -> Dict[str, Any]:
+    """Get export data as a dict (for non-streaming use cases).
+
+    Args:
+        resource_ids: List of RESOURCEID values to export
+        start_date: Start date in YYYY-MM-DD format
+        end_date: End date in YYYY-MM-DD format
+
+    Returns:
+        Dict with 'data', 'columns', 'total', or 'error' if query fails.
+    """
+    # Validate inputs
+    if not resource_ids:
+        return {'error': '請選擇至少一台設備'}
+
+    validation_error = validate_date_range(start_date, end_date)
+    if validation_error:
+        return {'error': validation_error}
+
+    try:
+        # Build resource filter
         resource_filter, resource_params = _build_resource_filter_sql(
             resource_ids, return_params=True
         )
-
-        # Load SQL template
-        sql = SQLLoader.load("job_query/job_txn_export")
-        sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
-
-        # Execute query
+
+        # Load SQL template
+        sql = SQLLoader.load("job_query/job_txn_export")
+        sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
+
+        # Execute query
         params = {
             'start_date': start_date,
             'end_date': end_date,
             **resource_params,
         }
-        df = read_sql_df(sql, params)
-
-        # Convert to records
-        data = []
-        for _, row in df.iterrows():
-            record = {}
-            for col in df.columns:
-                value = row[col]
-                if pd.isna(value):
-                    record[col] = None
-                elif isinstance(value, datetime):
-                    record[col] = value.strftime('%Y-%m-%d %H:%M:%S')
-                else:
-                    record[col] = value
-            data.append(record)
-
-        return {
-            'data': data,
-            'columns': list(df.columns),
-            'total': len(data)
-        }
-
+        df = read_sql_df(sql, params)
+
+        # Convert to records
+        data = []
+        for _, row in df.iterrows():
+            record = {}
+            for col in df.columns:
+                value = row[col]
+                if pd.isna(value):
+                    record[col] = None
+                elif isinstance(value, datetime):
+                    record[col] = value.strftime('%Y-%m-%d %H:%M:%S')
+                else:
+                    record[col] = value
+            data.append(record)
+
+        return {
+            'data': data,
+            'columns': list(df.columns),
+            'total': len(data)
+        }
+
     except Exception as exc:
         logger.exception("Export data query failed: %s", exc)
         return {'error': QUERY_ERROR_MESSAGE}
diff --git a/src/mes_dashboard/services/query_tool_service.py b/src/mes_dashboard/services/query_tool_service.py
index baa1c5c..43cc058 100644
--- a/src/mes_dashboard/services/query_tool_service.py
+++ b/src/mes_dashboard/services/query_tool_service.py
@@ -1128,7 +1128,7 @@ def get_lot_split_merge_history(
 
         if full_history:
             # Full mode uses dedicated slow query timeout path.
-            df = read_sql_df_slow(sql, params, timeout_seconds=120)
+            df = read_sql_df_slow(sql, params)
         else:
             df = read_sql_df(sql, params)
         data = _df_to_records(df)
diff --git a/src/mes_dashboard/services/reject_dataset_cache.py b/src/mes_dashboard/services/reject_dataset_cache.py
index 450a47d..d4418c6 100644
--- a/src/mes_dashboard/services/reject_dataset_cache.py
+++ b/src/mes_dashboard/services/reject_dataset_cache.py
@@ -21,7 +21,7 @@ from typing import Any, Dict, List, Optional
 import pandas as pd
 
 from mes_dashboard.core.cache import ProcessLevelCache, register_process_cache
-from mes_dashboard.core.database import read_sql_df
+from mes_dashboard.core.database import read_sql_df_slow as read_sql_df
 from mes_dashboard.core.redis_client import (
     REDIS_ENABLED,
     get_key,
diff --git a/src/mes_dashboard/services/reject_history_service.py b/src/mes_dashboard/services/reject_history_service.py
index 9147020..6616151 100644
--- a/src/mes_dashboard/services/reject_history_service.py
+++ b/src/mes_dashboard/services/reject_history_service.py
@@ -11,7 +11,7 @@ from typing import Any, Dict, Generator, Iterable, Optional
 
 import pandas as pd
 
-from mes_dashboard.core.database import read_sql_df
+from mes_dashboard.core.database import read_sql_df_slow as read_sql_df
 from mes_dashboard.services.scrap_reason_exclusion_cache import get_excluded_reasons
 from mes_dashboard.sql import QueryBuilder, SQLLoader
 
