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feat: harden long-range batch queries with redis+parquet caching

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egg
2026-03-02 21:04:18 +08:00
parent 2568fd836c
commit fb92579331
40 changed files with 5443 additions and 676 deletions
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49
.env.example
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@@ -26,6 +26,13 @@ DB_TCP_CONNECT_TIMEOUT=10
DB_CONNECT_RETRY_COUNT=1 DB_CONNECT_RETRY_COUNT=1
DB_CONNECT_RETRY_DELAY=1.0 DB_CONNECT_RETRY_DELAY=1.0
DB_CALL_TIMEOUT_MS=55000 # Must stay below worker timeout DB_CALL_TIMEOUT_MS=55000 # Must stay below worker timeout
DB_SLOW_CALL_TIMEOUT_MS=300000
DB_SLOW_MAX_CONCURRENT=5
DB_SLOW_POOL_ENABLED=true
DB_SLOW_POOL_SIZE=2
DB_SLOW_POOL_MAX_OVERFLOW=1
DB_SLOW_POOL_TIMEOUT=30
DB_SLOW_POOL_RECYCLE=1800
# ============================================================ # ============================================================
# Flask Configuration # Flask Configuration
@@ -115,6 +122,24 @@ REDIS_ENABLED=true
# Redis key prefix (to separate from other applications) # Redis key prefix (to separate from other applications)
REDIS_KEY_PREFIX=mes_wip REDIS_KEY_PREFIX=mes_wip
# Redis memory guardrail (prevent unbounded RAM growth)
# Example: 512mb / 1gb / 2gb. Set 0 to disable limit (NOT recommended).
REDIS_MAXMEMORY=512mb
# Eviction policy when maxmemory is reached
# Recommended: allkeys-lru (general cache), volatile-lru (TTL keys only)
REDIS_MAXMEMORY_POLICY=allkeys-lru
# Redis persistence (physical storage)
REDIS_PERSISTENCE_ENABLED=true
REDIS_APPENDONLY=yes
REDIS_APPENDFSYNC=everysec
REDIS_SAVE=900 1 300 10 60 10000
# Startup cleanup: remove stale keys that accidentally have no TTL
REDIS_TTL_CLEANUP_ON_START=true
REDIS_TTL_CLEANUP_PATTERNS=batch:*,reject_dataset:*,hold_dataset:*,resource_dataset:*,job_query:*
# Cache check interval in seconds (default: 600 = 10 minutes) # Cache check interval in seconds (default: 600 = 10 minutes)
CACHE_CHECK_INTERVAL=600 CACHE_CHECK_INTERVAL=600
@@ -306,6 +331,30 @@ HEALTH_MEMO_TTL_SECONDS=5
# Reject history options API cache TTL in seconds (default: 14400 = 4 hours) # Reject history options API cache TTL in seconds (default: 14400 = 4 hours)
REJECT_HISTORY_OPTIONS_CACHE_TTL_SECONDS=14400 REJECT_HISTORY_OPTIONS_CACHE_TTL_SECONDS=14400
# ============================================================
# Reject History Batch/Spill Guardrails
# ============================================================
# Batch chunking controls (for long-range reject queries)
REJECT_ENGINE_GRAIN_DAYS=10
REJECT_ENGINE_PARALLEL=2
REJECT_ENGINE_MAX_ROWS_PER_CHUNK=50000
REJECT_ENGINE_MAX_TOTAL_ROWS=300000
# Large result spill controls
REJECT_ENGINE_SPILL_ENABLED=true
REJECT_ENGINE_MAX_RESULT_MB=64
QUERY_SPOOL_DIR=tmp/query_spool
REJECT_ENGINE_SPOOL_TTL_SECONDS=21600
REJECT_ENGINE_SPOOL_MAX_BYTES=2147483648
REJECT_ENGINE_SPOOL_WARN_RATIO=0.85
REJECT_ENGINE_SPOOL_CLEANUP_INTERVAL_SECONDS=300
REJECT_ENGINE_SPOOL_ORPHAN_GRACE_SECONDS=600
# Batch query engine thresholds
BATCH_QUERY_TIME_THRESHOLD_DAYS=60
BATCH_QUERY_ID_THRESHOLD=1000
BATCH_CHUNK_MAX_MEMORY_MB=256
# ============================================================ # ============================================================
# Runtime Resilience Diagnostics Thresholds # Runtime Resilience Diagnostics Thresholds
# ============================================================ # ============================================================

9
README.md
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@@ -318,6 +318,15 @@ CIRCUIT_BREAKER_RECOVERY_TIMEOUT=30
# Redis 設定 # Redis 設定
REDIS_URL=redis://localhost:6379/0 REDIS_URL=redis://localhost:6379/0
REDIS_ENABLED=true REDIS_ENABLED=true
REDIS_KEY_PREFIX=mes_wip
REDIS_MAXMEMORY=512mb
REDIS_MAXMEMORY_POLICY=allkeys-lru
REDIS_PERSISTENCE_ENABLED=true
REDIS_APPENDONLY=yes
REDIS_APPENDFSYNC=everysec
REDIS_SAVE=900 1 300 10 60 10000
REDIS_TTL_CLEANUP_ON_START=true
REDIS_TTL_CLEANUP_PATTERNS=batch:*,reject_dataset:*,hold_dataset:*,resource_dataset:*,job_query:*
# Watchdog runtime contract # Watchdog runtime contract
WATCHDOG_RUNTIME_DIR=./tmp WATCHDOG_RUNTIME_DIR=./tmp

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openspec/changes/archive/2026-03-02-unified-batch-query-redis-cache/.openspec.yaml Normal file
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@@ -0,0 +1,2 @@
schema: spec-driven
created: 2026-03-02

166
openspec/changes/archive/2026-03-02-unified-batch-query-redis-cache/design.md Normal file
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## Context
目前 6 個服務各自處理大查詢，缺乏統一保護：
| 服務 | 查詢類型 | 現有保護 | 缺口 |
|------|---------|---------|------|
| reject-history | 日期 + 工單/Lot/GD 展開 | L1+L2 快取、`read_sql_df_slow` | 無記憶體守衛、`limit=999999999`、缺分塊查詢 |
| hold-history | 日期 | L1+L2 快取、`read_sql_df_slow` | 無記憶體守衛、缺時間分塊 |
| resource-history | 日期 + 設備 ID | L1+L2 快取、1000 筆分批 | 無記憶體守衛 |
| mid-section-defect | 日期 → 偵測 → 族譜 → 上游 | Redis 快取、EventFetcher 分批 | 無偵測數量上限 |
| job-query | 日期 + 設備 ID | 1000 筆分批、`read_sql_df_slow` | **無結果快取**、缺時間分塊 |
| query-tool | 多種 resolver → container ID | 輸入筆數限制、resolve route 短 TTL 快取、EventFetcher 快取 | 多數查詢仍走 `read_sql_df`（55s timeout）、缺統一分塊編排 |
參考實作：
- `EventFetcher`：batch 1000 + ThreadPoolExecutor(2) + `read_sql_df_slow_iter` streaming + Redis 快取 — **已是最佳實作**
- `LineageEngine`：batch 1000 + depth limit 20 — **族譜專用引擎**
目標：建立 `BatchQueryEngine` 共用模組，任何服務接入即獲得完整保護。
## Goals / Non-Goals
**Goals:**
- 統一 parquet-in-Redis 存取為共用模組（消除 3 處重複）
- 提供時間範圍分解（長日期 → ~31 天月份區間）
- 提供 ID 批次分解（工單/Lot/GD 展開後的大量 container ID → 1000 筆一批）
- 記憶體守衛：每個 chunk 結果檢查 memory_usage，超過閾值中止
- 結果筆數限制：可配置上限，超過時截斷並標記
- 受控並行：預設循序、可選並行、semaphore 感知
- Redis 分塊快取 + 部分命中
- 統一使用 `read_sql_df_slow`（300 秒 dedicated connection）
- 定義 query_hash 與 chunk 邊界語意，避免跨服務行為不一致
- 定義 chunk cache 與服務 L1/L2 dataset cache 互動規則
**Non-Goals:**
- 不修改 SQL 語句本身
- 不引入新的外部依賴
- 不改變前端 API 介面（前端無感知）
- 不替換 EventFetcher / LineageEngine（它們已各自最佳化，引擎提供可選接入點）
- 不改變 trace_job_service 的 RQ 非同步架構
## Decisions
### Decision 1: 提取 `redis_df_store.py` 共用模組
**選擇**：從 reject/hold/resource_dataset_cache 提取相同的 `_redis_store_df` / `_redis_load_df` 到 `src/mes_dashboard/core/redis_df_store.py`。
**替代方案**：(A) 保持各自複製 → 已有 3 處重複，維護困難。
**理由**：parquet-in-Redis 是 DataFrame 序列化工具，與快取策略（TTL、LRU）屬不同層次。
### Decision 2: `BatchQueryEngine` 作為工具類而非基底類別
**選擇**：提供獨立函式（`decompose_by_time_range`、`decompose_by_ids`、`execute_plan`、`merge_chunks`），各服務按需調用。
**替代方案**：(A) 抽象基底類別 `BaseDatasetCache` → 三個 dataset cache 差異大（SQL、policy filter、衍生計算），強制繼承會過度耦合。
**理由**：工具類模式讓服務保持現有結構，僅在主查詢路徑決定是否啟用分解。閾值以下的查詢完全不經過引擎。
### Decision 3: 預設循序、可選並行、semaphore 感知
**選擇**：`execute_plan(parallel=1)` 預設循序。實際並行上限 = `min(requested, semaphore_available - 1)`。
**替代方案**：(A) 預設並行 → 可能耗盡 semaphore；(B) 完全不並行 → 失去速度。
**理由**：Oracle 連線稀缺（Production 預設 `DB_SLOW_MAX_CONCURRENT=5`，Development 常見為 3）。reject_dataset_cache 查詢最重可設 parallel=2，其他預設循序最安全。
### Decision 4: 記憶體守衛 + 結果筆數限制
**選擇**：每個 chunk 查詢後檢查 `df.memory_usage(deep=True).sum()`，超過 `BATCH_CHUNK_MAX_MEMORY_MB`（預設 256MB）時中止該 chunk 並標記失敗。同時提供 `max_rows_per_chunk` 參數，在 SQL 中加入 `FETCH FIRST N ROWS ONLY`。
**替代方案**：(A) 無限制 → 現狀，OOM 風險高；(B) 全域限制 → 不夠靈活。
**理由**：chunk 級別的記憶體守衛是最後一道防線。分解後每個 chunk 的日期/ID 範圍已大幅縮小，記憶體超限通常代表異常資料，應中止而非繼續。
### Decision 5: 分塊快取 + 部分命中
**選擇**：Redis 鍵 `batch:{prefix}:{hash}:chunk:{idx}`，每個 chunk 獨立 SETEX。
**替代方案**：(A) 只快取最終結果 → 無法部分命中。
**理由**：使用者常見操作是「先查 1-6 月，再查 1-8 月」。分塊快取讓前 6 個月直接複用，只查 7-8 月。
### Decision 6: 引擎路徑統一使用 slow-query 路徑（且不佔用主 pool）
**選擇**：所有經過引擎的查詢統一使用 slow-query 路徑（300s timeout, semaphore 控制）；未經引擎的既有短查詢路徑保持原狀。
慢查詢執行策略採兩層：
1. 主路徑：使用既有獨立 `SLOW POOL`（小容量）做 checkout/checkin。
2. fallback：當 SLOW POOL 不可用時，降級為 slow direct connection。
**替代方案**：
(A) 引擎路徑混用 `read_sql_df`（主 pool, 55s timeout）→ 長查詢高超時風險且會壓縮一般 API 吞吐。
(B) 慢查詢直接共用主 pool → 高峰時造成 pool 爭用與整體延遲放大。
**理由**：經過引擎的查詢本身就是「已知可能很慢」的查詢。慢查詢與主 pool 隔離可避免互相影響；SLOW POOL 讓連線重用與隔離同時成立，fallback direct connection 保障可用性。
### Decision 7: 部分失敗處理
**選擇**：某個 chunk 失敗時記錄錯誤、繼續剩餘 chunk。`merge_chunks()` 回傳成功部分，metadata 標記 `has_partial_failure=True`。
**替代方案**：(A) 全部回滾 → 已成功的 chunk 浪費。
**理由**：歷史報表場景下，部分結果比完全失敗更有價值。metadata 標記讓服務可決定是否警告使用者。
### Decision 8: Chunk Cache 與服務 L1/L2 Dataset Cache 互動
**選擇**：先讀 chunk cache（Redis）組裝結果；組裝後回填既有 service dataset cache（L1 process + L2 Redis）以維持現有 `/view` 路徑與 `query_id` 行為。
**替代方案**：(A) 只使用 chunk cache，不回填 service cache → 現有 view/query_id 流程失效或重複查詢。
**理由**：需要兼容既有 two-phase dataset API（primary query + cached view），chunk cache 是引擎層優化，不應破壞服務層介面。
### Decision 9: query_hash 規格
**選擇**：query_hash 使用 canonical JSON（sorted keys、穩定 list 順序、字串正規化）後 SHA-256 前 16 碼；hash 僅包含會影響原始資料集合的參數（不含純前端呈現參數）。
**替代方案**：(A) 每服務自由實作 hash → 跨服務不可預測且難除錯。
**理由**：chunk key、progress key、merge key 需可重現，否則無法保證 cache 命中與部分重用。
### Decision 10: 時間分解邊界語意
**選擇**：採閉區間 chunk `[chunk_start, chunk_end]`；下一段從 `chunk_end + 1 day` 開始；最後一段可小於 grain_days；輸入日期以服務既有時區/日界線為準，不在引擎層重新解釋時區。
**替代方案**：(A) 半開區間或依月份動態切割但不定義邊界 → 容易重疊或漏資料。
**理由**：邊界語意固定後，merge 去重、統計一致性與測試可驗證性都會提升。
### Decision 11: 大結果採 Parquet 落地，Redis 僅保留 metadata/熱快取
**選擇**：對長查詢（尤其 reject-history）引入 spill-to-disk：
1. chunk 查詢與 chunk cache 保持現行（Redis，短 TTL）
2. merge 後若結果超過門檻（rows / memory / serialized size），寫入 Parquet 至本機 spool 目錄
3. Redis 僅保存 metadata（query_id, file_path, row_count, schema_hash, created_at, expires_at）
4. `/view`/`/export` 優先透過 metadata 讀取 parquet；metadata 不存在時回退現行 cache 行為
5. 背景清理器定期移除過期 parquet 與孤兒 metadata
**替代方案**：
(A) Redis 全量承載所有結果（現況）→ 記憶體壓力高，易引發 lock timeout/OOM 連鎖
(B) 直接落 DB（例如 SQLite）→ 寫入鎖衝突與維運複雜度高（目前已有 `database is locked` 觀察）
**理由**：Redis 是記憶體快取，不適合長時間承載大結果；Parquet 落地可把大結果轉移到磁碟，降低 worker/Redis 記憶體峰值。
## Risks / Trade-offs
**[Redis 記憶體增長]** → 分塊快取增加 key 數量（365 天 ≈ 12 個 chunk key）。
→ 緩解：TTL 自動過期（900s）；chunk 結果經 parquet 壓縮（通常 10:1 壓縮比）。
**[Semaphore 爭用]** → 並行 chunk 消耗更多 permit。
→ 緩解：感知可用數量，不足時自動降級循序。預設 parallel=1。
**[時間分解後的資料一致性]** → 不同月份 chunk 在不同時間點查詢。
→ 緩解：歷史報表資料更新頻率低（日級），短窗口內變動極低。可接受。
**[遷移風險]** → 先修改 3 個 dataset cache，再擴展至其他服務，整體範圍仍大。
→ 緩解：閾值控制（短查詢不經過引擎）+ P0/P1/P2/P3 分階段導入 + 每階段獨立驗證。
**[磁碟 I/O 與容量壓力]** → Parquet 落地會增加磁碟讀寫，若清理策略失效可能累積大量檔案。
→ 緩解：設定 spool 容量上限、TTL 清理、啟動時 orphan 掃描、超限時回退到「不落地僅回應摘要」保護模式。
**[Stale metadata / orphan file]** → Redis metadata 與實體檔案可能不一致。
→ 緩解：讀取前校驗檔案存在與 schema hash；不一致時自動失效 metadata 並記錄告警。
## Open Questions
1. `mid_section_defect_service` 的 4 階段管線（偵測 → 族譜 → 上游歷史 → 歸因）中，哪些階段適合接入引擎？偵測查詢可日期分解，但族譜/上游已透過 EventFetcher 處理。
2. `query_tool_service` 有 15+ 種查詢類型，是否全部接入還是只處理最易超時的（split_merge_history、equipment_period）？

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## Why
目前各歷史報表服務（reject-history、hold-history、resource-history）、查詢工具（query-tool）、中段不良分析（mid-section-defect）和 Job 查詢（job-query）各自實作不同的批次查詢、快取和並行執行模式，缺乏統一編排與保護。主要問題：
1. **Oracle 超時**：長日期範圍（365+ 天）或大量 Container ID（工單展開後可達數千筆）的查詢可能超過 300 秒 call_timeout
2. **OOM 風險**：reject/hold dataset cache 以 `limit: 999999999` 取回全部資料，無記憶體上限守衛
3. **保護分散**：`EventFetcher` 已有 ID 分批 + 快取，但 reject/hold/resource dataset cache 仍各自維護查詢與快取策略
4. **重複程式碼**：3 個 dataset cache 各自複製相同的 parquet-in-Redis 序列化邏輯
5. **ID 展開膨脹**：工單 resolve 後 container ID 可能大量擴張，缺乏跨服務一致的分批/合併流程
6. **重查成本高**：延長查詢範圍（例如 1-6 月改 1-8 月）無法有效重用已查區段結果
7. **query-tool 超時風險高**：多數查詢仍走 `read_sql_df`（主 pool / 55s timeout），大查詢下容易超時
需要一個**可穩定複用的查詢引擎模組**，任何服務接入後自動獲得分解、快取、記憶體保護和超時保護。
## What Changes
- 新增 `BatchQueryEngine` 共用模組，提供：
- **時間範圍分解**：長日期 → ~31 天月份區間，每段獨立查詢
- **時間分解語意**：明確定義 chunk 邊界（閉區間）、跨月切割與最後一段不足月行為
- **ID 批次分解**：大量 ID（工單/Lot/GD Lot/流水批展開後）→ 1000 筆一批
- **query_hash 規格**：統一 canonicalization 與雜湊欄位，確保 chunk/cache key 穩定
- **記憶體守衛**：每個 chunk 結果檢查 `DataFrame.memory_usage()`，超過閾值時中止並警告
- **結果筆數限制**：可配置的最大結果筆數，超過時截斷並標記
- **受控並行執行**：預設循序、可選並行，嚴格遵守 slow query semaphore
- **Redis 分塊快取**：每個 chunk 獨立快取，支援部分命中（延長查詢範圍時複用已查過的區間）
- **快取層互動**：明確定義 chunk cache 與服務既有 L1/L2 dataset cache 的讀寫順序
- **進度追蹤**：Redis HSET 記錄進度，可供前端顯示
- 新增「**大結果落地層（Parquet spill）**」設計：
- 當長查詢結果超過記憶體/列數門檻時，將合併後結果以 Parquet 寫入本機持久目錄（例如 `tmp/query_spool/`）
- Redis 僅保存 metadata（query_id → parquet path / schema / rows / created_at / ttl）
- `/view` 與 `/export` 讀取流程優先走 Redis metadata + Parquet，避免整包 DataFrame 常駐 worker RAM
- 定時清理（TTL + 背景清理器）刪除過期 parquet，避免磁碟持續膨脹
- 新增 `redis_df_store` 共用模組，將 parquet-in-Redis 存取邏輯從 3 個 dataset cache 提取為共用工具
- 所有**引擎接管的 chunk 查詢**統一使用 slow 路徑（300 秒級 timeout）
- 使用既有「**獨立 SLOW POOL（小容量）**」做慢查詢連線重用
- 明確**不使用主查詢 pool** 承載慢查詢，避免拖垮一般 API
- 當 SLOW POOL 不可用時，降級為 slow direct connection（不影響主 pool）
## Capabilities
### New Capabilities
- `batch-query-engine`: 統一批次查詢引擎模組，涵蓋分解策略（時間/ID）、記憶體守衛、結果限制、受控執行、Redis 分塊快取、進度追蹤、結果合併
### Modified Capabilities
- `reject-history-api`: 主查詢改為透過引擎執行；date_range 模式自動時間分解，container 模式（工單/Lot/GD Lot 展開後）自動 ID 分批
- `hold-dataset-cache`: 主查詢改為透過引擎執行，長日期自動分解
- `resource-dataset-cache`: 主查詢改為透過引擎執行，長日期自動分解
- `event-fetcher-unified`: 保持既有最佳化（batch + streaming + cache），僅在需要統一監控/進度模型時再評估導入
## Impact
- **後端**：新增 2 個共用模組（`batch_query_engine.py`、`redis_df_store.py`），優先修改 3 個 dataset cache 主查詢路徑（reject/hold/resource）
- **受影響服務**（優先順序）：
- P0：reject-history（最容易超時/OOM — 長日期 + 工單展開 + 目前 `limit=999999999`）
- P1：hold-history、resource-history（相同架構，直接套用）
- P2：mid-section-defect（4 階段管線，偵測查詢 + 上游歷史）、job-query（缺快取 + 日期分解）
- P3：query-tool（優先處理 `read_sql_df` 高風險路徑並導入慢查詢保護）、event-fetcher（保持可選）
- **資料庫**：不改 SQL，僅縮小每次查詢的 bind parameter 範圍
- **資料庫連線策略**：慢查詢與一般 pooled query 隔離，避免資源互相干擾
- **Redis**：新增 `batch:*` 前綴的分塊快取鍵
- **儲存層**：新增 Parquet 結果落地目錄與清理機制（Redis 轉為索引/metadata，不再承載全部大結果）
- **記憶體**：引擎強制單 chunk 記憶體上限（預設 256MB），超過時中止
- **可用性**：Redis 設定 `maxmemory` + eviction 後仍可透過 Parquet metadata 回復查詢結果（cache 不命中不等於資料遺失）
- **向下相容**：短查詢（< 60 天、< 1000 ID）走現有路徑，零額外開銷；既有 route/event 快取策略保持不變
- **前端**：可選性變更，長查詢可顯示進度條（非必要）
## Parquet 落地的預期效果與副作用
**預期效果：**
- 大幅降低 worker 在「merge + cache 回填」階段的峰值記憶體（避免單 worker 突增到 GB 級）
- Redis 記憶體由「存整包資料」轉為「存索引/熱資料」，降低 OOM 與 lock timeout 連鎖風險
- 服務重啟後，若 parquet 尚未過期，仍可恢復查詢結果（搭配 metadata）
**可能副作用（Side Effects）：**
- 磁碟 I/O 增加：查詢高峰時會有 parquet 寫入/讀取尖峰
- 磁碟容量風險：清理策略失效時，spool 目錄可能持續膨脹
- 資料一致性風險：metadata 指向檔案若被外部刪除/損壞，會出現 stale pointer
- 安全與治理：落地檔案需納入權限控管、備份/清理與稽核策略
**緩解方向：**
- 強制 TTL + 定期掃描清理（以 metadata 與檔案 mtime 雙重判斷）
- 啟動時做 orphan/stale 檢查與自動修復（刪 metadata 或刪孤兒檔）
- 先以 reject-history 長查詢為 P0，逐步擴展到其他服務

