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Github user gatorsmile commented on the issue:
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Yes, we need to extend the DDL parser to support the general user-defined
types.
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Github user maropu commented on the issue:
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Sorry to cutting in though, IMHO we need to have general logic to inject
user-defined types via `UDTRegistration` into the DDL parser
(`CatalystSqlParser`). If we have the logic, we could use the
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Github user sureshthalamati commented on the issue:
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@gatorsmile I like the DDL schema format approach. But the method
`CatalystSqlParser.parseTableSchema(sql)` will work only if user wants to
specify the target database datatype that also
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@gatorsmile sure. I will update the PR with the DDL format approach.
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Github user gatorsmile commented on the issue:
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@sureshthalamati https://github.com/apache/spark/pull/17171 has been
resolved. Can you update your PR by allowing users to specify the schema in DDL
format?
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Github user gatorsmile commented on the issue:
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This PR is following the same way as what AVRO did in Hive. [This linked
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Github user gatorsmile commented on the issue:
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retest this please
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Github user gatorsmile commented on the issue:
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@rxin @joshrosen @srowen Which options are preferred for supporting
customized column types? Table-level JDBC option or column-level metadata
property? Thanks!
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