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https://issues.apache.org/jira/browse/SPARK-60907?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-60907.
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Resolution: Fixed
Issue resolved by pull request 151
[https://github.com/apache/spark-connect-rust/pull/151]
> Send reader schemas in Spark Connect Rust as PySpark does, and accept a
> StructType in schema()
> ----------------------------------------------------------------------------------------------
>
> Key: SPARK-60907
> URL: https://issues.apache.org/jira/browse/SPARK-60907
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Hyukjin Kwon
> Assignee: Hyukjin Kwon
> Priority: Major
> Labels: pull-request-available
> Fix For: connect-rust-4.3.0
>
>
> In PySpark Connect, {{DataFrameReader.schema}} and
> {{DataStreamReader.schema}} take a StructType, which is sent as its JSON, or
> a string, which is sent as is; anything else raises {{NOT_EXPECTED_TYPE}}.
> The {{schema}} arguments of {{load()}}, {{csv()}}, {{json()}} and the other
> format methods go through the same method.
> The Rust-backed Python client differs:
> * {{schema()}} accepts only a string, so {{spark.read.schema(struct_type)}}
> and {{spark.readStream.schema(struct_type)}} raise {{TypeError}}.
> * The {{schema}} arguments of the format methods are parsed on the client and
> sent as a simple string. A StructType with field metadata, such as a comment,
> then fails on the server with {{PARSE_SYNTAX_ERROR}}, and a DDL string that
> the client parser does not accept, such as one with {{COMMENT}}, fails with
> {{ValueError}}. Data types other than a StructType are accepted rather than
> raising {{NOT_EXPECTED_TYPE}}.
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