diff --git a/src/mes_dashboard/services/resource_dataset_cache.py b/src/mes_dashboard/services/resource_dataset_cache.py
new file mode 100644
index 0000000..98f1c03
--- /dev/null
+++ b/src/mes_dashboard/services/resource_dataset_cache.py
@@ -0,0 +1,685 @@
+# -*- coding: utf-8 -*-
+"""Two-phase resource-history dataset cache.
+
+Primary query (POST /query) → Oracle → cache full per-resource × per-day DataFrame.
+Supplementary view (GET /view) → read cache → pandas derive kpi/trend/heatmap/comparison/detail.
+
+Cache layers:
+  L1: ProcessLevelCache (in-process, per-worker)
+  L2: Redis (cross-worker, parquet bytes encoded as base64 string)
+"""
+
+from __future__ import annotations
+
+import base64
+import hashlib
+import io
+import json
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import pandas as pd
+
+from mes_dashboard.core.cache import ProcessLevelCache, register_process_cache
+from mes_dashboard.core.database import read_sql_df_slow as read_sql_df
+from mes_dashboard.core.redis_client import (
+    REDIS_ENABLED,
+    get_key,
+    get_redis_client,
+)
+
+logger = logging.getLogger("mes_dashboard.resource_dataset_cache")
+
+_CACHE_TTL = 900  # 15 minutes
+_CACHE_MAX_SIZE = 8
+_REDIS_NAMESPACE = "resource_dataset"
+
+_dataset_cache = ProcessLevelCache(ttl_seconds=_CACHE_TTL, max_size=_CACHE_MAX_SIZE)
+register_process_cache(
+    "resource_dataset", _dataset_cache, "Resource Dataset (L1, 15min)"
+)
+
+_SQL_DIR = Path(__file__).resolve().parent.parent / "sql" / "resource_history"
+
+
+# ============================================================
+# SQL loading
+# ============================================================
+
+
+@lru_cache(maxsize=4)
+def _load_sql(name: str) -> str:
+    path = _SQL_DIR / f"{name}.sql"
+    return path.read_text(encoding="utf-8")
+
+
+# ============================================================
+# Query ID
+# ============================================================
+
+
+def _make_query_id(params: dict) -> str:
+    """Deterministic hash from primary query params."""
+    canonical = json.dumps(params, sort_keys=True, ensure_ascii=False, default=str)
+    return hashlib.sha256(canonical.encode("utf-8")).hexdigest()[:16]
+
+
+# ============================================================
+# Redis L2 helpers (parquet <-> base64 string)
+# ============================================================
+
+
+def _redis_key(query_id: str) -> str:
+    return get_key(f"{_REDIS_NAMESPACE}:{query_id}")
+
+
+def _redis_store_df(query_id: str, df: pd.DataFrame) -> None:
+    if not REDIS_ENABLED:
+        return
+    client = get_redis_client()
+    if client is None:
+        return
+    try:
+        buf = io.BytesIO()
+        df.to_parquet(buf, engine="pyarrow", index=False)
+        encoded = base64.b64encode(buf.getvalue()).decode("ascii")
+        client.setex(_redis_key(query_id), _CACHE_TTL, encoded)
+    except Exception as exc:
+        logger.warning("Failed to store DataFrame in Redis: %s", exc)
+
+
+def _redis_load_df(query_id: str) -> Optional[pd.DataFrame]:
+    if not REDIS_ENABLED:
+        return None
+    client = get_redis_client()
+    if client is None:
+        return None
+    try:
+        encoded = client.get(_redis_key(query_id))
+        if encoded is None:
+            return None
+        raw = base64.b64decode(encoded)
+        return pd.read_parquet(io.BytesIO(raw), engine="pyarrow")
+    except Exception as exc:
+        logger.warning("Failed to load DataFrame from Redis: %s", exc)
+        return None
+
+
+# ============================================================
+# Cache read (L1 -> L2 -> None)
+# ============================================================
+
+
+def _get_cached_df(query_id: str) -> Optional[pd.DataFrame]:
+    df = _dataset_cache.get(query_id)
+    if df is not None:
+        return df
+    df = _redis_load_df(query_id)
+    if df is not None:
+        _dataset_cache.set(query_id, df)
+    return df
+
+
+def _store_df(query_id: str, df: pd.DataFrame) -> None:
+    _dataset_cache.set(query_id, df)
+    _redis_store_df(query_id, df)
+
+
+# ============================================================
+# Resource cache integration (reuse existing service)
+# ============================================================
+
+
+def _get_filtered_resources_and_lookup(
+    *,
+    workcenter_groups: Optional[List[str]] = None,
+    families: Optional[List[str]] = None,
+    resource_ids: Optional[List[str]] = None,
+    is_production: bool = False,
+    is_key: bool = False,
+    is_monitor: bool = False,
+) -> tuple:
+    """Returns (resources_list, resource_lookup_dict, historyid_filter_sql)."""
+    from mes_dashboard.services.resource_history_service import (
+        _get_filtered_resources,
+        _build_resource_lookup,
+        _build_historyid_filter,
+    )
+
+    resources = _get_filtered_resources(
+        workcenter_groups=workcenter_groups,
+        families=families,
+        resource_ids=resource_ids,
+        is_production=is_production,
+        is_key=is_key,
+        is_monitor=is_monitor,
+    )
+    lookup = _build_resource_lookup(resources)
+    historyid_filter = _build_historyid_filter(resources)
+    return resources, lookup, historyid_filter
+
+
+def _get_resource_lookup() -> Dict[str, Dict[str, Any]]:
+    """Get current resource lookup from cache (for view-time dimension merge)."""
+    from mes_dashboard.services.resource_history_service import (
+        _get_filtered_resources,
+        _build_resource_lookup,
+    )
+
+    resources = _get_filtered_resources()
+    return _build_resource_lookup(resources)
+
+
+def _get_workcenter_mapping() -> Dict[str, Dict[str, Any]]:
+    from mes_dashboard.services.filter_cache import get_workcenter_mapping
+
+    return get_workcenter_mapping() or {}
+
+
+# ============================================================
+# Primary query
+# ============================================================
+
+
+def execute_primary_query(
+    *,
+    start_date: str,
+    end_date: str,
+    granularity: str = "day",
+    workcenter_groups: Optional[List[str]] = None,
+    families: Optional[List[str]] = None,
+    resource_ids: Optional[List[str]] = None,
+    is_production: bool = False,
+    is_key: bool = False,
+    is_monitor: bool = False,
+) -> Dict[str, Any]:
+    """Execute single Oracle query -> cache DataFrame -> return structured result."""
+
+    query_id_input = {
+        "start_date": start_date,
+        "end_date": end_date,
+        "workcenter_groups": sorted(workcenter_groups or []),
+        "families": sorted(families or []),
+        "resource_ids": sorted(resource_ids or []),
+        "is_production": is_production,
+        "is_key": is_key,
+        "is_monitor": is_monitor,
+    }
+    query_id = _make_query_id(query_id_input)
+
+    cached_df = _get_cached_df(query_id)
+    if cached_df is not None:
+        logger.info("Resource dataset cache hit for query_id=%s", query_id)
+    else:
+        logger.info(
+            "Resource dataset cache miss for query_id=%s, querying Oracle", query_id
+        )
+
+        resources, lookup, historyid_filter = _get_filtered_resources_and_lookup(
+            workcenter_groups=workcenter_groups,
+            families=families,
+            resource_ids=resource_ids,
+            is_production=is_production,