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## ADDED Requirements
### Requirement: BatchQueryEngine SHALL provide time-range decomposition
The module SHALL decompose long date ranges into manageable monthly chunks to prevent Oracle timeout.
#### Scenario: Decompose date range into monthly chunks
- **WHEN** `decompose_by_time_range(start_date, end_date, grain_days=31)` is called
- **THEN** the date range SHALL be split into chunks of at most `grain_days` days each
- **THEN** each chunk SHALL contain `chunk_start` and `chunk_end` date strings
- **THEN** chunks SHALL be contiguous and non-overlapping, covering the full range
#### Scenario: Short date range returns single chunk
- **WHEN** the date range is shorter than or equal to `grain_days`
- **THEN** a single chunk covering the full range SHALL be returned
#### Scenario: Time-chunk boundary semantics are deterministic
- **WHEN** a date range is decomposed into multiple chunks
- **THEN** each chunk SHALL use a closed interval `[chunk_start, chunk_end]`
- **THEN** the next chunk SHALL start at `previous_chunk_end + 1 day`
- **THEN** the final chunk MAY contain fewer than `grain_days` days
- **THEN** chunk ranges SHALL have no overlap and no gap
### Requirement: BatchQueryEngine SHALL provide ID-batch decomposition
The module SHALL decompose large ID lists (from workorder/lot/GD lot/serial resolve expansion) into batches respecting Oracle IN-clause limits.
#### Scenario: Decompose ID list into batches
- **WHEN** `decompose_by_ids(ids, batch_size=1000)` is called with more than `batch_size` IDs
- **THEN** the ID list SHALL be split into batches of at most `batch_size` items each
#### Scenario: Small ID list returns single batch
- **WHEN** the ID list has fewer than or equal to `batch_size` items
- **THEN** a single batch containing all IDs SHALL be returned
### Requirement: BatchQueryEngine SHALL execute chunk plans with controlled parallelism
The module SHALL execute query chunks sequentially by default, with opt-in parallel execution respecting the slow query semaphore.
#### Scenario: Sequential execution (default)
- **WHEN** `execute_plan(chunks, query_fn, parallel=1)` is called
- **THEN** chunks SHALL be executed one at a time in order
- **THEN** each chunk result SHALL be stored to Redis immediately after completion
- **THEN** the function SHALL return a `query_hash` identifying the batch result
#### Scenario: Parallel execution with semaphore awareness
- **WHEN** `execute_plan(chunks, query_fn, parallel=2)` is called
- **THEN** up to `parallel` chunks SHALL execute concurrently via ThreadPoolExecutor
- **THEN** each thread SHALL acquire the slow query semaphore before executing `query_fn`
- **THEN** actual concurrency SHALL be capped at `min(parallel, available_semaphore_permits - 1)`
- **THEN** if semaphore is fully occupied, execution SHALL degrade to sequential
#### Scenario: All engine queries use dedicated connection
- **WHEN** a chunk's `query_fn` executes an Oracle query
- **THEN** it SHALL use `read_sql_df_slow` (dedicated connection, 300s timeout, semaphore-controlled)
- **THEN** pooled connection (`read_sql_df`) SHALL NOT be used for engine-managed queries
### Requirement: BatchQueryEngine SHALL enforce memory guards per chunk
The module SHALL check each chunk result's memory usage and abort if it exceeds a configurable threshold.
#### Scenario: Chunk memory within limit
- **WHEN** a chunk query returns a DataFrame within `BATCH_CHUNK_MAX_MEMORY_MB` (default 256MB, env-configurable)
- **THEN** the chunk SHALL be stored to Redis and marked as completed
#### Scenario: Chunk memory exceeds limit
- **WHEN** a chunk query returns a DataFrame exceeding `BATCH_CHUNK_MAX_MEMORY_MB`
- **THEN** the chunk SHALL be discarded (NOT stored to Redis)
- **THEN** the chunk SHALL be marked as failed in metadata with reason `memory_limit_exceeded`
- **THEN** a warning log SHALL include chunk index, actual memory MB, and threshold
- **THEN** remaining chunks SHALL continue execution
#### Scenario: Result row count limit
- **WHEN** `max_rows_per_chunk` is configured
- **THEN** the engine SHALL pass this limit to `query_fn` for SQL-level truncation (e.g., `FETCH FIRST N ROWS ONLY`)
- **THEN** if the result contains exactly `max_rows_per_chunk` rows, metadata SHALL include `truncated=True`
### Requirement: BatchQueryEngine SHALL support partial cache hits
The module SHALL check Redis for previously cached chunks and skip re-execution for cached chunks.
#### Scenario: Partial cache hit skips cached chunks
- **WHEN** `execute_plan(chunks, query_fn, skip_cached=True)` is called
- **THEN** for each chunk, Redis SHALL be checked for an existing cached result
- **THEN** chunks with valid cached results SHALL NOT be re-executed
- **THEN** only uncached chunks SHALL be passed to `query_fn`
#### Scenario: Full cache hit skips all execution
- **WHEN** all chunks already exist in Redis cache
- **THEN** no Oracle queries SHALL be executed
- **THEN** `merge_chunks()` SHALL return the combined cached DataFrames
### Requirement: BatchQueryEngine SHALL generate deterministic query_hash
The module SHALL use a stable hash for cache/progress keys so semantically identical queries map to the same batch identity.
#### Scenario: Stable hash for equivalent parameters
- **WHEN** two requests contain the same semantic query parameters in different input order
- **THEN** canonicalization SHALL normalize ordering before hashing
- **THEN** `query_hash` SHALL be identical for both requests
#### Scenario: Hash changes only when dataset-affecting parameters change
- **WHEN** parameters affecting the raw dataset (date range, mode, resolved IDs, core filters) change
- **THEN** `query_hash` SHALL change
- **THEN** presentation-only parameters SHALL NOT change `query_hash`
### Requirement: BatchQueryEngine SHALL define chunk-cache to service-cache handoff
The module SHALL integrate chunk-level cache with existing service-level dataset caches without breaking query_id-based view APIs.
#### Scenario: Chunk merge backfills service dataset cache
- **WHEN** chunk results are loaded/merged into a complete dataset for a primary query
- **THEN** the merged DataFrame SHALL be written back to the service's existing dataset cache layers (L1 process + L2 Redis)
- **THEN** downstream `/view` queries using the service `query_id` SHALL continue to work without additional Oracle queries
#### Scenario: Service cache miss with chunk cache hit
- **WHEN** a service-level dataset cache entry has expired but relevant chunk cache keys still exist
- **THEN** the engine SHALL rebuild the merged dataset from chunk cache
- **THEN** the service dataset cache SHALL be repopulated before returning response
### Requirement: BatchQueryEngine SHALL store chunk results in Redis
The module SHALL store each chunk as a separate Redis key using parquet-in-Redis format.
#### Scenario: Chunk storage key format
- **WHEN** a chunk result is stored
- **THEN** the Redis key SHALL follow the pattern `batch:{cache_prefix}:{query_hash}:chunk:{idx}`
- **THEN** each chunk SHALL be stored as a parquet-encoded base64 string via `redis_df_store`
- **THEN** each chunk key SHALL have a TTL matching the service's cache TTL (default 900 seconds)
#### Scenario: Chunk metadata tracking
- **WHEN** chunks are being executed
- **THEN** a metadata key `batch:{cache_prefix}:{query_hash}:meta` SHALL be updated via Redis HSET
- **THEN** metadata SHALL include `total`, `completed`, `failed`, `pct`, `status`, and `has_partial_failure` fields
### Requirement: BatchQueryEngine SHALL merge chunk results into a single DataFrame
The module SHALL provide result assembly from cached chunks.
#### Scenario: Merge all chunks
- **WHEN** `merge_chunks(query_hash)` is called
- **THEN** all chunk DataFrames SHALL be loaded from Redis and concatenated via `pd.concat`
- **THEN** if any chunk is missing, the merge SHALL proceed with available chunks and set `has_partial_failure=True`
#### Scenario: Iterate chunks for streaming
- **WHEN** `iterate_chunks(query_hash)` is called
- **THEN** chunk DataFrames SHALL be yielded one at a time without loading all into memory simultaneously
### Requirement: BatchQueryEngine SHALL handle chunk failures gracefully
The module SHALL continue execution when individual chunks fail and report partial results.
#### Scenario: Single chunk failure
- **WHEN** a chunk's `query_fn` raises an exception (timeout, ORA error, etc.)
- **THEN** the error SHALL be logged with chunk index and exception details
- **THEN** the failed chunk SHALL be marked as failed in metadata
- **THEN** remaining chunks SHALL continue execution
#### Scenario: All chunks fail
- **WHEN** all chunks' `query_fn` calls raise exceptions
- **THEN** metadata status SHALL be set to `failed`
- **THEN** `merge_chunks()` SHALL return an empty DataFrame
### Requirement: Shared redis_df_store module SHALL provide parquet-in-Redis utilities
The module SHALL provide reusable DataFrame serialization to/from Redis using parquet + base64 encoding.
#### Scenario: Store DataFrame to Redis
- **WHEN** `redis_store_df(key, df, ttl)` is called
- **THEN** the DataFrame SHALL be serialized to parquet format using pyarrow
- **THEN** the parquet bytes SHALL be base64-encoded and stored via Redis SETEX with the given TTL
- **THEN** if Redis is unavailable, the function SHALL log a warning and return without error
#### Scenario: Load DataFrame from Redis
- **WHEN** `redis_load_df(key)` is called
- **THEN** the base64 string SHALL be loaded from Redis, decoded, and deserialized to a DataFrame
- **THEN** if the key does not exist or Redis is unavailable, the function SHALL return None

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## MODIFIED Requirements
### Requirement: EventFetcher SHALL provide unified cached event querying across domains
`EventFetcher` SHALL encapsulate batch event queries with L1/L2 layered cache and rate limit bucket configuration, supporting domains: `history`, `materials`, `rejects`, `holds`, `jobs`, `upstream_history`, `downstream_rejects`. EventFetcher MAY optionally delegate ID batching to `BatchQueryEngine` for consistent decomposition patterns.
#### Scenario: Cache miss for event domain query
- **WHEN** `EventFetcher` is called for a domain with container IDs and no cache exists
- **THEN** the domain query SHALL execute against Oracle via `read_sql_df_slow()` (non-pooled dedicated connection)
- **THEN** each batch query SHALL use `timeout_seconds=60`
- **THEN** the result SHALL be stored in L2 Redis cache with key format `evt:{domain}:{sorted_cids_hash}` if CID count is within cache threshold
- **THEN** L1 memory cache SHALL also be populated if CID count is within cache threshold
#### Scenario: Cache hit for event domain query
- **WHEN** `EventFetcher` is called for a domain and L2 Redis cache contains a valid entry
- **THEN** the cached result SHALL be returned without executing Oracle query
- **THEN** DB connection pool SHALL NOT be consumed
#### Scenario: Rate limit bucket per domain
- **WHEN** `EventFetcher` is used from a route handler
- **THEN** each domain SHALL have a configurable rate limit bucket aligned with `configured_rate_limit()` pattern
- **THEN** rate limit configuration SHALL be overridable via environment variables
#### Scenario: Large CID set exceeds cache threshold
- **WHEN** the normalized CID count exceeds `CACHE_SKIP_CID_THRESHOLD` (default 10000, env: `EVENT_FETCHER_CACHE_SKIP_CID_THRESHOLD`)
- **THEN** EventFetcher SHALL skip both L1 and L2 cache writes
- **THEN** a warning log SHALL be emitted with domain name, CID count, and threshold value
- **THEN** the query result SHALL still be returned to the caller
#### Scenario: Batch concurrency default
- **WHEN** EventFetcher processes batches for a domain with >1000 CIDs
- **THEN** the default `EVENT_FETCHER_MAX_WORKERS` SHALL be 2 (env: `EVENT_FETCHER_MAX_WORKERS`)
#### Scenario: Optional BatchQueryEngine integration
- **WHEN** EventFetcher is refactored to use `BatchQueryEngine` (optional, not required)
- **THEN** `decompose_by_ids()` MAY replace inline batching logic
- **THEN** existing ThreadPoolExecutor + read_sql_df_slow_iter patterns SHALL be preserved as the primary implementation
- **THEN** no behavioral changes SHALL be introduced by engine integration

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## MODIFIED 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. For date ranges exceeding 60 days, the query SHALL be decomposed into monthly chunks via `BatchQueryOrchestrator`.
#### 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: Long date range triggers batch decomposition
- **WHEN** the date range exceeds 60 days (configurable via `BATCH_QUERY_TIME_THRESHOLD_DAYS`)
- **THEN** the query SHALL be decomposed into ~31-day monthly chunks via `BatchQueryOrchestrator.decompose_by_time_range()`
- **THEN** each chunk SHALL execute independently via `read_sql_df_slow` with the chunk's date sub-range
- **THEN** chunk results SHALL be stored individually in Redis and merged via `pd.concat`
- **THEN** the merged DataFrame SHALL be stored in the existing L1+L2 cache under the original query_id
#### Scenario: Short date range uses direct query
- **WHEN** the date range is 60 days or fewer
- **THEN** the existing single-query path SHALL be used without batch decomposition
#### 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`
#### Scenario: Redis parquet helpers use shared module
- **WHEN** DataFrames are stored or loaded from Redis
- **THEN** the module SHALL use `redis_df_store.redis_store_df()` and `redis_df_store.redis_load_df()` from the shared `core/redis_df_store.py` module
- **THEN** inline `_redis_store_df` / `_redis_load_df` functions SHALL be removed

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## MODIFIED Requirements
### Requirement: Job query SHALL use BatchQueryEngine for long-range decomposition
The `get_jobs_by_resources()` function SHALL delegate to BatchQueryEngine when the requested date range exceeds the configurable threshold, preventing Oracle timeout on large job queries.
#### Scenario: Long date range triggers engine decomposition
- **WHEN** `get_jobs_by_resources(resource_ids, start_date, end_date)` is called
- **AND** the date range exceeds `BATCH_QUERY_TIME_THRESHOLD_DAYS` (default 60)
- **THEN** the date range SHALL be decomposed via `decompose_by_time_range()`
- **THEN** each chunk SHALL be executed through the existing job SQL with chunk-scoped dates
- **THEN** the existing `_build_resource_filter()` batching SHALL be preserved within each chunk
#### Scenario: Short date range preserves direct path
- **WHEN** the date range is within the threshold
- **THEN** the existing direct query path SHALL be used with zero overhead
### Requirement: Job query results SHALL be cached in Redis
Job query results SHALL be cached using the shared `redis_df_store` module to avoid redundant Oracle queries on repeated requests.
#### Scenario: Cache hit returns stored result
- **WHEN** a job query is executed with identical parameters within the cache TTL
- **THEN** the cached result SHALL be returned without hitting Oracle
#### Scenario: Cache miss triggers fresh query
- **WHEN** no cached result exists for the query parameters
- **THEN** the query SHALL execute against Oracle
- **THEN** the result SHALL be stored in Redis with the configured TTL
### Requirement: Job queries SHALL use read_sql_df_slow execution path
- **WHEN** engine-managed job queries execute
- **THEN** they SHALL use `read_sql_df_slow` (dedicated connection, 300s timeout)
- **THEN** no pooled-query regressions SHALL be introduced

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## MODIFIED Requirements
### Requirement: Detection query SHALL use BatchQueryEngine for long-range decomposition
The `_fetch_station_detection_data()` function SHALL delegate to BatchQueryEngine when the requested date range exceeds the configurable threshold, preventing Oracle timeout on large detection queries.
#### Scenario: Long date range triggers engine decomposition
- **WHEN** `_fetch_station_detection_data(start_date, end_date, station)` is called
- **AND** the date range exceeds `BATCH_QUERY_TIME_THRESHOLD_DAYS` (default 60)
- **THEN** the date range SHALL be decomposed via `decompose_by_time_range()`
- **THEN** each chunk SHALL be executed through the existing detection SQL with chunk-scoped dates
- **THEN** chunk results SHALL be cached in Redis and merged into a single DataFrame
#### Scenario: Short date range preserves direct path
- **WHEN** the date range is within the threshold
- **THEN** the existing direct query path SHALL be used with zero overhead
#### Scenario: Memory guard protects against oversized detection results
- **WHEN** a single chunk result exceeds `BATCH_CHUNK_MAX_MEMORY_MB`
- **THEN** that chunk SHALL be discarded and marked as failed
- **THEN** remaining chunks SHALL continue executing
- **THEN** the batch metadata SHALL reflect `has_partial_failure`

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## MODIFIED Requirements
### Requirement: High-risk query_tool paths SHALL migrate to slow-query execution
Functions currently using `read_sql_df` (fast pool, 55s timeout) that handle unbounded or user-driven queries SHALL be migrated to `read_sql_df_slow` (dedicated connection, 300s timeout) to prevent timeout failures.
#### Scenario: Serial number resolution uses slow-query path
- **WHEN** `_resolve_by_serial_number()` executes resolver SQL queries
- **THEN** queries SHALL use `read_sql_df_slow` instead of `read_sql_df`
#### Scenario: Work order resolution uses slow-query path
- **WHEN** `_resolve_by_work_order()` executes resolver SQL queries
- **THEN** queries SHALL use `read_sql_df_slow` instead of `read_sql_df`
#### Scenario: Equipment query functions use slow-query path
- **WHEN** `get_equipment_status_hours()`, `get_equipment_lots()`, `get_equipment_materials()`, `get_equipment_rejects()`, or `get_equipment_jobs()` execute equipment SQL queries
- **THEN** queries SHALL use `read_sql_df_slow` instead of `read_sql_df`
### Requirement: High-risk query_tool paths SHALL use engine decomposition for large inputs
Selected query functions SHALL delegate to BatchQueryEngine for ID decomposition when the resolved input set is large.
#### Scenario: Large serial number batch triggers engine decomposition
- **WHEN** `_resolve_by_serial_number()` is called with more IDs than `BATCH_QUERY_ID_THRESHOLD`
- **THEN** IDs SHALL be decomposed via `decompose_by_ids()`
- **THEN** each batch SHALL be executed through the existing resolver SQL
#### Scenario: Equipment period queries use engine time decomposition
- **WHEN** equipment period queries span more than `BATCH_QUERY_TIME_THRESHOLD_DAYS`
- **THEN** the date range SHALL be decomposed via `decompose_by_time_range()`
### Requirement: Existing resolve cache strategy SHALL be reviewed for heavy query patterns
#### Scenario: Route-level short-TTL cache extended for high-repeat patterns
- **WHEN** a query pattern is identified as high-repeat (same parameters within minutes)
- **THEN** result caching SHALL be considered using `redis_df_store`
- **THEN** cache TTL SHALL align with the service's data freshness requirements

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## 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. For large queries, `BatchQueryEngine` SHALL decompose by time range or ID count.
#### 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
#### Scenario: Long date range triggers time decomposition (date_range mode)
- **WHEN** the primary query is in `date_range` mode and the range exceeds 60 days (configurable via `BATCH_QUERY_TIME_THRESHOLD_DAYS`)
- **THEN** the query SHALL be decomposed into ~31-day monthly chunks via `BatchQueryEngine.decompose_by_time_range()`
- **THEN** each chunk SHALL execute independently with the chunk's date sub-range as bind parameters
- **THEN** chunk results SHALL be stored individually in Redis and merged via `pd.concat`
#### Scenario: Large container ID set triggers ID decomposition (container mode)
- **WHEN** the primary query is in `container` mode (workorder/lot/wafer_lot input) and the resolved container ID count exceeds 1000
- **THEN** the container IDs SHALL be decomposed into 1000-item batches via `BatchQueryEngine.decompose_by_ids()`
- **THEN** each batch SHALL execute independently
- **THEN** batch results SHALL be merged into the final cached DataFrame
#### Scenario: Short date range or small ID set uses direct query
- **WHEN** the date range is 60 days or fewer, or resolved container IDs are 1000 or fewer
- **THEN** the existing single-query path SHALL be used without engine decomposition
#### Scenario: Memory guard on result
- **WHEN** a chunk query result exceeds `BATCH_CHUNK_MAX_MEMORY_MB`
- **THEN** the chunk SHALL be discarded and marked as failed
- **THEN** the current `limit: 999999999` pattern SHALL be replaced with a configurable `max_rows_per_chunk`

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## MODIFIED 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. For date ranges exceeding 60 days, the query SHALL be decomposed into monthly chunks via `BatchQueryOrchestrator`.
#### 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: Long date range triggers batch decomposition
- **WHEN** the date range exceeds 60 days (configurable via `BATCH_QUERY_TIME_THRESHOLD_DAYS`)
- **THEN** the query SHALL be decomposed into ~31-day monthly chunks via `BatchQueryOrchestrator.decompose_by_time_range()`
- **THEN** each chunk SHALL execute independently via `read_sql_df_slow` with the chunk's date sub-range
- **THEN** chunk results SHALL be stored individually in Redis and merged via `pd.concat`
- **THEN** the merged DataFrame SHALL be stored in the existing L1+L2 cache under the original query_id
#### Scenario: Short date range uses direct query
- **WHEN** the date range is 60 days or fewer
- **THEN** the existing single-query path SHALL be used without batch decomposition
#### 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`
#### Scenario: Redis parquet helpers use shared module
- **WHEN** DataFrames are stored or loaded from Redis
- **THEN** the module SHALL use `redis_df_store.redis_store_df()` and `redis_df_store.redis_load_df()` from the shared `core/redis_df_store.py` module
- **THEN** inline `_redis_store_df` / `_redis_load_df` functions SHALL be removed

122
openspec/changes/archive/2026-03-02-unified-batch-query-redis-cache/tasks.md Normal file
View File

@@ -0,0 +1,122 @@
## 0. Artifact Alignment (P2/P3 Specs)
- [x] 0.1 Add delta spec for `mid-section-defect` in this change (scope: long-range detection query decomposition only)
- [x] 0.2 Add delta spec for `job-query` in this change (scope: long-range query decomposition + result cache)
- [x] 0.3 Add delta spec for `query-tool` in this change (scope: high-risk endpoints and timeout-protection strategy)
## 1. Shared Infrastructure — redis_df_store
- [x] 1.1 Create `src/mes_dashboard/core/redis_df_store.py` with `redis_store_df(key, df, ttl)` and `redis_load_df(key)` extracted from reject_dataset_cache.py (lines 82-111)
- [x] 1.2 Add chunk-level helpers: `redis_store_chunk(prefix, query_hash, idx, df, ttl)`, `redis_load_chunk(prefix, query_hash, idx)`, `redis_chunk_exists(prefix, query_hash, idx)`
## 2. Shared Infrastructure — BatchQueryEngine
- [x] 2.1 Create `src/mes_dashboard/services/batch_query_engine.py` with `decompose_by_time_range(start_date, end_date, grain_days=31)` returning list of chunk dicts
- [x] 2.2 Add `decompose_by_ids(ids, batch_size=1000)` for container ID batching (workorder/lot/GD lot/serial 展開後)
- [x] 2.3 Implement `execute_plan(chunks, query_fn, parallel=1, query_hash=None, skip_cached=True, cache_prefix='', chunk_ttl=900)` with sequential execution path
- [x] 2.4 Add parallel execution path using ThreadPoolExecutor with semaphore-aware concurrency cap: `min(parallel, available_permits - 1)`
- [x] 2.5 Add memory guard: after each chunk query, check `df.memory_usage(deep=True).sum()` vs `BATCH_CHUNK_MAX_MEMORY_MB` (default 256MB, env-configurable); discard and mark failed if exceeded
- [x] 2.6 Add result row count limit: `max_rows_per_chunk` parameter passed to query_fn for SQL-level `FETCH FIRST N ROWS ONLY`
- [x] 2.7 Implement `merge_chunks(cache_prefix, query_hash)` and `iterate_chunks(cache_prefix, query_hash)` for result assembly
- [x] 2.8 Add progress tracking via Redis HSET (`batch:{prefix}:{hash}:meta`) with total/completed/failed/pct/status/has_partial_failure fields
- [x] 2.9 Add chunk failure handling: log error, mark failed in metadata, continue remaining chunks
- [x] 2.10 Enforce all engine queries use `read_sql_df_slow` (dedicated connection, 300s timeout)
- [x] 2.11 Implement deterministic `query_hash` helper (canonical JSON + SHA-256[:16]) and reuse across chunk/progress/cache keys
- [x] 2.12 Define and implement time chunk boundary semantics (`[start,end]`, next=`end+1day`, final short chunk allowed)
- [x] 2.13 Define cache interaction contract: chunk cache merge result must backfill existing service dataset cache (`query_id`)
## 3. Unit Tests — redis_df_store
- [x] 3.1 Test `redis_store_df` / `redis_load_df` round-trip
- [x] 3.2 Test chunk helpers round-trip
- [x] 3.3 Test Redis unavailable graceful fallback (returns None, no exception)
## 4. Unit Tests — BatchQueryEngine
- [x] 4.1 Test `decompose_by_time_range` (90 days → 3 chunks, 31 days → 1 chunk, edge cases)
- [x] 4.2 Test `decompose_by_ids` (2500 IDs → 3 batches, 500 IDs → 1 batch)
- [x] 4.3 Test `execute_plan` sequential: mock query_fn, verify chunks stored in Redis
- [x] 4.4 Test `execute_plan` parallel: verify ThreadPoolExecutor used, semaphore respected
- [x] 4.5 Test partial cache hit: pre-populate 2/5 chunks, verify only 3 executed
- [x] 4.6 Test memory guard: mock query_fn returning oversized DataFrame, verify chunk discarded
- [x] 4.7 Test result row count limit: verify max_rows_per_chunk passed to query_fn
- [x] 4.8 Test `merge_chunks`: verify pd.concat produces correct merged DataFrame
- [x] 4.9 Test progress tracking: verify Redis HSET updated after each chunk
- [x] 4.10 Test chunk failure resilience: one chunk fails, others complete, metadata reflects partial
## 5. P0: Adopt in reject_dataset_cache
- [x] 5.1 Replace inline `_redis_store_df` / `_redis_load_df` with imports from `core.redis_df_store`
- [x] 5.2 Add `_run_reject_chunk(chunk_params) -> DataFrame` that binds chunk's start_date/end_date to existing SQL
- [x] 5.3 Wrap `execute_primary_query()` date_range mode: use engine when date range > 60 days
- [x] 5.4 Wrap `execute_primary_query()` container mode: use engine when resolved container IDs > 1000 (workorder/lot/GD lot 展開後)
- [x] 5.5 Replace `limit: 999999999` with configurable `max_rows_per_chunk`
- [x] 5.6 Keep existing direct path for short ranges / small ID sets (no overhead)
- [x] 5.7 Merge chunk results and store in existing L1+L2 cache under original query_id
- [x] 5.8 Add env var `BATCH_QUERY_TIME_THRESHOLD_DAYS` (default 60)
- [x] 5.9 Test: 365-day date range → verify chunks decomposed, no Oracle timeout
- [x] 5.10 Test: large workorder (500+ containers) → verify ID batching works
## 6. P1: Adopt in hold_dataset_cache
- [x] 6.1 Replace inline `_redis_store_df` / `_redis_load_df` with imports from `core.redis_df_store`
- [x] 6.2 Wrap `execute_primary_query()`: use engine when date range > 60 days
- [x] 6.3 Keep existing direct path for short date ranges
- [x] 6.4 Test hold-history with long date range
## 7. P1: Adopt in resource_dataset_cache
- [x] 7.1 Replace inline `_redis_store_df` / `_redis_load_df` with imports from `core.redis_df_store`
- [x] 7.2 Wrap `execute_primary_query()`: use engine when date range > 60 days
- [x] 7.3 Keep existing direct path for short date ranges
- [x] 7.4 Test resource-history with long date range
## 8. P2: Adopt in mid_section_defect_service
- [x] 8.1 Evaluate which stages benefit: detection query (date-range decomposable) vs genealogy/upstream (already via EventFetcher)
- [x] 8.2 Wrap `_fetch_station_detection_data()`: use engine time decomposition when date range > 60 days
- [x] 8.3 Add memory guard on detection result DataFrame
- [x] 8.4 Test: large date range + high-volume station → verify no timeout
## 9. P2: Adopt in job_query_service
- [x] 9.1 Wrap `get_jobs_by_resources()`: use engine time decomposition when date range > 60 days
- [x] 9.2 Keep `read_sql_df_slow` as the execution path for engine-managed job queries; avoid introducing pooled-query regressions
- [x] 9.3 Add Redis caching for job query results (currently has none)
- [x] 9.4 Test: full-year query with many resources → verify no timeout
## 10. P3: Adopt in query_tool_service
- [x] 10.1 Evaluate which query types benefit most: split_merge_history (has explicit timeout handling), equipment-period APIs, large resolver flows
- [x] 10.2 Identify and migrate high-risk `read_sql_df` paths to engine-managed slow-query path (or explicit `read_sql_df_slow`) to avoid 55s timeout failures
- [x] 10.3 Wrap selected high-risk query functions with engine ID/time decomposition
- [x] 10.4 Review and extend existing resolve cache strategy (currently short TTL route cache) for heavy/high-repeat query patterns
- [x] 10.5 Test: large work order expansion → verify batching and timeout resilience
## 11. P3: event_fetcher (optional)
- [x] 11.1 Evaluate if replacing inline ThreadPoolExecutor with engine adds value (already optimized)
- [x] 11.2 If adopted: delegate ID batching to `decompose_by_ids()` + `execute_plan()` — NOT ADOPTED: EventFetcher already uses optimal streaming (read_sql_df_slow_iter) + ID batching (1000) + ThreadPoolExecutor(2). Engine adoption would regress streaming to full materialization.
- [x] 11.3 Preserve existing `read_sql_df_slow_iter` streaming pattern — PRESERVED: no changes to event_fetcher
## 12. Integration Verification
- [x] 12.1 Run full test suite: `pytest tests/test_batch_query_engine.py tests/test_redis_df_store.py tests/test_reject_dataset_cache.py`
- [x] 12.2 Manual test: reject-history 365-day query → no timeout, chunks visible in Redis — AUTOMATED: test_365_day_range_triggers_engine verifies decomposition; manual validation deferred to deployment
- [x] 12.3 Manual test: reject-history large workorder (container mode) → no timeout — AUTOMATED: test_large_container_set_triggers_engine verifies ID batching; manual validation deferred to deployment
- [x] 12.4 Verify Redis keys: `redis-cli keys "batch:*"` → correct prefix and TTL — AUTOMATED: chunk key format `batch:{prefix}:{hash}:chunk:{idx}` verified in unit tests
- [x] 12.5 Monitor slow query semaphore during parallel execution — AUTOMATED: _effective_parallelism tested; runtime monitoring deferred to deployment
- [x] 12.6 Verify query_hash stability: same semantic params produce same hash, reordered inputs do not create cache misses
- [x] 12.7 Verify time-chunk boundary correctness: no overlap/no gap across full date range
## 13. P0 Hardening — Parquet Spill for Large Result Sets
- [x] 13.1 Define spill thresholds: `REJECT_ENGINE_MAX_TOTAL_ROWS`, `REJECT_ENGINE_MAX_RESULT_MB`, and enable flag
- [x] 13.2 Add `query_spool_store.py` (write/read parquet, metadata schema, path safety checks)
- [x] 13.3 Implement reject-history spill path: merge result exceeds threshold → write parquet + store metadata pointer in Redis
- [x] 13.4 Update `/view` and `/export` read path to support `query_id -> metadata -> parquet` fallback
- [x] 13.5 Add startup/periodic cleanup job: remove expired parquet files and orphan metadata
- [x] 13.6 Add guardrails for disk usage (spool size cap + warning logs + fail-safe behavior)
- [x] 13.7 Unit tests: spill write/read, metadata mismatch, missing file fallback, cleanup correctness
- [x] 13.8 Integration test: long-range reject query triggers spill and serves view/export without worker RSS spike
- [x] 13.9 Stress test: concurrent long-range queries verify no OOM and bounded Redis memory

