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DashBoard/tests/test_reject_dataset_cache.py
egg e79fb657a3 feat(reject-history): add materialized Pareto aggregate layer with feature-flagged rollout 
Pre-compute 6-dimension metric cubes from cached LOT-level DataFrames so
interactive Pareto requests read compact snapshots instead of re-scanning
detail rows on every filter change. Includes single-flight build guard,
TTL/size guardrails, cross-filter exclude-self evaluation, safe legacy
fallback, response metadata exposure, telemetry counters, and a 3-stage
rollout plan (telemetry-only → build-enabled → read-through).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-04 08:05:02 +08:00

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# -*- coding: utf-8 -*-
"""Unit tests for reject_dataset_cache helpers."""
from __future__ import annotations
import json
from decimal import Decimal
from unittest.mock import MagicMock
import pandas as pd
import pytest
from mes_dashboard.services import reject_dataset_cache as cache_svc
def test_compute_dimension_pareto_applies_policy_filters_before_grouping(monkeypatch):
"""Cached pareto should honor the same policy toggles as view/query paths."""
df = pd.DataFrame(
[
{
"CONTAINERID": "C1",
"LOSSREASONNAME": "001_A",
"LOSSREASON_CODE": "001_A",
"SCRAP_OBJECTTYPE": "MATERIAL",
"PRODUCTLINENAME": "(NA)",
"WORKCENTER_GROUP": "WB",
"REJECT_TOTAL_QTY": 100,
"DEFECT_QTY": 0,
"MOVEIN_QTY": 1000,
},
{
"CONTAINERID": "C2",
"LOSSREASONNAME": "001_A",
"LOSSREASON_CODE": "001_A",
"SCRAP_OBJECTTYPE": "LOT",
"PRODUCTLINENAME": "PKG-A",
"WORKCENTER_GROUP": "WB",
"REJECT_TOTAL_QTY": 50,
"DEFECT_QTY": 0,
"MOVEIN_QTY": 900,
},
]
)
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
monkeypatch.setattr(
"mes_dashboard.services.scrap_reason_exclusion_cache.get_excluded_reasons",
lambda: [],
)
excluded_material = cache_svc.compute_dimension_pareto(
query_id="qid-1",
dimension="package",
pareto_scope="all",
include_excluded_scrap=False,
exclude_material_scrap=True,
exclude_pb_diode=True,
)
kept_all = cache_svc.compute_dimension_pareto(
query_id="qid-1",
dimension="package",
pareto_scope="all",
include_excluded_scrap=False,
exclude_material_scrap=False,
exclude_pb_diode=True,
)
excluded_labels = {item.get("reason") for item in excluded_material.get("items", [])}
all_labels = {item.get("reason") for item in kept_all.get("items", [])}
assert "PKG-A" in excluded_labels
assert "(NA)" not in excluded_labels
assert "(NA)" in all_labels
def _build_detail_filter_df():
return pd.DataFrame(
[
{
"CONTAINERID": "C1",
"CONTAINERNAME": "LOT-001",
"TXN_DAY": pd.Timestamp("2026-02-01"),
"TXN_TIME": pd.Timestamp("2026-02-01 08:00:00"),
"WORKCENTERSEQUENCE_GROUP": 1,
"WORKCENTER_GROUP": "WB",
"WORKCENTERNAME": "WB-A",
"SPECNAME": "SPEC-A",
"WORKFLOWNAME": "WF-A",
"PRIMARY_EQUIPMENTNAME": "EQ-1",
"EQUIPMENTNAME": "EQ-1",