+            is_key=is_key,
+            is_monitor=is_monitor,
+        )
+
+        if not resources:
+            return {
+                "query_id": query_id,
+                "summary": _empty_summary(),
+                "detail": _empty_detail(),
+            }
+
+        sql = _load_sql("base_facts")
+        sql = sql.replace("{{ HISTORYID_FILTER }}", historyid_filter)
+        params = {"start_date": start_date, "end_date": end_date}
+        df = read_sql_df(sql, params)
+        if df is None:
+            df = pd.DataFrame()
+
+        if not df.empty:
+            _store_df(query_id, df)
+
+        cached_df = df
+
+    resource_lookup = _get_resource_lookup()
+    wc_mapping = _get_workcenter_mapping()
+
+    summary = _derive_summary(cached_df, resource_lookup, wc_mapping, granularity)
+    detail = _derive_detail(cached_df, resource_lookup, wc_mapping)
+
+    return {
+        "query_id": query_id,
+        "summary": summary,
+        "detail": detail,
+    }
+
+
+# ============================================================
+# View (supplementary — cache only)
+# ============================================================
+
+
+def apply_view(
+    *,
+    query_id: str,
+    granularity: str = "day",
+) -> Optional[Dict[str, Any]]:
+    """Read cache -> derive views. Returns None if expired."""
+    df = _get_cached_df(query_id)
+    if df is None:
+        return None
+
+    resource_lookup = _get_resource_lookup()
+    wc_mapping = _get_workcenter_mapping()
+
+    summary = _derive_summary(df, resource_lookup, wc_mapping, granularity)
+    detail = _derive_detail(df, resource_lookup, wc_mapping)
+
+    return {
+        "summary": summary,
+        "detail": detail,
+    }
+
+
+# ============================================================
+# Master derivation
+# ============================================================
+
+
+def _derive_summary(
+    df: pd.DataFrame,
+    resource_lookup: Dict[str, Dict[str, Any]],
+    wc_mapping: Dict[str, Dict[str, Any]],
+    granularity: str,
+) -> Dict[str, Any]:
+    if df is None or df.empty:
+        return _empty_summary()
+
+    return {
+        "kpi": _derive_kpi(df),
+        "trend": _derive_trend(df, granularity),
+        "heatmap": _derive_heatmap(df, resource_lookup, wc_mapping, granularity),
+        "workcenter_comparison": _derive_comparison(df, resource_lookup, wc_mapping),
+    }
+
+
+def _empty_summary() -> Dict[str, Any]:
+    return {
+        "kpi": _empty_kpi(),
+        "trend": [],
+        "heatmap": [],
+        "workcenter_comparison": [],
+    }
+
+
+def _empty_detail() -> Dict[str, Any]:
+    return {"data": [], "total": 0, "truncated": False, "max_records": None}
+
+
+def _empty_kpi() -> Dict[str, Any]:
+    return {
+        "ou_pct": 0,
+        "availability_pct": 0,
+        "prd_hours": 0,
+        "prd_pct": 0,
+        "sby_hours": 0,
+        "sby_pct": 0,
+        "udt_hours": 0,
+        "udt_pct": 0,
+        "sdt_hours": 0,
+        "sdt_pct": 0,
+        "egt_hours": 0,
+        "egt_pct": 0,
+        "nst_hours": 0,
+        "nst_pct": 0,
+        "machine_count": 0,
+    }
+
+
+# ============================================================
+# Helpers (reuse existing formulas)
+# ============================================================
+
+
+def _sf(value, default=0.0) -> float:
+    """Safe float."""
+    if value is None or pd.isna(value):
+        return default
+    return float(value)
+
+
+def _calc_ou_pct(prd, sby, udt, sdt, egt) -> float:
+    denom = prd + sby + udt + sdt + egt
+    return round(prd / denom * 100, 1) if denom > 0 else 0
+
+
+def _calc_avail_pct(prd, sby, udt, sdt, egt, nst) -> float:
+    num = prd + sby + egt
+    denom = prd + sby + egt + sdt + udt + nst
+    return round(num / denom * 100, 1) if denom > 0 else 0
+
+
+def _status_pct(val, total) -> float:
+    return round(val / total * 100, 1) if total > 0 else 0
+
+
+def _trunc_date(dt, granularity: str) -> str:
+    """Truncate a date value to the given granularity period string."""
+    if pd.isna(dt):
+        return ""
+    ts = pd.Timestamp(dt)
+    if granularity == "year":
+        return ts.strftime("%Y")
+    if granularity == "month":
+        return ts.strftime("%Y-%m")
+    if granularity == "week":
+        return (ts - pd.Timedelta(days=ts.weekday())).strftime("%Y-%m-%d")
+    return ts.strftime("%Y-%m-%d")
+
+
+# ============================================================
+# Derivation: KPI
+# ============================================================
+
+
+def _derive_kpi(df: pd.DataFrame) -> Dict[str, Any]:
+    if df is None or df.empty:
+        return _empty_kpi()
+
+    prd = _sf(df["PRD_HOURS"].sum())
+    sby = _sf(df["SBY_HOURS"].sum())
+    udt = _sf(df["UDT_HOURS"].sum())
+    sdt = _sf(df["SDT_HOURS"].sum())
+    egt = _sf(df["EGT_HOURS"].sum())
+    nst = _sf(df["NST_HOURS"].sum())
+    total = prd + sby + udt + sdt + egt + nst
+    machine_count = int(df["HISTORYID"].nunique())
+
+    return {
+        "ou_pct": _calc_ou_pct(prd, sby, udt, sdt, egt),
+        "availability_pct": _calc_avail_pct(prd, sby, udt, sdt, egt, nst),
+        "prd_hours": round(prd, 1),
+        "prd_pct": _status_pct(prd, total),
+        "sby_hours": round(sby, 1),
+        "sby_pct": _status_pct(sby, total),
+        "udt_hours": round(udt, 1),
+        "udt_pct": _status_pct(udt, total),
+        "sdt_hours": round(sdt, 1),
+        "sdt_pct": _status_pct(sdt, total),
+        "egt_hours": round(egt, 1),
+        "egt_pct": _status_pct(egt, total),
+        "nst_hours": round(nst, 1),
+        "nst_pct": _status_pct(nst, total),
+        "machine_count": machine_count,
+    }
+
+
+# ============================================================
+# Derivation: Trend
+# ============================================================
+
+
+def _derive_trend(df: pd.DataFrame, granularity: str) -> List[Dict[str, Any]]:
+    if df is None or df.empty:
+        return []
+
+    df = df.copy()
+    df["_period"] = df["DATA_DATE"].apply(lambda d: _trunc_date(d, granularity))
+
+    grouped = (
+        df.groupby("_period", sort=True)
+        .agg(
+            PRD_HOURS=("PRD_HOURS", "sum"),
+            SBY_HOURS=("SBY_HOURS", "sum"),
+            UDT_HOURS=("UDT_HOURS", "sum"),
+            SDT_HOURS=("SDT_HOURS", "sum"),
+            EGT_HOURS=("EGT_HOURS", "sum"),
+            NST_HOURS=("NST_HOURS", "sum"),
+        )
+        .reset_index()
+    )
+
+    items: List[Dict[str, Any]] = []
+    for _, row in grouped.iterrows():
+        prd = _sf(row["PRD_HOURS"])
+        sby = _sf(row["SBY_HOURS"])
+        udt = _sf(row["UDT_HOURS"])
+        sdt = _sf(row["SDT_HOURS"])
+        egt = _sf(row["EGT_HOURS"])
+        nst = _sf(row["NST_HOURS"])
+        items.append(
+            {
+                "date": row["_period"],
+                "ou_pct": _calc_ou_pct(prd, sby, udt, sdt, egt),
+                "availability_pct": _calc_avail_pct(prd, sby, udt, sdt, egt, nst),
+                "prd_hours": round(prd, 1),
+                "sby_hours": round(sby, 1),
+                "udt_hours": round(udt, 1),
+                "sdt_hours": round(sdt, 1),
+                "egt_hours": round(egt, 1),
+                "nst_hours": round(nst, 1),
+            }
+        )
+
+    return items
+
+
