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

tests/test_mid_section_defect_engine.py

@@ -0,0 +1,94 @@
+# -*- 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