"PRODUCTLINENAME": "PKG-A",
"PJ_TYPE": "TYPE-A",
"LOSSREASONNAME": "001_A",
"LOSSREASON_CODE": "001_A",
"SCRAP_OBJECTTYPE": "LOT",
"MOVEIN_QTY": 100,
"REJECT_TOTAL_QTY": 30,
"DEFECT_QTY": 0,
},
{
"CONTAINERID": "C2",
"CONTAINERNAME": "LOT-002",
"TXN_DAY": pd.Timestamp("2026-02-01"),
"TXN_TIME": pd.Timestamp("2026-02-01 09:00:00"),
"WORKCENTERSEQUENCE_GROUP": 1,
"WORKCENTER_GROUP": "WB",
"WORKCENTERNAME": "WB-B",
"SPECNAME": "SPEC-B",
"WORKFLOWNAME": "WF-B",
"PRIMARY_EQUIPMENTNAME": "EQ-2",
"EQUIPMENTNAME": "EQ-2",
"PRODUCTLINENAME": "PKG-B",
"PJ_TYPE": "TYPE-B",
"LOSSREASONNAME": "001_A",
"LOSSREASON_CODE": "001_A",
"SCRAP_OBJECTTYPE": "LOT",
"MOVEIN_QTY": 100,
"REJECT_TOTAL_QTY": 20,
"DEFECT_QTY": 0,
},
{
"CONTAINERID": "C3",
"CONTAINERNAME": "LOT-003",
"TXN_DAY": pd.Timestamp("2026-02-01"),
"TXN_TIME": pd.Timestamp("2026-02-01 10:00:00"),
"WORKCENTERSEQUENCE_GROUP": 1,
"WORKCENTER_GROUP": "WB",
"WORKCENTERNAME": "WB-C",
"SPECNAME": "SPEC-C",
"WORKFLOWNAME": "WF-C",
"PRIMARY_EQUIPMENTNAME": "EQ-3",
"EQUIPMENTNAME": "EQ-3",
"PRODUCTLINENAME": "PKG-C",
"PJ_TYPE": "TYPE-C",
"LOSSREASONNAME": "002_B",
"LOSSREASON_CODE": "002_B",
"SCRAP_OBJECTTYPE": "LOT",
"MOVEIN_QTY": 100,
"REJECT_TOTAL_QTY": 10,
"DEFECT_QTY": 0,
},
]
)
def test_apply_view_and_export_share_same_pareto_multi_select_filter(monkeypatch):
df = _build_detail_filter_df()
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
monkeypatch.setattr(
"mes_dashboard.services.scrap_reason_exclusion_cache.get_excluded_reasons",
lambda: [],
)
view_result = cache_svc.apply_view(
query_id="qid-2",
pareto_dimension="type",
pareto_values=["TYPE-A", "TYPE-C"],
)
export_rows = cache_svc.export_csv_from_cache(
query_id="qid-2",
pareto_dimension="type",
pareto_values=["TYPE-A", "TYPE-C"],
)
detail_items = view_result["detail"]["items"]
detail_types = {item["PJ_TYPE"] for item in detail_items}
exported_types = {row["TYPE"] for row in export_rows}
assert view_result["detail"]["pagination"]["total"] == 2
assert detail_types == {"TYPE-A", "TYPE-C"}
assert exported_types == {"TYPE-A", "TYPE-C"}
assert len(export_rows) == 2
def test_apply_view_rejects_invalid_pareto_dimension(monkeypatch):
df = _build_detail_filter_df()
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
with pytest.raises(ValueError, match="不支援的 pareto_dimension"):
cache_svc.apply_view(
query_id="qid-3",
pareto_dimension="invalid-dimension",
pareto_values=["X"],
)
with pytest.raises(ValueError, match="不支援的 pareto_dimension"):
cache_svc.export_csv_from_cache(
query_id="qid-3",
pareto_dimension="invalid-dimension",
pareto_values=["X"],
)
def test_compute_batch_pareto_applies_cross_filter_exclude_self(monkeypatch):
df = pd.DataFrame(
[
{
"CONTAINERID": "C1",
"TXN_DAY": pd.Timestamp("2026-02-01"),
"LOSSREASONNAME": "R-A",
"PRODUCTLINENAME": "PKG-1",
"PJ_TYPE": "TYPE-1",
"WORKFLOWNAME": "WF-1",