+# ============================================================
+# Derivation: Heatmap
+# ============================================================
+
+
+def _derive_heatmap(
+    df: pd.DataFrame,
+    resource_lookup: Dict[str, Dict[str, Any]],
+    wc_mapping: Dict[str, Dict[str, Any]],
+    granularity: str,
+) -> List[Dict[str, Any]]:
+    if df is None or df.empty:
+        return []
+
+    rows: List[Dict[str, Any]] = []
+    wc_seq_map: Dict[str, int] = {}
+
+    for _, row in df.iterrows():
+        historyid = row["HISTORYID"]
+        resource_info = resource_lookup.get(historyid, {})
+        if not resource_info:
+            continue
+
+        wc_name = resource_info.get("WORKCENTERNAME", "")
+        if not wc_name:
+            continue
+
+        wc_info = wc_mapping.get(wc_name, {})
+        wc_group = wc_info.get("group", wc_name)
+        wc_seq = wc_info.get("sequence", 999)
+        wc_seq_map[wc_group] = wc_seq
+        date_str = _trunc_date(row["DATA_DATE"], granularity)
+
+        rows.append(
+            {
+                "wc": wc_group,
+                "date": date_str,
+                "prd": _sf(row["PRD_HOURS"]),
+                "sby": _sf(row["SBY_HOURS"]),
+                "udt": _sf(row["UDT_HOURS"]),
+                "sdt": _sf(row["SDT_HOURS"]),
+                "egt": _sf(row["EGT_HOURS"]),
+            }
+        )
+
+    if not rows:
+        return []
+
+    tmp = pd.DataFrame(rows)
+    agg = (
+        tmp.groupby(["wc", "date"], sort=False)
+        .agg(prd=("prd", "sum"), sby=("sby", "sum"), udt=("udt", "sum"), sdt=("sdt", "sum"), egt=("egt", "sum"))
+        .reset_index()
+    )
+
+    items: List[Dict[str, Any]] = []
+    for _, r in agg.iterrows():
+        items.append(
+            {
+                "workcenter": r["wc"],
+                "workcenter_seq": wc_seq_map.get(r["wc"], 999),
+                "date": r["date"],
+                "ou_pct": _calc_ou_pct(r["prd"], r["sby"], r["udt"], r["sdt"], r["egt"]),
+            }
+        )
+
+    items.sort(key=lambda x: (x["workcenter_seq"], x["date"] or ""))
+    return items
+
+
+# ============================================================
+# Derivation: Workcenter Comparison
+# ============================================================
+
+
+def _derive_comparison(
+    df: pd.DataFrame,
+    resource_lookup: Dict[str, Dict[str, Any]],
+    wc_mapping: Dict[str, Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    if df is None or df.empty:
+        return []
+
+    # Aggregate by HISTORYID first
+    by_resource = (
+        df.groupby("HISTORYID", sort=False)
+        .agg(
+            prd=("PRD_HOURS", "sum"),
+            sby=("SBY_HOURS", "sum"),
+            udt=("UDT_HOURS", "sum"),
+            sdt=("SDT_HOURS", "sum"),
+            egt=("EGT_HOURS", "sum"),
+        )
+        .reset_index()
+    )
+
+    # Then aggregate by workcenter group
+    agg: Dict[str, Dict[str, float]] = {}
+    for _, row in by_resource.iterrows():
+        historyid = row["HISTORYID"]
+        resource_info = resource_lookup.get(historyid, {})
+        if not resource_info:
+            continue
+
+        wc_name = resource_info.get("WORKCENTERNAME", "")
+        if not wc_name:
+            continue
+
+        wc_info = wc_mapping.get(wc_name, {})
+        wc_group = wc_info.get("group", wc_name)
+
+        if wc_group not in agg:
+            agg[wc_group] = {"prd": 0, "sby": 0, "udt": 0, "sdt": 0, "egt": 0, "mc": 0}
+
+        agg[wc_group]["prd"] += _sf(row["prd"])
+        agg[wc_group]["sby"] += _sf(row["sby"])
+        agg[wc_group]["udt"] += _sf(row["udt"])
+        agg[wc_group]["sdt"] += _sf(row["sdt"])
+        agg[wc_group]["egt"] += _sf(row["egt"])
+        agg[wc_group]["mc"] += 1
+
+    items = [
+        {
+            "workcenter": wc,
+            "ou_pct": _calc_ou_pct(d["prd"], d["sby"], d["udt"], d["sdt"], d["egt"]),
+            "prd_hours": round(d["prd"], 1),
+            "machine_count": d["mc"],
+        }
+        for wc, d in agg.items()
+    ]
+    items.sort(key=lambda x: x["ou_pct"], reverse=True)
+    return items
+
+
+# ============================================================
+# Derivation: Detail
+# ============================================================
+
+
+def _derive_detail(
+    df: pd.DataFrame,
+    resource_lookup: Dict[str, Dict[str, Any]],
+    wc_mapping: Dict[str, Dict[str, Any]],
+) -> Dict[str, Any]:
+    if df is None or df.empty:
+        return _empty_detail()
+
+    by_resource = (
+        df.groupby("HISTORYID", sort=False)
+        .agg(
+            PRD_HOURS=("PRD_HOURS", "sum"),
+            SBY_HOURS=("SBY_HOURS", "sum"),
+            UDT_HOURS=("UDT_HOURS", "sum"),
+            SDT_HOURS=("SDT_HOURS", "sum"),
+            EGT_HOURS=("EGT_HOURS", "sum"),
+            NST_HOURS=("NST_HOURS", "sum"),
+            TOTAL_HOURS=("TOTAL_HOURS", "sum"),
+        )
+        .reset_index()
+    )
+
+    data: List[Dict[str, Any]] = []
+    for _, row in by_resource.iterrows():
+        historyid = row["HISTORYID"]
+        resource_info = resource_lookup.get(historyid, {})
+        if not resource_info:
+            continue
+
+        prd = _sf(row["PRD_HOURS"])
+        sby = _sf(row["SBY_HOURS"])
+        udt = _sf(row["UDT_HOURS"])
+        sdt = _sf(row["SDT_HOURS"])
+        egt = _sf(row["EGT_HOURS"])
+        nst = _sf(row["NST_HOURS"])
+        total = _sf(row["TOTAL_HOURS"])
+
+        wc_name = resource_info.get("WORKCENTERNAME", "")
+        wc_info = wc_mapping.get(wc_name, {})
+        wc_group = wc_info.get("group", wc_name)
+        wc_seq = wc_info.get("sequence", 999)
+        family = resource_info.get("RESOURCEFAMILYNAME", "")
+        resource_name = resource_info.get("RESOURCENAME", "")
+
+        data.append(
+            {
+                "workcenter": wc_group,
+                "workcenter_seq": wc_seq,
+                "family": family or "",
+                "resource": resource_name or "",
+                "ou_pct": _calc_ou_pct(prd, sby, udt, sdt, egt),
+                "availability_pct": _calc_avail_pct(prd, sby, udt, sdt, egt, nst),
+                "prd_hours": round(prd, 1),
+                "prd_pct": _status_pct(prd, total),
+                "sby_hours": round(sby, 1),
+                "sby_pct": _status_pct(sby, total),
+                "udt_hours": round(udt, 1),
+                "udt_pct": _status_pct(udt, total),
+                "sdt_hours": round(sdt, 1),
+                "sdt_pct": _status_pct(sdt, total),
+                "egt_hours": round(egt, 1),
+                "egt_pct": _status_pct(egt, total),
+                "nst_hours": round(nst, 1),
+                "nst_pct": _status_pct(nst, total),
+                "machine_count": 1,
+            }
+        )
+
+    data.sort(key=lambda x: (x["workcenter_seq"], x["family"], x["resource"]))
+
+    return {
+        "data": data,
+        "total": len(data),
+        "truncated": False,
+        "max_records": None,
+    }
diff --git a/src/mes_dashboard/services/resource_history_service.py b/src/mes_dashboard/services/resource_history_service.py
index 0e61d5b..09039d0 100644
--- a/src/mes_dashboard/services/resource_history_service.py
+++ b/src/mes_dashboard/services/resource_history_service.py
@@ -22,7 +22,7 @@ from typing import Optional, Dict, List, Any, Generator
 
