Skip to content

[WIP][SPARK-52751][PYTHON][CONNECT][FOLLOW-UP] Skip eager column name validation in df.col_name #53059

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
wants to merge 4 commits into
base: master
Choose a base branch
from
+4 −2
Draft

[WIP][SPARK-52751][PYTHON][CONNECT][FOLLOW-UP] Skip eager column name validation in df.col_name #53059

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
14ee4f8
test
zhengruifeng Nov 13, 2025
54dcf0d
test
zhengruifeng Nov 14, 2025
d407226
doc
zhengruifeng Nov 14, 2025
79b54c8
fix
zhengruifeng Nov 14, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion python/docs/source/migration_guide/pyspark_upgrade.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ Upgrading from PySpark 4.0 to 4.1
* In Spark 4.1, Python 3.9 support was dropped in PySpark.
* In Spark 4.1, the minimum supported version for PyArrow has been raised from 11.0.0 to 15.0.0 in PySpark.
* In Spark 4.1, the minimum supported version for Pandas has been raised from 2.0.0 to 2.2.0 in PySpark.
* In Spark 4.1, ``DataFrame['name']`` on Spark Connect Python Client no longer eagerly validate the column name. To restore the legacy behavior, set ``PYSPARK_VALIDATE_COLUMN_NAME_LEGACY`` environment variable to ``1``.
* In Spark 4.1, ``DataFrame['name']`` and ``DataFrame.name`` on Spark Connect Python Client no longer eagerly validate the column name. To restore the legacy behavior, set ``PYSPARK_VALIDATE_COLUMN_NAME_LEGACY`` environment variable to ``1``.
* In Spark 4.1, Arrow-optimized Python UDF supports UDT input / output instead of falling back to the regular UDF. To restore the legacy behavior, set ``spark.sql.execution.pythonUDF.arrow.legacy.fallbackOnUDT`` to ``true``.
* In Spark 4.1, unnecessary conversion to pandas instances is removed when ``spark.sql.execution.pythonUDF.arrow.enabled`` is enabled. As a result, the type coercion changes when the produced output has a schema different from the specified schema. To restore the previous behavior, enable ``spark.sql.legacy.execution.pythonUDF.pandas.conversion.enabled``.
* In Spark 4.1, unnecessary conversion to pandas instances is removed when ``spark.sql.execution.pythonUDTF.arrow.enabled`` is enabled. As a result, the type coercion changes when the produced output has a schema different from the specified schema. To restore the previous behavior, enable ``spark.sql.legacy.execution.pythonUDTF.pandas.conversion.enabled``.
Expand Down
4 changes: 3 additions & 1 deletion python/pyspark/sql/connect/dataframe.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1737,7 +1737,9 @@ def __getattr__(self, name: str) -> "Column":
errorClass="JVM_ATTRIBUTE_NOT_SUPPORTED", messageParameters={"attr_name": name}
)

if name not in self.columns:
if os.environ.get("PYSPARK_VALIDATE_COLUMN_NAME_LEGACY") == "1" and not any(
field.name == name for field in self._schema
):
raise PySparkAttributeError(
errorClass="ATTRIBUTE_NOT_SUPPORTED", messageParameters={"attr_name": name}
)
Expand Down