aminghadersohi commented on code in PR #44219:
URL: https://github.com/apache/superset/pull/44219#discussion_r4012148075
##########
superset/common/query_context_processor.py:
##########
@@ -568,7 +569,10 @@ def get_data(
)
return result or ""
- return df.to_dict(orient="records")
+ # QueryObject post-processing has completed before this materialization
+ # boundary. Canonicalize its trusted missing/non-finite scalar outputs,
+ # while downstream envelope validation still rejects injected infinity.
+ return df_to_records(df, convert_big_integers=False)
Review Comment:
Fixed in ee59e685f8. The query-context producer now uses the default
browser-safe integer conversion, and the regression test exercises both the
producer result and strict JSON browser-wire round trip for `2**53 + 1`.
##########
superset/dataframe.py:
##########
@@ -34,45 +43,54 @@ def _convert_big_integers(val: Any) -> Any:
:returns: the same value but recast as a string if it was an integer over
``JS_MAX_INTEGER``
"""
- return str(val) if isinstance(val, int) and abs(val) > JS_MAX_INTEGER else
val
-
-
-def _is_na(val: Any) -> bool:
- """
- Check if a value is NA/NaN for scalar values only.
-
- pd.isna() raises ValueError for arrays/lists, so we catch that case.
-
- :param val: the value to check
- :returns: True if the value is NA/NaN, False otherwise
- """
- try:
- return bool(pd.isna(val))
- except ValueError:
- # pd.isna raises ValueError for arrays (e.g., lists, dicts from JSON)
- return False
-
-
-def df_to_records(dframe: pd.DataFrame) -> list[dict[str, Any]]:
+ return str(val) if type(val) is int and abs(val) > JS_MAX_INTEGER else val
+
+
+def _is_trusted_missing_or_nonfinite(value: Any) -> bool:
+ """Recognize producer nulls without consulting object-column hooks."""
+ value_type = type(value)
+ if value_type in _PANDAS_MISSING_TYPES:
+ return True
+ if value_type is Decimal:
+ return not Decimal.is_finite(value)
+ if value_type is float:
+ return not math.isfinite(value)
+ if value_type in _NUMPY_FLOAT_TYPES:
+ return not math.isfinite(float(value))
Review Comment:
Fixed in ee59e685f8. NumPy floating values are classified with `np.isfinite`
before conversion, and record materialization preserves `np.longdouble` instead
of narrowing through Python float. Direct coverage verifies finite `1e400`
survives while ±inf/NaN become null.
##########
superset/dataframe.py:
##########
@@ -34,45 +43,54 @@ def _convert_big_integers(val: Any) -> Any:
:returns: the same value but recast as a string if it was an integer over
``JS_MAX_INTEGER``
"""
- return str(val) if isinstance(val, int) and abs(val) > JS_MAX_INTEGER else
val
-
-
-def _is_na(val: Any) -> bool:
- """
- Check if a value is NA/NaN for scalar values only.
-
- pd.isna() raises ValueError for arrays/lists, so we catch that case.
-
- :param val: the value to check
- :returns: True if the value is NA/NaN, False otherwise
- """
- try:
- return bool(pd.isna(val))
- except ValueError:
- # pd.isna raises ValueError for arrays (e.g., lists, dicts from JSON)
- return False
-
-
-def df_to_records(dframe: pd.DataFrame) -> list[dict[str, Any]]:
+ return str(val) if type(val) is int and abs(val) > JS_MAX_INTEGER else val
+
+
+def _is_trusted_missing_or_nonfinite(value: Any) -> bool:
+ """Recognize producer nulls without consulting object-column hooks."""
+ value_type = type(value)
+ if value_type in _PANDAS_MISSING_TYPES:
+ return True
+ if value_type is Decimal:
+ return not Decimal.is_finite(value)
+ if value_type is float:
+ return not math.isfinite(value)
+ if value_type in _NUMPY_FLOAT_TYPES:
+ return not math.isfinite(float(value))
+ return False
Review Comment:
Fixed in ee59e685f8 with `np.isfinite` on the original NumPy scalar and
non-narrowing row materialization. Producer/browser-wire regression coverage
accompanies the direct longdouble test; the focused suite passes (152 tests).
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