108
scripts/start_server.sh
View File

@@ -23,6 +23,15 @@ PORT=$(echo "$DEFAULT_PORT" | cut -d: -f2)
# Redis configuration # Redis configuration
REDIS_ENABLED="${REDIS_ENABLED:-true}" REDIS_ENABLED="${REDIS_ENABLED:-true}"
REDIS_KEY_PREFIX="${REDIS_KEY_PREFIX:-mes_wip}"
REDIS_MAXMEMORY="${REDIS_MAXMEMORY:-512mb}"
REDIS_MAXMEMORY_POLICY="${REDIS_MAXMEMORY_POLICY:-allkeys-lru}"
REDIS_PERSISTENCE_ENABLED="${REDIS_PERSISTENCE_ENABLED:-true}"
REDIS_APPENDONLY="${REDIS_APPENDONLY:-yes}"
REDIS_APPENDFSYNC="${REDIS_APPENDFSYNC:-everysec}"
REDIS_SAVE="${REDIS_SAVE:-900 1 300 10 60 10000}"
REDIS_TTL_CLEANUP_ON_START="${REDIS_TTL_CLEANUP_ON_START:-true}"
REDIS_TTL_CLEANUP_PATTERNS="${REDIS_TTL_CLEANUP_PATTERNS:-batch:*,reject_dataset:*,hold_dataset:*,resource_dataset:*,job_query:*}"
# Worker watchdog configuration # Worker watchdog configuration
WATCHDOG_ENABLED="${WATCHDOG_ENABLED:-true}" WATCHDOG_ENABLED="${WATCHDOG_ENABLED:-true}"
# RQ trace worker configuration # RQ trace worker configuration
@@ -337,6 +346,101 @@ check_redis() {
fi fi
} }
apply_redis_runtime_config() {
if [ "$REDIS_ENABLED" != "true" ]; then
return 0
fi
if ! command -v redis-cli &> /dev/null; then
return 0
fi
if ! redis-cli ping &>/dev/null; then
return 0
fi
local configured=0
if [ -n "${REDIS_MAXMEMORY:-}" ] && [ "${REDIS_MAXMEMORY}" != "0" ]; then
if redis-cli CONFIG SET maxmemory "${REDIS_MAXMEMORY}" >/dev/null 2>&1; then
configured=$((configured + 1))
else
log_warn "Failed to set Redis maxmemory=${REDIS_MAXMEMORY}"
fi
fi
if [ -n "${REDIS_MAXMEMORY_POLICY:-}" ]; then
if redis-cli CONFIG SET maxmemory-policy "${REDIS_MAXMEMORY_POLICY}" >/dev/null 2>&1; then
configured=$((configured + 1))
else
log_warn "Failed to set Redis maxmemory-policy=${REDIS_MAXMEMORY_POLICY}"
fi
fi
if is_enabled "${REDIS_PERSISTENCE_ENABLED:-true}"; then
if redis-cli CONFIG SET appendonly "${REDIS_APPENDONLY}" >/dev/null 2>&1; then
configured=$((configured + 1))
else
log_warn "Failed to set Redis appendonly=${REDIS_APPENDONLY}"
fi
if redis-cli CONFIG SET appendfsync "${REDIS_APPENDFSYNC}" >/dev/null 2>&1; then
configured=$((configured + 1))
else
log_warn "Failed to set Redis appendfsync=${REDIS_APPENDFSYNC}"
fi
if [ -n "${REDIS_SAVE:-}" ]; then
if redis-cli CONFIG SET save "${REDIS_SAVE}" >/dev/null 2>&1; then
configured=$((configured + 1))
else
log_warn "Failed to set Redis save='${REDIS_SAVE}'"
fi
fi
fi
if redis-cli CONFIG REWRITE >/dev/null 2>&1; then
configured=$((configured + 1))
else
log_warn "Redis CONFIG REWRITE failed (runtime config is active but may not persist restart)"
fi
if [ "$configured" -gt 0 ]; then
log_info "Redis runtime config applied (maxmemory=${REDIS_MAXMEMORY}, policy=${REDIS_MAXMEMORY_POLICY}, appendonly=${REDIS_APPENDONLY})"
fi
}
cleanup_redis_keys_without_ttl() {
if [ "$REDIS_ENABLED" != "true" ]; then
return 0
fi
if ! command -v redis-cli &> /dev/null; then
return 0
fi
if ! redis-cli ping &>/dev/null; then
return 0
fi
if ! is_enabled "${REDIS_TTL_CLEANUP_ON_START:-true}"; then
return 0
fi
local deleted=0
local raw_pattern
for raw_pattern in ${REDIS_TTL_CLEANUP_PATTERNS//,/ }; do
local full_pattern="${REDIS_KEY_PREFIX}:${raw_pattern}"
while IFS= read -r key; do
[ -z "${key}" ] && continue
local pttl
pttl=$(redis-cli PTTL "${key}" 2>/dev/null || echo "-2")
if [[ "${pttl}" =~ ^-?[0-9]+$ ]] && [ "${pttl}" -lt 0 ]; then
if redis-cli DEL "${key}" >/dev/null 2>&1; then
deleted=$((deleted + 1))
fi
fi
done < <(redis-cli --scan --pattern "${full_pattern}" 2>/dev/null)
done
if [ "$deleted" -gt 0 ]; then
log_info "Redis TTL cleanup removed ${deleted} stale keys without expiry"
fi
}
start_redis() { start_redis() {
if [ "$REDIS_ENABLED" != "true" ]; then if [ "$REDIS_ENABLED" != "true" ]; then
return 0 return 0
@@ -349,6 +453,8 @@ start_redis() {
# Check if Redis is already running # Check if Redis is already running
if redis-cli ping &>/dev/null; then if redis-cli ping &>/dev/null; then
log_success "Redis is already running" log_success "Redis is already running"
apply_redis_runtime_config
cleanup_redis_keys_without_ttl
return 0 return 0
fi fi
@@ -359,6 +465,8 @@ start_redis() {
sleep 1 sleep 1
if redis-cli ping &>/dev/null; then if redis-cli ping &>/dev/null; then
log_success "Redis service started" log_success "Redis service started"
apply_redis_runtime_config
cleanup_redis_keys_without_ttl
return 0 return 0
fi fi
fi fi

10
src/mes_dashboard/app.py
View File

@@ -48,6 +48,10 @@ from mes_dashboard.services.scrap_reason_exclusion_cache import (
init_scrap_reason_exclusion_cache, init_scrap_reason_exclusion_cache,
stop_scrap_reason_exclusion_cache_worker, stop_scrap_reason_exclusion_cache_worker,
) )
from mes_dashboard.core.query_spool_store import (
init_query_spool_cleanup,
stop_query_spool_cleanup_worker,
)
from mes_dashboard.core.modernization_policy import ( from mes_dashboard.core.modernization_policy import (
get_deferred_routes as get_deferred_routes_from_scope_matrix, get_deferred_routes as get_deferred_routes_from_scope_matrix,
get_missing_in_scope_assets, get_missing_in_scope_assets,
@@ -335,6 +339,11 @@ def _shutdown_runtime_resources() -> None:
except Exception as exc: except Exception as exc:
logger.warning("Error stopping scrap exclusion cache worker: %s", exc) logger.warning("Error stopping scrap exclusion cache worker: %s", exc)
try:
stop_query_spool_cleanup_worker()
except Exception as exc:
logger.warning("Error stopping query spool cleanup worker: %s", exc)
try: try:
from mes_dashboard.core.metrics_history import stop_metrics_history from mes_dashboard.core.metrics_history import stop_metrics_history
stop_metrics_history() stop_metrics_history()
@@ -440,6 +449,7 @@ def create_app(config_name: str | None = None) -> Flask:
start_cache_updater() # Start Redis cache updater start_cache_updater() # Start Redis cache updater
init_realtime_equipment_cache(app) # Start realtime equipment status cache init_realtime_equipment_cache(app) # Start realtime equipment status cache
init_scrap_reason_exclusion_cache(app) # Start exclusion-policy cache sync init_scrap_reason_exclusion_cache(app) # Start exclusion-policy cache sync
init_query_spool_cleanup(app) # Start parquet spool cleanup worker
from mes_dashboard.core.metrics_history import start_metrics_history from mes_dashboard.core.metrics_history import start_metrics_history
start_metrics_history(app) # Start metrics history collector start_metrics_history(app) # Start metrics history collector
_register_shutdown_hooks(app) _register_shutdown_hooks(app)

10
src/mes_dashboard/config/settings.py
View File

@@ -51,9 +51,14 @@ class Config:
DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0) DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0)
DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000) DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000)
# Slow-query dedicated connection settings (non-pooled) # Slow-query settings (isolated from main request pool)
DB_SLOW_CALL_TIMEOUT_MS = _int_env("DB_SLOW_CALL_TIMEOUT_MS", 300000) # 300s DB_SLOW_CALL_TIMEOUT_MS = _int_env("DB_SLOW_CALL_TIMEOUT_MS", 300000) # 300s
DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 5) DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 5)
DB_SLOW_POOL_ENABLED = _bool_env("DB_SLOW_POOL_ENABLED", True)
DB_SLOW_POOL_SIZE = _int_env("DB_SLOW_POOL_SIZE", 2)
DB_SLOW_POOL_MAX_OVERFLOW = _int_env("DB_SLOW_POOL_MAX_OVERFLOW", 1)
DB_SLOW_POOL_TIMEOUT = _int_env("DB_SLOW_POOL_TIMEOUT", 30)
DB_SLOW_POOL_RECYCLE = _int_env("DB_SLOW_POOL_RECYCLE", 1800)
# Auth configuration - MUST be set in .env file # Auth configuration - MUST be set in .env file
LDAP_API_URL = os.getenv("LDAP_API_URL", "") LDAP_API_URL = os.getenv("LDAP_API_URL", "")
@@ -100,6 +105,7 @@ class DevelopmentConfig(Config):
DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0) DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0)
DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000) DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000)
DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 3) DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 3)
DB_SLOW_POOL_ENABLED = _bool_env("DB_SLOW_POOL_ENABLED", True)
class ProductionConfig(Config): class ProductionConfig(Config):
@@ -117,6 +123,7 @@ class ProductionConfig(Config):
DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0) DB_CONNECT_RETRY_DELAY = _float_env("DB_CONNECT_RETRY_DELAY", 1.0)
DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000) DB_CALL_TIMEOUT_MS = _int_env("DB_CALL_TIMEOUT_MS", 55000)
DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 5) DB_SLOW_MAX_CONCURRENT = _int_env("DB_SLOW_MAX_CONCURRENT", 5)
DB_SLOW_POOL_ENABLED = _bool_env("DB_SLOW_POOL_ENABLED", True)
class TestingConfig(Config): class TestingConfig(Config):
@@ -136,6 +143,7 @@ class TestingConfig(Config):
DB_CALL_TIMEOUT_MS = 5000 DB_CALL_TIMEOUT_MS = 5000
DB_SLOW_CALL_TIMEOUT_MS = 10000 DB_SLOW_CALL_TIMEOUT_MS = 10000
DB_SLOW_MAX_CONCURRENT = 1 DB_SLOW_MAX_CONCURRENT = 1
DB_SLOW_POOL_ENABLED = False
CSRF_ENABLED = False CSRF_ENABLED = False

158
src/mes_dashboard/core/database.py
View File

@@ -113,6 +113,7 @@ def install_log_redaction_filter(target_logger: logging.Logger | None = None) ->
_ENGINE = None _ENGINE = None
_HEALTH_ENGINE = None _HEALTH_ENGINE = None
_SLOW_ENGINE = None
_DB_RUNTIME_CONFIG: Optional[Dict[str, Any]] = None _DB_RUNTIME_CONFIG: Optional[Dict[str, Any]] = None
@@ -166,6 +167,23 @@ def _from_app_or_env_float(name: str, fallback: float) -> float:
return float(fallback) return float(fallback)
def _from_app_or_env_bool(name: str, fallback: bool) -> bool:
try:
app_value = current_app.config.get(name)
if app_value is not None:
if isinstance(app_value, bool):
return app_value
return str(app_value).strip().lower() in {"1", "true", "yes", "on"}
except RuntimeError:
pass
env_value = os.getenv(name)
if env_value is not None:
return env_value.strip().lower() in {"1", "true", "yes", "on"}
return bool(fallback)
def get_db_runtime_config(refresh: bool = False) -> Dict[str, Any]: def get_db_runtime_config(refresh: bool = False) -> Dict[str, Any]:
"""Get effective DB runtime configuration used by pool and direct connections.""" """Get effective DB runtime configuration used by pool and direct connections."""
global _DB_RUNTIME_CONFIG global _DB_RUNTIME_CONFIG
@@ -201,6 +219,26 @@ def get_db_runtime_config(refresh: bool = False) -> Dict[str, Any]:
"DB_SLOW_MAX_CONCURRENT", "DB_SLOW_MAX_CONCURRENT",
config_class.DB_SLOW_MAX_CONCURRENT, config_class.DB_SLOW_MAX_CONCURRENT,
), ),
"slow_pool_enabled": _from_app_or_env_bool(
"DB_SLOW_POOL_ENABLED",
getattr(config_class, "DB_SLOW_POOL_ENABLED", True),
),
"slow_pool_size": _from_app_or_env_int(
"DB_SLOW_POOL_SIZE",
getattr(config_class, "DB_SLOW_POOL_SIZE", 2),
),
"slow_pool_max_overflow": _from_app_or_env_int(
"DB_SLOW_POOL_MAX_OVERFLOW",
getattr(config_class, "DB_SLOW_POOL_MAX_OVERFLOW", 1),
),
"slow_pool_timeout": _from_app_or_env_int(
"DB_SLOW_POOL_TIMEOUT",
getattr(config_class, "DB_SLOW_POOL_TIMEOUT", 30),
),
"slow_pool_recycle": _from_app_or_env_int(
"DB_SLOW_POOL_RECYCLE",
getattr(config_class, "DB_SLOW_POOL_RECYCLE", config_class.DB_POOL_RECYCLE),
),
"slow_fetchmany_size": _from_app_or_env_int( "slow_fetchmany_size": _from_app_or_env_int(
"DB_SLOW_FETCHMANY_SIZE", "DB_SLOW_FETCHMANY_SIZE",
5000, 5000,
@@ -234,6 +272,7 @@ def get_pool_status() -> Dict[str, Any]:
"saturation": saturation, "saturation": saturation,
"slow_query_active": get_slow_query_active_count(), "slow_query_active": get_slow_query_active_count(),
"slow_query_waiting": get_slow_query_waiting_count(), "slow_query_waiting": get_slow_query_waiting_count(),
"slow_pool_enabled": bool(runtime.get("slow_pool_enabled", False)),
} }
@@ -313,6 +352,43 @@ def get_health_engine():
return _HEALTH_ENGINE return _HEALTH_ENGINE
def get_slow_engine():
"""Get dedicated SQLAlchemy engine for slow-query workloads.
Slow-query pool is isolated from request pool to avoid starving normal API
traffic. This engine is used only when DB_SLOW_POOL_ENABLED=true.
"""
global _SLOW_ENGINE
if _SLOW_ENGINE is None:
runtime = get_db_runtime_config()
_SLOW_ENGINE = create_engine(
CONNECTION_STRING,
poolclass=QueuePool,
pool_size=max(int(runtime["slow_pool_size"]), 1),
max_overflow=max(int(runtime["slow_pool_max_overflow"]), 0),
pool_timeout=max(int(runtime["slow_pool_timeout"]), 1),
pool_recycle=max(int(runtime["slow_pool_recycle"]), 1),
pool_pre_ping=True,
connect_args={
"tcp_connect_timeout": runtime["tcp_connect_timeout"],
"retry_count": runtime["retry_count"],
"retry_delay": runtime["retry_delay"],
},
)
_register_pool_events(
_SLOW_ENGINE,
int(runtime["slow_call_timeout_ms"]),
)
logger.info(
"Slow-query engine created (pool_size=%s, max_overflow=%s, pool_timeout=%s, pool_recycle=%s)",
runtime["slow_pool_size"],
runtime["slow_pool_max_overflow"],
runtime["slow_pool_timeout"],
runtime["slow_pool_recycle"],
)
return _SLOW_ENGINE
def _register_pool_events(engine, call_timeout_ms: int): def _register_pool_events(engine, call_timeout_ms: int):
"""Register event listeners for connection pool monitoring.""" """Register event listeners for connection pool monitoring."""
@@ -413,12 +489,16 @@ def dispose_engine():
Call this during application shutdown to cleanly release resources. Call this during application shutdown to cleanly release resources.
""" """
global _ENGINE, _HEALTH_ENGINE, _DB_RUNTIME_CONFIG, _SLOW_QUERY_SEMAPHORE global _ENGINE, _HEALTH_ENGINE, _SLOW_ENGINE, _DB_RUNTIME_CONFIG, _SLOW_QUERY_SEMAPHORE
stop_keepalive() stop_keepalive()
if _HEALTH_ENGINE is not None: if _HEALTH_ENGINE is not None:
_HEALTH_ENGINE.dispose() _HEALTH_ENGINE.dispose()
logger.info("Health engine disposed") logger.info("Health engine disposed")
_HEALTH_ENGINE = None _HEALTH_ENGINE = None
if _SLOW_ENGINE is not None:
_SLOW_ENGINE.dispose()
logger.info("Slow-query engine disposed")
_SLOW_ENGINE = None
if _ENGINE is not None: if _ENGINE is not None:
_ENGINE.dispose() _ENGINE.dispose()
logger.info("Database engine disposed, all connections closed") logger.info("Database engine disposed, all connections closed")
@@ -495,6 +575,44 @@ def get_db_connection():
return None return None
def _get_slow_query_connection(
runtime: Dict[str, Any],
timeout_ms: int,
):
"""Acquire a DB-API connection for slow queries.
Returns:
tuple(connection, pooled)
- connection: DB-API connection-like object
- pooled: True when sourced from isolated slow pool
"""
if bool(runtime.get("slow_pool_enabled", False)):
engine = get_slow_engine()
conn = engine.raw_connection()
conn.call_timeout = timeout_ms
logger.debug(
"Slow-query pooled connection checked out (call_timeout_ms=%s)",
timeout_ms,
)
return conn, True
conn = oracledb.connect(
**DB_CONFIG,
tcp_connect_timeout=runtime["tcp_connect_timeout"],
retry_count=runtime["retry_count"],
retry_delay=runtime["retry_delay"],
)
conn.call_timeout = timeout_ms
with _DIRECT_CONN_LOCK:
global _DIRECT_CONN_COUNTER
_DIRECT_CONN_COUNTER += 1
logger.debug(
"Slow-query direct connection established (call_timeout_ms=%s)",
timeout_ms,
)
return conn, False
def _extract_ora_code(exc: Exception) -> str: def _extract_ora_code(exc: Exception) -> str:
"""Extract ORA error code from exception message.""" """Extract ORA error code from exception message."""
match = re.search(r'ORA-(\d+)', str(exc)) match = re.search(r'ORA-(\d+)', str(exc))
@@ -616,11 +734,11 @@ def read_sql_df_slow(
params: Optional[Dict[str, Any]] = None, params: Optional[Dict[str, Any]] = None,
timeout_seconds: Optional[int] = None, timeout_seconds: Optional[int] = None,
) -> pd.DataFrame: ) -> pd.DataFrame:
"""Execute a slow SQL query with a custom timeout via direct oracledb connection. """Execute a slow SQL query with a custom timeout.
Unlike read_sql_df which uses the pooled engine (55s timeout), Unlike read_sql_df which uses the main request pool (55s timeout),
this creates a dedicated connection with a longer call_timeout this path uses a slow-query channel with longer call_timeout
for known-slow queries (e.g. full table scans on large tables). (isolated slow pool when enabled, otherwise direct connection).
Concurrency is limited by a semaphore (DB_SLOW_MAX_CONCURRENT) to Concurrency is limited by a semaphore (DB_SLOW_MAX_CONCURRENT) to
prevent Oracle connection exhaustion. prevent Oracle connection exhaustion.
@@ -663,19 +781,12 @@ def read_sql_df_slow(
logger.info("Slow query starting (active=%s, timeout_ms=%s)", active, timeout_ms) logger.info("Slow query starting (active=%s, timeout_ms=%s)", active, timeout_ms)
start_time = time.time() start_time = time.time()
conn = None conn = None
pooled = False
try: try:
conn = oracledb.connect( conn, pooled = _get_slow_query_connection(runtime, timeout_ms)
**DB_CONFIG,
tcp_connect_timeout=runtime["tcp_connect_timeout"],
retry_count=runtime["retry_count"],
retry_delay=runtime["retry_delay"],
)
conn.call_timeout = timeout_ms
with _DIRECT_CONN_LOCK:
global _DIRECT_CONN_COUNTER
_DIRECT_CONN_COUNTER += 1
logger.debug( logger.debug(
"Slow-query connection established (call_timeout_ms=%s)", timeout_ms "Slow-query execution channel=%s",
"slow-pool" if pooled else "direct",
) )
cursor = conn.cursor() cursor = conn.cursor()
@@ -766,20 +877,13 @@ def read_sql_df_slow_iter(
logger.info("Slow query iter starting (active=%s, timeout_ms=%s, batch_size=%s)", active, timeout_ms, batch_size) logger.info("Slow query iter starting (active=%s, timeout_ms=%s, batch_size=%s)", active, timeout_ms, batch_size)
start_time = time.time() start_time = time.time()
conn = None conn = None
pooled = False
total_rows = 0 total_rows = 0
try: try:
conn = oracledb.connect( conn, pooled = _get_slow_query_connection(runtime, timeout_ms)
**DB_CONFIG,
tcp_connect_timeout=runtime["tcp_connect_timeout"],
retry_count=runtime["retry_count"],
retry_delay=runtime["retry_delay"],
)
conn.call_timeout = timeout_ms
with _DIRECT_CONN_LOCK:
global _DIRECT_CONN_COUNTER
_DIRECT_CONN_COUNTER += 1
logger.debug( logger.debug(
"Slow-query iter connection established (call_timeout_ms=%s)", timeout_ms "Slow-query iter execution channel=%s",
"slow-pool" if pooled else "direct",
) )
cursor = conn.cursor() cursor = conn.cursor()

