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322 | class DuckBudgetRuntime(BaseBudgetRuntime[DuckBudgetExecutor]):
"""DuckDB-specific budget runtime with plan-based estimation."""
DEFAULT_GUARD = BudgetGuard(
env_var="FF_DUCKDB_MAX_BYTES",
estimator_attr="_estimate_query_bytes",
engine_label="DuckDB",
what="query",
)
_FIXED_TYPE_SIZES: ClassVar[dict[str, int]] = {
"boolean": 1,
"bool": 1,
"tinyint": 1,
"smallint": 2,
"integer": 4,
"int": 4,
"bigint": 8,
"float": 4,
"real": 4,
"double": 8,
"double precision": 8,
"decimal": 16,
"numeric": 16,
"uuid": 16,
"json": 64,
"jsonb": 64,
"timestamp": 8,
"timestamp_ntz": 8,
"timestamp_ltz": 8,
"timestamptz": 8,
"date": 4,
"time": 4,
"interval": 16,
}
_VARCHAR_DEFAULT_WIDTH = 64
_VARCHAR_MAX_WIDTH = 1024
_DEFAULT_ROW_WIDTH = 128
def __init__(self, executor: DuckBudgetExecutor, guard: BudgetGuard | None = None):
super().__init__(executor, guard)
self._table_row_width_cache: dict[tuple[str | None, str], int] = {}
# ------------------------------------------------------------------ #
# Cost estimation used by BudgetGuard #
# ------------------------------------------------------------------ #
def estimate_query_bytes(self, sql: str) -> int | None:
"""
Estimate query size via DuckDB's EXPLAIN (FORMAT JSON).
"""
# Try to normalize to a SELECT/CTE body if the executor exposes it
body = self.executor._selectable_body(sql).strip().rstrip(";\n\t ")
lower = body.lower()
if not lower.startswith(("select", "with")):
return None
explain_sql = f"EXPLAIN (FORMAT JSON) {body}"
try:
rows = self.executor._execute_fetchall(explain_sql)
except Exception:
return None
if not rows:
return None
fragments: list[str] = []
for row in rows:
for cell in row:
if cell is None:
continue
fragments.append(str(cell))
if not fragments:
return None
plan_text = "\n".join(fragments).strip()
start = plan_text.find("[")
end = plan_text.rfind("]")
if start == -1 or end == -1 or end <= start:
return None
try:
plan_data = json.loads(plan_text[start : end + 1])
except Exception:
return None
estimate = self._max_cardinality(plan_data)
if estimate <= 0:
return None
tables = self._collect_tables_from_plan(
plan_data if isinstance(plan_data, list) else [plan_data]
)
row_width = self._row_width_for_tables(tables)
if row_width <= 0:
row_width = self._DEFAULT_ROW_WIDTH
bytes_estimate = int(estimate * row_width)
return bytes_estimate if bytes_estimate > 0 else None
def _max_cardinality(self, plan_data: Any) -> int:
def _to_int(value: Any) -> int | None:
if value is None:
return None
if isinstance(value, (int, float)):
try:
converted = int(value)
except Exception:
return None
return converted
text = str(value)
match = re.search(r"(\d+(?:\.\d+)?)", text)
if not match:
return None
try:
return int(float(match.group(1)))
except ValueError:
return None
def _walk_node(node: dict[str, Any]) -> int:
best = 0
extra = node.get("extra_info") or {}
for key in (
"Estimated Cardinality",
"estimated_cardinality",
"Cardinality",
"cardinality",
):
candidate = _to_int(extra.get(key))
if candidate is not None:
best = max(best, candidate)
candidate = _to_int(node.get("cardinality"))
if candidate is not None:
best = max(best, candidate)
for child in node.get("children") or []:
if isinstance(child, dict):
best = max(best, _walk_node(child))
return best
nodes = plan_data if isinstance(plan_data, list) else [plan_data]
estimate = 0
for entry in nodes:
if isinstance(entry, dict):
estimate = max(estimate, _walk_node(entry))
return estimate
def _collect_tables_from_plan(self, nodes: list[dict[str, Any]]) -> set[tuple[str | None, str]]:
tables: set[tuple[str | None, str]] = set()
def _walk(entry: dict[str, Any]) -> None:
extra = entry.get("extra_info") or {}
table_val = extra.get("Table")
schema_val = extra.get("Schema") or extra.get("Database") or extra.get("Catalog")
if isinstance(table_val, str) and table_val.strip():
schema, table = self._split_identifier(table_val, schema_val)
if table:
tables.add((schema, table))