"WORKCENTER_GROUP": "WB-1",
"PRIMARY_EQUIPMENTNAME": "EQ-1",
"SCRAP_OBJECTTYPE": "LOT",
"LOSSREASON_CODE": "001_A",
"MOVEIN_QTY": 100,
"REJECT_TOTAL_QTY": 100,
"DEFECT_QTY": 0,
},
{
"CONTAINERID": "C2",
"TXN_DAY": pd.Timestamp("2026-02-01"),
"LOSSREASONNAME": "R-A",
"PRODUCTLINENAME": "PKG-2",
"PJ_TYPE": "TYPE-2",
"WORKFLOWNAME": "WF-2",
"WORKCENTER_GROUP": "WB-2",
"PRIMARY_EQUIPMENTNAME": "EQ-2",
"SCRAP_OBJECTTYPE": "LOT",
"LOSSREASON_CODE": "001_A",
"MOVEIN_QTY": 100,
"REJECT_TOTAL_QTY": 50,
"DEFECT_QTY": 0,
},
{
"CONTAINERID": "C3",
"TXN_DAY": pd.Timestamp("2026-02-01"),
"LOSSREASONNAME": "R-B",
"PRODUCTLINENAME": "PKG-1",
"PJ_TYPE": "TYPE-2",
"WORKFLOWNAME": "WF-2",
"WORKCENTER_GROUP": "WB-1",
"PRIMARY_EQUIPMENTNAME": "EQ-1",
"SCRAP_OBJECTTYPE": "LOT",
"LOSSREASON_CODE": "002_B",
"MOVEIN_QTY": 100,
"REJECT_TOTAL_QTY": 40,
"DEFECT_QTY": 0,
},
{
"CONTAINERID": "C4",
"TXN_DAY": pd.Timestamp("2026-02-01"),
"LOSSREASONNAME": "R-B",
"PRODUCTLINENAME": "PKG-3",
"PJ_TYPE": "TYPE-3",
"WORKFLOWNAME": "WF-3",
"WORKCENTER_GROUP": "WB-3",
"PRIMARY_EQUIPMENTNAME": "EQ-3",
"SCRAP_OBJECTTYPE": "LOT",
"LOSSREASON_CODE": "002_B",
"MOVEIN_QTY": 100,
"REJECT_TOTAL_QTY": 30,
"DEFECT_QTY": 0,
},
]
)
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
monkeypatch.setattr(
"mes_dashboard.services.scrap_reason_exclusion_cache.get_excluded_reasons",
lambda: [],
)
result = cache_svc.compute_batch_pareto(
query_id="qid-batch-1",
metric_mode="reject_total",
pareto_scope="all",
include_excluded_scrap=True,
pareto_selections={
"reason": ["R-A"],
"type": ["TYPE-2"],
},
)
reason_items = result["dimensions"]["reason"]["items"]
type_items = result["dimensions"]["type"]["items"]
package_items = result["dimensions"]["package"]["items"]
assert {item["reason"] for item in reason_items} == {"R-A", "R-B"}
assert {item["reason"] for item in type_items} == {"TYPE-1", "TYPE-2"}
assert [item["reason"] for item in package_items] == ["PKG-2"]
def test_compute_batch_pareto_memory_guard_rejects_large_cached_dataset(monkeypatch):
df = _build_detail_filter_df()
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
monkeypatch.setattr(cache_svc, "_df_memory_mb", lambda _df: 128.0)
monkeypatch.setattr(cache_svc, "_REJECT_DERIVE_MAX_INPUT_MB", 64)
with pytest.raises(MemoryError, match="超過 64 MB 上限"):
cache_svc.compute_batch_pareto(
query_id="qid-batch-mem-guard",
metric_mode="reject_total",
pareto_scope="all",
)
def test_apply_pareto_selection_filter_supports_multi_dimension_and_logic():
df = _build_detail_filter_df()
filtered = cache_svc._apply_pareto_selection_filter(
df,
pareto_selections={
"reason": ["001_A"],
"type": ["TYPE-B"],
},
)
assert len(filtered) == 1
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_engine_chunk_uses_paged_fetch_without_truncation(self, monkeypatch):
"""Engine chunk should fetch all pages (offset paging), not truncate at page size."""