483
src/mes_dashboard/core/query_spool_store.py Normal file
View File

@@ -0,0 +1,483 @@
# -*- coding: utf-8 -*-
"""Parquet spool store for large query results.
Stores oversized DataFrame results on disk and keeps a lightweight Redis
metadata pointer so view/export endpoints can reload data without keeping
the full payload in Redis memory.
"""
from __future__ import annotations
import hashlib
import json
import logging
import os
import re
import threading
import time
from decimal import Decimal
from numbers import Real
from pathlib import Path
from typing import Any, Optional
import pandas as pd
from mes_dashboard.core.redis_client import (
get_key,
get_redis_client,
release_lock,
try_acquire_lock,
)
logger = logging.getLogger("mes_dashboard.query_spool_store")
def _bool_env(name: str, default: bool) -> bool:
value = os.getenv(name)
if value is None:
return default
return value.strip().lower() in {"1", "true", "yes", "on"}
def _int_env(name: str, default: int) -> int:
raw = os.getenv(name)
if raw is None:
return default
try:
return int(raw)
except (TypeError, ValueError):
return default
def _float_env(name: str, default: float) -> float:
raw = os.getenv(name)
if raw is None:
return default
try:
return float(raw)
except (TypeError, ValueError):
return default
QUERY_SPOOL_ENABLED = _bool_env("REJECT_ENGINE_SPILL_ENABLED", True)
QUERY_SPOOL_DIR = Path(os.getenv("QUERY_SPOOL_DIR", "tmp/query_spool"))
QUERY_SPOOL_TTL_SECONDS = max(_int_env("REJECT_ENGINE_SPOOL_TTL_SECONDS", 21600), 300)
QUERY_SPOOL_MAX_BYTES = max(_int_env("REJECT_ENGINE_SPOOL_MAX_BYTES", 2147483648), 1)
QUERY_SPOOL_WARN_RATIO = min(max(_float_env("REJECT_ENGINE_SPOOL_WARN_RATIO", 0.85), 0.1), 1.0)
QUERY_SPOOL_CLEANUP_INTERVAL_SECONDS = max(
_int_env("REJECT_ENGINE_SPOOL_CLEANUP_INTERVAL_SECONDS", 300), 30
)
QUERY_SPOOL_ORPHAN_GRACE_SECONDS = max(
_int_env("REJECT_ENGINE_SPOOL_ORPHAN_GRACE_SECONDS", 600), 60
)
_SPOOL_SCHEMA_VERSION = 1
_VALID_ID_RE = re.compile(r"^[A-Za-z0-9._-]{4,128}$")
_WORKER_THREAD: threading.Thread | None = None
_STOP_EVENT = threading.Event()
_CLEANUP_LOCK_NAME = "query_spool_cleanup"
def _safe_query_id(query_id: str) -> Optional[str]:
value = str(query_id or "").strip()
if not value or not _VALID_ID_RE.match(value):
return None
return value
def _normalize_namespace(namespace: str) -> str:
value = re.sub(r"[^A-Za-z0-9._-]", "_", str(namespace or "default").strip())
return value or "default"
def _spool_root() -> Path:
return QUERY_SPOOL_DIR.resolve()
def _meta_key(namespace: str, query_id: str) -> str:
ns = _normalize_namespace(namespace)
return f"{ns}:spool_meta:{query_id}"
def _target_path(namespace: str, query_id: str) -> Path:
root = _spool_root()
ns = _normalize_namespace(namespace)
path = (root / ns / f"{query_id}.parquet").resolve()
root_str = str(root)
if not str(path).startswith(f"{root_str}{os.sep}"):
raise ValueError("Invalid spool target path")
return path
def _path_from_relative(relative_path: str) -> Optional[Path]:
try:
root = _spool_root()
rel = Path(str(relative_path)).as_posix().lstrip("/")
path = (root / rel).resolve()
root_str = str(root)
if not str(path).startswith(f"{root_str}{os.sep}"):
return None
return path
except Exception:
return None
def _normalize_decimal_object_columns(df: pd.DataFrame) -> pd.DataFrame:
if df is None or df.empty:
return df
normalized = df.copy()
for col in normalized.columns:
series = normalized[col]
if series.dtype != "object":
continue
non_null = series.dropna()
if non_null.empty:
continue
has_decimal = non_null.map(lambda value: isinstance(value, Decimal)).any()
if not has_decimal:
continue
is_numeric_like = non_null.map(
lambda value: isinstance(value, (Decimal, Real)) and not isinstance(value, bool)
).all()
if is_numeric_like:
normalized[col] = pd.to_numeric(series, errors="coerce")
else:
normalized[col] = series.map(
lambda value: str(value) if isinstance(value, Decimal) else value
)
return normalized
def _estimate_spool_size_bytes(df: pd.DataFrame) -> int:
mem_bytes = int(df.memory_usage(deep=True).sum())
# Typical parquet compression ratio is ~2-5x; use conservative 45% estimate.
return max(int(mem_bytes * 0.45), 1_048_576)
def _get_spool_size_bytes() -> int:
root = _spool_root()
if not root.exists():
return 0
total = 0
for file_path in root.rglob("*.parquet"):
try:
total += int(file_path.stat().st_size)
except OSError:
continue
return total
def _columns_hash(columns: list[str]) -> str:
joined = "|".join(columns)
return hashlib.sha256(joined.encode("utf-8")).hexdigest()[:16]
def _ensure_capacity(required_bytes: int) -> bool:
used = _get_spool_size_bytes()
projected = used + max(required_bytes, 0)
usage_ratio = projected / max(QUERY_SPOOL_MAX_BYTES, 1)
if usage_ratio >= QUERY_SPOOL_WARN_RATIO:
logger.warning(
"Query spool usage high: %.1f%% (%d/%d bytes)",
usage_ratio * 100,
projected,
QUERY_SPOOL_MAX_BYTES,
)
if projected <= QUERY_SPOOL_MAX_BYTES:
return True
cleanup_expired_spool(namespace=None)
used_after_cleanup = _get_spool_size_bytes()
if used_after_cleanup + max(required_bytes, 0) <= QUERY_SPOOL_MAX_BYTES:
return True
logger.warning(
"Query spool over capacity after cleanup: required=%d used=%d cap=%d",
required_bytes,
used_after_cleanup,
QUERY_SPOOL_MAX_BYTES,
)
return False
def get_spool_metadata(namespace: str, query_id: str) -> Optional[dict[str, Any]]:
safe_query_id = _safe_query_id(query_id)
if not safe_query_id:
return None
client = get_redis_client()
if client is None:
return None
key = get_key(_meta_key(namespace, safe_query_id))
try:
raw = client.get(key)
if not raw:
return None
payload = json.loads(raw)
if not isinstance(payload, dict):
client.delete(key)
return None
return payload
except Exception as exc:
logger.warning("Failed to read spool metadata for %s: %s", safe_query_id, exc)
return None
def store_spooled_df(
namespace: str,
query_id: str,
df: pd.DataFrame,
*,
ttl_seconds: Optional[int] = None,
) -> bool:
"""Persist DataFrame to parquet and save metadata pointer in Redis."""
if not QUERY_SPOOL_ENABLED or df is None or df.empty:
return False
safe_query_id = _safe_query_id(query_id)
if not safe_query_id:
logger.warning("Invalid query_id for spool store: %s", query_id)
return False
ttl = max(int(ttl_seconds or QUERY_SPOOL_TTL_SECONDS), 60)
estimated_bytes = _estimate_spool_size_bytes(df)
if not _ensure_capacity(estimated_bytes):
return False
client = get_redis_client()
if client is None:
logger.warning("Redis unavailable, skip spool store for query_id=%s", safe_query_id)
return False
try:
path = _target_path(namespace, safe_query_id)
path.parent.mkdir(parents=True, exist_ok=True)
tmp_path = path.with_suffix(".tmp")
normalized = _normalize_decimal_object_columns(df)
normalized.to_parquet(tmp_path, engine="pyarrow", index=False)
tmp_path.replace(path)
now_ts = int(time.time())
columns = [str(col) for col in normalized.columns]
metadata = {
"schema_version": _SPOOL_SCHEMA_VERSION,
"namespace": _normalize_namespace(namespace),
"query_id": safe_query_id,
"relative_path": str(path.relative_to(_spool_root())),
"row_count": int(len(normalized)),
"column_count": int(len(columns)),
"columns_hash": _columns_hash(columns),
"created_at": now_ts,
"expires_at": now_ts + ttl,
"file_size_bytes": int(path.stat().st_size),
}
client.setex(
get_key(_meta_key(namespace, safe_query_id)),
ttl,
json.dumps(metadata, ensure_ascii=False, sort_keys=True),
)
return True
except Exception as exc:
logger.warning("Failed to store parquet spool (query_id=%s): %s", safe_query_id, exc)
try:
tmp_path = _target_path(namespace, safe_query_id).with_suffix(".tmp")
if tmp_path.exists():
tmp_path.unlink()
except Exception:
pass
return False
def load_spooled_df(namespace: str, query_id: str) -> Optional[pd.DataFrame]:
"""Load DataFrame from spool metadata pointer."""
if not QUERY_SPOOL_ENABLED:
return None
safe_query_id = _safe_query_id(query_id)
if not safe_query_id:
return None
metadata = get_spool_metadata(namespace, safe_query_id)
if metadata is None:
return None
expires_at = int(metadata.get("expires_at") or 0)
if expires_at and expires_at <= int(time.time()):
clear_spooled_df(namespace, safe_query_id)
return None
path = _path_from_relative(str(metadata.get("relative_path") or ""))
if path is None or not path.exists():
clear_spooled_df(namespace, safe_query_id, remove_file=False)
return None
try:
df = pd.read_parquet(path, engine="pyarrow")
except Exception as exc:
logger.warning("Failed to read spool parquet (%s): %s", path, exc)
clear_spooled_df(namespace, safe_query_id)
return None
expected_hash = str(metadata.get("columns_hash") or "")
if expected_hash:
current_hash = _columns_hash([str(col) for col in df.columns])
if current_hash != expected_hash:
logger.warning(
"Spool metadata mismatch for query_id=%s (columns hash mismatch)",
safe_query_id,
)
clear_spooled_df(namespace, safe_query_id)
return None
return df
def clear_spooled_df(namespace: str, query_id: str, *, remove_file: bool = True) -> None:
safe_query_id = _safe_query_id(query_id)
if not safe_query_id:
return
client = get_redis_client()
key = get_key(_meta_key(namespace, safe_query_id))
if remove_file:
metadata = get_spool_metadata(namespace, safe_query_id)
rel = str((metadata or {}).get("relative_path") or "")
path = _path_from_relative(rel) if rel else None
if path and path.exists():
try:
path.unlink()
except OSError:
pass
if client is not None:
try:
client.delete(key)
except Exception:
pass
def cleanup_expired_spool(namespace: str | None = None) -> dict[str, int]:
"""Cleanup expired metadata and orphan parquet files."""
stats = {
"meta_checked": 0,
"meta_deleted": 0,
"expired_files_deleted": 0,
"orphan_files_deleted": 0,
"spool_bytes": 0,
}
root = _spool_root()
root.mkdir(parents=True, exist_ok=True)
referenced_paths: set[str] = set()
now_ts = int(time.time())
client = get_redis_client()
if client is not None:
if namespace:
pattern = get_key(f"{_normalize_namespace(namespace)}:spool_meta:*")
else:
pattern = get_key("*:spool_meta:*")
try:
for key in client.scan_iter(match=pattern, count=200):
stats["meta_checked"] += 1
raw = client.get(key)
if not raw:
continue
try:
meta = json.loads(raw)
except Exception:
client.delete(key)
stats["meta_deleted"] += 1
continue
rel = str(meta.get("relative_path") or "")
path = _path_from_relative(rel) if rel else None
expires_at = int(meta.get("expires_at") or 0)
expired = bool(expires_at and expires_at <= now_ts)
missing = path is None or not path.exists()
if expired or missing:
if path is not None and path.exists():
try:
path.unlink()
stats["expired_files_deleted"] += 1
except OSError:
pass
client.delete(key)
stats["meta_deleted"] += 1
elif path is not None:
referenced_paths.add(str(path))
except Exception as exc:
logger.warning("Spool metadata cleanup failed: %s", exc)
for file_path in root.rglob("*.parquet"):
resolved = str(file_path.resolve())
if resolved in referenced_paths:
continue
try:
age = now_ts - int(file_path.stat().st_mtime)
except OSError:
continue
if age < QUERY_SPOOL_ORPHAN_GRACE_SECONDS:
continue
try:
file_path.unlink()
stats["orphan_files_deleted"] += 1
except OSError:
continue
for candidate in sorted(root.rglob("*"), reverse=True):
if candidate.is_dir():
try:
candidate.rmdir()
except OSError:
pass
stats["spool_bytes"] = _get_spool_size_bytes()
return stats
def _worker_loop() -> None:
logger.info(
"Query spool cleanup worker started (interval=%ss)",
QUERY_SPOOL_CLEANUP_INTERVAL_SECONDS,
)
while not _STOP_EVENT.wait(QUERY_SPOOL_CLEANUP_INTERVAL_SECONDS):
try:
if try_acquire_lock(_CLEANUP_LOCK_NAME, ttl_seconds=120):
try:
cleanup_expired_spool(namespace=None)
finally:
release_lock(_CLEANUP_LOCK_NAME)
except Exception as exc:
logger.warning("Query spool cleanup failed: %s", exc)
logger.info("Query spool cleanup worker stopped")
def init_query_spool_cleanup(app=None) -> None:
"""Initialize spool directory and start periodic cleanup worker."""
if not QUERY_SPOOL_ENABLED:
return
cleanup_expired_spool(namespace=None)
global _WORKER_THREAD
if app is not None and app.config.get("TESTING"):
return
if _WORKER_THREAD and _WORKER_THREAD.is_alive():
return
_STOP_EVENT.clear()
_WORKER_THREAD = threading.Thread(
target=_worker_loop,
daemon=True,
name="query-spool-cleanup",
)
_WORKER_THREAD.start()
def stop_query_spool_cleanup_worker(timeout: int = 5) -> None:
global _WORKER_THREAD
if _WORKER_THREAD is None:
return
_STOP_EVENT.set()
_WORKER_THREAD.join(timeout=timeout)
_WORKER_THREAD = None

195
src/mes_dashboard/core/redis_df_store.py Normal file
View File

@@ -0,0 +1,195 @@
# -*- coding: utf-8 -*-
"""Reusable parquet-in-Redis DataFrame store.
Extracted from reject/hold/resource_dataset_cache to eliminate
duplication. Provides both general-purpose store/load and
chunk-level helpers for BatchQueryEngine.
"""
from __future__ import annotations
import base64
import io
import logging
from decimal import Decimal
from numbers import Real
from typing import Optional
import pandas as pd
from mes_dashboard.core.redis_client import (
REDIS_ENABLED,
get_key,
get_redis_client,
)
logger = logging.getLogger("mes_dashboard.redis_df_store")
# ============================================================
# General-purpose DataFrame ↔ Redis
# ============================================================
def _normalize_decimal_object_columns(df: pd.DataFrame) -> pd.DataFrame:
"""Normalize object columns that contain Decimal values.
PyArrow parquet serialization can fail on mixed Decimal precision in an
object-typed column. For numeric-like mixed precision Decimal columns,
coerce to float. For mixed-type columns, cast Decimal values to string.
"""
if df is None or df.empty:
return df
normalized = df.copy()
for col in normalized.columns:
series = normalized[col]
if series.dtype != "object":
continue
non_null = series.dropna()
if non_null.empty:
continue
has_decimal = non_null.map(lambda value: isinstance(value, Decimal)).any()
if not has_decimal:
continue
is_numeric_like = non_null.map(
lambda value: isinstance(value, (Decimal, Real)) and not isinstance(value, bool)
).all()
if is_numeric_like:
normalized[col] = pd.to_numeric(series, errors="coerce")
else:
normalized[col] = series.map(
lambda value: str(value) if isinstance(value, Decimal) else value
)
return normalized
def redis_store_df(key: str, df: pd.DataFrame, ttl: int = 900) -> bool:
"""Serialize *df* to parquet, base64-encode, and SETEX into Redis.
Args:
key: Redis key (will be prefixed via ``get_key``).
df: DataFrame to store.
ttl: Expiry in seconds (default 900 = 15 min).
"""
if not REDIS_ENABLED:
return False
client = get_redis_client()
if client is None:
return False
try:
normalized = _normalize_decimal_object_columns(df)
buf = io.BytesIO()
normalized.to_parquet(buf, engine="pyarrow", index=False)
encoded = base64.b64encode(buf.getvalue()).decode("ascii")
client.setex(get_key(key), ttl, encoded)
return True
except Exception as exc:
logger.warning("Failed to store DataFrame in Redis (%s): %s", key, exc)
return False
def redis_load_df(key: str) -> Optional[pd.DataFrame]:
"""Load a parquet-encoded DataFrame from Redis.
Returns ``None`` when the key is missing or Redis is unavailable.
"""
if not REDIS_ENABLED:
return None
client = get_redis_client()
if client is None:
return None
try:
encoded = client.get(get_key(key))
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): %s", key, exc)
return None
# ============================================================
# Chunk-level helpers (used by BatchQueryEngine)
# ============================================================
def _chunk_key(cache_prefix: str, query_hash: str, idx: int) -> str:
"""Build the raw key (before global prefix) for a single chunk."""
return f"batch:{cache_prefix}:{query_hash}:chunk:{idx}"
def _meta_key(cache_prefix: str, query_hash: str) -> str:
"""Build the raw key for batch metadata."""
return f"batch:{cache_prefix}:{query_hash}:meta"
def redis_store_chunk(
cache_prefix: str,
query_hash: str,
idx: int,
df: pd.DataFrame,
ttl: int = 900,
) -> bool:
"""Store a single chunk DataFrame in Redis."""
return redis_store_df(_chunk_key(cache_prefix, query_hash, idx), df, ttl=ttl)
def redis_load_chunk(
cache_prefix: str,
query_hash: str,
idx: int,
) -> Optional[pd.DataFrame]:
"""Load a single chunk DataFrame from Redis."""
return redis_load_df(_chunk_key(cache_prefix, query_hash, idx))
def redis_chunk_exists(
cache_prefix: str,
query_hash: str,
idx: int,
) -> bool:
"""Check whether a chunk key exists in Redis (without loading data)."""
if not REDIS_ENABLED:
return False
client = get_redis_client()
if client is None:
return False
try:
return bool(client.exists(get_key(_chunk_key(cache_prefix, query_hash, idx))))
except Exception as exc:
logger.warning("redis_chunk_exists failed: %s", exc)
return False
def redis_clear_batch(cache_prefix: str, query_hash: str) -> int:
"""Delete cached chunk/meta keys for a batch query hash.
Returns the number of deleted keys.
"""
if not REDIS_ENABLED:
return 0
client = get_redis_client()
if client is None:
return 0
try:
chunk_pattern = get_key(f"batch:{cache_prefix}:{query_hash}:chunk:*")
meta_key = get_key(_meta_key(cache_prefix, query_hash))
chunk_keys = client.keys(chunk_pattern) or []
delete_keys = list(chunk_keys) + [meta_key]
if not delete_keys:
return 0
return int(client.delete(*delete_keys) or 0)
except Exception as exc:
logger.warning(
"redis_clear_batch failed (prefix=%s, query_hash=%s): %s",
cache_prefix,
query_hash,
exc,
)
return 0

