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https://issues.apache.org/jira/browse/FLINK-29267?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18094579#comment-18094579
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Dale Lane commented on FLINK-29267:
-----------------------------------
{quote}We provide a table of supported mapping data types.
{quote}
How did you envisage this working?
I'm wondering if a single central table of supported mappings would be
difficult to handle in a way that would make sense for all formats and
connectors (e.g. different external systems might have different meanings for
types with a given name)
What about a table of supported mappings for each connector and format? It does
perhaps distribute it more than is ideal, but the implementation will be
distributed anyway, so is that okay?
> Support external type systems in DDL
> ------------------------------------
>
> Key: FLINK-29267
> URL: https://issues.apache.org/jira/browse/FLINK-29267
> Project: Flink
> Issue Type: Improvement
> Components: Connectors / JDBC, Formats (JSON, Avro, Parquet, ORC,
> SequenceFile), Table SQL / Ecosystem
> Reporter: Timo Walther
> Assignee: Timo Walther
> Priority: Major
>
> Many connectors and formats require supporting external data types. Postgres
> users request UUID support, Avro users require enum support, etc.
> FLINK-19869 implemented support for Postgres UUIDs poorly and even impacts
> performance with regular strings.
> The long-term solution should be user-defined types in Flink. This is however
> a bigger effort that requires a FLIP and a bigger amount of resources.
> As a mid-term solution, we should offer a consistent approach based on DDL
> options that allows to define a mapping from Flink type system to the
> external type system. I suggest the following:
> {code}
> CREATE TABLE MyTable (
> ...
> ) WITH(
> 'mapping.data-types' = '<Flink field name>: <External field data type>'
> )
> {code}
> The mapping defines a map from Flink data type to external data type. The
> external data type should be string parsable. This works for most connectors
> and formats (e.g. Avro schema string).
> Examples:
> {code}
> CREATE TABLE MyTable (
> regular_col STRING,
> uuid_col STRING,
> point_col ARRAY<DOUBLE>,
> box_col ARRAY<ARRAY<DOUBLE>>
> ) WITH(
> 'mapping.data-types' = 'uuid_col: uuid, point_col: point, box_col: box'
> )
> {code}
> We provide a table of supported mapping data types. E.g. the {{point}} type
> is always maped to {{ARRAY<DOUBLE>}}. In general we choose a data type in
> Flink that comes closest to the required functionality.
> Future work:
> In theory, we can also offer mapping of field names. It might be a
> requirement that Flink's column name is different from the external system's
> one.
> {code}
> CREATE TABLE MyTable (
> ...
> ) WITH(
> 'mapping.names' = '<Flink field name>: <External field name>'
> )
> {code}
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