import mes_dashboard.services.batch_query_engine as engine_mod
offsets = []
captured = {"df": pd.DataFrame(), "merge_kwargs": None}
def fake_read_sql(sql, params):
offset = int(params.get("offset", 0))
limit = int(params.get("limit", 0))
offsets.append(offset)
total_rows = 5
remaining = max(total_rows - offset, 0)
take = min(limit, remaining)
if take <= 0:
return pd.DataFrame()
return pd.DataFrame(
{
"CONTAINERID": [f"C{offset + i}" for i in range(take)],
"LOSSREASONNAME": ["R1"] * take,
"REJECT_TOTAL_QTY": [1] * take,
}
)
def fake_execute_plan(chunks, query_fn, **kwargs):
page_size = kwargs.get("max_rows_per_chunk")
captured["df"] = query_fn(chunks[0], max_rows_per_chunk=page_size)
return kwargs.get("query_hash", "qh")
def fake_merge_chunks(prefix, qhash, **kwargs):
captured["merge_kwargs"] = kwargs
return captured["df"]
monkeypatch.setattr(cache_svc, "_REJECT_ENGINE_MAX_ROWS_PER_CHUNK", 2)
monkeypatch.setattr(engine_mod, "should_decompose_by_time", lambda *_a, **_kw: True)
monkeypatch.setattr(
engine_mod,
"decompose_by_time_range",
lambda *_a, **_kw: [{"chunk_start": "2025-01-01", "chunk_end": "2025-01-31"}],
)
monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
monkeypatch.setattr(cache_svc, "read_sql_df", fake_read_sql)
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _qid: 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, **_kw: None)
monkeypatch.setattr(cache_svc, "_apply_policy_filters", lambda df, **kw: df)
monkeypatch.setattr(cache_svc, "_store_query_result", lambda *_a, **_kw: None)
monkeypatch.setattr(cache_svc, "redis_clear_batch", lambda *_a, **_kw: 0)
monkeypatch.setattr(
cache_svc,
"_build_primary_response",
lambda qid, df, meta, ri: {"query_id": qid, "rows": len(df)},
)
result = cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-01-01",
end_date="2025-03-01",
)
assert result["rows"] == 5
assert offsets == [0, 2, 4]
assert captured["merge_kwargs"] == {}
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="2025-06-10",
)
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="2025-01-05",
)
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)
def test_resolve_containers_deduplicates_container_ids(monkeypatch):
monkeypatch.setattr(
cache_svc,
"_RESOLVERS",
{
"lot": lambda values: {
"data": [
{"container_id": "CID-1"},
{"container_id": "CID-1"},
{"container_id": "CID-2"},
],
"input_count": len(values),
"not_found": [],
"expansion_info": {"LOT%": 2},
}
},
)
monkeypatch.setenv("CONTAINER_RESOLVE_MAX_EXPANSION_PER_TOKEN", "10")
monkeypatch.setenv("CONTAINER_RESOLVE_MAX_CONTAINER_IDS", "10")
resolved = cache_svc.resolve_containers("lot", ["LOT%"])
assert resolved["container_ids"] == ["CID-1", "CID-2"]
assert resolved["resolution_info"]["resolved_count"] == 2
def test_resolve_containers_allows_oversized_expansion_and_sets_guardrail(monkeypatch):
monkeypatch.setattr(
cache_svc,
"_RESOLVERS",
{
"lot": lambda values: {
"data": [{"container_id": "CID-1"}],
"input_count": len(values),
"not_found": [],
"expansion_info": {"GA%": 999},
}
},
)
monkeypatch.setenv("CONTAINER_RESOLVE_MAX_EXPANSION_PER_TOKEN", "50")
monkeypatch.setenv("CONTAINER_RESOLVE_PATTERN_MIN_PREFIX_LEN", "2")
resolved = cache_svc.resolve_containers("lot", ["GA%"])
guardrail = resolved["resolution_info"].get("guardrail") or {}
assert guardrail.get("overflow") is True
assert len(guardrail.get("expansion_offenders") or []) == 1
def test_partial_failure_in_response_meta(monkeypatch):
import mes_dashboard.services.batch_query_engine as engine_mod
df = pd.DataFrame({"CONTAINERID": ["C1"], "LOSSREASONNAME": ["R1"], "REJECT_TOTAL_QTY": [1]})
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _qid: None)
monkeypatch.setattr(cache_svc, "_validate_range", lambda *_a, **_kw: 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, "_apply_policy_filters", lambda data, **kw: data)
monkeypatch.setattr(cache_svc, "_store_query_result", lambda *_a, **_kw: False)
monkeypatch.setattr(cache_svc, "redis_clear_batch", lambda *_a, **_kw: None)