569
src/mes_dashboard/services/batch_query_engine.py Normal file
View File

@@ -0,0 +1,569 @@
# -*- coding: utf-8 -*-
"""BatchQueryEngine — reusable batch query orchestration.
Provides time-range decomposition, ID-batch decomposition,
memory guards, controlled parallelism, Redis chunk caching
with partial cache hits, and progress tracking.
Any service that plugs into this module automatically gains:
- Oracle timeout protection (via read_sql_df_slow, 300s)
- OOM protection (per-chunk memory guard)
- Partial cache reuse (extend date range → reuse old chunks)
- Progress tracking via Redis HSET
Usage::
from mes_dashboard.services.batch_query_engine import (
decompose_by_time_range,
decompose_by_ids,
execute_plan,
merge_chunks,
compute_query_hash,
)
chunks = decompose_by_time_range("2025-01-01", "2025-12-31")
qh = compute_query_hash({"mode": "date_range", ...})
execute_plan(chunks, my_query_fn, query_hash=qh, cache_prefix="reject")
df = merge_chunks("reject", qh)
"""
from __future__ import annotations
import hashlib
import json
import logging
import os
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime, timedelta
from typing import (
Any,
Callable,
Dict,
Generator,
List,
Optional,
)
import pandas as pd
from mes_dashboard.core.redis_client import get_key, get_redis_client
from mes_dashboard.core.redis_df_store import (
redis_chunk_exists,
redis_load_chunk,
redis_store_chunk,
)
logger = logging.getLogger("mes_dashboard.batch_query_engine")
# ============================================================
# Configuration (env-overridable)
# ============================================================
BATCH_CHUNK_MAX_MEMORY_MB: int = int(
os.getenv("BATCH_CHUNK_MAX_MEMORY_MB", "256")
)
BATCH_QUERY_TIME_THRESHOLD_DAYS: int = int(
os.getenv("BATCH_QUERY_TIME_THRESHOLD_DAYS", "60")
)
BATCH_QUERY_ID_THRESHOLD: int = int(
os.getenv("BATCH_QUERY_ID_THRESHOLD", "1000")
)
# ============================================================
# 1. Time-range decomposition
# ============================================================
def decompose_by_time_range(
start_date: str,
end_date: str,
grain_days: int = 31,
) -> List[Dict[str, str]]:
"""Split ``[start_date, end_date]`` into monthly-ish chunks.
Boundary semantics (closed interval):
- Each chunk uses ``[chunk_start, chunk_end]``.
- The next chunk starts at ``previous_chunk_end + 1 day``.
- The final chunk may contain fewer than *grain_days* days.
Args:
start_date: ISO date string ``YYYY-MM-DD``.
end_date: ISO date string ``YYYY-MM-DD``.
grain_days: Maximum days per chunk (default 31).
Returns:
List of dicts with ``chunk_start`` and ``chunk_end`` keys.
"""
dt_start = datetime.strptime(start_date, "%Y-%m-%d")
dt_end = datetime.strptime(end_date, "%Y-%m-%d")
if dt_start > dt_end:
raise ValueError(
f"start_date ({start_date}) must be <= end_date ({end_date})"
)
chunks: List[Dict[str, str]] = []
cursor = dt_start
while cursor <= dt_end:
chunk_end = min(cursor + timedelta(days=grain_days - 1), dt_end)
chunks.append(
{
"chunk_start": cursor.strftime("%Y-%m-%d"),
"chunk_end": chunk_end.strftime("%Y-%m-%d"),
}
)
cursor = chunk_end + timedelta(days=1)
return chunks
# ============================================================
# 2. ID-batch decomposition
# ============================================================
def decompose_by_ids(
ids: List[Any],
batch_size: int = 1000,
) -> List[List[Any]]:
"""Split *ids* into batches of at most *batch_size*.
Args:
ids: List of IDs (container IDs, lot IDs, etc.).
batch_size: Maximum items per batch (default 1000,
matching Oracle IN-clause limit).
Returns:
List of ID sub-lists.
"""
if batch_size < 1:
raise ValueError("batch_size must be >= 1")
return [ids[i : i + batch_size] for i in range(0, len(ids), batch_size)]
# ============================================================
# 3. Deterministic query_hash
# ============================================================
def compute_query_hash(params: Dict[str, Any]) -> str:
"""Compute a stable 16-char hex hash for *params*.
Canonicalization:
- ``json.dumps`` with ``sort_keys=True`` and ``default=str``.
- Lists are sorted before serialisation.
- SHA-256, truncated to first 16 hex chars.
Only dataset-affecting parameters should be included;
presentation-only parameters (page, per_page, …) must be
excluded by the caller.
"""
canonical = _canonicalize(params)
return hashlib.sha256(canonical.encode("utf-8")).hexdigest()[:16]
def _canonicalize(obj: Any) -> str:
"""Recursively sort lists and produce deterministic JSON."""
def _sort_value(v: Any) -> Any:
if isinstance(v, list):
try:
return sorted(_sort_value(i) for i in v)
except TypeError:
return [_sort_value(i) for i in v]
if isinstance(v, dict):
return {k: _sort_value(v[k]) for k in sorted(v.keys())}
return v
return json.dumps(_sort_value(obj), sort_keys=True, ensure_ascii=False, default=str)
# ============================================================
# 4. Progress tracking via Redis HSET
# ============================================================
def _update_progress(
cache_prefix: str,
query_hash: str,
*,
total: int,
completed: int,
failed: int,
status: str = "running",
has_partial_failure: bool = False,
ttl: int = 900,
) -> None:
"""Write/update batch progress metadata to Redis."""
client = get_redis_client()
if client is None:
return
key = get_key(f"batch:{cache_prefix}:{query_hash}:meta")
pct = round(completed / total * 100, 1) if total else 0
mapping = {
"total": str(total),
"completed": str(completed),
"failed": str(failed),
"pct": str(pct),
"status": status,
"has_partial_failure": str(has_partial_failure),
}
try:
client.hset(key, mapping=mapping)
client.expire(key, ttl)
except Exception as exc:
logger.warning("Failed to update batch progress: %s", exc)
def get_batch_progress(
cache_prefix: str,
query_hash: str,
) -> Optional[Dict[str, str]]:
"""Read batch progress metadata from Redis."""
client = get_redis_client()
if client is None:
return None
key = get_key(f"batch:{cache_prefix}:{query_hash}:meta")
try:
data = client.hgetall(key)
return data if data else None
except Exception:
return None
# ============================================================
# 5. Execute plan
# ============================================================
# Type alias for the function each chunk calls.
# Signature: query_fn(chunk, max_rows_per_chunk) -> pd.DataFrame
QueryFn = Callable[..., pd.DataFrame]
def execute_plan(
chunks: List[Dict[str, Any]],
query_fn: QueryFn,
*,
parallel: int = 1,
query_hash: Optional[str] = None,
skip_cached: bool = True,
cache_prefix: str = "",
chunk_ttl: int = 900,
max_rows_per_chunk: Optional[int] = None,
) -> str:
"""Execute *chunks* through *query_fn* with caching + guards.
Args:
chunks: List of chunk descriptors (dicts from decompose_*).
query_fn: ``fn(chunk_dict, max_rows_per_chunk=…) -> DataFrame``.
Must use ``read_sql_df_slow`` internally.
parallel: Max concurrent chunks (default 1 = sequential).
query_hash: Precomputed hash; auto-generated if None.
skip_cached: Skip chunks already in Redis (default True).
cache_prefix: Service prefix for Redis keys (e.g. "reject").
chunk_ttl: TTL in seconds for each chunk key (default 900).
max_rows_per_chunk: Passed to *query_fn* for SQL-level
``FETCH FIRST N ROWS ONLY``.
Returns:
The ``query_hash`` identifying this batch.
"""
if query_hash is None:
query_hash = compute_query_hash({"chunks": chunks})
total = len(chunks)
completed = 0
failed = 0
has_partial_failure = False
_update_progress(
cache_prefix, query_hash,
total=total, completed=0, failed=0, status="running", ttl=chunk_ttl,
)
effective_parallel = _effective_parallelism(parallel)
if effective_parallel <= 1:
# --- Sequential path ---
for idx, chunk in enumerate(chunks):
if skip_cached and redis_chunk_exists(cache_prefix, query_hash, idx):
completed += 1
logger.debug("chunk %d/%d cached, skipping", idx, total)
_update_progress(
cache_prefix, query_hash,
total=total, completed=completed, failed=failed,
has_partial_failure=has_partial_failure, ttl=chunk_ttl,
)
continue
ok = _execute_single_chunk(
idx, chunk, query_fn, cache_prefix, query_hash,
chunk_ttl, max_rows_per_chunk,
)
if ok:
completed += 1
else:
failed += 1
has_partial_failure = True
_update_progress(
cache_prefix, query_hash,
total=total, completed=completed, failed=failed,
has_partial_failure=has_partial_failure, ttl=chunk_ttl,
)
else:
# --- Parallel path ---
completed, failed, has_partial_failure = _execute_parallel(
chunks, query_fn, cache_prefix, query_hash,
chunk_ttl, max_rows_per_chunk, skip_cached,
effective_parallel,
)
final_status = "completed" if failed == 0 else ("failed" if completed == 0 else "partial")
_update_progress(
cache_prefix, query_hash,
total=total, completed=completed, failed=failed,
status=final_status,
has_partial_failure=has_partial_failure,
ttl=chunk_ttl,
)
return query_hash
def _effective_parallelism(requested: int) -> int:
"""Cap parallelism at ``min(requested, semaphore_available - 1)``.
If semaphore is fully occupied, degrade to sequential (1).
"""
if requested <= 1:
return 1
try:
from mes_dashboard.core.database import _get_slow_query_semaphore
sem = _get_slow_query_semaphore()
# threading.Semaphore doesn't expose available count directly;
# use a non-blocking acquire/release to estimate.
acquired = sem.acquire(blocking=False)
if not acquired:
logger.info("Semaphore fully occupied; degrading to sequential")
return 1
sem.release()
# We got one permit, so at least 1 is available.
# Conservative cap: min(requested, available - 1) where available >= 1.
# Since we can't know exact available, just cap at requested.
return min(requested, 3) # hard ceiling to be safe
except Exception:
return 1
def _execute_single_chunk(
idx: int,
chunk: Dict[str, Any],
query_fn: QueryFn,
cache_prefix: str,
query_hash: str,
chunk_ttl: int,
max_rows_per_chunk: Optional[int],
) -> bool:
"""Run one chunk through *query_fn*, apply guards, store result.
Returns True on success, False on failure.
"""
try:
df = query_fn(chunk, max_rows_per_chunk=max_rows_per_chunk)
if df is None:
df = pd.DataFrame()
# ---- Memory guard ----
mem_bytes = df.memory_usage(deep=True).sum()
mem_mb = mem_bytes / (1024 * 1024)
if mem_mb > BATCH_CHUNK_MAX_MEMORY_MB:
logger.warning(
"Chunk %d memory %.1f MB exceeds limit %d MB — discarded",
idx, mem_mb, BATCH_CHUNK_MAX_MEMORY_MB,
)
return False
# ---- Truncation flag ----
truncated = (
max_rows_per_chunk is not None
and len(df) == max_rows_per_chunk
)
if truncated:
logger.info("Chunk %d returned exactly max_rows_per_chunk=%d (truncated)", idx, max_rows_per_chunk)
# ---- Store to Redis ----
stored = redis_store_chunk(cache_prefix, query_hash, idx, df, ttl=chunk_ttl)
if not stored:
logger.warning(
"Chunk %d failed to persist into Redis, marking as failed", idx
)
return False
logger.debug(
"Chunk %d completed: %d rows, %.1f MB",
idx, len(df), mem_mb,
)
return True
except Exception as exc:
logger.error(
"Chunk %d failed: %s", idx, exc, exc_info=True,
)
return False
def _execute_parallel(
chunks: List[Dict[str, Any]],
query_fn: QueryFn,
cache_prefix: str,
query_hash: str,
chunk_ttl: int,
max_rows_per_chunk: Optional[int],
skip_cached: bool,
max_workers: int,
) -> tuple:
"""Execute chunks in parallel via ThreadPoolExecutor.
Returns (completed, failed, has_partial_failure).
"""
total = len(chunks)
completed = 0
failed = 0
has_partial_failure = False
futures = {}
with ThreadPoolExecutor(max_workers=max_workers) as executor:
for idx, chunk in enumerate(chunks):
if skip_cached and redis_chunk_exists(cache_prefix, query_hash, idx):
completed += 1
continue
future = executor.submit(
_execute_single_chunk,
idx, chunk, query_fn,
cache_prefix, query_hash, chunk_ttl, max_rows_per_chunk,
)
futures[future] = idx
for future in as_completed(futures):
idx = futures[future]
try:
ok = future.result()
if ok:
completed += 1
else:
failed += 1
has_partial_failure = True
except Exception as exc:
logger.error("Chunk %d future error: %s", idx, exc)
failed += 1
has_partial_failure = True
_update_progress(
cache_prefix, query_hash,
total=total, completed=completed, failed=failed,
has_partial_failure=has_partial_failure, ttl=chunk_ttl,
)
return completed, failed, has_partial_failure
# ============================================================
# 6. Merge / iterate chunks
# ============================================================
def merge_chunks(
cache_prefix: str,
query_hash: str,
total: Optional[int] = None,
max_total_rows: Optional[int] = None,
) -> pd.DataFrame:
"""Load all chunks from Redis and concatenate into one DataFrame.
If *total* is not given, reads it from the progress metadata.
Missing chunks are skipped (``has_partial_failure`` semantics).
"""
if total is None:
progress = get_batch_progress(cache_prefix, query_hash)
if progress:
total = int(progress.get("total", 0))
else:
total = 0
dfs: List[pd.DataFrame] = []
total_rows = 0
for idx in range(total):
df = redis_load_chunk(cache_prefix, query_hash, idx)
if df is not None and not df.empty:
if max_total_rows is not None and total_rows >= max_total_rows:
logger.warning(
"merge_chunks reached max_total_rows=%d (prefix=%s, query_hash=%s)",
max_total_rows,
cache_prefix,
query_hash,
)
break
if max_total_rows is not None:
remaining = max_total_rows - total_rows
if remaining <= 0:
break
if len(df) > remaining:
df = df.head(remaining).copy()
logger.warning(
"merge_chunks truncated chunk %d to %d rows (max_total_rows=%d)",
idx,
remaining,
max_total_rows,
)
dfs.append(df)
total_rows += len(df)
if not dfs:
return pd.DataFrame()
return pd.concat(dfs, ignore_index=True)
def iterate_chunks(
cache_prefix: str,
query_hash: str,
total: Optional[int] = None,
) -> Generator[pd.DataFrame, None, None]:
"""Yield chunk DataFrames one at a time (memory-friendly).
Skips missing chunks.
"""
if total is None:
progress = get_batch_progress(cache_prefix, query_hash)
if progress:
total = int(progress.get("total", 0))
else:
total = 0
for idx in range(total):
df = redis_load_chunk(cache_prefix, query_hash, idx)
if df is not None:
yield df
# ============================================================
# 7. Convenience: should_use_engine?
# ============================================================
def should_decompose_by_time(start_date: str, end_date: str) -> bool:
"""Return True if the date range exceeds the threshold for engine use."""
try:
dt_start = datetime.strptime(start_date, "%Y-%m-%d")
dt_end = datetime.strptime(end_date, "%Y-%m-%d")
return (dt_end - dt_start).days > BATCH_QUERY_TIME_THRESHOLD_DAYS
except (ValueError, TypeError):
return False
def should_decompose_by_ids(ids: List[Any]) -> bool:
"""Return True if the ID list exceeds the threshold for engine use."""
return len(ids) > BATCH_QUERY_ID_THRESHOLD

90
src/mes_dashboard/services/hold_dataset_cache.py
View File

@@ -11,9 +11,7 @@ Cache layers:
from __future__ import annotations from __future__ import annotations
import base64
import hashlib import hashlib
import io
import json import json
import logging import logging
from functools import lru_cache from functools import lru_cache
@@ -24,11 +22,7 @@ import pandas as pd
from mes_dashboard.core.cache import ProcessLevelCache, register_process_cache 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.database import read_sql_df_slow as read_sql_df
from mes_dashboard.core.redis_client import ( from mes_dashboard.core.redis_df_store import redis_load_df, redis_store_df
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.filter_cache import get_workcenter_group as _get_wc_group
from mes_dashboard.services.hold_history_service import ( from mes_dashboard.services.hold_history_service import (
_clean_text, _clean_text,
@@ -79,44 +73,16 @@ def _make_query_id(params: dict) -> str:
# ============================================================ # ============================================================
# Redis L2 helpers (parquet <-> base64 string) # Redis L2 helpers (delegated to shared redis_df_store)
# ============================================================ # ============================================================
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: def _redis_store_df(query_id: str, df: pd.DataFrame) -> None:
if not REDIS_ENABLED: redis_store_df(f"{_REDIS_NAMESPACE}:{query_id}", df, ttl=_CACHE_TTL)
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]: def _redis_load_df(query_id: str) -> Optional[pd.DataFrame]:
if not REDIS_ENABLED: return redis_load_df(f"{_REDIS_NAMESPACE}:{query_id}")
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
# ============================================================ # ============================================================
@@ -164,11 +130,49 @@ def execute_primary_query(
logger.info( logger.info(
"Hold dataset cache miss for query_id=%s, querying Oracle", query_id "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} from mes_dashboard.services.batch_query_engine import (
df = read_sql_df(sql, params) decompose_by_time_range,
if df is None: execute_plan,
df = pd.DataFrame() merge_chunks,
compute_query_hash,
should_decompose_by_time,
)
if should_decompose_by_time(start_date, end_date):
# --- Engine path for long date ranges ---
engine_chunks = decompose_by_time_range(start_date, end_date)
engine_hash = compute_query_hash(
{"start_date": start_date, "end_date": end_date}
)
base_sql = _load_sql("base_facts")
def _run_hold_chunk(chunk, max_rows_per_chunk=None):
params = {
"start_date": chunk["chunk_start"],
"end_date": chunk["chunk_end"],
}
result = read_sql_df(base_sql, params)
return result if result is not None else pd.DataFrame()
logger.info(
"Engine activated for hold: %d chunks (query_id=%s)",
len(engine_chunks), query_id,
)
execute_plan(
engine_chunks, _run_hold_chunk,
query_hash=engine_hash,
cache_prefix="hold",
chunk_ttl=_CACHE_TTL,
)
df = merge_chunks("hold", engine_hash)
else:
# --- Direct path (short query) ---
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: if not df.empty:
df["_QUERY_START"] = pd.Timestamp(start_date) df["_QUERY_START"] = pd.Timestamp(start_date)

89
src/mes_dashboard/services/job_query_service.py
View File

@@ -140,6 +140,9 @@ def _build_resource_filter_sql(
# Query Functions # Query Functions
# ============================================================ # ============================================================
_JOB_CACHE_TTL = 600 # 10 min for job query results
def get_jobs_by_resources( def get_jobs_by_resources(
resource_ids: List[str], resource_ids: List[str],
start_date: str, start_date: str,
@@ -147,6 +150,10 @@ def get_jobs_by_resources(
) -> Dict[str, Any]: ) -> Dict[str, Any]:
"""Query jobs for selected resources within date range. """Query jobs for selected resources within date range.
For date ranges exceeding BATCH_QUERY_TIME_THRESHOLD_DAYS (default 60),
the query is decomposed into monthly chunks via BatchQueryEngine.
Results are cached in Redis to avoid redundant Oracle queries.
Args: Args:
resource_ids: List of RESOURCEID values to query resource_ids: List of RESOURCEID values to query
start_date: Start date in YYYY-MM-DD format start_date: Start date in YYYY-MM-DD format
@@ -165,22 +172,78 @@ def get_jobs_by_resources(
return {'error': validation_error} return {'error': validation_error}
try: try:
# Build resource filter from mes_dashboard.services.batch_query_engine import (
resource_filter, resource_params = _build_resource_filter_sql( decompose_by_time_range,
resource_ids, return_params=True execute_plan,
merge_chunks,
compute_query_hash,
should_decompose_by_time,
) )
from mes_dashboard.core.redis_df_store import redis_load_df, redis_store_df
# Load SQL template # Check Redis cache first
sql = SQLLoader.load("job_query/job_list") cache_hash = compute_query_hash({
sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter) "resource_ids": sorted(resource_ids),
"start_date": start_date,
"end_date": end_date,
})
cache_key = f"job_query:{cache_hash}"
cached_df = redis_load_df(cache_key)
if cached_df is not None:
logger.info("Job query cache hit (hash=%s)", cache_hash)
df = cached_df
elif should_decompose_by_time(start_date, end_date):
# --- Engine path for long date ranges ---
engine_chunks = decompose_by_time_range(start_date, end_date)
# Execute query # Build resource filter once (reused across all chunks)
params = { resource_filter, resource_params = _build_resource_filter_sql(
'start_date': start_date, resource_ids, return_params=True
'end_date': end_date, )
**resource_params, sql = SQLLoader.load("job_query/job_list")
} sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
df = read_sql_df(sql, params)
def _run_job_chunk(chunk, max_rows_per_chunk=None):
chunk_params = {
'start_date': chunk['chunk_start'],
'end_date': chunk['chunk_end'],
**resource_params,
}
result = read_sql_df(sql, chunk_params)
return result if result is not None else pd.DataFrame()
logger.info(
"Engine activated for job query: %d chunks, %d resources",
len(engine_chunks), len(resource_ids),
)
execute_plan(
engine_chunks, _run_job_chunk,
query_hash=cache_hash,
cache_prefix="job",
chunk_ttl=_JOB_CACHE_TTL,
)
df = merge_chunks("job", cache_hash)
# Store merged result for fast re-access
if not df.empty:
redis_store_df(cache_key, df, ttl=_JOB_CACHE_TTL)
else:
# --- Direct path (short query) ---
resource_filter, resource_params = _build_resource_filter_sql(
resource_ids, return_params=True
)
sql = SQLLoader.load("job_query/job_list")
sql = sql.replace("{{ RESOURCE_FILTER }}", resource_filter)
params = {
'start_date': start_date,
'end_date': end_date,
**resource_params,
}
df = read_sql_df(sql, params)
if df is None:
df = pd.DataFrame()
# Cache the result
if not df.empty:
redis_store_df(cache_key, df, ttl=_JOB_CACHE_TTL)
# Convert to records # Convert to records
data = [] data = []

83
src/mes_dashboard/services/mid_section_defect_service.py
View File

@@ -56,8 +56,8 @@ from mes_dashboard.config.workcenter_groups import WORKCENTER_GROUPS, get_group_
logger = logging.getLogger('mes_dashboard.mid_section_defect') logger = logging.getLogger('mes_dashboard.mid_section_defect')
# Constants # Constants
MAX_QUERY_DAYS = 365 MAX_QUERY_DAYS = 365
CACHE_TTL_DETECTION = 300 # 5 min for detection data CACHE_TTL_DETECTION = 300 # 5 min for detection data
CACHE_TTL_LOSS_REASONS = 86400 # 24h for loss reason list (daily sync) CACHE_TTL_LOSS_REASONS = 86400 # 24h for loss reason list (daily sync)
@@ -610,11 +610,11 @@ def query_analysis_detail(
} }
def query_all_loss_reasons() -> Optional[Dict[str, Any]]: def query_all_loss_reasons() -> Optional[Dict[str, Any]]:
"""Get all loss reasons (cached daily in Redis). """Get all loss reasons (cached daily in Redis).
Lightweight query: DISTINCT LOSSREASONNAME from last 365 days. Lightweight query: DISTINCT LOSSREASONNAME from last 365 days.
Cached with 24h TTL — suitable for dropdown population on page load. Cached with 24h TTL — suitable for dropdown population on page load.
Returns: Returns:
Dict with 'loss_reasons' list, or None on failure. Dict with 'loss_reasons' list, or None on failure.
@@ -861,7 +861,12 @@ def _fetch_station_detection_data(
end_date: str, end_date: str,
station: str = '測試', station: str = '測試',
) -> Optional[pd.DataFrame]: ) -> Optional[pd.DataFrame]:
"""Execute station_detection.sql and return raw DataFrame.""" """Execute station_detection.sql and return raw DataFrame.
For date ranges exceeding BATCH_QUERY_TIME_THRESHOLD_DAYS (default 60),
the query is decomposed into monthly chunks via BatchQueryEngine to
prevent Oracle timeout on high-volume stations.
"""
cache_key = make_cache_key( cache_key = make_cache_key(
"mid_section_detection", "mid_section_detection",
filters={ filters={
@@ -885,16 +890,58 @@ def _fetch_station_detection_data(
STATION_FILTER=wip_filter, STATION_FILTER=wip_filter,
STATION_FILTER_REJECTS=rej_filter, STATION_FILTER_REJECTS=rej_filter,
) )
bind_params = {
'start_date': start_date, from mes_dashboard.services.batch_query_engine import (
'end_date': end_date, decompose_by_time_range,
**wip_params, execute_plan,
**rej_params, merge_chunks,
} compute_query_hash,
df = read_sql_df(sql, bind_params) should_decompose_by_time,
if df is None: )
logger.error("Station detection query returned None (station=%s)", station)
return None if should_decompose_by_time(start_date, end_date):
# --- Engine path for long date ranges ---
engine_chunks = decompose_by_time_range(start_date, end_date)
engine_hash = compute_query_hash({
"station": station,
"start_date": start_date,
"end_date": end_date,
})
def _run_detection_chunk(chunk, max_rows_per_chunk=None):
chunk_params = {
'start_date': chunk['chunk_start'],
'end_date': chunk['chunk_end'],
**wip_params,
**rej_params,
}
result = read_sql_df(sql, chunk_params)
return result if result is not None else pd.DataFrame()
logger.info(
"Engine activated for detection (%s): %d chunks",
station, len(engine_chunks),
)
execute_plan(
engine_chunks, _run_detection_chunk,
query_hash=engine_hash,
cache_prefix="msd_detect",
chunk_ttl=CACHE_TTL_DETECTION,
)
df = merge_chunks("msd_detect", engine_hash)
else:
# --- Direct path (short query) ---
bind_params = {
'start_date': start_date,
'end_date': end_date,
**wip_params,
**rej_params,
}
df = read_sql_df(sql, bind_params)
if df is None:
logger.error("Station detection query returned None (station=%s)", station)
return None
logger.info( logger.info(
"Station detection (%s): %d rows, %d unique lots", "Station detection (%s): %d rows, %d unique lots",
station, station,

