Add initial work from Codex

backend/app/services/warehouse_service.py

@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+import hashlib
+import logging
+from collections.abc import Sequence
+from time import perf_counter
+
+import pandas as pd
+from opentelemetry import metrics, trace
+from sqlalchemy import text
+from sqlalchemy.engine import Engine
+from sqlalchemy.exc import SQLAlchemyError
+
+from app.db import queries
+
+LOGGER = logging.getLogger(__name__)
+
+
+class ReadOnlyWarehouseClient:
+    def __init__(self, engines: dict[str, Engine]) -> None:
+        self.engines = engines
+        self.tracer = trace.get_tracer(__name__)
+        self.meter = metrics.get_meter(__name__)
+        self.query_counter = self.meter.create_counter(
+            name="warehouse_queries_total",
+            description="Total warehouse query executions",
+        )
+        self.query_latency = self.meter.create_histogram(
+            name="warehouse_query_latency_ms",
+            unit="ms",
+            description="Warehouse query latency",
+        )
+
+    def _validate_read_only_query(self, sql: str) -> None:
+        normalized = sql.strip().lower()
+        if not (normalized.startswith("select") or normalized.startswith("with")):
+            raise ValueError("Only read-only SELECT/CTE SQL statements are allowed.")
+
+    def _run_query_list(
+        self, source: str, sql_candidates: Sequence[str]
+    ) -> pd.DataFrame:
+        engine = self.engines[source]
+        last_error: Exception | None = None
+
+        for candidate in sql_candidates:
+            self._validate_read_only_query(candidate)
+            query_hash = hashlib.sha256(candidate.encode("utf-8")).hexdigest()[:12]
+            with self.tracer.start_as_current_span("warehouse.query") as span:
+                span.set_attribute("db.system", "mssql")
+                span.set_attribute("db.source", source)
+                span.set_attribute("db.query.hash", query_hash)
+                started = perf_counter()
+                try:
+                    with engine.connect() as conn:
+                        with self.tracer.start_as_current_span(
+                            "warehouse.query.execute"
+                        ):
+                            df = pd.read_sql_query(sql=text(candidate), con=conn)
+                    elapsed_ms = (perf_counter() - started) * 1000
+                    self.query_latency.record(elapsed_ms, attributes={"source": source})
+                    self.query_counter.add(
+                        1, attributes={"source": source, "status": "ok"}
+                    )
+                    return df
+                except SQLAlchemyError as exc:
+                    last_error = exc
+                    elapsed_ms = (perf_counter() - started) * 1000
+                    self.query_latency.record(elapsed_ms, attributes={"source": source})
+                    self.query_counter.add(
+                        1, attributes={"source": source, "status": "error"}
+                    )
+                    LOGGER.warning(
+                        "Query failed for %s with hash %s: %s", source, query_hash, exc
+                    )
+
+        if last_error is not None:
+            raise RuntimeError(
+                f"All query candidates failed for source '{source}'."
+            ) from last_error
+        return pd.DataFrame()
+
+    def fetch_daily_sales(self) -> pd.DataFrame:
+        aw = self._run_query_list("aw", queries.AW_DAILY_SALES_QUERIES)
+        aw["source"] = "AdventureWorks2022DWH"
+        wwi = self._run_query_list("wwi", queries.WWI_DAILY_SALES_QUERIES)
+        wwi["source"] = "WorldWideImporters"
+        return pd.concat([aw, wwi], ignore_index=True)
+
+    def fetch_product_performance(self) -> pd.DataFrame:
+        aw = self._run_query_list("aw", queries.AW_PRODUCT_PERFORMANCE_QUERIES)
+        aw["source"] = "AdventureWorks2022DWH"
+        wwi = self._run_query_list("wwi", queries.WWI_PRODUCT_PERFORMANCE_QUERIES)
+        wwi["source"] = "WorldWideImporters"
+        return pd.concat([aw, wwi], ignore_index=True)
+
+    def fetch_customer_performance(self) -> pd.DataFrame:
+        aw = self._run_query_list("aw", queries.AW_CUSTOMER_QUERIES)
+        aw["source"] = "AdventureWorks2022DWH"
+        wwi = self._run_query_list("wwi", queries.WWI_CUSTOMER_QUERIES)
+        wwi["source"] = "WorldWideImporters"
+        return pd.concat([aw, wwi], ignore_index=True)