for child in entry.get("children") or []:
if isinstance(child, dict):
_walk(child)
for node in nodes:
if isinstance(node, dict):
_walk(node)
return tables
def _split_identifier(
self, identifier: str, explicit_schema: str | None
) -> tuple[str | None, str]:
parts = [part.strip() for part in identifier.split(".") if part.strip()]
if not parts:
return explicit_schema, identifier
if len(parts) >= 2:
schema_candidate = self._strip_quotes(parts[-2])
table_candidate = self._strip_quotes(parts[-1])
return schema_candidate or explicit_schema, table_candidate
return explicit_schema, self._strip_quotes(parts[-1])
def _strip_quotes(self, value: str) -> str:
if value.startswith('"') and value.endswith('"'):
return value[1:-1]
return value
def _row_width_for_tables(self, tables: Iterable[tuple[str | None, str]]) -> int:
widths: list[int] = []
for schema, table in tables:
width = self._row_width_for_table(schema, table)
if width > 0:
widths.append(width)
return max(widths) if widths else 0
def _row_width_for_table(self, schema: str | None, table: str) -> int:
key = (schema or "", table.lower())
cached = self._table_row_width_cache.get(key)
if cached:
return cached
columns = self._columns_for_table(table, schema)
width = sum(self._estimate_column_width(col) for col in columns)
if width <= 0:
width = self._DEFAULT_ROW_WIDTH
self._table_row_width_cache[key] = width
return width
def _columns_for_table(
self, table: str, schema: str | None
) -> list[tuple[str | None, int | None, int | None, int | None]]:
table_lower = table.lower()
columns: list[tuple[str | None, int | None, int | None, int | None]] = []
seen_schemas: set[str | None] = set()
for candidate in self._schema_candidates(schema):
if candidate in seen_schemas:
continue
seen_schemas.add(candidate)
try:
if candidate is not None:
rows = self.executor._execute_fetchall(
"""
select lower(data_type) as dtype,
character_maximum_length,
numeric_precision,
numeric_scale
from information_schema.columns
where lower(table_name)=lower(?)
and lower(table_schema)=lower(?)
order by ordinal_position
""",
[table_lower, candidate.lower()],
)
else:
rows = self.executor._execute_fetchall(
"""
select lower(data_type) as dtype,
character_maximum_length,
numeric_precision,
numeric_scale
from information_schema.columns
where lower(table_name)=lower(?)
order by lower(table_schema), ordinal_position
""",
[table_lower],
)
except Exception:
continue
if rows:
return rows
return columns
def _schema_candidates(self, schema: str | None) -> list[str | None]:
candidates: list[str | None] = []
def _add(value: str | None) -> None:
normalized = self._normalize_schema(value)
if normalized not in candidates:
candidates.append(normalized)
_add(schema)
_add(getattr(self.executor, "schema", None))
for alt in ("main", "temp"):
_add(alt)
_add(None)
return candidates
def _normalize_schema(self, schema: str | None) -> str | None:
if not schema:
return None
stripped = schema.strip()
return stripped or None
def _estimate_column_width(
self, column_info: tuple[str | None, int | None, int | None, int | None]
) -> int:
dtype_raw, char_max, numeric_precision, _ = column_info
dtype = self._normalize_data_type(dtype_raw)
if dtype and dtype in self._FIXED_TYPE_SIZES:
return self._FIXED_TYPE_SIZES[dtype]
if dtype in {"character", "varchar", "char", "text", "string"}:
if char_max and char_max > 0:
return min(char_max, self._VARCHAR_MAX_WIDTH)
return self._VARCHAR_DEFAULT_WIDTH
if dtype in {"varbinary", "blob", "binary"}:
if char_max and char_max > 0:
return min(char_max, self._VARCHAR_MAX_WIDTH)
return self._VARCHAR_DEFAULT_WIDTH
if dtype in {"numeric", "decimal"} and numeric_precision and numeric_precision > 0:
return min(max(int(numeric_precision), 16), 128)
return 16
def _normalize_data_type(self, dtype: str | None) -> str | None:
if not dtype:
return None
stripped = dtype.strip().lower()
if "(" in stripped:
stripped = stripped.split("(", 1)[0].strip()
if stripped.endswith("[]"):
stripped = stripped[:-2]
return stripped or None
|