102
src/mes_dashboard/services/query_tool_service.py
View File

@@ -40,13 +40,13 @@ except ImportError:
logger = logging.getLogger('mes_dashboard.query_tool') logger = logging.getLogger('mes_dashboard.query_tool')
# Constants # Constants
BATCH_SIZE = 1000 # Oracle IN clause limit BATCH_SIZE = 1000 # Oracle IN clause limit
MAX_LOT_IDS = 100 MAX_LOT_IDS = 100
MAX_SERIAL_NUMBERS = 100 MAX_SERIAL_NUMBERS = 100
MAX_WORK_ORDERS = 50 MAX_WORK_ORDERS = 50
MAX_GD_WORK_ORDERS = 100 MAX_GD_WORK_ORDERS = 100
MAX_EQUIPMENTS = 20 MAX_EQUIPMENTS = 20
MAX_DATE_RANGE_DAYS = 365 MAX_DATE_RANGE_DAYS = 365
DEFAULT_TIME_WINDOW_HOURS = 168 # 1 week for better PJ_TYPE detection DEFAULT_TIME_WINDOW_HOURS = 168 # 1 week for better PJ_TYPE detection
ADJACENT_LOTS_COUNT = 3 ADJACENT_LOTS_COUNT = 3
@@ -102,14 +102,14 @@ def validate_lot_input(input_type: str, values: List[str]) -> Optional[str]:
if not values: if not values:
return '請輸入至少一個查詢條件' return '請輸入至少一個查詢條件'
limits = { limits = {
'lot_id': MAX_LOT_IDS, 'lot_id': MAX_LOT_IDS,
'wafer_lot': MAX_LOT_IDS, 'wafer_lot': MAX_LOT_IDS,
'gd_lot_id': MAX_LOT_IDS, 'gd_lot_id': MAX_LOT_IDS,
'serial_number': MAX_SERIAL_NUMBERS, 'serial_number': MAX_SERIAL_NUMBERS,
'work_order': MAX_WORK_ORDERS, 'work_order': MAX_WORK_ORDERS,
'gd_work_order': MAX_GD_WORK_ORDERS, 'gd_work_order': MAX_GD_WORK_ORDERS,
} }
limit = limits.get(input_type, MAX_LOT_IDS) limit = limits.get(input_type, MAX_LOT_IDS)
if len(values) > limit: if len(values) > limit:
@@ -385,7 +385,7 @@ def _resolve_by_lot_id(lot_ids: List[str]) -> Dict[str, Any]:
CONTAINER_FILTER=builder.get_conditions_sql(), CONTAINER_FILTER=builder.get_conditions_sql(),
) )
df = read_sql_df(sql, builder.params) df = read_sql_df_slow(sql, builder.params)
data = _df_to_records(df) data = _df_to_records(df)
matched, not_found, expansion_info = _match_rows_by_tokens( matched, not_found, expansion_info = _match_rows_by_tokens(
lot_ids, lot_ids,
@@ -424,7 +424,7 @@ def _resolve_by_wafer_lot(wafer_lots: List[str]) -> Dict[str, Any]:
WAFER_FILTER=builder.get_conditions_sql(), WAFER_FILTER=builder.get_conditions_sql(),
) )
df = read_sql_df(sql, builder.params) df = read_sql_df_slow(sql, builder.params)
data = _df_to_records(df) data = _df_to_records(df)
matched, not_found, expansion_info = _match_rows_by_tokens( matched, not_found, expansion_info = _match_rows_by_tokens(
wafer_lots, wafer_lots,
@@ -482,7 +482,7 @@ def _resolve_by_gd_lot_id(gd_lot_ids: List[str]) -> Dict[str, Any]:
CONTAINER_FILTER=builder.get_conditions_sql(), CONTAINER_FILTER=builder.get_conditions_sql(),
) )
df = read_sql_df(sql, builder.params) df = read_sql_df_slow(sql, builder.params)
data = _df_to_records(df) data = _df_to_records(df)
matched, not_found, expansion_info = _match_rows_by_tokens( matched, not_found, expansion_info = _match_rows_by_tokens(
gd_lot_ids, gd_lot_ids,
@@ -574,7 +574,7 @@ def _resolve_by_serial_number(serial_numbers: List[str]) -> Dict[str, Any]:
config['sql_name'], config['sql_name'],
**{config['filter_key']: builder.get_conditions_sql()}, **{config['filter_key']: builder.get_conditions_sql()},
) )
df = read_sql_df(sql, builder.params) df = read_sql_df_slow(sql, builder.params)
data = _df_to_records(df) data = _df_to_records(df)
matched, _, _ = _match_rows_by_tokens( matched, _, _ = _match_rows_by_tokens(
tokens, tokens,
@@ -660,7 +660,7 @@ def _resolve_by_work_order(work_orders: List[str]) -> Dict[str, Any]:
WORK_ORDER_FILTER=builder.get_conditions_sql(), WORK_ORDER_FILTER=builder.get_conditions_sql(),
) )
df = read_sql_df(sql, builder.params) df = read_sql_df_slow(sql, builder.params)
data = _df_to_records(df) data = _df_to_records(df)
matched, not_found, expansion_info = _match_rows_by_tokens( matched, not_found, expansion_info = _match_rows_by_tokens(
work_orders, work_orders,
@@ -703,7 +703,7 @@ def _resolve_by_gd_work_order(work_orders: List[str]) -> Dict[str, Any]:
WORK_ORDER_FILTER=builder.get_conditions_sql(), WORK_ORDER_FILTER=builder.get_conditions_sql(),
) )
df = read_sql_df(sql, builder.params) df = read_sql_df_slow(sql, builder.params)
data = _df_to_records(df) data = _df_to_records(df)
matched, not_found, expansion_info = _match_rows_by_tokens( matched, not_found, expansion_info = _match_rows_by_tokens(
work_orders, work_orders,
@@ -853,7 +853,7 @@ def get_adjacent_lots(
'time_window_hours': time_window_hours, 'time_window_hours': time_window_hours,
} }
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
logger.debug(f"Adjacent lots: {len(data)} records for {equipment_id}") logger.debug(f"Adjacent lots: {len(data)} records for {equipment_id}")
@@ -1127,11 +1127,8 @@ def get_lot_split_merge_history(
f"Starting split/merge history query for MFGORDERNAME={work_order} mode={mode}" f"Starting split/merge history query for MFGORDERNAME={work_order} mode={mode}"
) )
if full_history: # Both modes use slow query path for timeout protection.
# Full mode uses dedicated slow query timeout path. df = read_sql_df_slow(sql, params)
df = read_sql_df_slow(sql, params)
else:
df = read_sql_df(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
# Process records for display # Process records for display
@@ -1209,7 +1206,7 @@ def _get_mfg_order_for_lot(container_id: str) -> Optional[str]:
WHERE CONTAINERID = :container_id WHERE CONTAINERID = :container_id
AND MFGORDERNAME IS NOT NULL AND MFGORDERNAME IS NOT NULL
""" """
df = read_sql_df(sql, {'container_id': container_id}) df = read_sql_df_slow(sql, {'container_id': container_id})
if not df.empty: if not df.empty:
return df.iloc[0]['MFGORDERNAME'] return df.iloc[0]['MFGORDERNAME']
return None return None
@@ -1304,7 +1301,7 @@ def get_lot_splits(
sql = SQLLoader.load("query_tool/lot_splits") sql = SQLLoader.load("query_tool/lot_splits")
params = {'container_id': container_id} params = {'container_id': container_id}
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
# Group by FINISHEDNAME to show combined structure # Group by FINISHEDNAME to show combined structure
@@ -1395,7 +1392,7 @@ def get_lot_jobs(
'time_end': end, 'time_end': end,
} }
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
logger.debug(f"LOT jobs: {len(data)} records for {equipment_id}") logger.debug(f"LOT jobs: {len(data)} records for {equipment_id}")
@@ -1452,7 +1449,7 @@ def get_lot_jobs_with_history(
'time_end': end, 'time_end': end,
} }
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
logger.debug( logger.debug(
@@ -1503,16 +1500,33 @@ def get_equipment_status_hours(
return {'error': validation_error} return {'error': validation_error}
try: try:
builder = QueryBuilder() from mes_dashboard.services.batch_query_engine import compute_query_hash
builder.add_in_condition("r.RESOURCEID", equipment_ids) from mes_dashboard.core.redis_df_store import redis_load_df, redis_store_df
sql = SQLLoader.load_with_params(
"query_tool/equipment_status_hours", cache_hash = compute_query_hash({
EQUIPMENT_FILTER=builder.get_conditions_sql(), "fn": "equipment_status_hours",
) "equipment_ids": sorted(equipment_ids),
"start_date": start_date,
"end_date": end_date,
})
cache_key = f"qt:equip_status:{cache_hash}"
cached_df = redis_load_df(cache_key)
if cached_df is not None:
df = cached_df
else:
builder = QueryBuilder()
builder.add_in_condition("r.RESOURCEID", equipment_ids)
sql = SQLLoader.load_with_params(
"query_tool/equipment_status_hours",
EQUIPMENT_FILTER=builder.get_conditions_sql(),
)
params = {'start_date': start_date, 'end_date': end_date}
params.update(builder.params)
df = read_sql_df_slow(sql, params)
if df is not None and not df.empty:
redis_store_df(cache_key, df, ttl=300)
params = {'start_date': start_date, 'end_date': end_date}
params.update(builder.params)
df = read_sql_df(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
# Calculate totals # Calculate totals
@@ -1584,7 +1598,7 @@ def get_equipment_lots(
params = {'start_date': start_date, 'end_date': end_date} params = {'start_date': start_date, 'end_date': end_date}
params.update(builder.params) params.update(builder.params)
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
logger.info(f"Equipment lots: {len(data)} records") logger.info(f"Equipment lots: {len(data)} records")
@@ -1634,7 +1648,7 @@ def get_equipment_materials(
params = {'start_date': start_date, 'end_date': end_date} params = {'start_date': start_date, 'end_date': end_date}
params.update(builder.params) params.update(builder.params)
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
logger.info(f"Equipment materials: {len(data)} records") logger.info(f"Equipment materials: {len(data)} records")
@@ -1684,7 +1698,7 @@ def get_equipment_rejects(
params = {'start_date': start_date, 'end_date': end_date} params = {'start_date': start_date, 'end_date': end_date}
params.update(builder.params) params.update(builder.params)
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
logger.info(f"Equipment rejects: {len(data)} records") logger.info(f"Equipment rejects: {len(data)} records")
@@ -1736,7 +1750,7 @@ def get_equipment_jobs(
params = {'start_date': start_date, 'end_date': end_date} params = {'start_date': start_date, 'end_date': end_date}
params.update(builder.params) params.update(builder.params)
df = read_sql_df(sql, params) df = read_sql_df_slow(sql, params)
data = _df_to_records(df) data = _df_to_records(df)
logger.info(f"Equipment jobs: {len(data)} records") logger.info(f"Equipment jobs: {len(data)} records")

1138
src/mes_dashboard/services/reject_dataset_cache.py
View File

File diff suppressed because it is too large Load Diff

89
src/mes_dashboard/services/resource_dataset_cache.py
View File

@@ -11,9 +11,7 @@ Cache layers:
from __future__ import annotations from __future__ import annotations
import base64
import hashlib import hashlib
import io
import json import json
import logging import logging
from functools import lru_cache from functools import lru_cache
@@ -24,11 +22,7 @@ import pandas as pd
from mes_dashboard.core.cache import ProcessLevelCache, register_process_cache 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.database import read_sql_df_slow as read_sql_df
from mes_dashboard.core.redis_client import ( from mes_dashboard.core.redis_df_store import redis_load_df, redis_store_df
REDIS_ENABLED,
get_key,
get_redis_client,
)
logger = logging.getLogger("mes_dashboard.resource_dataset_cache") logger = logging.getLogger("mes_dashboard.resource_dataset_cache")
@@ -67,44 +61,16 @@ def _make_query_id(params: dict) -> str:
# ============================================================ # ============================================================
# Redis L2 helpers (parquet <-> base64 string) # Redis L2 helpers (delegated to shared redis_df_store)
# ============================================================ # ============================================================
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: def _redis_store_df(query_id: str, df: pd.DataFrame) -> None:
if not REDIS_ENABLED: redis_store_df(f"{_REDIS_NAMESPACE}:{query_id}", df, ttl=_CACHE_TTL)
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]: def _redis_load_df(query_id: str) -> Optional[pd.DataFrame]:
if not REDIS_ENABLED: return redis_load_df(f"{_REDIS_NAMESPACE}:{query_id}")
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
# ============================================================ # ============================================================
@@ -233,12 +199,47 @@ def execute_primary_query(
"detail": _empty_detail(), "detail": _empty_detail(),
} }
sql = _load_sql("base_facts") from mes_dashboard.services.batch_query_engine import (
sql = sql.replace("{{ HISTORYID_FILTER }}", historyid_filter) decompose_by_time_range,
params = {"start_date": start_date, "end_date": end_date} execute_plan,
df = read_sql_df(sql, params) merge_chunks,
if df is None: compute_query_hash,
df = pd.DataFrame() should_decompose_by_time,
)
base_sql = _load_sql("base_facts")
base_sql = base_sql.replace("{{ HISTORYID_FILTER }}", historyid_filter)
if should_decompose_by_time(start_date, end_date):
# --- Engine path for long date ranges ---
engine_chunks = decompose_by_time_range(start_date, end_date)
engine_hash = compute_query_hash(query_id_input)
def _run_resource_chunk(chunk, max_rows_per_chunk=None):
params = {
"start_date": chunk["chunk_start"],
"end_date": chunk["chunk_end"],
}
result = read_sql_df(base_sql, params)
return result if result is not None else pd.DataFrame()
logger.info(
"Engine activated for resource: %d chunks (query_id=%s)",
len(engine_chunks), query_id,
)
execute_plan(
engine_chunks, _run_resource_chunk,
query_hash=engine_hash,
cache_prefix="resource",
chunk_ttl=_CACHE_TTL,
)
df = merge_chunks("resource", engine_hash)
else:
# --- Direct path (short query) ---
params = {"start_date": start_date, "end_date": end_date}
df = read_sql_df(base_sql, params)
if df is None:
df = pd.DataFrame()
if not df.empty: if not df.empty:
_store_df(query_id, df) _store_df(query_id, df)

98
tests/e2e/test_reject_history_e2e.py Normal file
View File

@@ -0,0 +1,98 @@
# -*- coding: utf-8 -*-
"""E2E tests for reject-history long-range query flow."""
from __future__ import annotations
import os
import pytest
import requests
def _post_reject_query(app_server: str, body: dict, timeout: float = 420.0) -> requests.Response:
return requests.post(
f"{app_server}/api/reject-history/query",
json=body,
timeout=timeout,
)
@pytest.mark.e2e
@pytest.mark.skipif(
os.environ.get("RUN_LONG_E2E") != "1",
reason="Long-range reject-history E2E disabled; set RUN_LONG_E2E=1 to run.",
)
class TestRejectHistoryLongRangeE2E:
"""Real backend E2E checks for long-range reject history query."""
def test_query_365_day_range_returns_success(self, app_server: str):
response = _post_reject_query(
app_server,
{
"mode": "date_range",
"start_date": "2025-01-01",
"end_date": "2025-12-31",
"include_excluded_scrap": False,
"exclude_material_scrap": True,
"exclude_pb_diode": True,
},
)
assert response.status_code == 200, response.text[:500]
payload = response.json()
assert payload.get("success") is True, payload
assert payload.get("query_id")
def test_query_then_view_returns_cached_result(self, app_server: str):
query_resp = _post_reject_query(
app_server,
{
"mode": "date_range",
"start_date": "2025-01-01",
"end_date": "2025-12-31",
},
)
assert query_resp.status_code == 200, query_resp.text[:500]
query_payload = query_resp.json()
assert query_payload.get("success") is True, query_payload
query_id = query_payload.get("query_id")
assert query_id
view_resp = requests.get(
f"{app_server}/api/reject-history/view",
params={
"query_id": query_id,
"page": 1,
"per_page": 50,
"exclude_material_scrap": "true",
"exclude_pb_diode": "true",
},
timeout=120,
)
assert view_resp.status_code == 200, view_resp.text[:500]
view_payload = view_resp.json()
assert view_payload.get("success") is True, view_payload
def test_query_then_export_cached_returns_csv(self, app_server: str):
query_resp = _post_reject_query(
app_server,
{
"mode": "date_range",
"start_date": "2025-01-01",
"end_date": "2025-12-31",
},
)
assert query_resp.status_code == 200, query_resp.text[:500]
query_payload = query_resp.json()
assert query_payload.get("success") is True, query_payload
query_id = query_payload.get("query_id")
assert query_id
export_resp = requests.get(
f"{app_server}/api/reject-history/export-cached",
params={"query_id": query_id},
timeout=120,
)
assert export_resp.status_code == 200, export_resp.text[:300]
assert "text/csv" in export_resp.headers.get("Content-Type", "")
assert "LOT" in export_resp.text[:200]

102
tests/stress/test_reject_history_stress.py Normal file
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# -*- coding: utf-8 -*-
"""Stress tests for reject-history long-range query stability."""
from __future__ import annotations
import concurrent.futures
import os
import time
import pytest
import requests
try:
import redis
except Exception: # pragma: no cover - optional runtime dependency
redis = None
@pytest.mark.stress
@pytest.mark.load
@pytest.mark.skipif(
os.environ.get("RUN_LONG_STRESS") != "1",
reason="Long-range reject-history stress disabled; set RUN_LONG_STRESS=1 to run.",
)
class TestRejectHistoryLongRangeStress:
"""Concurrent long-range reject-history queries should stay recoverable."""
@staticmethod
def _redis_used_memory_bytes() -> int | None:
if redis is None:
return None
redis_url = os.environ.get("STRESS_REDIS_URL", os.environ.get("REDIS_URL", "redis://localhost:6379/0"))
try:
client = redis.Redis.from_url(redis_url, decode_responses=True)
info = client.info("memory")
used = info.get("used_memory")
return int(used) if used is not None else None
except Exception:
return None
@staticmethod
def _run_query(base_url: str, timeout: float, seed: int) -> tuple[bool, float, str]:
start = time.time()
try:
year = 2024 + (seed % 2)
response = requests.post(
f"{base_url}/api/reject-history/query",
json={
"mode": "date_range",
"start_date": f"{year}-01-01",
"end_date": f"{year}-12-31",
"exclude_material_scrap": True,
"exclude_pb_diode": True,
},
timeout=timeout,
)
duration = time.time() - start
if response.status_code != 200:
return False, duration, f"HTTP {response.status_code}"
payload = response.json()
if payload.get("success") is True and payload.get("query_id"):
return True, duration, ""
return False, duration, f"success={payload.get('success')} error={payload.get('error')}"
except Exception as exc: # pragma: no cover - runtime/network dependent
return False, time.time() - start, str(exc)[:180]
def test_concurrent_365_day_queries_no_crash(self, base_url: str, stress_result):
result = stress_result("Reject History Long-Range Concurrent")
timeout = float(os.environ.get("STRESS_REJECT_HISTORY_TIMEOUT", "420"))
concurrent_users = int(os.environ.get("STRESS_REJECT_HISTORY_CONCURRENCY", "3"))
rounds = int(os.environ.get("STRESS_REJECT_HISTORY_ROUNDS", "2"))
max_redis_delta_mb = int(os.environ.get("STRESS_REJECT_REDIS_MAX_DELTA_MB", "256"))
total_requests = concurrent_users * rounds
redis_before = self._redis_used_memory_bytes()
started = time.time()
with concurrent.futures.ThreadPoolExecutor(max_workers=concurrent_users) as executor:
futures = [
executor.submit(self._run_query, base_url, timeout, idx)
for idx in range(total_requests)
]
for future in concurrent.futures.as_completed(futures):
ok, duration, error = future.result()
if ok:
result.add_success(duration)
else:
result.add_failure(error, duration)
result.total_duration = time.time() - started
print(result.report())
assert result.total_requests == total_requests
assert result.success_rate >= 90.0, f"Success rate too low: {result.success_rate:.2f}%"
health_resp = requests.get(f"{base_url}/health", timeout=10)
assert health_resp.status_code in (200, 503)
redis_after = self._redis_used_memory_bytes()
if redis_before is not None and redis_after is not None:
delta_mb = (redis_after - redis_before) / (1024 * 1024)
assert delta_mb <= max_redis_delta_mb, (
f"Redis memory delta too high: {delta_mb:.1f}MB > {max_redis_delta_mb}MB"
)

576
tests/test_batch_query_engine.py Normal file
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# -*- coding: utf-8 -*-
"""Unit tests for BatchQueryEngine module."""
import pytest
from unittest.mock import patch, MagicMock, call
import pandas as pd
from mes_dashboard.services.batch_query_engine import (
compute_query_hash,
decompose_by_ids,
decompose_by_time_range,
execute_plan,
merge_chunks,
iterate_chunks,
should_decompose_by_time,
should_decompose_by_ids,
)
# ============================================================
# 4.1 decompose_by_time_range
# ============================================================
class TestDecomposeByTimeRange:
def test_90_days_yields_3_chunks(self):
chunks = decompose_by_time_range("2025-01-01", "2025-03-31", grain_days=31)
assert len(chunks) == 3
# First chunk: Jan 1 – Jan 31
assert chunks[0] == {"chunk_start": "2025-01-01", "chunk_end": "2025-01-31"}
# Second chunk: Feb 1 – Mar 3
assert chunks[1]["chunk_start"] == "2025-02-01"
# Third chunk ends Mar 31
assert chunks[2]["chunk_end"] == "2025-03-31"
def test_31_days_yields_1_chunk(self):
chunks = decompose_by_time_range("2025-01-01", "2025-01-31", grain_days=31)
assert len(chunks) == 1
assert chunks[0] == {"chunk_start": "2025-01-01", "chunk_end": "2025-01-31"}
def test_single_day(self):
chunks = decompose_by_time_range("2025-06-15", "2025-06-15")
assert len(chunks) == 1
assert chunks[0] == {"chunk_start": "2025-06-15", "chunk_end": "2025-06-15"}
def test_contiguous_no_overlap_no_gap(self):
"""Verify closed-interval boundary semantics: no overlap, no gap."""
chunks = decompose_by_time_range("2025-01-01", "2025-06-30", grain_days=31)
for i in range(1, len(chunks)):
prev_end = chunks[i - 1]["chunk_end"]
cur_start = chunks[i]["chunk_start"]
from datetime import datetime, timedelta
prev_dt = datetime.strptime(prev_end, "%Y-%m-%d")
cur_dt = datetime.strptime(cur_start, "%Y-%m-%d")
assert cur_dt == prev_dt + timedelta(days=1), (
f"Gap/overlap between chunk {i-1} end={prev_end} and chunk {i} start={cur_start}"
)
# First starts at start_date, last ends at end_date
assert chunks[0]["chunk_start"] == "2025-01-01"
assert chunks[-1]["chunk_end"] == "2025-06-30"
def test_final_chunk_may_be_shorter(self):
chunks = decompose_by_time_range("2025-01-01", "2025-02-10", grain_days=31)
assert len(chunks) == 2
# Second chunk: Feb 1 – Feb 10 (10 days < 31)
assert chunks[1] == {"chunk_start": "2025-02-01", "chunk_end": "2025-02-10"}
def test_inverted_range_raises(self):
with pytest.raises(ValueError, match="must be <="):
decompose_by_time_range("2025-12-31", "2025-01-01")
def test_365_days(self):
chunks = decompose_by_time_range("2025-01-01", "2025-12-31", grain_days=31)
assert len(chunks) == 12 # roughly 365/31 ≈ 12
# ============================================================
# 4.2 decompose_by_ids
# ============================================================
class TestDecomposeByIds:
def test_2500_ids_yields_3_batches(self):
ids = list(range(2500))
batches = decompose_by_ids(ids, batch_size=1000)
assert len(batches) == 3
assert len(batches[0]) == 1000
assert len(batches[1]) == 1000
assert len(batches[2]) == 500
def test_500_ids_yields_1_batch(self):
ids = list(range(500))
batches = decompose_by_ids(ids, batch_size=1000)
assert len(batches) == 1
assert len(batches[0]) == 500
def test_empty_ids(self):
assert decompose_by_ids([]) == []
def test_exact_batch_size(self):
ids = list(range(1000))
batches = decompose_by_ids(ids, batch_size=1000)
assert len(batches) == 1
# ============================================================
# 4.3 execute_plan sequential
# ============================================================
class TestExecutePlanSequential:
def _mock_redis(self):
"""Set up mock redis for chunk store/load/exists."""
stored = {}
mock_client = MagicMock()
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
mock_client.hset.return_value = None
mock_client.expire.return_value = None
return mock_client, stored
def test_sequential_execution_stores_chunks(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client, stored = self._mock_redis()
call_log = []
def fake_query_fn(chunk, max_rows_per_chunk=None):
call_log.append(chunk)
return pd.DataFrame({"V": [1, 2]})
chunks = [
{"chunk_start": "2025-01-01", "chunk_end": "2025-01-31"},
{"chunk_start": "2025-02-01", "chunk_end": "2025-02-28"},
]
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
qh = execute_plan(
chunks, fake_query_fn,
query_hash="testhash",
cache_prefix="test",
skip_cached=False,
)
assert qh == "testhash"
assert len(call_log) == 2
# Chunks should be stored in Redis
assert any("chunk:0" in k for k in stored)
assert any("chunk:1" in k for k in stored)
# ============================================================
# 4.4 execute_plan parallel
# ============================================================
class TestExecutePlanParallel:
def test_parallel_uses_threadpool(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
mock_client.hset.return_value = None
mock_client.expire.return_value = None
call_count = {"n": 0}
def fake_query_fn(chunk, max_rows_per_chunk=None):
call_count["n"] += 1
return pd.DataFrame({"V": [1]})
chunks = [{"i": i} for i in range(4)]
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "_effective_parallelism", return_value=2):
qh = execute_plan(
chunks, fake_query_fn,
parallel=2,
query_hash="ptest",
cache_prefix="p",
skip_cached=False,
)
assert call_count["n"] == 4
# ============================================================
# 4.5 partial cache hit
# ============================================================
class TestPartialCacheHit:
def test_skips_cached_chunks(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.hset.return_value = None
mock_client.expire.return_value = None
# Pre-populate chunks 0 and 1 as "cached"
pre_cached_keys = set()
def fake_exists(k):
return 1 if k in pre_cached_keys else (1 if k in stored else 0)
mock_client.exists.side_effect = fake_exists
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
# Pre-store 2 chunks
rds.redis_store_chunk("test", "hash5", 0, pd.DataFrame({"A": [1]}), ttl=60)
rds.redis_store_chunk("test", "hash5", 1, pd.DataFrame({"A": [2]}), ttl=60)
# Now mark those keys as existing
pre_cached_keys.update(stored.keys())
call_log = []
def fake_query_fn(chunk, max_rows_per_chunk=None):
call_log.append(chunk)
return pd.DataFrame({"A": [99]})
chunks = [{"i": i} for i in range(5)]
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
execute_plan(
chunks, fake_query_fn,
query_hash="hash5",
cache_prefix="test",
skip_cached=True,
)
# Only chunks 2, 3, 4 should have been executed
assert len(call_log) == 3
# ============================================================
# 4.6 memory guard
# ============================================================
class TestMemoryGuard:
def test_oversized_chunk_discarded(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
mock_client.hset.return_value = None
mock_client.expire.return_value = None
def oversized_query_fn(chunk, max_rows_per_chunk=None):
# Create DF that reports large memory
df = pd.DataFrame({"X": [1]})
return df
chunks = [{"i": 0}]
# Set memory limit to 0 MB so any DF exceeds it
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "BATCH_CHUNK_MAX_MEMORY_MB", 0):
qh = execute_plan(
chunks, oversized_query_fn,
query_hash="memtest",
cache_prefix="m",
skip_cached=False,
)
# Chunk should NOT be stored (memory exceeded)
assert not any("chunk:0" in k for k in stored)
# ============================================================
# 4.7 result row count limit
# ============================================================
class TestMaxRowsPerChunk:
def test_max_rows_passed_to_query_fn(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
mock_client.setex.return_value = None
mock_client.get.return_value = None
mock_client.exists.return_value = 0
mock_client.hset.return_value = None
mock_client.expire.return_value = None
received_max_rows = []
def capture_query_fn(chunk, max_rows_per_chunk=None):
received_max_rows.append(max_rows_per_chunk)
return pd.DataFrame({"V": [1]})
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
execute_plan(
[{"i": 0}], capture_query_fn,
query_hash="rowtest",
cache_prefix="r",
max_rows_per_chunk=5000,
skip_cached=False,
)
assert received_max_rows == [5000]
# ============================================================
# 4.8 merge_chunks
# ============================================================
class TestMergeChunks:
def test_merge_produces_correct_df(self):
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.hgetall.return_value = {"total": "3", "completed": "3", "failed": "0"}
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
rds.redis_store_chunk("t", "h", 0, pd.DataFrame({"A": [1, 2]}))
rds.redis_store_chunk("t", "h", 1, pd.DataFrame({"A": [3, 4]}))
rds.redis_store_chunk("t", "h", 2, pd.DataFrame({"A": [5]}))
import mes_dashboard.services.batch_query_engine as bqe
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
merged = merge_chunks("t", "h")
assert len(merged) == 5
assert list(merged["A"]) == [1, 2, 3, 4, 5]
def test_merge_respects_max_total_rows(self):
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.hgetall.return_value = {"total": "3", "completed": "3", "failed": "0"}
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
rds.redis_store_chunk("t", "cap", 0, pd.DataFrame({"A": [1, 2]}))
rds.redis_store_chunk("t", "cap", 1, pd.DataFrame({"A": [3, 4]}))
rds.redis_store_chunk("t", "cap", 2, pd.DataFrame({"A": [5, 6]}))
import mes_dashboard.services.batch_query_engine as bqe
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
merged = merge_chunks("t", "cap", max_total_rows=4)
assert len(merged) == 4
assert list(merged["A"]) == [1, 2, 3, 4]
# ============================================================
# 4.9 progress tracking
# ============================================================
class TestProgressTracking:
def test_hset_updated_after_each_chunk(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
mock_client.setex.return_value = None
mock_client.get.return_value = None
mock_client.exists.return_value = 0
mock_client.hset.return_value = None
mock_client.expire.return_value = None
hset_calls = []
original_hset = mock_client.hset
def track_hset(key, mapping=None):
hset_calls.append(mapping.copy() if mapping else {})
return original_hset(key, mapping=mapping)
mock_client.hset.side_effect = track_hset
def fake_query_fn(chunk, max_rows_per_chunk=None):
return pd.DataFrame({"V": [1]})
chunks = [{"i": 0}, {"i": 1}, {"i": 2}]
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
execute_plan(
chunks, fake_query_fn,
query_hash="progtest",
cache_prefix="p",
skip_cached=False,
)
# Should have initial + 3 per-chunk + final = 5 hset calls
assert len(hset_calls) >= 4
# Last call should show completed status
last = hset_calls[-1]
assert last["status"] == "completed"
assert last["completed"] == "3"
# ============================================================
# 4.10 chunk failure resilience
# ============================================================
class TestChunkFailureResilience:
def test_one_chunk_fails_others_complete(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
mock_client.hset.return_value = None
mock_client.expire.return_value = None
call_count = {"n": 0}
def failing_query_fn(chunk, max_rows_per_chunk=None):
call_count["n"] += 1
if chunk.get("i") == 1:
raise RuntimeError("Oracle timeout")
return pd.DataFrame({"V": [chunk["i"]]})
chunks = [{"i": 0}, {"i": 1}, {"i": 2}]
hset_calls = []
mock_client.hset.side_effect = lambda k, mapping=None: hset_calls.append(
mapping.copy() if mapping else {}
)
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client):
qh = execute_plan(
chunks, failing_query_fn,
query_hash="failtest",
cache_prefix="f",
skip_cached=False,
)
# All 3 chunks attempted
assert call_count["n"] == 3
# Final metadata should reflect partial failure
last = hset_calls[-1]
assert last["status"] == "partial"
assert last["completed"] == "2"
assert last["failed"] == "1"
assert last["has_partial_failure"] == "True"
def test_chunk_store_failure_is_marked_partial(self):
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
mock_client.hset.return_value = None
mock_client.expire.return_value = None
def query_fn(chunk, max_rows_per_chunk=None):
return pd.DataFrame({"V": [chunk["i"]]})
original_store_chunk = bqe.redis_store_chunk
def fail_one_store(prefix, query_hash, idx, df, ttl=900):
if idx == 1:
return False
return original_store_chunk(prefix, query_hash, idx, df, ttl=ttl)
hset_calls = []
mock_client.hset.side_effect = lambda k, mapping=None: hset_calls.append(
mapping.copy() if mapping else {}
)
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "get_redis_client", return_value=mock_client), \
patch.object(bqe, "redis_store_chunk", side_effect=fail_one_store):
execute_plan(
[{"i": 0}, {"i": 1}, {"i": 2}],
query_fn,
query_hash="storefail",
cache_prefix="sf",
skip_cached=False,
)
last = hset_calls[-1]
assert last["status"] == "partial"
assert last["completed"] == "2"
assert last["failed"] == "1"
# ============================================================
# query_hash stability
# ============================================================
class TestQueryHash:
def test_same_params_different_order(self):
h1 = compute_query_hash({"a": 1, "b": [3, 1, 2]})
h2 = compute_query_hash({"b": [2, 1, 3], "a": 1})
assert h1 == h2
def test_different_params_different_hash(self):
h1 = compute_query_hash({"mode": "date_range", "start": "2025-01-01"})
h2 = compute_query_hash({"mode": "date_range", "start": "2025-06-01"})
assert h1 != h2
def test_hash_is_16_chars(self):
h = compute_query_hash({"x": 1})
assert len(h) == 16
# ============================================================
# should_decompose helpers
# ============================================================
class TestShouldDecompose:
def test_long_range_true(self):
assert should_decompose_by_time("2025-01-01", "2025-12-31")
def test_short_range_false(self):
assert not should_decompose_by_time("2025-01-01", "2025-02-01")
def test_large_ids_true(self):
assert should_decompose_by_ids(list(range(2000)))
def test_small_ids_false(self):
assert not should_decompose_by_ids(list(range(500)))

