feat: new file

backend/app/services/db_sample.py

@@ -0,0 +1,105 @@
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any
+
+import pandas as pd
+from sqlalchemy import Engine, text
+
+from ..db import get_engine
+from ..services.schema_discovery import discover_schema
+from ..settings import settings
+
+log = logging.getLogger("db_sample")
+
+
+def _truncate_value(v: Any, max_len: int) -> Any:
+    if v is None:
+        return None
+    if isinstance(v, (int, float, bool)):
+        return v
+    s = str(v)
+    if len(s) <= max_len:
+        return s
+    return s[: max_len - 3] + "..."
+
+
+def _df_to_records(df: pd.DataFrame, max_str_len: int) -> list[dict[str, Any]]:
+    out: list[dict[str, Any]] = []
+    for _, row in df.iterrows():
+        rec: dict[str, Any] = {}
+        for k, v in row.items():
+            rec[str(k)] = _truncate_value(v, max_str_len)
+        out.append(rec)
+    return out
+
+
+def _list_tables(engine: Engine) -> list[str]:
+    with engine.connect() as conn:
+        rows = conn.execute(
+            text(
+                """
+                SELECT table_name
+                FROM information_schema.tables
+                WHERE table_schema = DATABASE()
+                ORDER BY table_name
+                """
+            )
+        ).all()
+    return [r[0] for r in rows]
+
+
+def _table_row_count(engine: Engine, table: str) -> int | None:
+    try:
+        with engine.connect() as conn:
+            v = conn.execute(text(f"SELECT COUNT(*) FROM {table}")).scalar()
+        return int(v) if v is not None else None
+    except Exception:
+        return None
+
+
+def _sample_table(engine: Engine, table: str, limit: int) -> pd.DataFrame:
+    with engine.connect() as conn:
+        return pd.read_sql(text(f"SELECT * FROM {table} LIMIT {limit}"), conn)
+
+
+def print_db_sample_to_logs(limit: int | None = None) -> None:
+    """
+    打印数据库结构与样例行到后端日志，便于分析。
+    注意：会截断长字符串，避免日志爆炸。
+    """
+    engine = get_engine()
+    schema = discover_schema(engine)
+
+    eff_limit = int(limit or settings.debug_db_sample_limit)
+    max_str_len = int(settings.debug_db_sample_max_str_len)
+
+    tables = _list_tables(engine)
+    log.warning("DB SAMPLE: discovered schema=%s", schema.model_dump())
+    log.warning("DB SAMPLE: tables=%s", tables)
+
+    # prioritize discovered tables first
+    prioritized: list[str] = []
+    for t in [schema.sales_table, schema.products_table]:
+        if t and t in tables and t not in prioritized:
+            prioritized.append(t)
+    for t in tables:
+        if t not in prioritized:
+            prioritized.append(t)
+
+    for t in prioritized[: min(len(prioritized), 8)]:
+        cnt = _table_row_count(engine, t)
+        try:
+            df = _sample_table(engine, t, eff_limit)
+            recs = _df_to_records(df, max_str_len=max_str_len)
+            log.warning(
+                "DB SAMPLE: table=%s rows=%s cols=%s sample=%s",
+                t,
+                cnt,
+                list(df.columns),
+                json.dumps(recs, ensure_ascii=False),
+            )
+        except Exception as e:
+            log.warning("DB SAMPLE: table=%s rows=%s sample_failed=%s", t, cnt, str(e))
+