perf: optimize revenue forecast effective document links

tests/test_revenue_forecast_effective_links_performance.py

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+from contextlib import nullcontext
+from datetime import date
+
+import app.document_reconciliation_service as document_service
+import app.revenue_forecast_service as forecast_service
+
+
+class _Result:
+    def __init__(self, rows=(), scalar_value=False):
+        self.rows = list(rows)
+        self.scalar_value = scalar_value
+
+    def mappings(self):
+        return self
+
+    def all(self):
+        return self.rows
+
+    def scalar(self):
+        return self.scalar_value
+
+
+class _Connection:
+    def __init__(self, *, v2=False):
+        self.v2 = v2
+        self.statements = []
+
+    def execute(self, statement, params=None):
+        sql = str(statement)
+        self.statements.append(sql)
+        if "to_regclass" in sql:
+            return _Result(scalar_value=self.v2)
+        return _Result()
+
+
+class _Engine:
+    def __init__(self, connection):
+        self.connection = connection
+
+    def begin(self):
+        return nullcontext(self.connection)
+
+
+def test_forecast_prepares_effective_links_once(monkeypatch):
+    connection = _Connection()
+    calls = []
+    flags = []
+    monkeypatch.setattr(forecast_service, "engine", _Engine(connection))
+    monkeypatch.setattr(forecast_service, "ensure_revenue_forecast_schema", lambda: None)
+    monkeypatch.setattr(forecast_service, "get_sales_target", lambda **kwargs: {"target_amount": 0})
+    monkeypatch.setattr(document_service, "prepare_effective_document_links", lambda conn: calls.append(conn) or "_effective_document_links")
+    monkeypatch.setattr(forecast_service, "_historical_stage_rates", lambda conn: {})
+
+    def realised(conn, **kwargs):
+        flags.append(kwargs["effective_links_prepared"])
+        return {"amount": 0.0, "count": 0, "opportunity_ids": set(), "items": []}
+
+    def realised_ids(conn, **kwargs):
+        flags.append(kwargs["effective_links_prepared"])
+        return set()
+
+    monkeypatch.setattr(forecast_service, "_realised_for_period", realised)
+    monkeypatch.setattr(forecast_service, "_already_realised_ids", realised_ids)
+
+    forecast_service.get_revenue_forecast()
+
+    assert calls == [connection]
+    assert flags == [True, True]
+
+
+def test_helpers_still_prepare_when_called_in_isolation(monkeypatch):
+    connection = _Connection()
+    calls = []
+    monkeypatch.setattr(document_service, "prepare_effective_document_links", lambda conn: calls.append(conn))
+
+    forecast_service._realised_for_period(
+        connection, metric="invoiced", period_start=date(2026, 8, 1), period_end=date(2026, 8, 31),
+    )
+    forecast_service._already_realised_ids(connection, metric="invoiced")
+
+    assert calls == [connection, connection]
+
+
+def test_set_based_preparation_has_constant_statement_count_and_no_resolver(monkeypatch):
+    monkeypatch.setattr(
+        document_service, "resolve_document_links",
+        lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("per-opportunity resolver called")),
+    )
+    for v2 in (False, True):
+        connection = _Connection(v2=v2)
+        document_service.prepare_effective_document_links(connection)
+        assert len(connection.statements) == 4
+        assert sum("INSERT INTO _effective_document_links" in sql for sql in connection.statements) == 1
+
+
+def test_set_based_legacy_relationship_case_matches_resolver_vocabulary():
+    source = document_service.prepare_effective_document_links.__code__.co_consts
+    sql_fragments = " ".join(value for value in source if isinstance(value, str))
+    for relationship in ("REMOVED", "PRIMARY", "HISTORICAL", "SECONDARY"):
+        assert relationship in sql_fragments
+    assert "NOT EXISTS" in sql_fragments
+    assert "l.ended_at IS NULL" in sql_fragments