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https://issues.apache.org/jira/browse/SPARK-61029?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon updated SPARK-61029:
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Description:
In PySpark Connect, {{spark.createDataFrame(data, schema)}} parses a DDL string
schema on the server ({{AnalyzePlanRequest.ddl_parse}}), as do {{mapInPandas}},
{{mapInArrow}} and the apply functions of their schemas, so they take any DDL
that Spark does: {{"st struct<a:int,b:double>"}}, {{"arr
array<struct<c:int>>"}}, {{"m map<string,struct<x:int>>"}}, backtick-quoted
names such as {{"`a b` int"}}, collations such as {{"s STRING COLLATE
UTF8_LCASE"}}, and {{NOT NULL}} and {{COMMENT}} clauses. They raise the errors
of Spark's parser, such as {{PARSE_SYNTAX_ERROR}} for {{"a INT,"}}.
{{transformWithStateInPandas}} sends a DDL output schema as an
{{UnparsedDataType}}, for the server to parse.
The Rust-backed Python client parses it on the client, which raises
{{ValueError: invalid type string 'st struct<a:int,b:double>': SparkError {
kind: ValueError, error_class: "INVALID_DATATYPE_FORMAT", ... }}} for these. It
takes {{"a INT,"}} as {{a INT}}, and sends a type of {{"s STRING COLLATE
UTF8_LCASE"}} that the server fails to parse ({{PARSE_SYNTAX_ERROR}}). Parse
them as PySpark does.
was:
In PySpark Connect, {{spark.createDataFrame(data, schema)}} parses a DDL string
schema on the server ({{AnalyzePlanRequest.ddl_parse}}), as do {{mapInPandas}},
{{mapInArrow}} and the apply functions of their schemas, so they take any DDL
that Spark does: {{"st struct<a:int,b:double>"}}, {{"arr
array<struct<c:int>>"}}, {{"m map<string,struct<x:int>>"}}, backtick-quoted
names such as {{"`a b` int"}}, and {{NOT NULL}} and {{COMMENT}} clauses.
The Rust-backed Python client parses it on the client, which raises
{{ValueError: invalid type string 'st struct<a:int,b:double>': SparkError {
kind: ValueError, error_class: "INVALID_DATATYPE_FORMAT", ... }}} for these.
Parse them as PySpark does.
> Parse the DDL schema strings of createDataFrame and the map functions on the
> server in the Python client as PySpark does
> ------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-61029
> URL: https://issues.apache.org/jira/browse/SPARK-61029
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Hyukjin Kwon
> Priority: Major
> Fix For: connect-rust-4.3.0
>
>
> In PySpark Connect, {{spark.createDataFrame(data, schema)}} parses a DDL
> string schema on the server ({{AnalyzePlanRequest.ddl_parse}}), as do
> {{mapInPandas}}, {{mapInArrow}} and the apply functions of their schemas, so
> they take any DDL that Spark does: {{"st struct<a:int,b:double>"}}, {{"arr
> array<struct<c:int>>"}}, {{"m map<string,struct<x:int>>"}}, backtick-quoted
> names such as {{"`a b` int"}}, collations such as {{"s STRING COLLATE
> UTF8_LCASE"}}, and {{NOT NULL}} and {{COMMENT}} clauses. They raise the
> errors of Spark's parser, such as {{PARSE_SYNTAX_ERROR}} for {{"a INT,"}}.
> {{transformWithStateInPandas}} sends a DDL output schema as an
> {{UnparsedDataType}}, for the server to parse.
> The Rust-backed Python client parses it on the client, which raises
> {{ValueError: invalid type string 'st struct<a:int,b:double>': SparkError {
> kind: ValueError, error_class: "INVALID_DATATYPE_FORMAT", ... }}} for these.
> It takes {{"a INT,"}} as {{a INT}}, and sends a type of {{"s STRING COLLATE
> UTF8_LCASE"}} that the server fails to parse ({{PARSE_SYNTAX_ERROR}}). Parse
> them as PySpark does.
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