 import pandas as pd
 
-from mes_dashboard.core.database import read_sql_df
+from mes_dashboard.core.database import read_sql_df_slow as read_sql_df
 from mes_dashboard.sql import QueryBuilder, SQLLoader
 from mes_dashboard.config.field_contracts import get_export_headers, get_export_api_keys
 
diff --git a/src/mes_dashboard/sql/hold_history/base_facts.sql b/src/mes_dashboard/sql/hold_history/base_facts.sql
new file mode 100644
index 0000000..33a990a
--- /dev/null
+++ b/src/mes_dashboard/sql/hold_history/base_facts.sql
@@ -0,0 +1,101 @@
+/*
+ * Base facts query for hold_dataset_cache.
+ * Fetches ALL hold/release records for the date range with all columns
+ * needed to derive trend, reason pareto, duration, and detail list.
+ *
+ * Shift boundary: HOLDTXNDATE before 07:30 → same day, after 07:30 → next day.
+ */
+WITH history_base AS (
+  SELECT
+    CASE
+      WHEN TO_CHAR(h.HOLDTXNDATE, 'HH24MI') >= '0730' THEN TRUNC(h.HOLDTXNDATE) + 1
+      ELSE TRUNC(h.HOLDTXNDATE)
+    END AS hold_day,
+    CASE
+      WHEN h.RELEASETXNDATE IS NULL THEN NULL
+      WHEN TO_CHAR(h.RELEASETXNDATE, 'HH24MI') >= '0730' THEN TRUNC(h.RELEASETXNDATE) + 1
+      ELSE TRUNC(h.RELEASETXNDATE)
+    END AS release_day,
+    h.CONTAINERID,
+    NVL(TRIM(c.CONTAINERNAME), TRIM(h.CONTAINERID)) AS LOT_ID,
+    h.PJ_WORKORDER,
+    c.PRODUCTNAME,
+    h.WORKCENTERNAME,
+    h.HOLDREASONID,
+    h.HOLDREASONNAME,
+    NVL(h.QTY, 0) AS QTY,
+    h.HOLDTXNDATE,
+    h.HOLDEMP,
+    h.HOLDCOMMENTS,
+    h.RELEASETXNDATE,
+    h.RELEASEEMP,
+    h.RELEASECOMMENTS,
+    h.NCRID,
+    h.FUTUREHOLDCOMMENTS,
+    CASE
+      WHEN h.RELEASETXNDATE IS NULL THEN (SYSDATE - h.HOLDTXNDATE) * 24
+      ELSE (h.RELEASETXNDATE - h.HOLDTXNDATE) * 24
+    END AS HOLD_HOURS,
+    CASE
+      WHEN h.HOLDREASONNAME IN ({{ NON_QUALITY_REASONS }}) THEN 'non-quality'
+      ELSE 'quality'
+    END AS HOLD_TYPE,
+    CASE
+      WHEN h.FUTUREHOLDCOMMENTS IS NOT NULL THEN 1
+      ELSE 0
+    END AS IS_FUTURE_HOLD,
+    ROW_NUMBER() OVER (
+      PARTITION BY
+        h.CONTAINERID,
+        CASE
+          WHEN TO_CHAR(h.HOLDTXNDATE, 'HH24MI') >= '0730' THEN TRUNC(h.HOLDTXNDATE) + 1
+          ELSE TRUNC(h.HOLDTXNDATE)
+        END
+      ORDER BY h.HOLDTXNDATE DESC
+    ) AS RN_HOLD_DAY,
+    ROW_NUMBER() OVER (
+      PARTITION BY h.CONTAINERID, h.HOLDREASONID
+      ORDER BY h.HOLDTXNDATE
+    ) AS RN_FUTURE_REASON
+  FROM DWH.DW_MES_HOLDRELEASEHISTORY h
+  LEFT JOIN DWH.DW_MES_CONTAINER c ON c.CONTAINERID = h.CONTAINERID
+  WHERE (
+      h.HOLDTXNDATE >= TO_DATE(:start_date || ' 073000', 'YYYY-MM-DD HH24MISS') - 1
+      OR h.RELEASETXNDATE >= TO_DATE(:start_date || ' 073000', 'YYYY-MM-DD HH24MISS') - 1
+      OR h.RELEASETXNDATE IS NULL
+    )
+    AND (
+      h.HOLDTXNDATE <= TO_DATE(:end_date || ' 073000', 'YYYY-MM-DD HH24MISS')
+      OR h.RELEASETXNDATE <= TO_DATE(:end_date || ' 073000', 'YYYY-MM-DD HH24MISS')
+      OR h.RELEASETXNDATE IS NULL
+    )
+)
+SELECT
+  hold_day,
+  release_day,
+  CONTAINERID,
+  LOT_ID,
+  PJ_WORKORDER,
+  PRODUCTNAME,
+  WORKCENTERNAME,
+  HOLDREASONID,
+  HOLDREASONNAME,
+  QTY,
+  HOLDTXNDATE,
+  HOLDEMP,
+  HOLDCOMMENTS,
+  RELEASETXNDATE,
+  RELEASEEMP,
+  RELEASECOMMENTS,
+  NCRID,
+  FUTUREHOLDCOMMENTS,
+  HOLD_HOURS,
+  HOLD_TYPE,
+  IS_FUTURE_HOLD,
+  RN_HOLD_DAY,
+  RN_FUTURE_REASON,
+  CASE
+    WHEN IS_FUTURE_HOLD = 1 AND RN_FUTURE_REASON <> 1 THEN 0
+    ELSE 1
+  END AS FUTURE_HOLD_FLAG
+FROM history_base
diff --git a/src/mes_dashboard/sql/resource_history/base_facts.sql b/src/mes_dashboard/sql/resource_history/base_facts.sql
new file mode 100644
index 0000000..1cc7700
--- /dev/null
+++ b/src/mes_dashboard/sql/resource_history/base_facts.sql
@@ -0,0 +1,31 @@
+-- Base facts query for resource_dataset_cache.
+-- Fetches ALL shift-status records per resource per day for the date range.
+-- Used as single Oracle query; all views (kpi, trend, heatmap, comparison, detail)
+-- are derived in-memory from this cached DataFrame.
+--
+-- Placeholders:
+--   HISTORYID_FILTER - Resource ID filter condition (e.g., HISTORYID IN (...))
+-- Parameters:
+--   :start_date - Start date (YYYY-MM-DD)
+--   :end_date   - End date (YYYY-MM-DD)
+
+WITH shift_data AS (
+    SELECT /*+ MATERIALIZE */ HISTORYID, TXNDATE, OLDSTATUSNAME, HOURS
+    FROM DWH.DW_MES_RESOURCESTATUS_SHIFT
+    WHERE TXNDATE >= TO_DATE(:start_date, 'YYYY-MM-DD')
+      AND TXNDATE < TO_DATE(:end_date, 'YYYY-MM-DD') + 1
+      AND {{ HISTORYID_FILTER }}
+)
+SELECT
+    HISTORYID,
+    TRUNC(TXNDATE) as DATA_DATE,
+    SUM(CASE WHEN OLDSTATUSNAME = 'PRD' THEN HOURS ELSE 0 END) as PRD_HOURS,
+    SUM(CASE WHEN OLDSTATUSNAME = 'SBY' THEN HOURS ELSE 0 END) as SBY_HOURS,
+    SUM(CASE WHEN OLDSTATUSNAME = 'UDT' THEN HOURS ELSE 0 END) as UDT_HOURS,
+    SUM(CASE WHEN OLDSTATUSNAME = 'SDT' THEN HOURS ELSE 0 END) as SDT_HOURS,
+    SUM(CASE WHEN OLDSTATUSNAME = 'EGT' THEN HOURS ELSE 0 END) as EGT_HOURS,
+    SUM(CASE WHEN OLDSTATUSNAME = 'NST' THEN HOURS ELSE 0 END) as NST_HOURS,
+    SUM(HOURS) as TOTAL_HOURS
+FROM shift_data
+GROUP BY HISTORYID, TRUNC(TXNDATE)
+ORDER BY HISTORYID, TRUNC(TXNDATE)
diff --git a/tests/test_hold_history_routes.py b/tests/test_hold_history_routes.py
index e7dad48..0710132 100644
--- a/tests/test_hold_history_routes.py
+++ b/tests/test_hold_history_routes.py
@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Unit tests for Hold History API routes."""
+"""Unit tests for Hold History API routes (two-phase query/view pattern)."""
 
 import json
 import unittest
@@ -35,215 +35,220 @@ class TestHoldHistoryPageRoute(TestHoldHistoryRoutesBase):
         self.assertTrue(response.location.endswith('/portal-shell/hold-history'))
 
 
-class TestHoldHistoryTrendRoute(TestHoldHistoryRoutesBase):
-    """Test GET /api/hold-history/trend endpoint."""
+class TestHoldHistoryQueryRoute(TestHoldHistoryRoutesBase):
+    """Test POST /api/hold-history/query endpoint."""
 
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_trend')
-    def test_trend_passes_date_range(self, mock_trend):
-        mock_trend.return_value = {
-            'days': [
-                {
-                    'date': '2026-02-01',
-                    'quality': {'holdQty': 10, 'newHoldQty': 2, 'releaseQty': 3, 'futureHoldQty': 1},
-                    'non_quality': {'holdQty': 5, 'newHoldQty': 1, 'releaseQty': 2, 'futureHoldQty': 0},
-                    'all': {'holdQty': 15, 'newHoldQty': 3, 'releaseQty': 5, 'futureHoldQty': 1},
-                }
-            ]
-        }
-
-        response = self.client.get('/api/hold-history/trend?start_date=2026-02-01&end_date=2026-02-07')
-        payload = json.loads(response.data)
-
-        self.assertEqual(response.status_code, 200)
-        self.assertTrue(payload['success'])
-        self.assertIn('days', payload['data'])
-        mock_trend.assert_called_once_with('2026-02-01', '2026-02-07')
-
-    def test_trend_invalid_date_returns_400(self):
-        response = self.client.get('/api/hold-history/trend?start_date=2026/02/01&end_date=2026-02-07')
+    def test_query_missing_dates_returns_400(self):
+        response = self.client.post(
+            '/api/hold-history/query',
+            json={'start_date': '2026-02-01'},
+        )
         payload = json.loads(response.data)
 
         self.assertEqual(response.status_code, 400)
         self.assertFalse(payload['success'])
 