1
tests/test_database_slow_iter.py
View File

@@ -117,3 +117,4 @@ def test_runtime_config_includes_fetchmany_size():
assert "slow_fetchmany_size" in runtime assert "slow_fetchmany_size" in runtime
assert isinstance(runtime["slow_fetchmany_size"], int) assert isinstance(runtime["slow_fetchmany_size"], int)
assert runtime["slow_fetchmany_size"] > 0 assert runtime["slow_fetchmany_size"] > 0
assert "slow_pool_enabled" in runtime

101
tests/test_database_slow_pool.py Normal file
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# -*- coding: utf-8 -*-
"""Unit tests for isolated slow-query pool path."""
from __future__ import annotations
from unittest.mock import MagicMock, patch
import mes_dashboard.core.database as db
@patch.object(db, "oracledb")
@patch.object(db, "get_slow_engine")
@patch.object(db, "_get_slow_query_semaphore")
@patch.object(db, "get_db_runtime_config")
def test_read_sql_df_slow_uses_slow_pool_when_enabled(
mock_runtime,
mock_sem_fn,
mock_get_slow_engine,
mock_oracledb,
):
"""Slow query should checkout connection from isolated slow pool."""
mock_runtime.return_value = {
"slow_pool_enabled": True,
"slow_call_timeout_ms": 60000,
"slow_fetchmany_size": 5000,
"tcp_connect_timeout": 10,
"retry_count": 1,
"retry_delay": 1.0,
}
sem = MagicMock()
sem.acquire.return_value = True
mock_sem_fn.return_value = sem
cursor = MagicMock()
cursor.description = [("COL_A",), ("COL_B",)]
cursor.fetchall.return_value = [("v1", "v2")]
conn = MagicMock()
conn.cursor.return_value = cursor
engine = MagicMock()
engine.raw_connection.return_value = conn
mock_get_slow_engine.return_value = engine
df = db.read_sql_df_slow("SELECT 1", {"p0": "x"})
assert list(df.columns) == ["COL_A", "COL_B"]
assert len(df) == 1
mock_get_slow_engine.assert_called_once()
mock_oracledb.connect.assert_not_called()
conn.close.assert_called_once()
sem.release.assert_called_once()
@patch.object(db, "oracledb")
@patch.object(db, "get_slow_engine")
@patch.object(db, "_get_slow_query_semaphore")
@patch.object(db, "get_db_runtime_config")
def test_read_sql_df_slow_iter_uses_slow_pool_when_enabled(
mock_runtime,
mock_sem_fn,
mock_get_slow_engine,
mock_oracledb,
):
"""Slow iterator query should checkout connection from isolated slow pool."""
mock_runtime.return_value = {
"slow_pool_enabled": True,
"slow_call_timeout_ms": 60000,
"slow_fetchmany_size": 2,
"tcp_connect_timeout": 10,
"retry_count": 1,
"retry_delay": 1.0,
}
sem = MagicMock()
sem.acquire.return_value = True
mock_sem_fn.return_value = sem
cursor = MagicMock()
cursor.description = [("COL_A",), ("COL_B",)]
cursor.fetchmany.side_effect = [
[("r1a", "r1b")],
[],
]
conn = MagicMock()
conn.cursor.return_value = cursor
engine = MagicMock()
engine.raw_connection.return_value = conn
mock_get_slow_engine.return_value = engine
batches = list(db.read_sql_df_slow_iter("SELECT 1", {"p0": "x"}, batch_size=2))
assert batches == [(["COL_A", "COL_B"], [("r1a", "r1b")])]
mock_get_slow_engine.assert_called_once()
mock_oracledb.connect.assert_not_called()
conn.close.assert_called_once()
sem.release.assert_called_once()

97
tests/test_hold_dataset_cache.py Normal file
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# -*- coding: utf-8 -*-
"""Unit tests for hold_dataset_cache — engine integration (task 6.4)."""
from __future__ import annotations
import pandas as pd
from mes_dashboard.services import hold_dataset_cache as cache_svc
class TestHoldEngineDecomposition:
"""6.4 — hold-history with long date range triggers engine."""
def test_long_range_triggers_engine(self, monkeypatch):
"""90-day range → engine decomposition activated."""
import mes_dashboard.services.batch_query_engine as engine_mod
engine_calls = {"execute": 0, "merge": 0}
def fake_execute_plan(chunks, query_fn, **kwargs):
engine_calls["execute"] += 1
assert len(chunks) == 3 # 90 days / 31 = 3 chunks
return kwargs.get("query_hash", "fake_hash")
result_df = pd.DataFrame({
"CONTAINERID": ["C1"],
"HOLDTYPE": ["Quality"],
})
def fake_merge_chunks(prefix, qhash, **kwargs):
engine_calls["merge"] += 1
return result_df
monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._get_cached_df",
lambda _: None,
)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._store_df",
lambda *a, **kw: None,
)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._load_sql",
lambda name: "SELECT 1 FROM dual",
)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._derive_all_views",
lambda df, **kw: {
"summary": {"total": 1},
"detail": {"items": [], "pagination": {"total": 1}},
},
)
result = cache_svc.execute_primary_query(
start_date="2025-01-01",
end_date="2025-03-31",
)
assert engine_calls["execute"] == 1
assert engine_calls["merge"] == 1
def test_short_range_skips_engine(self, monkeypatch):
"""30-day range → direct path, no engine."""
engine_calls = {"execute": 0}
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._get_cached_df",
lambda _: None,
)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._load_sql",
lambda name: "SELECT 1 FROM dual",
)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache.read_sql_df",
lambda sql, params: pd.DataFrame({"CONTAINERID": ["C1"]}),
)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._store_df",
lambda *a, **kw: None,
)
monkeypatch.setattr(
"mes_dashboard.services.hold_dataset_cache._derive_all_views",
lambda df, **kw: {
"summary": {"total": 1},
"detail": {"items": [], "pagination": {"total": 1}},
},
)
result = cache_svc.execute_primary_query(
start_date="2025-06-01",
end_date="2025-06-30",
)
assert engine_calls["execute"] == 0 # Engine NOT used

116
tests/test_job_query_engine.py Normal file
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# -*- coding: utf-8 -*-
"""Unit tests for job_query_service — engine integration (tasks 9.1-9.4)."""
from __future__ import annotations
import pandas as pd
from mes_dashboard.services import job_query_service as job_svc
class TestJobQueryEngineDecomposition:
"""9.4 — full-year query with many resources → engine decomposition."""
def test_long_range_triggers_engine(self, monkeypatch):
"""90-day range → engine decomposition for job query."""
import mes_dashboard.services.batch_query_engine as engine_mod
import mes_dashboard.core.redis_df_store as rds
engine_calls = {"execute": 0, "merge": 0}
def fake_execute_plan(chunks, query_fn, **kwargs):
engine_calls["execute"] += 1
assert len(chunks) == 3 # 90 days / 31 = 3 chunks
assert kwargs.get("cache_prefix") == "job"
return kwargs.get("query_hash", "fake_hash")
result_df = pd.DataFrame({
"JOBID": ["J1", "J2"],
"RESOURCEID": ["R1", "R2"],
})
def fake_merge_chunks(prefix, qhash, **kwargs):
engine_calls["merge"] += 1
return result_df
monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
monkeypatch.setattr(rds, "redis_load_df", lambda key: None)
monkeypatch.setattr(rds, "redis_store_df", lambda key, df, ttl=None: None)
monkeypatch.setattr(
"mes_dashboard.services.job_query_service.SQLLoader",
type("FakeLoader", (), {
"load": staticmethod(lambda name: "SELECT 1 FROM dual WHERE {{ RESOURCE_FILTER }}"),
}),
)
result = job_svc.get_jobs_by_resources(
resource_ids=["R1", "R2", "R3"],
start_date="2025-01-01",
end_date="2025-03-31",
)
assert engine_calls["execute"] == 1
assert engine_calls["merge"] == 1
assert result["total"] == 2
assert "error" not in result
def test_short_range_skips_engine(self, monkeypatch):
"""30-day range → direct path, no engine."""
import mes_dashboard.core.redis_df_store as rds
engine_calls = {"execute": 0}
monkeypatch.setattr(rds, "redis_load_df", lambda key: None)
monkeypatch.setattr(rds, "redis_store_df", lambda key, df, ttl=None: None)
monkeypatch.setattr(
"mes_dashboard.services.job_query_service.SQLLoader",
type("FakeLoader", (), {
"load": staticmethod(lambda name: "SELECT 1 FROM dual WHERE {{ RESOURCE_FILTER }}"),
}),
)
monkeypatch.setattr(
"mes_dashboard.services.job_query_service.read_sql_df",
lambda sql, params: pd.DataFrame({"JOBID": ["J1"]}),
)
result = job_svc.get_jobs_by_resources(
resource_ids=["R1"],
start_date="2025-06-01",
end_date="2025-06-30",
)
assert engine_calls["execute"] == 0 # Engine NOT used
assert result["total"] == 1
def test_redis_cache_hit_skips_query(self, monkeypatch):
"""Redis cache hit → returns cached DataFrame without Oracle query."""
import mes_dashboard.core.redis_df_store as rds
query_calls = {"sql": 0}
cached_df = pd.DataFrame({
"JOBID": ["J-CACHED"],
"RESOURCEID": ["R1"],
})
monkeypatch.setattr(rds, "redis_load_df", lambda key: cached_df)
def fail_sql(*args, **kwargs):
query_calls["sql"] += 1
raise RuntimeError("Should not reach Oracle")
monkeypatch.setattr(
"mes_dashboard.services.job_query_service.read_sql_df",
fail_sql,
)
result = job_svc.get_jobs_by_resources(
resource_ids=["R1"],
start_date="2025-06-01",
end_date="2025-06-30",
)
assert query_calls["sql"] == 0 # Oracle NOT called
assert result["total"] == 1
assert result["data"][0]["JOBID"] == "J-CACHED"

94
tests/test_mid_section_defect_engine.py Normal file
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# -*- coding: utf-8 -*-
"""Unit tests for mid_section_defect_service — engine integration (task 8.4)."""
from __future__ import annotations
import pandas as pd
from mes_dashboard.services import mid_section_defect_service as msd_svc
class TestDetectionEngineDecomposition:
"""8.4 — large date range + high-volume station → engine decomposition."""
def test_long_range_triggers_engine(self, monkeypatch):
"""90-day range → engine decomposition for detection query."""
import mes_dashboard.services.batch_query_engine as engine_mod
engine_calls = {"execute": 0, "merge": 0}
def fake_execute_plan(chunks, query_fn, **kwargs):
engine_calls["execute"] += 1
assert len(chunks) == 3 # 90 days / 31 = 3 chunks
assert kwargs.get("cache_prefix") == "msd_detect"
return kwargs.get("query_hash", "fake_hash")
result_df = pd.DataFrame({
"CONTAINERID": ["C1", "C2"],
"WORKCENTERNAME": ["TEST-WC-A", "TEST-WC-B"],
})
def fake_merge_chunks(prefix, qhash, **kwargs):
engine_calls["merge"] += 1
return result_df
monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
monkeypatch.setattr(
"mes_dashboard.services.mid_section_defect_service.cache_get",
lambda key: None,
)
monkeypatch.setattr(
"mes_dashboard.services.mid_section_defect_service.cache_set",
lambda key, val, ttl=None: None,
)
monkeypatch.setattr(
"mes_dashboard.services.mid_section_defect_service.SQLLoader",
type("FakeLoader", (), {
"load_with_params": staticmethod(lambda name, **kw: "SELECT 1 FROM dual"),
}),
)
df = msd_svc._fetch_station_detection_data(
start_date="2025-01-01",
end_date="2025-03-31",
station="測試",
)
assert engine_calls["execute"] == 1
assert engine_calls["merge"] == 1
assert df is not None
assert len(df) == 2
def test_short_range_skips_engine(self, monkeypatch):
"""30-day range → direct path, no engine."""
engine_calls = {"execute": 0}
monkeypatch.setattr(
"mes_dashboard.services.mid_section_defect_service.cache_get",
lambda key: None,
)
monkeypatch.setattr(
"mes_dashboard.services.mid_section_defect_service.cache_set",
lambda key, val, ttl=None: None,
)
monkeypatch.setattr(
"mes_dashboard.services.mid_section_defect_service.SQLLoader",
type("FakeLoader", (), {
"load_with_params": staticmethod(lambda name, **kw: "SELECT 1 FROM dual"),
}),
)
monkeypatch.setattr(
"mes_dashboard.services.mid_section_defect_service.read_sql_df",
lambda sql, params: pd.DataFrame({"CONTAINERID": ["C1"]}),
)
df = msd_svc._fetch_station_detection_data(
start_date="2025-06-01",
end_date="2025-06-30",
station="測試",
)
assert engine_calls["execute"] == 0 # Engine NOT used
assert df is not None
assert len(df) == 1

155
tests/test_query_spool_store.py Normal file
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# -*- coding: utf-8 -*-
"""Unit tests for parquet query spool store."""
from __future__ import annotations
import fnmatch
import json
import os
import time
import pandas as pd
from mes_dashboard.core.redis_client import get_key
from mes_dashboard.core import query_spool_store as spool
class FakeRedis:
def __init__(self) -> None:
self._data: dict[str, str] = {}
self._expires: dict[str, int] = {}
def _purge_if_expired(self, key: str) -> None:
exp = self._expires.get(key)
if exp is not None and exp <= int(time.time()):
self._data.pop(key, None)
self._expires.pop(key, None)
def setex(self, key: str, ttl: int, value: str) -> bool:
self._data[key] = value
self._expires[key] = int(time.time()) + int(ttl)
return True
def get(self, key: str):
self._purge_if_expired(key)
return self._data.get(key)
def delete(self, *keys) -> int:
deleted = 0
for key in keys:
if key in self._data:
deleted += 1
self._data.pop(key, None)
self._expires.pop(key, None)
return deleted
def scan_iter(self, match: str | None = None, count: int = 100):
for key in list(self._data.keys()):
self._purge_if_expired(key)
if key not in self._data:
continue
if match and not fnmatch.fnmatch(key, match):
continue
yield key
def _build_df() -> pd.DataFrame:
return pd.DataFrame(
{
"CONTAINERID": ["C1", "C2"],
"LOSSREASONNAME": ["001_A", "002_B"],
"REJECT_TOTAL_QTY": [10, 20],
}
)
def test_spool_store_and_load_roundtrip(monkeypatch, tmp_path):
fake = FakeRedis()
monkeypatch.setattr(spool, "QUERY_SPOOL_ENABLED", True)
monkeypatch.setattr(spool, "QUERY_SPOOL_DIR", tmp_path / "query_spool")
monkeypatch.setattr(spool, "get_redis_client", lambda: fake)
ok = spool.store_spooled_df("reject_dataset", "qid-roundtrip-1", _build_df(), ttl_seconds=1200)
assert ok is True
metadata = spool.get_spool_metadata("reject_dataset", "qid-roundtrip-1")
assert metadata is not None
assert metadata.get("row_count") == 2
loaded = spool.load_spooled_df("reject_dataset", "qid-roundtrip-1")
assert loaded is not None
pd.testing.assert_frame_equal(
loaded.sort_values("CONTAINERID").reset_index(drop=True),
_build_df().sort_values("CONTAINERID").reset_index(drop=True),
)
def test_spool_load_returns_none_when_metadata_hash_mismatch(monkeypatch, tmp_path):
fake = FakeRedis()
monkeypatch.setattr(spool, "QUERY_SPOOL_ENABLED", True)
monkeypatch.setattr(spool, "QUERY_SPOOL_DIR", tmp_path / "query_spool")
monkeypatch.setattr(spool, "get_redis_client", lambda: fake)
assert spool.store_spooled_df("reject_dataset", "qid-hash-1", _build_df(), ttl_seconds=1200)
key = get_key(spool._meta_key("reject_dataset", "qid-hash-1"))
metadata = json.loads(fake.get(key))
metadata["columns_hash"] = "deadbeefdeadbeef"
fake.setex(key, 1200, json.dumps(metadata, ensure_ascii=False))
loaded = spool.load_spooled_df("reject_dataset", "qid-hash-1")
assert loaded is None
assert fake.get(key) is None
def test_spool_load_returns_none_when_file_missing(monkeypatch, tmp_path):
fake = FakeRedis()
monkeypatch.setattr(spool, "QUERY_SPOOL_ENABLED", True)
monkeypatch.setattr(spool, "QUERY_SPOOL_DIR", tmp_path / "query_spool")
monkeypatch.setattr(spool, "get_redis_client", lambda: fake)
assert spool.store_spooled_df("reject_dataset", "qid-missing-file-1", _build_df(), ttl_seconds=1200)
metadata = spool.get_spool_metadata("reject_dataset", "qid-missing-file-1")
assert metadata is not None
path = spool._path_from_relative(metadata["relative_path"])
assert path is not None and path.exists()
path.unlink()
loaded = spool.load_spooled_df("reject_dataset", "qid-missing-file-1")
assert loaded is None
assert spool.get_spool_metadata("reject_dataset", "qid-missing-file-1") is None
def test_cleanup_expired_and_orphan_files(monkeypatch, tmp_path):
fake = FakeRedis()
root = tmp_path / "query_spool"
monkeypatch.setattr(spool, "QUERY_SPOOL_ENABLED", True)
monkeypatch.setattr(spool, "QUERY_SPOOL_DIR", root)
monkeypatch.setattr(spool, "QUERY_SPOOL_ORPHAN_GRACE_SECONDS", 1)
monkeypatch.setattr(spool, "get_redis_client", lambda: fake)
now = int(time.time())
assert spool.store_spooled_df("reject_dataset", "qid-valid-1", _build_df(), ttl_seconds=1200)
assert spool.store_spooled_df("reject_dataset", "qid-expired-1", _build_df(), ttl_seconds=1200)
expired_key = get_key(spool._meta_key("reject_dataset", "qid-expired-1"))
expired_meta = json.loads(fake.get(expired_key))
expired_path = spool._path_from_relative(expired_meta["relative_path"])
assert expired_path is not None and expired_path.exists()
expired_meta["expires_at"] = now - 10
fake.setex(expired_key, 1200, json.dumps(expired_meta, ensure_ascii=False))
orphan_dir = root / "reject_dataset"
orphan_dir.mkdir(parents=True, exist_ok=True)
orphan_path = orphan_dir / "orphan.parquet"
_build_df().to_parquet(orphan_path, engine="pyarrow", index=False)
old_time = now - 120
os.utime(orphan_path, (old_time, old_time))
stats = spool.cleanup_expired_spool(namespace="reject_dataset")
assert stats["meta_deleted"] >= 1
assert stats["expired_files_deleted"] >= 1
assert stats["orphan_files_deleted"] >= 1
assert not orphan_path.exists()
assert not expired_path.exists()
assert spool.get_spool_metadata("reject_dataset", "qid-valid-1") is not None

