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https://issues.apache.org/jira/browse/SPARK-59515?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-59515:
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Labels: pull-request-available (was: )
> assertSchemaEqual ignores non-nullability type parameters
> ---------------------------------------------------------
>
> Key: SPARK-59515
> URL: https://issues.apache.org/jira/browse/SPARK-59515
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 5.0.0
> Reporter: Akshay Thorat
> Priority: Major
> Labels: pull-request-available
>
> h2. Problem
> With ignoreNullable=True, assertSchemaEqual accepts remaining leaf types
> solely because typeName matches. This ignores semantic parameters unrelated
> to nullability, including collation, character length, time precision, and
> interval bounds.
> h2. Reproduction
> Reproduced on upstream master 39776477a3d (PySpark 5.0.0.dev0), Python
> 3.10.11. No SparkSession is required.
> {code:python}
> from pyspark.sql.types import StringType, StructField, StructType
> from pyspark.testing import assertSchemaEqual
> a = StructType([StructField("s", StringType())])
> b = StructType([StructField("s", StringType("UTF8_LCASE"))])
> assertSchemaEqual(a, b) # Returns without raising
> {code}
> h2. Expected behavior
> Different collation semantics should raise DIFFERENT_SCHEMA even when
> nullability is ignored. The same applies to different CharType/VarcharType
> lengths, TimeType precision, and interval bounds.
> h2. Proposed fix and verification
> Use data-type equality for leaf types after the existing recursive
> nullability handling. Regression tests cover seven type pairs across scalar,
> array, map key, map value, and nested struct contexts. Nested nullability
> continues to be ignored. User tests relying on the previous loose matching
> may now fail correctly with DIFFERENT_SCHEMA.
> Regression tests fail before the fix and pass afterward. Python compilation,
> custom-error checks and Ruff checks pass.
> Pull request: https://github.com/apache/spark/pull/58791
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