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_trend')
+    def test_query_invalid_date_format_returns_400(self):
+        response = self.client.post(
+            '/api/hold-history/query',
+            json={'start_date': '2026/02/01', 'end_date': '2026-02-07'},
+        )
+        payload = json.loads(response.data)
+
+        self.assertEqual(response.status_code, 400)
+        self.assertFalse(payload['success'])
+
+    def test_query_end_before_start_returns_400(self):
+        response = self.client.post(
+            '/api/hold-history/query',
+            json={'start_date': '2026-02-07', 'end_date': '2026-02-01'},
+        )
+        payload = json.loads(response.data)
+
+        self.assertEqual(response.status_code, 400)
+        self.assertFalse(payload['success'])
+
+    def test_query_invalid_record_type_returns_400(self):
+        response = self.client.post(
+            '/api/hold-history/query',
+            json={
+                'start_date': '2026-02-01',
+                'end_date': '2026-02-07',
+                'record_type': 'invalid',
+            },
+        )
+        payload = json.loads(response.data)
+
+        self.assertEqual(response.status_code, 400)
+        self.assertFalse(payload['success'])
+
+    @patch('mes_dashboard.routes.hold_history_routes.execute_primary_query')
+    def test_query_success(self, mock_exec):
+        mock_exec.return_value = {
+            'query_id': 'abc123',
+            'trend': {'days': []},
+            'reason_pareto': {'items': []},
+            'duration': {'items': []},
+            'list': {'items': [], 'pagination': {}},
+        }
+
+        response = self.client.post(
+            '/api/hold-history/query',
+            json={
+                'start_date': '2026-02-01',
+                'end_date': '2026-02-07',
+                'hold_type': 'quality',
+            },
+        )
+        payload = json.loads(response.data)
+
+        self.assertEqual(response.status_code, 200)
+        self.assertTrue(payload['success'])
+        self.assertIn('query_id', payload['data'])
+
+    @patch('mes_dashboard.routes.hold_history_routes.execute_primary_query')
+    def test_query_passes_params(self, mock_exec):
+        mock_exec.return_value = {'query_id': 'x', 'trend': {}, 'reason_pareto': {}, 'duration': {}, 'list': {}}
+
+        self.client.post(
+            '/api/hold-history/query',
+            json={
+                'start_date': '2026-02-01',
+                'end_date': '2026-02-07',
+                'hold_type': 'non-quality',
+                'record_type': 'on_hold',
+            },
+        )
+
+        mock_exec.assert_called_once_with(
+            start_date='2026-02-01',
+            end_date='2026-02-07',
+            hold_type='non-quality',
+            record_type='on_hold',
+        )
+
+    @patch('mes_dashboard.routes.hold_history_routes.execute_primary_query')
     @patch('mes_dashboard.core.rate_limit.check_and_record', return_value=(True, 8))
-    def test_trend_rate_limited_returns_429(self, _mock_limit, mock_service):
-        response = self.client.get('/api/hold-history/trend?start_date=2026-02-01&end_date=2026-02-07')
+    def test_query_rate_limited_returns_429(self, _mock_limit, mock_exec):
+        response = self.client.post(
+            '/api/hold-history/query',
+            json={'start_date': '2026-02-01', 'end_date': '2026-02-07'},
+        )
         payload = json.loads(response.data)
 
         self.assertEqual(response.status_code, 429)
         self.assertEqual(payload['error']['code'], 'TOO_MANY_REQUESTS')
         self.assertEqual(response.headers.get('Retry-After'), '8')
-        mock_service.assert_not_called()
+        mock_exec.assert_not_called()
 
 
-class TestHoldHistoryReasonParetoRoute(TestHoldHistoryRoutesBase):
-    """Test GET /api/hold-history/reason-pareto endpoint."""
+class TestHoldHistoryViewRoute(TestHoldHistoryRoutesBase):
+    """Test GET /api/hold-history/view endpoint."""
 
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_reason_pareto')
-    def test_reason_pareto_passes_hold_type_and_record_type(self, mock_service):
-        mock_service.return_value = {'items': []}
+    def test_view_missing_query_id_returns_400(self):
+        response = self.client.get('/api/hold-history/view')
+        payload = json.loads(response.data)
 
+        self.assertEqual(response.status_code, 400)
+        self.assertFalse(payload['success'])
+        self.assertIn('query_id', payload['error'])
+
+    def test_view_invalid_record_type_returns_400(self):
         response = self.client.get(
-            '/api/hold-history/reason-pareto?start_date=2026-02-01&end_date=2026-02-07'
-            '&hold_type=non-quality&record_type=on_hold'
-        )
-
-        self.assertEqual(response.status_code, 200)
-        mock_service.assert_called_once_with('2026-02-01', '2026-02-07', 'non-quality', 'on_hold')
-
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_reason_pareto')
-    def test_reason_pareto_defaults_record_type_to_new(self, mock_service):
-        mock_service.return_value = {'items': []}
-
-        response = self.client.get(
-            '/api/hold-history/reason-pareto?start_date=2026-02-01&end_date=2026-02-07&hold_type=quality'
-        )
-
-        self.assertEqual(response.status_code, 200)
-        mock_service.assert_called_once_with('2026-02-01', '2026-02-07', 'quality', 'new')
-
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_reason_pareto')
-    def test_reason_pareto_multi_record_type(self, mock_service):
-        mock_service.return_value = {'items': []}
-
-        response = self.client.get(
-            '/api/hold-history/reason-pareto?start_date=2026-02-01&end_date=2026-02-07'
-            '&hold_type=quality&record_type=on_hold,released'
-        )
-
-        self.assertEqual(response.status_code, 200)
-        mock_service.assert_called_once_with('2026-02-01', '2026-02-07', 'quality', 'on_hold,released')
-
-    def test_reason_pareto_invalid_record_type_returns_400(self):
-        response = self.client.get(
-            '/api/hold-history/reason-pareto?start_date=2026-02-01&end_date=2026-02-07&record_type=invalid'
+            '/api/hold-history/view?query_id=abc123&record_type=bogus'
         )
         payload = json.loads(response.data)
 
         self.assertEqual(response.status_code, 400)
         self.assertFalse(payload['success'])
 
-    def test_reason_pareto_partial_invalid_record_type_returns_400(self):
+    def test_view_invalid_duration_range_returns_400(self):
         response = self.client.get(
-            '/api/hold-history/reason-pareto?start_date=2026-02-01&end_date=2026-02-07&record_type=on_hold,bogus'
+            '/api/hold-history/view?query_id=abc123&duration_range=invalid'
         )
         payload = json.loads(response.data)
 
         self.assertEqual(response.status_code, 400)
         self.assertFalse(payload['success'])
 
+    @patch('mes_dashboard.routes.hold_history_routes.apply_view')
+    def test_view_cache_expired_returns_410(self, mock_view):
+        mock_view.return_value = None
 
-class TestHoldHistoryDurationRoute(TestHoldHistoryRoutesBase):
-    """Test GET /api/hold-history/duration endpoint."""
-
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_duration')
-    def test_duration_failure_returns_500(self, mock_service):
-        mock_service.return_value = None
-
-        response = self.client.get('/api/hold-history/duration?start_date=2026-02-01&end_date=2026-02-07')
+        response = self.client.get('/api/hold-history/view?query_id=abc123')
         payload = json.loads(response.data)
 
-        self.assertEqual(response.status_code, 500)
+        self.assertEqual(response.status_code, 410)
         self.assertFalse(payload['success'])
+        self.assertEqual(payload['error'], 'cache_expired')
 
