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https://issues.apache.org/jira/browse/SPARK-60921?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-60921.
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Resolution: Duplicate
> Make data types in Spark Connect Rust compare and hash as PySpark's do
> ----------------------------------------------------------------------
>
> Key: SPARK-60921
> URL: https://issues.apache.org/jira/browse/SPARK-60921
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Hyukjin Kwon
> Priority: Major
> Labels: pull-request-available
> Fix For: connect-rust-4.3.0
>
>
> In PySpark, two data types are equal when they are of the same class and have
> the same attributes ({{DataType.__eq__}}), and a data type hashes as
> {{hash(str(self))}}. The Rust-backed Python client's data types, except
> {{StructField}}, define no equality, so they compare by identity, and so
> these PySpark 4.3.0 results differ:
> * {{IntegerType() == IntegerType()}}, {{DecimalType(10, 2) == DecimalType(10,
> 2)}}, {{ArrayType(IntegerType()) == ArrayType(IntegerType())}},
> {{MapType(StringType(), LongType()) == MapType(StringType(), LongType())}}
> and the same for {{StructType}} are true. The client returns false for all of
> them, and true for {{!=}}.
> * So does every comparison of a schema or a field type, as in {{df.schema ==
> StructType(...)}}, {{field.dataType == StringType()}}, or a data type and its
> pickled copy.
> * Equal data types hash equally, and {{StructField}} is hashable. The
> client's hashes differ, and its {{StructField}} is unhashable.
> Make data types compare and hash as PySpark's do.
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