a275c30c0e
Chunk failures in BatchQueryEngine were silently discarded — `has_partial_failure` was tracked in Redis but never surfaced to the API response or frontend. Users could see incomplete data without any warning. This commit closes the gap end-to-end: Backend: - Track failed chunk time ranges (`failed_ranges`) in batch engine progress metadata - Add single retry for transient Oracle errors (timeout, connection) in `_execute_single_chunk` - Read `get_batch_progress()` after merge but before `redis_clear_batch()` cleanup - Inject `has_partial_failure`, `failed_chunk_count`, `failed_ranges` into API response meta - Persist partial failure flag to independent Redis key with TTL aligned to data storage layer - Add shared container-resolution policy module with wildcard/expansion guardrails - Refactor reason filter from single-value to multi-select (`reason` → `reasons`) Frontend: - Add client-side date range validation (730-day limit) before API submission - Display amber warning banner on partial failure with specific failed date ranges - Support generic fallback message for container-mode queries without date ranges - Update FilterPanel to support multi-select reason chips Specs & tests: - Create batch-query-resilience spec; update reject-history-api and reject-history-page specs - Add 7 new tests for retry, memory guard, failed ranges, partial failure propagation, TTL - Cross-service regression verified (hold, resource, job, msd — 411 tests pass) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
# -*- coding: utf-8 -*-
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"""Unit tests for job_query_service — engine integration (tasks 9.1-9.4)."""
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from __future__ import annotations
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import pandas as pd
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from mes_dashboard.services import job_query_service as job_svc
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class TestJobQueryEngineDecomposition:
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"""9.4 — full-year query with many resources → engine decomposition."""
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def test_long_range_triggers_engine(self, monkeypatch):
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"""90-day range → engine decomposition for job query."""
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import mes_dashboard.services.batch_query_engine as engine_mod
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import mes_dashboard.core.redis_df_store as rds
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engine_calls = {"execute": 0, "merge": 0}
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def fake_execute_plan(chunks, query_fn, **kwargs):
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engine_calls["execute"] += 1
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assert len(chunks) == 3 # 90 days / 31 = 3 chunks
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assert kwargs.get("cache_prefix") == "job"
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return kwargs.get("query_hash", "fake_hash")
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result_df = pd.DataFrame({
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"JOBID": ["J1", "J2"],
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"RESOURCEID": ["R1", "R2"],
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})
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def fake_merge_chunks(prefix, qhash, **kwargs):
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engine_calls["merge"] += 1
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return result_df
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monkeypatch.setattr(engine_mod, "execute_plan", fake_execute_plan)
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monkeypatch.setattr(engine_mod, "merge_chunks", fake_merge_chunks)
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monkeypatch.setattr(rds, "redis_load_df", lambda key: None)
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monkeypatch.setattr(rds, "redis_store_df", lambda key, df, ttl=None: None)
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monkeypatch.setattr(
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"mes_dashboard.services.job_query_service.SQLLoader",
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type("FakeLoader", (), {
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"load": staticmethod(lambda name: "SELECT 1 FROM dual WHERE {{ RESOURCE_FILTER }}"),
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}),
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)
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result = job_svc.get_jobs_by_resources(
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resource_ids=["R1", "R2", "R3"],
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start_date="2025-01-01",
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end_date="2025-03-31",
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)
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assert engine_calls["execute"] == 1
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assert engine_calls["merge"] == 1
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assert result["total"] == 2
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assert "error" not in result
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def test_short_range_skips_engine(self, monkeypatch):
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"""30-day range → direct path, no engine."""
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import mes_dashboard.core.redis_df_store as rds
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engine_calls = {"execute": 0}
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monkeypatch.setattr(rds, "redis_load_df", lambda key: None)
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monkeypatch.setattr(rds, "redis_store_df", lambda key, df, ttl=None: None)
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monkeypatch.setattr(
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"mes_dashboard.services.job_query_service.SQLLoader",
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type("FakeLoader", (), {
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"load": staticmethod(lambda name: "SELECT 1 FROM dual WHERE {{ RESOURCE_FILTER }}"),
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}),
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)
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monkeypatch.setattr(
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"mes_dashboard.services.job_query_service.read_sql_df",
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lambda sql, params: pd.DataFrame({"JOBID": ["J1"]}),
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)
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result = job_svc.get_jobs_by_resources(
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resource_ids=["R1"],
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start_date="2025-06-01",
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end_date="2025-06-05",
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)
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assert engine_calls["execute"] == 0 # Engine NOT used
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assert result["total"] == 1
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def test_redis_cache_hit_skips_query(self, monkeypatch):
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"""Redis cache hit → returns cached DataFrame without Oracle query."""
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import mes_dashboard.core.redis_df_store as rds
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query_calls = {"sql": 0}
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cached_df = pd.DataFrame({
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"JOBID": ["J-CACHED"],
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"RESOURCEID": ["R1"],
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})
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monkeypatch.setattr(rds, "redis_load_df", lambda key: cached_df)
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def fail_sql(*args, **kwargs):
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query_calls["sql"] += 1
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raise RuntimeError("Should not reach Oracle")
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monkeypatch.setattr(
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"mes_dashboard.services.job_query_service.read_sql_df",
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fail_sql,
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)
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result = job_svc.get_jobs_by_resources(
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resource_ids=["R1"],
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start_date="2025-06-01",
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end_date="2025-06-30",
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)
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assert query_calls["sql"] == 0 # Oracle NOT called
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assert result["total"] == 1
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assert result["data"][0]["JOBID"] == "J-CACHED"
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