-    def test_duration_invalid_hold_type(self):
-        response = self.client.get(
-            '/api/hold-history/duration?start_date=2026-02-01&end_date=2026-02-07&hold_type=invalid'
-        )
-        payload = json.loads(response.data)
-
-        self.assertEqual(response.status_code, 400)
-        self.assertFalse(payload['success'])
-
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_duration')
-    def test_duration_passes_record_type(self, mock_service):
-        mock_service.return_value = {'items': []}
-
-        response = self.client.get(
-            '/api/hold-history/duration?start_date=2026-02-01&end_date=2026-02-07&record_type=released'
-        )
-
-        self.assertEqual(response.status_code, 200)
-        mock_service.assert_called_once_with('2026-02-01', '2026-02-07', 'quality', 'released')
-
-    def test_duration_invalid_record_type_returns_400(self):
-        response = self.client.get(
-            '/api/hold-history/duration?start_date=2026-02-01&end_date=2026-02-07&record_type=bogus'
-        )
-        payload = json.loads(response.data)
-
-        self.assertEqual(response.status_code, 400)
-        self.assertFalse(payload['success'])
-
-
-class TestHoldHistoryListRoute(TestHoldHistoryRoutesBase):
-    """Test GET /api/hold-history/list endpoint."""
-
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_list')
-    def test_list_caps_per_page_and_sets_page_floor(self, mock_service):
-        mock_service.return_value = {
-            'items': [],
-            'pagination': {'page': 1, 'perPage': 200, 'total': 0, 'totalPages': 1},
+    @patch('mes_dashboard.routes.hold_history_routes.apply_view')
+    def test_view_success(self, mock_view):
+        mock_view.return_value = {
+            'trend': {'days': []},
+            'reason_pareto': {'items': []},
+            'duration': {'items': []},
+            'list': {'items': [], 'pagination': {}},
         }
 
         response = self.client.get(
-            '/api/hold-history/list?start_date=2026-02-01&end_date=2026-02-07'
-            '&hold_type=all&page=0&per_page=500&reason=品質確認'
+            '/api/hold-history/view?query_id=abc123&hold_type=non-quality&reason=品質確認&page=2&per_page=20'
         )
+        payload = json.loads(response.data)
 
         self.assertEqual(response.status_code, 200)
-        mock_service.assert_called_once_with(
-            start_date='2026-02-01',
-            end_date='2026-02-07',
-            hold_type='all',
+        self.assertTrue(payload['success'])
+
+        mock_view.assert_called_once_with(
+            query_id='abc123',
+            hold_type='non-quality',
             reason='品質確認',
             record_type='new',
             duration_range=None,
-            page=1,
-            per_page=200,
+            page=2,
+            per_page=20,
         )
 
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_list')
-    def test_list_passes_duration_range(self, mock_service):
-        mock_service.return_value = {
-            'items': [],
-            'pagination': {'page': 1, 'perPage': 50, 'total': 0, 'totalPages': 1},
+    @patch('mes_dashboard.routes.hold_history_routes.apply_view')
+    def test_view_caps_per_page(self, mock_view):
+        mock_view.return_value = {
+            'trend': {'days': []},
+            'reason_pareto': {'items': []},
+            'duration': {'items': []},
+            'list': {'items': [], 'pagination': {}},
         }
 
-        response = self.client.get(
-            '/api/hold-history/list?start_date=2026-02-01&end_date=2026-02-07&duration_range=<4h'
+        self.client.get(
+            '/api/hold-history/view?query_id=abc123&page=0&per_page=500'
         )
 
-        self.assertEqual(response.status_code, 200)
-        mock_service.assert_called_once_with(
-            start_date='2026-02-01',
-            end_date='2026-02-07',
-            hold_type='quality',
-            reason=None,
-            record_type='new',
-            duration_range='<4h',
-            page=1,
-            per_page=50,
+        call_kwargs = mock_view.call_args[1]
+        self.assertEqual(call_kwargs['page'], 1)
+        self.assertEqual(call_kwargs['per_page'], 200)
+
+    @patch('mes_dashboard.routes.hold_history_routes.apply_view')
+    def test_view_passes_duration_range(self, mock_view):
+        mock_view.return_value = {
+            'trend': {'days': []},
+            'reason_pareto': {'items': []},
+            'duration': {'items': []},
+            'list': {'items': [], 'pagination': {}},
+        }
+
+        self.client.get(
+            '/api/hold-history/view?query_id=abc123&duration_range=<4h'
         )
 
-    def test_list_invalid_duration_range_returns_400(self):
-        response = self.client.get(
-            '/api/hold-history/list?start_date=2026-02-01&end_date=2026-02-07&duration_range=invalid'
-        )
-        payload = json.loads(response.data)
+        call_kwargs = mock_view.call_args[1]
+        self.assertEqual(call_kwargs['duration_range'], '<4h')
 
-        self.assertEqual(response.status_code, 400)
-        self.assertFalse(payload['success'])
-
-    @patch('mes_dashboard.routes.hold_history_routes.get_hold_history_list')
+    @patch('mes_dashboard.routes.hold_history_routes.apply_view')
     @patch('mes_dashboard.core.rate_limit.check_and_record', return_value=(True, 5))
-    def test_list_rate_limited_returns_429(self, _mock_limit, mock_service):
-        response = self.client.get('/api/hold-history/list?start_date=2026-02-01&end_date=2026-02-07')
+    def test_view_rate_limited_returns_429(self, _mock_limit, mock_view):
+        response = self.client.get('/api/hold-history/view?query_id=abc123')
         payload = json.loads(response.data)
 
         self.assertEqual(response.status_code, 429)
         self.assertEqual(payload['error']['code'], 'TOO_MANY_REQUESTS')
         self.assertEqual(response.headers.get('Retry-After'), '5')
-        mock_service.assert_not_called()
+        mock_view.assert_not_called()
diff --git a/tests/test_resource_history_routes.py b/tests/test_resource_history_routes.py
index c72f4a6..11c2290 100644
--- a/tests/test_resource_history_routes.py
+++ b/tests/test_resource_history_routes.py
@@ -61,8 +61,8 @@ class TestResourceHistoryOptionsAPI(unittest.TestCase):
         self.assertIn('error', data)
 
 
-class TestResourceHistorySummaryAPI(unittest.TestCase):
-    """Integration tests for /api/resource/history/summary endpoint."""
+class TestResourceHistoryQueryAPI(unittest.TestCase):
+    """Integration tests for POST /api/resource/history/query endpoint."""
 
     def setUp(self):
         """Set up test client."""
@@ -73,7 +73,10 @@ class TestResourceHistorySummaryAPI(unittest.TestCase):
 
     def test_missing_start_date(self):
         """Missing start_date should return 400."""
-        response = self.client.get('/api/resource/history/summary?end_date=2024-01-31')
+        response = self.client.post(
+            '/api/resource/history/query',
+            json={'end_date': '2024-01-31'},
+        )
 
         self.assertEqual(response.status_code, 400)
         data = json.loads(response.data)
@@ -82,74 +85,69 @@ class TestResourceHistorySummaryAPI(unittest.TestCase):
 
     def test_missing_end_date(self):
         """Missing end_date should return 400."""
-        response = self.client.get('/api/resource/history/summary?start_date=2024-01-01')
+        response = self.client.post(
+            '/api/resource/history/query',
+            json={'start_date': '2024-01-01'},
+        )
 
         self.assertEqual(response.status_code, 400)
         data = json.loads(response.data)
         self.assertFalse(data['success'])
         self.assertIn('end_date', data['error'])
 