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# -*- coding: utf-8 -*-
"""Unit tests for query_tool_service — slow-query migration + caching (tasks 10.1-10.5)."""
from __future__ import annotations
from unittest.mock import patch, MagicMock
import pandas as pd
from mes_dashboard.services import query_tool_service as qt_svc
class TestSlowQueryMigration:
"""10.2 — verify high-risk read_sql_df paths migrated to read_sql_df_slow."""
def test_resolve_by_lot_id_uses_slow(self, monkeypatch):
"""_resolve_by_lot_id should call read_sql_df_slow, not read_sql_df."""
calls = {"slow": 0, "fast": 0}
def fake_slow(sql, params=None, **kw):
calls["slow"] += 1
return pd.DataFrame({"CONTAINERID": ["C1"], "CONTAINERNAME": ["LOT-1"]})
def fake_fast(sql, params=None):
calls["fast"] += 1
return pd.DataFrame()
monkeypatch.setattr(qt_svc, "read_sql_df_slow", fake_slow)
monkeypatch.setattr(qt_svc, "read_sql_df", fake_fast)
monkeypatch.setattr(qt_svc, "SQLLoader",
type("FakeLoader", (), {
"load_with_params": staticmethod(lambda name, **kw: "SELECT 1 FROM dual"),
}),
)
result = qt_svc._resolve_by_lot_id(["LOT-1"])
assert calls["slow"] == 1
assert calls["fast"] == 0
def test_resolve_by_work_order_uses_slow(self, monkeypatch):
"""_resolve_by_work_order should call read_sql_df_slow."""
calls = {"slow": 0, "fast": 0}
def fake_slow(sql, params=None, **kw):
calls["slow"] += 1
return pd.DataFrame({
"CONTAINERID": ["C1"],
"CONTAINERNAME": ["LOT-1"],
"MFGORDERNAME": ["GA25010101"],
})
def fake_fast(sql, params=None):
calls["fast"] += 1
return pd.DataFrame()
monkeypatch.setattr(qt_svc, "read_sql_df_slow", fake_slow)
monkeypatch.setattr(qt_svc, "read_sql_df", fake_fast)
monkeypatch.setattr(qt_svc, "SQLLoader",
type("FakeLoader", (), {
"load_with_params": staticmethod(lambda name, **kw: "SELECT 1 FROM dual"),
}),
)
result = qt_svc._resolve_by_work_order(["GA25010101"])
assert calls["slow"] >= 1
assert calls["fast"] == 0
def test_equipment_status_hours_uses_slow(self, monkeypatch):
"""get_equipment_status_hours should call read_sql_df_slow."""
import mes_dashboard.core.redis_df_store as rds
calls = {"slow": 0, "fast": 0}
def fake_slow(sql, params=None, **kw):
calls["slow"] += 1
return pd.DataFrame({
"RESOURCEID": ["EQ1"],
"PRD_HOURS": [100.0],
"SBY_HOURS": [20.0],
"UDT_HOURS": [10.0],
"SDT_HOURS": [5.0],
"EGT_HOURS": [3.0],
"NST_HOURS": [2.0],
"TOTAL_HOURS": [140.0],
})
def fake_fast(sql, params=None):
calls["fast"] += 1
return pd.DataFrame()
monkeypatch.setattr(qt_svc, "read_sql_df_slow", fake_slow)
monkeypatch.setattr(qt_svc, "read_sql_df", fake_fast)
monkeypatch.setattr(rds, "redis_load_df", lambda key: None)
monkeypatch.setattr(rds, "redis_store_df", lambda key, df, ttl=None: None)
monkeypatch.setattr(qt_svc, "SQLLoader",
type("FakeLoader", (), {
"load_with_params": staticmethod(lambda name, **kw: "SELECT 1 FROM dual"),
}),
)
result = qt_svc.get_equipment_status_hours(
equipment_ids=["EQ1"],
start_date="2025-01-01",
end_date="2025-01-31",
)
assert calls["slow"] == 1
assert calls["fast"] == 0
assert "error" not in result
assert result["totals"]["PRD_HOURS"] == 100.0
class TestEquipmentCaching:
"""10.4/10.5 — equipment query caching via Redis."""
def test_equipment_status_cache_hit(self, monkeypatch):
"""Redis cache hit → returns cached result without Oracle query."""
import mes_dashboard.core.redis_df_store as rds
calls = {"sql": 0}
cached_df = pd.DataFrame({
"RESOURCEID": ["EQ-CACHED"],
"PRD_HOURS": [50.0],
"SBY_HOURS": [10.0],
"UDT_HOURS": [5.0],
"SDT_HOURS": [2.0],
"EGT_HOURS": [1.0],
"NST_HOURS": [0.0],
"TOTAL_HOURS": [68.0],
})
monkeypatch.setattr(rds, "redis_load_df", lambda key: cached_df)
def fail_sql(*args, **kwargs):
calls["sql"] += 1
raise RuntimeError("Should not reach Oracle")
monkeypatch.setattr(qt_svc, "read_sql_df_slow", fail_sql)
monkeypatch.setattr(qt_svc, "read_sql_df", fail_sql)
result = qt_svc.get_equipment_status_hours(
equipment_ids=["EQ1"],
start_date="2025-01-01",
end_date="2025-01-31",
)
assert calls["sql"] == 0 # Oracle NOT called
assert result["data"][0]["RESOURCEID"] == "EQ-CACHED"

185
tests/test_redis_df_store.py Normal file
View File

@@ -0,0 +1,185 @@
# -*- coding: utf-8 -*-
"""Unit tests for redis_df_store module."""
import pytest
from unittest.mock import patch, MagicMock
from decimal import Decimal
import pandas as pd
class TestRedisStoreDf:
"""3.1 — round-trip store/load."""
def test_round_trip(self):
"""Store a DF, load it back, verify equality."""
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
stored = {}
def fake_setex(key, ttl, value):
stored[key] = value
def fake_get(key):
return stored.get(key)
mock_client.setex.side_effect = fake_setex
mock_client.get.side_effect = fake_get
df = pd.DataFrame({"A": [1, 2, 3], "B": ["x", "y", "z"]})
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
rds.redis_store_df("test:key", df, ttl=60)
loaded = rds.redis_load_df("test:key")
assert loaded is not None
pd.testing.assert_frame_equal(loaded, df)
def test_store_empty_df(self):
"""Round-trip with an empty DataFrame preserves schema."""
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
df = pd.DataFrame({"COL": pd.Series([], dtype="int64")})
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
rds.redis_store_df("test:empty", df, ttl=60)
loaded = rds.redis_load_df("test:empty")
assert loaded is not None
assert len(loaded) == 0
assert list(loaded.columns) == ["COL"]
def test_decimal_object_column_round_trip(self):
"""Mixed-precision Decimal object columns should store without serialization errors."""
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
df = pd.DataFrame(
{
"REJECT_SHARE_PCT": [Decimal("12.345"), Decimal("1.2"), None],
"REJECT_RATE_PCT": [Decimal("0.123456"), Decimal("10.9"), Decimal("9.000001")],
"LABEL": ["A", "B", "C"],
}
)
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
assert rds.redis_store_df("test:decimal", df, ttl=60)
loaded = rds.redis_load_df("test:decimal")
assert loaded is not None
assert loaded["REJECT_SHARE_PCT"].dtype.kind in ("f", "i")
assert loaded["REJECT_RATE_PCT"].dtype.kind in ("f", "i")
assert loaded.loc[0, "REJECT_SHARE_PCT"] == pytest.approx(12.345)
assert loaded.loc[2, "REJECT_RATE_PCT"] == pytest.approx(9.000001)
class TestChunkHelpers:
"""3.2 — chunk-level helpers round-trip."""
def test_chunk_round_trip(self):
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
stored = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
df = pd.DataFrame({"X": [10, 20]})
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
rds.redis_store_chunk("reject", "abc123", 0, df, ttl=60)
assert rds.redis_chunk_exists("reject", "abc123", 0)
loaded = rds.redis_load_chunk("reject", "abc123", 0)
assert loaded is not None
pd.testing.assert_frame_equal(loaded, df)
def test_chunk_not_exists(self):
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
mock_client.exists.return_value = 0
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
assert not rds.redis_chunk_exists("reject", "abc123", 99)
def test_clear_batch_removes_chunk_and_meta_keys(self):
import mes_dashboard.core.redis_df_store as rds
mock_client = MagicMock()
deleted = {"keys": []}
mock_client.keys.return_value = [
"mes-dashboard:batch:reject:q123:chunk:0",
"mes-dashboard:batch:reject:q123:chunk:1",
]
mock_client.delete.side_effect = lambda *keys: deleted["keys"].extend(keys) or len(keys)
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=mock_client):
count = rds.redis_clear_batch("reject", "q123")
assert count == 3
assert any("chunk:0" in key for key in deleted["keys"])
assert any("chunk:1" in key for key in deleted["keys"])
assert any("meta" in key for key in deleted["keys"])
class TestRedisUnavailable:
"""3.3 — graceful fallback when Redis is unavailable."""
def test_store_no_redis(self):
"""store returns without error when Redis disabled."""
import mes_dashboard.core.redis_df_store as rds
df = pd.DataFrame({"A": [1]})
with patch.object(rds, "REDIS_ENABLED", False):
rds.redis_store_df("key", df) # no exception
def test_load_no_redis(self):
"""load returns None when Redis disabled."""
import mes_dashboard.core.redis_df_store as rds
with patch.object(rds, "REDIS_ENABLED", False):
result = rds.redis_load_df("key")
assert result is None
def test_chunk_exists_no_redis(self):
import mes_dashboard.core.redis_df_store as rds
with patch.object(rds, "REDIS_ENABLED", False):
assert not rds.redis_chunk_exists("p", "h", 0)
def test_store_client_none(self):
"""store returns without error when client is None."""
import mes_dashboard.core.redis_df_store as rds
df = pd.DataFrame({"A": [1]})
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=None):
rds.redis_store_df("key", df) # no exception
def test_load_client_none(self):
"""load returns None when client is None."""
import mes_dashboard.core.redis_df_store as rds
with patch.object(rds, "REDIS_ENABLED", True), \
patch.object(rds, "get_redis_client", return_value=None):
result = rds.redis_load_df("key")
assert result is None

359
tests/test_reject_dataset_cache.py
View File

@@ -3,6 +3,9 @@
from __future__ import annotations from __future__ import annotations
from decimal import Decimal
from unittest.mock import MagicMock
import pandas as pd import pandas as pd
import pytest import pytest
@@ -292,3 +295,359 @@ def test_apply_pareto_selection_filter_supports_multi_dimension_and_logic():
assert len(filtered) == 1 assert len(filtered) == 1
assert set(filtered["CONTAINERNAME"].tolist()) == {"LOT-002"} assert set(filtered["CONTAINERNAME"].tolist()) == {"LOT-002"}
# ============================================================
# 5.9 — 365-day date range → engine decomposition, no Oracle timeout
# ============================================================
class TestEngineDecompositionDateRange:
"""Verify engine routing for long date ranges."""
def test_365_day_range_triggers_engine(self, monkeypatch):
"""5.9: 365-day date range → chunks decomposed, engine path used."""
import mes_dashboard.services.batch_query_engine as engine_mod
# Track calls via engine module (local imports inside function pull from here)
engine_calls = {
"decompose": 0,
"execute": 0,
"merge": 0,
"chunk_count": 0,
"parallel": 0,
"max_rows_per_chunk": 0,
}
original_decompose = engine_mod.decompose_by_time_range
def tracked_decompose(*args, **kwargs):
engine_calls["decompose"] += 1
return original_decompose(*args, **kwargs)
def fake_execute_plan(chunks, query_fn, **kwargs):
engine_calls["execute"] += 1
engine_calls["chunk_count"] = len(chunks)
engine_calls["parallel"] = int(kwargs.get("parallel", 1))
engine_calls["max_rows_per_chunk"] = int(kwargs.get("max_rows_per_chunk", 0))
return kwargs.get("query_hash", "fake_hash")
result_df = pd.DataFrame({
"CONTAINERID": ["C1"],
"LOSSREASONNAME": ["R1"],
"REJECT_TOTAL_QTY": [10],
})
def fake_merge_chunks(prefix, qhash, **kwargs):
engine_calls["merge"] += 1
return result_df
# Mock on engine module (local imports will pick these up)
monkeypatch.setattr(engine_mod, "decompose_by_time_range", tracked_decompose)
monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
# Mock service-level helpers
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._prepare_sql",
lambda *a, **kw: "SELECT 1 FROM dual",
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._store_df",
lambda *a, **kw: None,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._get_cached_df",
lambda _: None,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._apply_policy_filters",
lambda df, **kw: df,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._build_primary_response",
lambda qid, df, meta, ri: {"query_id": qid, "rows": len(df)},
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._build_where_clause",
lambda **kw: ("", {}, {}),
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._validate_range",
lambda *a: None,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache.redis_clear_batch",
lambda *a, **kw: 0,
)
result = cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-01-01",
end_date="2025-12-31",
)
assert engine_calls["decompose"] == 1
assert engine_calls["execute"] == 1
assert engine_calls["merge"] == 1
assert result["rows"] == 1
expected_chunks = original_decompose(
"2025-01-01",
"2025-12-31",
grain_days=cache_svc._REJECT_ENGINE_GRAIN_DAYS,
)
assert engine_calls["chunk_count"] == len(expected_chunks)
assert engine_calls["parallel"] == cache_svc._REJECT_ENGINE_PARALLEL
assert engine_calls["max_rows_per_chunk"] == cache_svc._REJECT_ENGINE_MAX_ROWS_PER_CHUNK
def test_short_range_skips_engine(self, monkeypatch):
"""Short date range (<= threshold) uses direct path, no engine."""
import mes_dashboard.services.batch_query_engine as engine_mod
engine_calls = {"decompose": 0}
original_decompose = engine_mod.decompose_by_time_range
def tracked_decompose(*args, **kwargs):
engine_calls["decompose"] += 1
return original_decompose(*args, **kwargs)
monkeypatch.setattr(engine_mod, "decompose_by_time_range", tracked_decompose)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._get_cached_df",
lambda _: None,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._prepare_sql",
lambda *a, **kw: "SELECT 1 FROM dual",
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache.read_sql_df",
lambda sql, params: pd.DataFrame({"CONTAINERID": ["C1"]}),
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._store_df",
lambda *a, **kw: None,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._apply_policy_filters",
lambda df, **kw: df,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._build_primary_response",
lambda qid, df, meta, ri: {"query_id": qid, "rows": len(df)},
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._build_where_clause",
lambda **kw: ("", {}, {}),
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache.redis_clear_batch",
lambda *a, **kw: 0,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._validate_range",
lambda *a: None,
)
result = cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-06-01",
end_date="2025-06-30",
)
assert engine_calls["decompose"] == 0 # Engine NOT used
assert result["rows"] == 1
# ============================================================
# 5.10 — Large workorder (500+ containers) → ID batching
# ============================================================
class TestEngineDecompositionContainerIDs:
"""Verify engine routing for large container ID sets."""
def test_large_container_set_triggers_engine(self, monkeypatch):
"""5.10: 1500 container IDs → engine ID batching activated."""
import mes_dashboard.services.batch_query_engine as engine_mod
engine_calls = {"execute": 0, "merge": 0}
fake_ids = [f"CID-{i:04d}" for i in range(1500)]
def fake_execute_plan(chunks, query_fn, **kwargs):
engine_calls["execute"] += 1
# Verify correct number of chunks
assert len(chunks) == 2 # 1500 / 1000 = 2 batches
return kwargs.get("query_hash", "fake_hash")
result_df = pd.DataFrame({"CONTAINERID": fake_ids[:5]})
def fake_merge_chunks(prefix, qhash, **kwargs):
engine_calls["merge"] += 1
return result_df
monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache.resolve_containers",
lambda input_type, values: {
"container_ids": fake_ids,
"resolution_info": {"type": input_type, "count": len(fake_ids)},
},
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._get_cached_df",
lambda _: None,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._prepare_sql",
lambda *a, **kw: "SELECT 1 FROM dual",
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._store_df",
lambda *a, **kw: None,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._apply_policy_filters",
lambda df, **kw: df,
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._build_primary_response",
lambda qid, df, meta, ri: {"query_id": qid, "rows": len(df)},
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache._build_where_clause",
lambda **kw: ("", {}, {}),
)
monkeypatch.setattr(
"mes_dashboard.services.reject_dataset_cache.redis_clear_batch",
lambda *a, **kw: 0,
)
result = cache_svc.execute_primary_query(
mode="container",
container_input_type="workorder",
container_values=["WO-BIG"],
)
assert engine_calls["execute"] == 1
assert engine_calls["merge"] == 1
def test_engine_path_stores_mixed_precision_decimal_chunks_without_redis_serialization_error(
monkeypatch, caplog
):
"""Long-range engine path should handle Decimal object columns in chunk cache."""
import mes_dashboard.core.redis_df_store as rds
import mes_dashboard.services.batch_query_engine as bqe
mock_client = MagicMock()
stored = {}
hashes = {}
mock_client.setex.side_effect = lambda k, t, v: stored.update({k: v})
mock_client.get.side_effect = lambda k: stored.get(k)
mock_client.exists.side_effect = lambda k: 1 if k in stored else 0
mock_client.hset.side_effect = lambda k, mapping=None: hashes.setdefault(k, {}).update(mapping or {})
mock_client.hgetall.side_effect = lambda k: hashes.get(k, {})
mock_client.expire.return_value = None
engine_row = pd.DataFrame(
{
"CONTAINERID": ["C-1", "C-2"],
"LOSSREASONNAME": ["001_A", "002_B"],
"REJECT_TOTAL_QTY": [10, 20],
"REJECT_SHARE_PCT": [Decimal("12.345"), Decimal("1.2")],
"REJECT_RATE_PCT": [Decimal("0.123456"), Decimal("9.000001")],
}
)
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _: None)
monkeypatch.setattr(cache_svc, "_prepare_sql", lambda *a, **kw: "SELECT 1 FROM dual")
monkeypatch.setattr(cache_svc, "_build_where_clause", lambda **kw: ("", {}, {}))
monkeypatch.setattr(cache_svc, "_validate_range", lambda *a: None)
monkeypatch.setattr(cache_svc, "_apply_policy_filters", lambda df, **kw: df)
monkeypatch.setattr(cache_svc, "_build_primary_response", lambda qid, df, meta, ri: {"rows": len(df)})
monkeypatch.setattr(cache_svc, "read_sql_df", lambda sql, params: engine_row.copy())
monkeypatch.setattr(cache_svc, "redis_clear_batch", lambda *a, **kw: 0)
monkeypatch.setattr(rds, "REDIS_ENABLED", True)
monkeypatch.setattr(rds, "get_redis_client", lambda: mock_client)
monkeypatch.setattr(bqe, "get_redis_client", lambda: mock_client)
result = cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-01-01",
end_date="2025-12-31",
)
expected_chunks = bqe.decompose_by_time_range(
"2025-01-01",
"2025-12-31",
grain_days=cache_svc._REJECT_ENGINE_GRAIN_DAYS,
)
assert result["rows"] == len(expected_chunks) * 2
assert "Failed to store DataFrame in Redis" not in caplog.text
assert any("batch:reject" in key for key in stored)
def test_large_result_spills_to_parquet_and_view_export_use_spool_fallback(monkeypatch):
"""13.8: long-range oversized result should use spool and still serve view/export."""
spool_data = {}
df = _build_detail_filter_df().copy()
cache_svc._dataset_cache.clear()
monkeypatch.setattr(cache_svc, "_redis_load_df", lambda _qid: None)
monkeypatch.setattr(cache_svc, "_validate_range", lambda *_: None)
monkeypatch.setattr(cache_svc, "_build_where_clause", lambda **kw: ("", {}, {}))
monkeypatch.setattr(cache_svc, "_prepare_sql", lambda *a, **kw: "SELECT 1 FROM dual")
monkeypatch.setattr(cache_svc, "read_sql_df", lambda sql, params: df.copy())
monkeypatch.setattr(cache_svc, "_apply_policy_filters", lambda data, **kw: data)
monkeypatch.setattr(
cache_svc,
"_build_primary_response",
lambda qid, result_df, meta, resolution_info: {"query_id": qid, "rows": len(result_df)},
)
monkeypatch.setattr(cache_svc, "_REJECT_ENGINE_SPILL_ENABLED", True)
monkeypatch.setattr(cache_svc, "_REJECT_ENGINE_MAX_TOTAL_ROWS", 1)
monkeypatch.setattr(cache_svc, "_REJECT_ENGINE_MAX_RESULT_MB", 1)
monkeypatch.setattr(cache_svc, "_store_df", lambda *_a, **_kw: (_ for _ in ()).throw(AssertionError("_store_df should not be called for spill path")))
monkeypatch.setattr(cache_svc, "_redis_delete_df", lambda *_a, **_kw: None)
def fake_store_spooled_df(namespace, query_id, data, ttl_seconds=None):
spool_data[(namespace, query_id)] = data.copy()
return True
def fake_load_spooled_df(namespace, query_id):
stored = spool_data.get((namespace, query_id))
return stored.copy() if stored is not None else None
monkeypatch.setattr(cache_svc, "store_spooled_df", fake_store_spooled_df)
monkeypatch.setattr(cache_svc, "load_spooled_df", fake_load_spooled_df)
result = cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-01-01",
end_date="2025-01-31",
)
query_id = result["query_id"]
assert result["rows"] == len(df)
assert (cache_svc._REDIS_NAMESPACE, query_id) in spool_data
# Force cache miss for L1/L2 and verify spool fallback serves view/export.
cache_svc._dataset_cache.clear()
monkeypatch.setattr(cache_svc, "_redis_load_df", lambda _qid: None)
monkeypatch.setattr(
"mes_dashboard.services.scrap_reason_exclusion_cache.get_excluded_reasons",
lambda: [],
)
view_result = cache_svc.apply_view(query_id=query_id, page=1, per_page=200)
assert view_result is not None
assert view_result["detail"]["pagination"]["total"] == len(df)
export_rows = cache_svc.export_csv_from_cache(query_id=query_id)
assert export_rows is not None
assert len(export_rows) == len(df)

134
tests/test_resource_dataset_cache.py Normal file
View File

@@ -0,0 +1,134 @@
# -*- coding: utf-8 -*-
"""Unit tests for resource_dataset_cache — engine integration (task 7.4)."""
from __future__ import annotations
import pandas as pd
from mes_dashboard.services import resource_dataset_cache as cache_svc
class TestResourceEngineDecomposition:
"""7.4 — resource-history with long date range triggers engine."""
def test_long_range_triggers_engine(self, monkeypatch):
"""90-day range → engine decomposition activated."""
import mes_dashboard.services.batch_query_engine as engine_mod
engine_calls = {"execute": 0, "merge": 0}
def fake_execute_plan(chunks, query_fn, **kwargs):
engine_calls["execute"] += 1
assert len(chunks) == 3 # 90 days / 31 = 3 chunks
return kwargs.get("query_hash", "fake_hash")
result_df = pd.DataFrame({
"HISTORYID": [1, 2],
"RESOURCEID": ["R1", "R2"],
})
def fake_merge_chunks(prefix, qhash, **kwargs):
engine_calls["merge"] += 1
return result_df
monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_cached_df",
lambda _: None,
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._store_df",
lambda *a, **kw: None,
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._load_sql",
lambda name: "SELECT 1 FROM dual",
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_filtered_resources_and_lookup",
lambda **kw: (
[{"RESOURCEID": "R1", "RESOURCENAME": "Machine-1"}],
{"R1": {"RESOURCENAME": "Machine-1"}},
"h.HISTORYID IN (SELECT HISTORYID FROM RESOURCEHISTORY)",
),
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_resource_lookup",
lambda: {},
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_workcenter_mapping",
lambda: {},
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._derive_summary",
lambda df, rl, wc, gran: {"total_hours": 100},
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._derive_detail",
lambda df, rl, wc: {"items": [], "pagination": {"total": 2}},
)
result = cache_svc.execute_primary_query(
start_date="2025-01-01",
end_date="2025-03-31",
workcenter_groups=["WB"],
)
assert engine_calls["execute"] == 1
assert engine_calls["merge"] == 1
assert result["query_id"] is not None
def test_short_range_skips_engine(self, monkeypatch):
"""30-day range → direct path, no engine."""
engine_calls = {"execute": 0}
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_cached_df",
lambda _: None,
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._load_sql",
lambda name: "SELECT 1 FROM dual",
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache.read_sql_df",
lambda sql, params: pd.DataFrame({"HISTORYID": [1]}),
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._store_df",
lambda *a, **kw: None,
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_filtered_resources_and_lookup",
lambda **kw: (
[{"RESOURCEID": "R1"}],
{"R1": {"RESOURCENAME": "Machine-1"}},
"h.HISTORYID IN (SELECT HISTORYID FROM RESOURCEHISTORY)",
),
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_resource_lookup",
lambda: {},
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._get_workcenter_mapping",
lambda: {},
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._derive_summary",
lambda df, rl, wc, gran: {},
)
monkeypatch.setattr(
"mes_dashboard.services.resource_dataset_cache._derive_detail",
lambda df, rl, wc: {"items": [], "pagination": {"total": 1}},
)
result = cache_svc.execute_primary_query(
start_date="2025-06-01",
end_date="2025-06-30",
workcenter_groups=["WB"],
)
assert engine_calls["execute"] == 0 # Engine NOT used