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

tests/test_query_tool_engine.py

@@ -0,0 +1,151 @@
+# -*- 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"