monkeypatch.setattr(
cache_svc,
"_build_primary_response",
lambda qid, result_df, meta, resolution_info: {"query_id": qid, "meta": meta},
)
monkeypatch.setattr(cache_svc, "_store_partial_failure_flag", lambda *_a, **_kw: None)
monkeypatch.setattr(engine_mod, "should_decompose_by_time", lambda *_a, **_kw: True)
monkeypatch.setattr(
engine_mod,
"decompose_by_time_range",
lambda *_a, **_kw: [{"chunk_start": "2025-01-01", "chunk_end": "2025-01-10"}],
)
monkeypatch.setattr(engine_mod, "execute_plan", lambda *a, **kw: kw.get("query_hash"))
monkeypatch.setattr(engine_mod, "merge_chunks", lambda *a, **kw: df.copy())
monkeypatch.setattr(
engine_mod,
"get_batch_progress",
lambda *_a, **_kw: {
"has_partial_failure": "True",
"failed": "2",
"failed_ranges": json.dumps([{"start": "2025-01-01", "end": "2025-01-10"}]),
},
)
result = cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-01-01",
end_date="2025-03-01",
)
meta = result.get("meta") or {}
assert meta.get("has_partial_failure") is True
assert meta.get("failed_chunk_count") == 2
assert meta.get("failed_ranges") == [{"start": "2025-01-01", "end": "2025-01-10"}]
def test_cache_hit_restores_partial_failure(monkeypatch):
cached_df = pd.DataFrame({"CONTAINERID": ["C1"], "LOSSREASONNAME": ["R1"], "REJECT_TOTAL_QTY": [1]})
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _qid: cached_df)
monkeypatch.setattr(cache_svc, "_validate_range", lambda *_a, **_kw: None)
monkeypatch.setattr(cache_svc, "_build_where_clause", lambda **kw: ("", {}, {}))
monkeypatch.setattr(cache_svc, "_apply_policy_filters", lambda data, **kw: data)
monkeypatch.setattr(
cache_svc,
"_load_partial_failure_flag",
lambda _qid: {
"has_partial_failure": True,
"failed_chunk_count": 3,
"failed_ranges": [],
},
)
monkeypatch.setattr(
cache_svc,
"_build_primary_response",
lambda qid, result_df, meta, resolution_info: {"query_id": qid, "meta": meta},
)
result = cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-01-01",
end_date="2025-01-31",
)
meta = result.get("meta") or {}
assert meta.get("has_partial_failure") is True
assert meta.get("failed_chunk_count") == 3
assert meta.get("failed_ranges") == []
@pytest.mark.parametrize(
"store_result,expected_ttl",
[
(True, cache_svc._REJECT_ENGINE_SPOOL_TTL_SECONDS),
(False, cache_svc._CACHE_TTL),
],
)
def test_partial_failure_ttl_matches_spool(monkeypatch, store_result, expected_ttl):
import mes_dashboard.services.batch_query_engine as engine_mod
df = pd.DataFrame({"CONTAINERID": ["C1"], "LOSSREASONNAME": ["R1"], "REJECT_TOTAL_QTY": [1]})
captured = {"ttls": []}
monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _qid: None)
monkeypatch.setattr(cache_svc, "_validate_range", lambda *_a, **_kw: 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, "_apply_policy_filters", lambda data, **kw: data)
monkeypatch.setattr(cache_svc, "_store_query_result", lambda *_a, **_kw: store_result)
monkeypatch.setattr(cache_svc, "redis_clear_batch", lambda *_a, **_kw: None)
monkeypatch.setattr(
cache_svc,
"_build_primary_response",
lambda qid, result_df, meta, resolution_info: {"query_id": qid, "meta": meta},
)
monkeypatch.setattr(
cache_svc,
"_store_partial_failure_flag",
lambda _qid, _failed, _ranges, ttl: captured["ttls"].append(ttl),
)
monkeypatch.setattr(engine_mod, "should_decompose_by_time", lambda *_a, **_kw: True)
monkeypatch.setattr(
engine_mod,
"decompose_by_time_range",
lambda *_a, **_kw: [{"chunk_start": "2025-01-01", "chunk_end": "2025-01-10"}],
)
monkeypatch.setattr(engine_mod, "execute_plan", lambda *a, **kw: kw.get("query_hash"))
monkeypatch.setattr(engine_mod, "merge_chunks", lambda *a, **kw: df.copy())
monkeypatch.setattr(
engine_mod,
"get_batch_progress",
lambda *_a, **_kw: {"has_partial_failure": "True", "failed": "1", "failed_ranges": "[]"},
)
cache_svc.execute_primary_query(
mode="date_range",
start_date="2025-01-01",
end_date="2025-03-01",
)
assert captured["ttls"] == [expected_ttl]
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