-    @patch('mes_dashboard.routes.resource_history_routes.query_summary')
-    def test_date_range_exceeds_limit(self, mock_query):
-        """Date range exceeding 730 days should return error."""
-        mock_query.return_value = {'error': '查詢範圍不可超過 730 天（兩年）'}
-
-        response = self.client.get(
-            '/api/resource/history/summary?start_date=2024-01-01&end_date=2026-01-02'
-        )
-
-        self.assertEqual(response.status_code, 400)
-        data = json.loads(response.data)
-        self.assertFalse(data['success'])
-        self.assertIn('730', data['error'])
-
-    @patch('mes_dashboard.routes.resource_history_routes.query_summary')
-    def test_successful_summary(self, mock_query):
-        """Successful summary request should return all data sections."""
+    @patch('mes_dashboard.routes.resource_history_routes.execute_primary_query')
+    def test_successful_query(self, mock_query):
+        """Successful query should return query_id, summary, and detail."""
         mock_query.return_value = {
-            'kpi': {
-                'ou_pct': 80.0,
-                'prd_hours': 800,
-                'sby_hours': 100,
-                'udt_hours': 50,
-                'sdt_hours': 30,
-                'egt_hours': 20,
-                'nst_hours': 100,
-                'machine_count': 10
+            'query_id': 'abc123',
+            'summary': {
+                'kpi': {'ou_pct': 80.0, 'machine_count': 10},
+                'trend': [{'date': '2024-01-01', 'ou_pct': 80.0}],
+                'heatmap': [{'workcenter': 'WC01', 'date': '2024-01-01', 'ou_pct': 80.0}],
+                'workcenter_comparison': [{'workcenter': 'WC01', 'ou_pct': 80.0}],
+            },
+            'detail': {
+                'data': [{'workcenter': 'WC01', 'ou_pct': 80.0}],
+                'total': 1,
+                'truncated': False,
+                'max_records': None,
             },
-            'trend': [{'date': '2024-01-01', 'ou_pct': 80.0}],
-            'heatmap': [{'workcenter': 'WC01', 'date': '2024-01-01', 'ou_pct': 80.0}],
-            'workcenter_comparison': [{'workcenter': 'WC01', 'ou_pct': 80.0}]
         }
 
-        response = self.client.get(
-            '/api/resource/history/summary?start_date=2024-01-01&end_date=2024-01-07'
+        response = self.client.post(
+            '/api/resource/history/query',
+            json={'start_date': '2024-01-01', 'end_date': '2024-01-07'},
         )
 
         self.assertEqual(response.status_code, 200)
         data = json.loads(response.data)
         self.assertTrue(data['success'])
-        self.assertIn('kpi', data['data'])
-        self.assertIn('trend', data['data'])
-        self.assertIn('heatmap', data['data'])
-        self.assertIn('workcenter_comparison', data['data'])
+        self.assertIn('query_id', data)
+        self.assertIn('summary', data)
+        self.assertIn('detail', data)
+        self.assertIn('kpi', data['summary'])
+        self.assertIn('trend', data['summary'])
 
-    @patch('mes_dashboard.routes.resource_history_routes.query_summary')
-    def test_summary_with_filters(self, mock_query):
-        """Summary with filters should pass them to service."""
-        mock_query.return_value = {'kpi': {}, 'trend': [], 'heatmap': [], 'workcenter_comparison': []}
+    @patch('mes_dashboard.routes.resource_history_routes.execute_primary_query')
+    def test_query_with_filters(self, mock_query):
+        """Query with filters should pass them to service."""
+        mock_query.return_value = {
+            'query_id': 'abc123',
+            'summary': {'kpi': {}, 'trend': [], 'heatmap': [], 'workcenter_comparison': []},
+            'detail': {'data': [], 'total': 0, 'truncated': False, 'max_records': None},
+        }
 
-        response = self.client.get(
-            '/api/resource/history/summary'
-            '?start_date=2024-01-01'
-            '&end_date=2024-01-07'
-            '&granularity=week'
-            '&workcenter_groups=焊接_DB'
-            '&workcenter_groups=成型'
-            '&families=FAM01'
-            '&families=FAM02'
-            '&is_production=1'
-            '&is_key=1'
+        response = self.client.post(
+            '/api/resource/history/query',
+            json={
+                'start_date': '2024-01-01',
+                'end_date': '2024-01-07',
+                'granularity': 'week',
+                'workcenter_groups': ['焊接_DB', '成型'],
+                'families': ['FAM01', 'FAM02'],
+                'is_production': True,
+                'is_key': True,
+            },
         )
 
         self.assertEqual(response.status_code, 200)
@@ -162,8 +160,8 @@ class TestResourceHistorySummaryAPI(unittest.TestCase):
         self.assertTrue(call_kwargs['is_key'])
 
 
-class TestResourceHistoryDetailAPI(unittest.TestCase):
-    """Integration tests for /api/resource/history/detail endpoint."""
+class TestResourceHistoryViewAPI(unittest.TestCase):
+    """Integration tests for GET /api/resource/history/view endpoint."""
 
     def setUp(self):
         """Set up test client."""
@@ -172,60 +170,54 @@ class TestResourceHistoryDetailAPI(unittest.TestCase):
         self.app.config['TESTING'] = True
         self.client = self.app.test_client()
 
-    def test_missing_dates(self):
-        """Missing dates should return 400."""
-        response = self.client.get('/api/resource/history/detail')
+    def test_missing_query_id(self):
+        """Missing query_id should return 400."""
+        response = self.client.get('/api/resource/history/view')
 
         self.assertEqual(response.status_code, 400)
         data = json.loads(response.data)
         self.assertFalse(data['success'])
+        self.assertIn('query_id', data['error'])
 
-    @patch('mes_dashboard.routes.resource_history_routes.query_detail')
-    def test_successful_detail(self, mock_query):
-        """Successful detail request should return data with total and truncated flag."""
-        mock_query.return_value = {
-            'data': [
-                {'workcenter': 'WC01', 'family': 'FAM01', 'resource': 'RES01', 'ou_pct': 80.0}
-            ],
-            'total': 100,
-            'truncated': False,
-            'max_records': None
+    @patch('mes_dashboard.routes.resource_history_routes.apply_view')
+    def test_cache_expired(self, mock_view):
+        """Expired cache should return 410."""
+        mock_view.return_value = None
+
+        response = self.client.get('/api/resource/history/view?query_id=abc123')
+
+        self.assertEqual(response.status_code, 410)
+        data = json.loads(response.data)
+        self.assertFalse(data['success'])
+        self.assertEqual(data['error'], 'cache_expired')
+
+    @patch('mes_dashboard.routes.resource_history_routes.apply_view')
+    def test_successful_view(self, mock_view):
+        """Successful view should return summary and detail."""
+        mock_view.return_value = {
+            'summary': {
+                'kpi': {'ou_pct': 80.0},
+                'trend': [],
+                'heatmap': [],
+                'workcenter_comparison': [],
+            },
+            'detail': {
+                'data': [{'workcenter': 'WC01', 'ou_pct': 80.0}],
+                'total': 1,
+                'truncated': False,
+                'max_records': None,
+            },
         }
 
         response = self.client.get(
-            '/api/resource/history/detail?start_date=2024-01-01&end_date=2024-01-07'
+            '/api/resource/history/view?query_id=abc123&granularity=week'
         )
 
         self.assertEqual(response.status_code, 200)
         data = json.loads(response.data)
         self.assertTrue(data['success'])
-        self.assertIn('data', data)
-        self.assertIn('total', data)
-        self.assertIn('truncated', data)
-        self.assertFalse(data['truncated'])
-
-    @patch('mes_dashboard.routes.resource_history_routes.query_detail')
-    def test_detail_truncated_warning(self, mock_query):
-        """Detail with truncated data should return truncated flag and max_records."""
-        mock_query.return_value = {
-            'data': [{'workcenter': 'WC01', 'family': 'FAM01', 'resource': 'RES01', 'ou_pct': 80.0}],
-            'total': 6000,
-            'truncated': True,
-            'max_records': 5000
-        }
-
-        response = self.client.get(
-            '/api/resource/history/detail'
-            '?start_date=2024-01-01'
-            '&end_date=2024-01-07'
-        )
-
-        self.assertEqual(response.status_code, 200)
-        data = json.loads(response.data)
-        self.assertTrue(data['success'])
-        self.assertTrue(data['truncated'])
-        self.assertEqual(data['max_records'], 5000)
-        self.assertEqual(data['total'], 6000)
+        self.assertIn('summary', data)
+        self.assertIn('detail', data)
 
 
 class TestResourceHistoryExportAPI(unittest.TestCase):